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DAGIM, S., E. V. PUSTOVALOV, A. N. FEDORETS, and A. M. FROLOV. "EXPLORING AMORPHOUS ALLOYS: ADVANCED ELECTRON MICROSCOPY AND CLUSTER ANALYSIS." Computational nanotechnology 11, no. 1 (2024): 112–20. http://dx.doi.org/10.33693/2313-223x-2024-11-1-112-120.
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In this study, we explored the atomic structure and orderliness of amorphous alloys through advanced electron microscopy and analytical techniques. Amorphous alloys, characterized by disordered atomic structures, exhibit promising applications in technology. The research addresses a crucial knowledge gap by investigating cluster distribution, particle arrangement, and orderliness within the amorphous matrix. High-resolution electron microscopy (HREM) images are analyzed using diverse algorithms and software tools. The study establishes a correlation between angles approaching 180 degrees and increased orderliness within clusters, highlighting the reliability of angle distribution analysis. Robust indicators, including Div (SP(B/V)) and Div (Mu(B/V)) metrics, assess and compare amorphous alloy samples. Kullback-Leibler (K-L) divergence indicates the significance of cluster ordering, validated by the S-K test. Radial Distribution Function (RDF) analysis uncovers local short-range order, deepening understanding despite limited orderliness discernment. These findings not only enhance our understanding of metallic glasses or amorphous alloys but also offer opportunities for tailored design and improved applications across various technological domains.
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Feng, Guohuan, Junchen Lin, and Keyi Wang. "Researches Advanced in Clustering Algorithms." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 168–77. http://dx.doi.org/10.54097/hset.v16i.2498.
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Clustering is a technique to find the intrinsic structure between data and is a fundamental problem in many data-driven application fields. Currently, clustering is generally modeled as an unsupervised learning task, aiming to mine similar features between different samples and cluster samples with similar features into clusters. Ideally, objects in the same cluster are expected to be similar in the clustering results, while objects in different clusters are quite different. This study summarizes the research status of clustering algorithms in recent years. Specifically, the relevant critical steps of clustering algorithms are first introduced. From two aspects of partition and hierarchical clustering, representative clustering algorithms such as K-means, K-medoids, CLARANS, BIRCH, DBSCAN, and CURE are further detailed. This study also analyzes and summarizes the above algorithms in terms of critical technologies, algorithm ideas, benefits, and shortcomings and compares the distance accuracy of different algorithms on standard data sets. The above work will provide a valuable reference for cluster analysis and data mining research.
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Yadav, Pradeep Kumar, R. S. Sikarwar, Badal Verma, Sushma Tiwari, and D. K. Shrivastava. "Genetic Divergence for Grain Yield and Its Components in Bread Wheat (Triticum aestivum L.): Experimental Investigation." International Journal of Environment and Climate Change 13, no. 5 (2023): 340–48. http://dx.doi.org/10.9734/ijecc/2023/v13i51776.
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The present investigation Comprises 34 advanced breeding lines including checks of bread wheat and experiment was conducted in a complete randomized block design with three replications at the research farm department of genetics and plant breeding, RVSKVV, B.M. College of Agriculture, Khandwa during Rabi season (November 2021 to April 2022) for estimation of the multivariate analysis of divergence. The advanced breeding lines were grouped into seven clusters. Cluster III contained the highest number of advanced breeding lines(12) and clusters V, VI, and VII contained the lowest (1 each). The inter-cluster distance in most cases was larger than the intra cluster distance which indicated that wider diversity is present among the advanced breeding lines of distant grouped. The highest intra cluster distance was observed in cluster IV revealed maximum genetic divergence among its constituents. The highest inter-cluster distance was found between cluster VI and VII and the lowest was between cluster V and VI. Highest cluster mean exhibited in cluster VII for most of the agro-morphological traits i.e. number of tillers/plant, spike length, spike weight, number of grain/spike followed by cluster II for grain filling period, days to maturity and plant height. On the basis of genetic diversity analysis, maximum percent contribution towards genetic divergence in 34 advanced breeding lines were found in grain filling period, days to maturity, number of grain/spike, days to 50% flowering, biological yield per plant and harvest index. Such differences in the genetic component of traits studied in the manuscript can be applied as a source of variation in other breeding programmes and crossing nurseries for wheat improvement.
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Siddika, A., A. K. M. Aminul Islam, M. G. Rasul, M. A. K. Mian, and J. U. Ahmed. "GENETIC DIVERSITY IN ADVANCED GENERATION OF VEGETABLE PEA (Pisum sativum L.)." Bangladesh Journal of Plant Breeding and Genetics 27, no. 1 (2015): 9–16. http://dx.doi.org/10.3329/bjpbg.v27i1.23972.
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Twenty five advanced lines among them twelve lines obtained from the cross between Edible Podded Pea and IPSA Motorsuty-1, nine obtained from the cross between Local White and IPSA Motorsuty-3 and five parental lines were included to measure genetic diversity. The field experiment was conducted at the research farm, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh. Analysis of variance showed significant differences among the genotypes for all characters. Multivariate analysis based 14 agronomic characters indicated that the 26 genotypes fell into five distant clusters. Fifty percent germination was found to be contributed maximum towards the total divergence. The inter cluster (D2 values) distance was maximum between cluster I and cluster II and intra-cluster distance was in cluster III. Cluster V comprising five genotypes, namely, G11, G14, G21, G22, G25 and scored first position for 50% germination, pod per plant, 100 green seed weight and seed yield per plant (6.02). Genotypes belonging to the cluster I, II and V having greater inter cluster distance and higher cluster means for various characters could be recommended for inclusion in future breeding program as they are expected to produce good segregates.
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Streur, Megan, Sarah J. Ratcliffe, David Callans, M. Benjamin Shoemaker, and Barbara Riegel. "Atrial fibrillation symptom clusters and associated clinical characteristics and outcomes: A cross-sectional secondary data analysis." European Journal of Cardiovascular Nursing 17, no. 8 (2018): 707–16. http://dx.doi.org/10.1177/1474515118778445.
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Background: Symptom clusters among adults with atrial fibrillation have previously been identified but no study has examined the relationship between symptom clusters and outcomes. Aims: The purpose of this study was to identify atrial fibrillation-specific symptom clusters, characterize individuals with each cluster, and determine whether symptom cluster membership is associated with healthcare utilization. Methods: This was a cross-sectional secondary data analysis of 1501 adults from the Vanderbilt Atrial Fibrillation Registry with verified atrial fibrillation. Self-reported symptoms were measured with the University of Toronto Atrial Fibrillation Severity Scale. We used hierarchical cluster analysis (Ward’s method) to identify clusters and dendrograms, pseudo F, and pseudo T-squared to determine the ideal number of clusters. Next, we used regression analysis to examine the association between cluster membership and healthcare utilization. Results: Males predominated (67%) and the average age was 58.4 years. Two symptom clusters were identified, a Weary cluster (3.7%, n=56, fatigue at rest, shortness of breath at rest, chest pain, and dizziness) and an Exertional cluster (32.7%, n=491, shortness of breath with activity and exercise intolerance). Several sociodemographic and clinical characteristics varied by symptom cluster group membership, including age, gender, atrial fibrillation type, body mass index, comorbidity status, and treatment strategy. Women were more likely to experience either cluster ( p<0.001). The Weary cluster was associated with nearly triple the rate of emergency department utilization (incident rate ratio [IRR] 2.8, p<0.001) and twice the rate of hospitalizations (IRR 1.9, p<0.001). Conclusion: We identified two symptom clusters. The Weary cluster was associated with a significantly increased rate of healthcare utilization.
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Dalcin, Thais, Alsones Balestrin, and Eduardo Künzel Teixeira. "Start-Up Cluster Development: A Multi-Case Analysis in the Brazilian Context." International Journal of Innovation and Technology Management 14, no. 06 (2017): 1750035. http://dx.doi.org/10.1142/s0219877017500353.
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This paper aims to contribute to knowledge on start-up cluster development by describing the core resource configurations in the development trajectory of start-up clusters in an emergent country. This research involves two start-up clusters in the Brazilian context. The results provide a framework formed by four stages that indicates the trajectory of start-up cluster development from the basic level, in which the most evident resources are tangible and endogenous, to the more advanced level, which comprises intangible and exogenous resources. In the fourth stage, start-up clusters improve resources, such as quality of life, cultural diversity and political and economic conditions to foster entrepreneurship and innovation.
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Randler, Christoph. "An Analysis of Heterogeneity in German Speaking Birdwatchers Reveals Three Distinct Clusters and Gender Differences." Birds 2, no. 3 (2021): 250–60. http://dx.doi.org/10.3390/birds2030018.
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The purpose of this study was to segment birdwatchers into clusters. Members from a wide range of bird related organizations, from highly specialized birders as well as Facebook bird group members were studied to provide a diverse dataset (n = 2766; 50.5% men). Birding specialization was measured with a battery of questionnaires. Birding specialization encompassed the three constructs of skill/competence, behavior, personal and behavioral commitment. Additionally, involvement, measured by centrality to lifestyle, attraction, social bonding, and identity, was used. The NbClust analyses showed that a three-cluster solution was the optimal solution. Then, k-means cluster analysis was applied on three groups: casual/novice, intermediate, and specialist/advanced birdwatchers. More men than women were in the specialist/advanced group and more women than men in the casual/novice group. As a conclusion, this study confirms a three-cluster solution for segmenting German birdwatchers based on a large and diverse sample and a broad conceptualization of the construct birding specialization. These data can be used to address different target audiences (novices, advanced birders) with different programs, e.g., in nature conservation.
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Bates, Ian, Sherly Meilianti, Lina Bader, Rishi Gandhi, Rachael Leng, and Kirsten Galbraith. "Strengthening Primary Healthcare through accelerated advancement of the global pharmacy workforce: a cross-sectional survey of 88 countries." BMJ Open 12, no. 5 (2022): e061860. http://dx.doi.org/10.1136/bmjopen-2022-061860.
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ObjectiveAdvancing the pharmacy workforce contributes to strengthening primary healthcare and accelerating progress towards universal health coverage. This study aimed to identify key enablers to support policy development for national pharmacy workforce advancement.DesignA cross-sectional country-level questionnaire was distributed from July 2018 to March 2019.SettingNational-level or country-level pharmacy workforce development policy.ParticipantsProfessional leadership associations and national agencies of the International Pharmaceutical Federation (FIP). The FIP global database included 129 countries.MeasuresThe questionnaire was designed to collate data on the scope of advanced and specialist practice in respondent countries. Multiple correspondence analysis and subsequent cluster analysis were conducted to explore the associations and patterns of country-level attributes of systems in place for the pharmacy workforce advancement in order to develop a general transnational model for country-level advanced practice development.ResultsEighty-eight countries (68.2% response rate) responded to the questionnaire. Factors that enhance and contribute to advanced practice policy development include the country’s socioeconomic factors and the availability of national practice advancement concepts. The essential advancement concepts include the availability of framework and professional recognition systems, programmes assisting advanced practice development and workforce advancement and recognition opportunities. Cluster analysis identified three clusters of country respondents. First cluster included low-income and middle-income with poor pharmacy advancement implementation, second cluster included a higher socioeconomic status with weaker pharmacy workforce advancement implementation and third cluster included upper middle-income to high-income countries and high rates of pharmacy advancement implementation.ConclusionThe key factors identified in this study can be used to support a transnational approach to pharmacy workforce advancement. The three clusters identified highlighted that workforce advancement was not an exclusive trait of higher-income countries. Lessons from countries that have already adopted concepts of advancement in pharmacy practice could be adopted to other countries to accelerate the progress of advanced practice globally.
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Pahuja, Anjali K., Kundan Singh Chufal, Irfan Ahmad, et al. "Identifying Prognostic Groups Using Machine Learning Tools in Patients Undergoing Chemoradiation for Inoperable Locally Advanced Nonsmall Cell Lung Carcinoma." Asian Journal of Oncology 05, no. 02 (2019): 056–63. http://dx.doi.org/10.1055/s-0039-3401437.
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Abstract Introduction Unresectable stage III nonsmall cell lung cancer (NSCLC) continues to have dismal 5-year overall survival (OS) rate. However, a subset of the patients treated with chemoradiation show significantly better outcome. Prediction of treatment outcome can be improved by utilizing machine learning tools, such as cluster analysis (CA), and is capable of identifying complex interactions among many variables. We have utilized CA to identify a cluster with good prognosis within stage III NSCLC. Materials and Methods Retrospective analysis of treatment outcomes was done for 92 patients who underwent chemoradiation for inoperable locally advanced NSCLC from 2012 to 2018. Using various patient- and treatment-related variables, an exploratory factor analysis was performed to extract factors with eigenvalue > 1. An appropriate number of homogeneous groups were identified using agglomerative hierarchical cluster analysis. Further K-mean cluster analysis was applied to classify each patient into their homogeneous clusters. The newly formed cluster variable was used as an independent variable to estimate survival over time using Kaplan–Meier method. Results With a median follow-up of 18 months, median OS was 14 months. Using CA, three prognostic clusters were obtained. Cluster 2 with 36 patients had a median OS of 36 months, whereas Cluster 1 with 34 patients had a median OS of 20 months (p = 0.004). Conclusion A cluster could thus be identified with a relatively good prognosis within stage III NSCLC. Using CA, we have attempted to create a model which may provide more specific prognostic information in addition to that provided by tumor node metastasis-based models.
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Yennu, Sriram, Janet L. Williams, Gary B. Chisholm, and Eduardo Bruera. "The effects of dexamethasone and placebo on symptom clusters in advanced cancer patients: A preliminary report." Journal of Clinical Oncology 33, no. 29_suppl (2015): 187. http://dx.doi.org/10.1200/jco.2015.33.29_suppl.187.
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187 Background: Advanced cancer patients frequently experience debilitating symptoms that occur in clusters, but few pharmacological studies have targeted symptom clusters. Our objective was to examine the effects of dexamethasone on symptom clusters. Methods: Secondary analysis of a recent RCT of dexamethasone (DEX) vs placebo (PL) on cancer symptoms as assessed by FACIT-F-Fatigue; FAACT-Anorexia-Cachexia; BPI - Pain; HADS- Anxiety-Depression; ESAS: Sleep, Drowsiness, Dyspnea. Symptom clusters were identified based on baseline symptoms [ESAS] using principal component analysis. Cluster scores were computed by adding each scale divided by the maximum value for the scale: Fatigue- Anorexia-Depression = (Fatigue /52 + Anorexia/48+ HADS-Depression/21); Sleep-Anxiety-Drowsiness = (Sleep/10+HADS-Anxiety/21+Drowsiness /10); Pain-Dyspnea = (BPI/10 +Dyspnea /10). Higher number indicates better QOL. Correlations and change in the severity of symptom clusters were analyzed. Results: In 114 evaluable patients, 3 clusters accounted for 63% of the total variance at baseline: Fatigue-anorexia/cachexia-depression cluster (FAD); sleep-anxiety-drowsiness cluster (SAD) and Pain-Dyspnea cluster (PD). Median (IQR) improvement in the FAD cluster at Day 15 and Day 8 was significantly higher in the DEX than in the PL group [0.22 (-0.04, 0.45) vs. 0.06 (-.30, .20), P = 0.016)] and [0.15 (-0.84, 0.35) vs-0.095 (-0.35, 0.16), p = 0.017] respectively. There was no significant change observed in SAD and PD after DEX. Median (IQR) scores for FAD and PD of the DEX group at baseline, day 8, and day 15 were 1.42(1.1,1.7),1.71(1.3,2.1),1.78(1.4,2.2); [1.1(0.8,1.4); 1.38(.04,1.6); 1.43(1.3,1.7) respectively and significantly correlated over time at Day 8 (r = 0.76; p < 0.001) Day 15 (r = 0.55;p < 0.001) [FAD]; Day 8 (r = 0.36; p < 0.001) Day 15 (r = 0.45; p < 0.001) [PD]. Conclusions: FAD cluster showed improvement with dexamethasone and consistent correlation overtime, as compared to SAD and PD cluster. These findings suggest that fatigue-anorexia/cachexia- and depression share a common a common pathophysiologic basis. Further studies are needed to investigate this cluster and target anti-inflammatory therapies. Clinical trial information: NCT00489307.
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Pryadko, S. N., and S. A. Kucheryavenko. "Knowledge-Intensive Market: Geomarketing and Cluster Approach to Functioning and Analysis." Proceedings of the Southwest State University. Series: Economics. Sociology. Management 13, no. 5 (2023): 128–40. http://dx.doi.org/10.21869/2223-1552-2023-13-5-128-140.
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Relevance. Innovative development is one of the highest priority tasks of the economy at the national and regional level. One of the directions for solving these problems is the formation of local knowledge-intensive markets, within which the interaction of the main participants in the innovation cycle takes place - from conducting fundamental scientific research to introducing a knowledge-intensive product to the market. The object of the study was the local knowledge-intensive market of the Belgorod region. The sources of information for the study were data from the World Intellectual Property Organization (WIPO); research results presented in the eLibrary.Ru search engine; data from statistical reporting of the Territorial Body of the Federal State Statistics Service for the Belgorod Region.The purpose of the study is a cluster grouping of developed advanced production technologies in organizations of the Belgorod region.Objectives: content analysis of theoretical data on the research problem in the eLibrary.Ru search engine; analysis of thematic publications and regulatory documents, data from foreign countries; classification of tools for analyzing indicators of knowledge-intensive markets; grouping of quantitative indicators; testing of research results in the conditions of a regional knowledge-intensive market.Methodology. Retrospective content analysis, static data analysis, cluster analysis and grouping of developed advanced production technologies in organizations of the Belgorod region were used as research methods.Results. The article presents a brief analysis of the effectiveness of implementing these tasks from the perspective of geomarketing and cluster approaches. The main stages of the formation of cluster policy in the Russian Federation are highlighted and the conditions for the formation of this policy at the national level are analyzed. As a result of the research, the main geomarketing tools for analyzing the local knowledge-intensive market were structured; a grouping of quantitative parameters is proposed for conducting geomarketing and cluster analysis of a knowledge-intensive local market.Conclusion. Based on cluster analysis, advanced production technologies used in the region were grouped into four clusters, which can become the basis for further innovative development.
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Ringer, Simon P. "Advanced Nanostructural Analysis of Aluminium Alloys Using Atom Probe Tomography." Materials Science Forum 519-521 (July 2006): 25–34. http://dx.doi.org/10.4028/www.scientific.net/msf.519-521.25.
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This paper sets out the needs for and recent advances in microscopy in Al alloys, using solutesolute and solute-vacancy clustering as examples. Cluster-assisted nucleation and cluster strengthening are discussed and this is followed by a discussion of the local electrode atom probe. Heuristic and algorithmic tools for assessing the nanoscale microstructure or nanostructure of Al alloys acquired from atom probe tomography experiments are then presented.
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Aktas, Aynur, Declan Walsh, Lisa A. Rybicki, and Anne Fitz. "Symptom clusters and demographic characteristics in advanced cancer." Journal of Clinical Oncology 31, no. 15_suppl (2013): 9638. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.9638.
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9638 Background: Little is known about demographic variations in cancer symptom clusters (SC). Our objective was to determine whether SC are associated with age, gender, race, performance status (PS), or primary cancer site. Methods: Symptoms from 1000 advanced cancer patients referred to a palliative medicine program were recorded prospectively. Among 922 patients with complete symptom data, hierarchical cluster analysis identified 7 SC. A SC was considered present if the patient had ≥50% of the symptoms in the cluster. Comparisons were made between patients with and without each cluster using the chi-square test (age <65 vs. ≥65 years; gender female (F) vs. male (M); race Caucasian (C) vs. African American (AA); 10 primary site groups (PSG), or Wilcoxon rank sum test (ECOG PS 0-4). A p value <0.05 indicated statistical significance. Results: 83% of patients were C, 52% ≥65 years, 56% M, and 55% had ECOG PS 3-4 Most common PSG were lung (25%), genitourinary (18%), and gastrointestinal (GI) (11%). Fatigue/anorexia-cachexia cluster was associated with race (58% AA vs. 68% C, p=0.032) and PSG (range 47% melanoma to 83% pancreas, p=0.012); Neuropsychological cluster was associated with older age (29% ≥65 vs. 39% <65, p<0.001) and race (22% AA vs. 36% C, p=0.001). Upper GI cluster was associated with female gender (16% M vs. 22% F, p=0.035) and PSG (range 8% Head & Neck to 32% pancreas, p=0.035). Nausea/Vomiting cluster was associated with younger age (35% ≥65 vs. 43% <65, p=0.010) and female gender (33% M vs. 47% F, p<0.001). Aerodigestive cluster was associated with male gender (36% F vs. 44% M, p=0.010) and PSG (range 24% pancreas to 58% Head & Neck, p<0.001). Debility cluster was associated with race (33% AA vs. 44% C, p=0.016) and poor PS (range 17% PS0 to 54% PS4, p<0.001). Pain cluster was associated with younger age (88% ≥65 vs. 92% <65, p=0.028). Conclusions: We identified 7 SC whose prevalence were influenced by age, gender, race, PS, or primary cancer site. This supports the clinical relevance of the cluster concept in palliative and supportive care. Demographic characteristics may warrant different clinical approaches to patient care. Identification of these differences may help develop more effective cancer treatment and management strategies.
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Christy A., Joy. "An advanced ilrcpsd technique for bridging the competency and cognitive skills of students in higher education." International Journal of Engineering & Technology 7, no. 1.3 (2017): 37. http://dx.doi.org/10.14419/ijet.v7i1.3.8984.
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Data mining refers to the extraction of meaningful knowledge from large data sources as it may contain hidden potential facts. In general the analysis of data mining can either be predictive or descriptive. Predictive analysis of data mining interprets the inference of the existing results so as to identify the future outputs and the descriptive analysis of data mining interprets the intrinsic characteristics or nature of the data. Clustering is one of the descriptive analysis techniques of data mining which groups the objects of similar types in such a way that objects in a cluster are closer to each other than the objects of other clusters. K-means is the most popular and widely used clustering algorithm that starts by selecting the k-random initial centroids as equal to number of clusters given by the user. It then computes the distance between initial centroids with the remaining data objects and groups the data objects into the cluster centroids with minimum distance. This process is repeated until there is no change in the cluster centroids or cluster members. But, still k-means has been suffered from several issues such as optimum number of k, random initial centroids, unknown number of iterations, global optimum solutions of clusters and more importantly the creation of meaningful clusters when dealing with the analysis of datasets from various domains. The accuracy involved with clustering should never be compromised. Thus, in this paper, a novel classification via clustering algorithm called Iterative Linear Regression Clustering with Percentage Split Distribution (ILRCPSD) is introduced as an alternate solution to the problems encountered in traditional clustering algorithms. The proposed algorithm is examined over an educational dataset to identify the hidden group of students having similar cognitive and competency skills. The performance of the proposed algorithm is well-compared with the accuracy of the traditional k-means clustering in terms of building meaningful clusters and to prove its real time usefulness.
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Lee, You Won, Eun Ji Lee, Seung Yeon Oh, et al. "Abstract 5935: Phenotype profiling of tumor microenvironment in EGFR mutant lung adenocarcinoma with multi-omics data." Cancer Research 83, no. 7_Supplement (2023): 5935. http://dx.doi.org/10.1158/1538-7445.am2023-5935.
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Abstract Introduction: EGFR mutations holds the major targets for drug in lung adenocarcinoma (LUAD). Despite the tremendous study of EGFR mutant (MT) LUAD, the comprehensive interpretation of the heterogeneous character of LUAD harboring EGFR MT remains a key challenge. Here, we investigated the heterogeneity of EGFR MT LUAD and explored the tumor microenvironment (TME) in EGFR MT LUAD. Method: We performed single-cell RNA sequencing (scRNA-seq) from 135 LUAD patients which consist of normal(n=24), EGFR wild (WT)(n=18), and MT(n=93). Also, we used whole genome sequencing and bulk-RNA sequencing to validate with scRNA-seq results. From 898,648 cells, main cell types were classified. To explore the various characteristics of MT LUAD tumor cells, we used two ways: i) We re-clustered epithelial cells populating the normal, WT, and MT. ii) We re-clustered only MT epithelial cells. In each analysis, we identified the tumor character in the clusters using differential expressed genes analysis, lineage tracing, clinical information, mutation, and trajectory analysis. Also, we extracted each main cell type except epithelial cells, and identified subtypes of main cell types. Finally, we revealed the interaction of cellular components in TME. Results: In the analysis of epithelial cells, we identified characteristics of specific EGFR MT by comparing of EGFR WT and MT tumors in clusters with similar biological features. The cluster represented by alveolar type 2 (AT2) known as initiation of LUAD was populating normal, WT, and MT. In this cluster, MT- and WT-associated pathway shared but differently significant between MT and WT in the pathway analysis. The cluster represented by proliferative is mostly comprised tumor cells and we found significantly increased the expression of MDK, CD24 in the MT of the cluster. In the analysis of only MT epithelial cells, 2 of clusters were stage-specific cluster: i) The cluster annotated as early stage cluster, ii) The cluster annotated as advanced stage cluster. Trajectory showed that there is a pseudotemporal continuum, following the stage from early stage cluster to advanced stage cluster. Also, based on the lineage tracing, 2 of clusters revealed lineage-specific clusters: i) The cluster annotated as AT2 was enriched from early stage cells, ii) The cluster annotated as basal cell known as origin of lung squamous cell carcinoma(LUSC) was enriched from advanced stage cells. Psedotemporal ordering of these cluster revealed AT2 cluster transdifferentiate into basal cell cluster which implied the possibility of LUAD to LUSC transition by drug resistance. In the interaction of MT and WT TME, the number of signaling received epithelial cells from myeloid cells, endothelial cells, and fibroblasts as sender increased compared with the interaction of normal. Conclusion: We shed light on the ecosystem of TME according to clinical and biological feature of tumor in EGFR mutant LUAD. Citation Format: You Won Lee, Eun Ji Lee, Seung Yeon Oh, Kyoung-Ho Pyo, Seong Gu Heo, YoungJoon Park, Su-Jin Choi, Kyumin Lim, Ju-hyeon Lee, Jae Hwan Kim, Jii Bum Lee, Ji Yoon Lee, Sun Min Lim, Chang Gon Kim, Min Hee Hong, Mi Ran Yun, Byoung Chul Cho. Phenotype profiling of tumor microenvironment in EGFR mutant lung adenocarcinoma with multi-omics data. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5935.
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Pahria, Tuti, Yuliana Yuliana, Atlastieka Praptiwi, Maha Atout, and Hana Rizmadewi Agustina. "Symptom Clusters and Quality of Life among Women Living with Cancer." Jurnal Keperawatan Soedirman 18, no. 3 (2023): 129. http://dx.doi.org/10.20884/1.jks.2023.18.3.8530.
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The prevalence of cancer in Indonesia has increased and is one of the biggest causes of death. Symptom clusters, a collection of symptoms in cancer patients that appear together and are related to one another, can affect the quality of life of cancer patients. This study aims to identify the relationship between symptom clusters and the quality of life of advanced cancer patients. This research used cross-sectional quantitative survey data from a cancer patient care unit at a referral hospital in West Java Province with a total of 140 respondents. Consecutive sampling was conducted for three months in stage III or IV cancer patients who were undergoing therapy. This study used descriptive analysis, factor analysis with the Principal Components Analysis (PCA) approach, and multiple linear regression analysis. Five symptom clusters were identified: the psychological cluster, the gastrointestinal cluster, the numbness cluster, the pain cluster, and the respiratory distress cluster. The results showed that symptom clusters influence the quality-of-life dimension. The symptom clusters’ coefficient of determination (R2) for the physical dimension was 0.231 (weak), the role dimension was 0.191 (very weak), the emotional dimension was 0.484 (moderate), the cognitive dimension was 0.011 (very weak), the social dimension was 0.420 (moderate), and the general-health dimension was 0.202 (weak).
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Vahldiek, Kai, and Frank Klawonn. "Cluster-Centered Visualization Techniques for Fuzzy Clustering Results to Judge Single Clusters." Applied Sciences 14, no. 3 (2024): 1102. http://dx.doi.org/10.3390/app14031102.
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Fuzzy clustering, as a powerful method for pattern recognition and data analysis, often produces complex results that require careful examination of individual clusters. In this paper, advanced visualization techniques are presented that aim to facilitate the analysis of fuzzy clustering results by focusing on the evaluation and interpretation of individual clusters. The presented approach is based on the development of cluster-centric visualization techniques that consider the inherent uncertainty of fuzzy clustering results. The novelty is an assessment of individual clusters with the proposed visualizations. In general, three cluster-centered visualization techniques are presented. These approaches are intended not only to illustrate the overall structure of the fuzzy clustering results but also to enable detailed individual cluster analysis. The performance of the presented visualization techniques is demonstrated by their application to real data sets from different areas. The results show that the techniques provide an effective way to judge individual clusters in fuzzy clustering results for complex data structures.
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Campregher, Paulo V., Santosh K. Srivastava, Nobuharu Fujii, H. Joachim Deeg, Harlan S. Robins та Edus Warren. "Analysis of the αβ T Cell Receptor Repertoire in Advanced Myelodysplastic Syndrome". Blood 112, № 11 (2008): 3650. http://dx.doi.org/10.1182/blood.v112.11.3650.3650.
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Abstract Analysis of the αβ T cell receptor (TCR) repertoire in patients with myelodysplastic syndrome (MDS) using the technique of TCR β chain spectratyping has provided valuable insights into the pathophysiology of cytopenias in a subset of patients. TCR β chain spectratypes represent complex datasets, however, and statistical tools for their comprehensive analysis are limited. To enable global comparison of spectratype data from different individuals, we developed a robust statistical method based on k-means clustering analysis, and applied this method to analysis of the αβ TCR repertoires in the peripheral blood of 50 MDS patients and 23 age-matched healthy controls. From each of the 23 CDR3 length distributions (one for each of 23 Vβ families) comprising each spectratype, 4 features were extracted: the number of peaks in the distribution, the relative height of the largest peak, the skewness of the distribution, and the kurtosis. K-means clustering was applied at the Vβ family level to the extracted feature data from the CDR3 length distributions across the 73 subjects. This analysis typically identified two distinct clusters: a “normal” cluster characterized by a higher number of peaks, lower maximum relative height, lower skewness, and lower kurtosis, and a second “abnormal” cluster with the opposite characteristics. Thus, each CDR3 length distribution was classified as normal or abnormal according to its assignment to one of these two clusters. The mean number of abnormal CDR3 length distributions per individual was 1.6 (range, 0 to 5) for the age-matched controls and 3.7 (range, 1 to 18) (p=0.03) for the MDS patients. K-means clustering was also applied at the individual level to composite datasets consisting of the four features extracted from each of the 23 CRD3 length distributions in each individual’s spectratype. This higher-level clustering again generated 2 clusters. The “normal” cluster contained all of the age-matched control subjects as well as 39 MDS patients, while the “abnormal” cluster contained the remaining 11 MDS patients, all of whom had profoundly abnormal TCR Vβ spectratypes. The median age in the abnormal group was higher than in the normal group, 67 versus 61 years, respectively (p=0.031). When the individuals in the two groups were analyzed according to the IPSS and WHO classification systems, 82% of patients in the abnormal group had high-risk disease (IPSS int-2 and high), compared with only 45% in the normal group (p=0.03), and 73% of patients in the abnormal group had advanced disease by WHO classification (>5% blasts), as opposed to 41% in the normal group (p=0.027). The 11 MDS patients in the abnormal cluster also had a higher median expression level of the Wilms’ tumor-1 (WT1) gene, as determined by quantitative RT-PCR, in the peripheral blood (0.034 versus 0.0062, p=0.047), and a higher median bone marrow blast count (10% versus 2%, p=0.056). The two groups of MDS patients were evaluated for potential differences in three variables that could potentially confound the analysis of TCR Vβ spectratyping: peripheral blood lymphopenia, active infection, and a history of transfusion. The median peripheral blood lymphocyte count (1310 × 103/ml versus 1410 × 103/ml, respectively; p=0.37), a history of transfusion (70% versus 70%), and the incidence of MDS-related infection (27% versus 21%, respectively, p=0.69), as defined by a viral, fungal or bacterial infection identified after the diagnosis of MDS but before sample acquisition, were also not significantly different between the normal and abnormal groups. In order to evaluate the stability of spectratypes over time and during therapy, serial Vβ spectratyping analysis of bone marrow mononuclear cells was performed in 4 patients before and after treatment with azacytidine and etanercept. In all 4 cases, the spectratypes remained stably abnormal over months of observation, during which time 2 patients achieved complete and 2 achieved partial remissions of their disease. In conclusion, we have developed a new statistical algorithm for spectratyping analysis that enabled the identification of a group of MDS patients with high-risk disease and highly abnormal αβ TCR repertoires. These findings further highlight the biological and clinical heterogeneity of MDS and provide the rationale for further studies of the αβ TCR repertoire in MDS.
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Nuruzzaman, Md, Md Shohel Rana, Aleya Ferdausi, Md Monjurul Huda, Lutful Hassan, and Shamsun Nahar Begum. "Clustering and Principal Component Analysis of Nerica Mutant Rice Lines Growing Under Rainfed Condition." International Journal of Applied Sciences and Biotechnology 7, no. 3 (2019): 327–34. http://dx.doi.org/10.3126/ijasbt.v7i3.25703.
Texte intégralRésumé :
A field experiment was conducted at subtropical region in Bangladesh to assess the contribution of morphological traits to variability in some NERICA mutant rice lines. The experiment was conducted following RCBD with three replications. Thirty-one NERICA rice genotypes (twenty-eight mutant lines along with three parents) of advanced generations were used. Data were collected on twelve morphological traits. The results of the principal component analysis showed that the first four components account for 80% of total variation giving a clear idea of the structure underlying the variables analyzed. This result was also supported by scree test. Cluster analysis using Ward's method classified the thirty-one genotypes into five distinct groups. The maximum inter-cluster distance was observed between clusters indicating the possibility of high heterosis if individuals from these clusters are cross-bred. The results of PCA were closely in line with those of the cluster analysis. These results can now be used by breeders to develop drought tolerant high yielding rice varieties and new breeding protocols for rice improvement.
 Int. J. Appl. Sci. Biotechnol. Vol 7(3): 327-334
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Paoloni, Francesco, Federica Pecci, Giulia Bruschi, et al. "Pan-cancer G2C-Pro: A two-stage Gaussian clustering to prognostically stratify patients with advanced tumors treated with immune checkpoint inhibitors." Journal of Clinical Oncology 42, no. 16_suppl (2024): e13605-e13605. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e13605.
Texte intégralRésumé :
e13605 Background: Immune checkpoint inhibitors (ICIs) have revamped the clinical outcomes of patients (pts) affected by advanced solid tumors. The aim of our study is to develop a two-stage Gaussian Clustering algorithm (G2C) integrated with a logistic regression model (Pro) to prognostically stratifypts with advanced solid tumors treated with ICIs (pan-cancer G2C-Pro) based on baseline features. Methods: Data extraction involved, retrospectively, pts with advanced solid tumors (lung, melanoma, renal cancer, head and neck, urothelial carcinoma) treated with ICIs at Department of Medical Oncology, Ancona. Baseline body mass composition (BC) was assessed through computed tomography (CT) scan at L3 level, abstracting subcutaneous and visceral fat and muscle mass indicators. Moreover, baseline clinicopathologic features, nutritional status through The Controlling Nutritional Status (CONUT) score, and comorbidities were collected. An unsupervised clustering analysis was used to identify two groups of pts with different BC phenotype risk groups. Then, another unsupervised clustering analysis was used to identify two groups of pts within the dataset that hold prognostic significance according to BC risk groups, clinicopathologic features, CONUT score, and comorbidities. G2C was constructed by sequentially integrating two Gaussian Mixture models, each employing K-means++ initialization. Next, Pro was used to predict clusters’ label. The metrics used to evaluate the average performance of Pro on test sets were ACCuracy (ACC) and Area Under the Curve (AUC). The model was developed in Python on-cloud using the Google Colab service. Results: A total of 87 pts with complete data available were included in the final analysis. The two generated clusters for BC phenotype were BC_Low_Risk (n = 39) and BC_High_Risk (n = 48). Then, considering BC risk groups, clinicopathologic features, nutritional status, and comorbidities, two generated clusters were cluster 1 (n = 55) and cluster 2 (n = 32). Looking at clinical outcomes, median progression free survival was 16.1 months for cluster 1 versus 7.1 months for cluster 2 (HR: 0.57, 95% CI: 0.33-0.98, p = 0.04), and median overall survival was 41.8 months for cluster 1 versus 10.5 months for cluster 2 (HR: 0.47, 95% CI: 0.27-0.83, p = 0.008). The average ACC and AUC across all splits achieved by Pro model for patient classification into clusters were 0.94 and 0.89, respectively. From the feature ranking, it emerged that the one with higher importance was CONUT score, followed by BC phenotype risk groups and neutrophil-to-lymphocyte ratio. Conclusions: By using an easy-to-obtain and reproducible baseline BC, clinicopathologic, nutritional features, and comorbidities, we demonstrated that pan-cancer G2C-Pro is a promising and accurate prognostic model for stratifying pts with advanced tumors treated with ICIs.
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Mayvani, Titov Chuk's, Rifai Afin, Alifah Rokhmah Idialis, and Sariyani Sariyani. "Analysis of Growth and Tourism Clusters in Madura." Jurnal Ekonomi dan Studi Pembangunan 14, no. 1 (2022): 59. http://dx.doi.org/10.17977/um002v14i12022p059.
Texte intégralRésumé :
The tourism sector is one of the essential sectors that drive economic growth. There are many tourist destinations in Madura Island that have uniqueness, where the tourism is based on beaches, culture, history, and even religion. However, the existence of tourist destinations has not been widely felt by the Madurese community economically. Therefore, tourism development in Madura needs attention, considering the tourism sector plays a vital role in encouraging economic growth. This research uses scalogram analysis and K-Means Clustering analysis. The scalogram analysis consists of Location Coefficient (LC) analysis which reflects the level of importance of a facility in an area, and functional index analysis, which is used to measure the hierarchy of facilities in each district or region. Meanwhile, the K-Means Clustering analysis is intended to see the Madura tourism clusters. This study indicates that Sumenep Regency can be the centre of tourism growth in attractions, amenities, and accessibility. The analysis shows that Sumenep Regency is the closest distance to the cluster centre or can be categorized as an advanced cluster. Then, Pamekasan Regency is a less developed cluster because it is far from the cluster centre. This research provides some recommendation in increasing economic growth in Madura.
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Singha, Tanoy, Md Asif Mahamud, Shahin Imran, et al. "Genetic diversity analysis of advanced rice lines for salt tolerance using SSR markers." Asian Journal of Medical and Biological Research 7, no. 2 (2021): 214–21. http://dx.doi.org/10.3329/ajmbr.v7i2.55001.
Texte intégralRésumé :
Twenty-two rice lines were used to evaluate salt tolerance at the Laboratory of Biotechnology Division of Bangladesh Institute of Nuclear Agriculture (BINA), Mymensingh. Four SSR markers viz. AP3206f, RM1287, RM7075, and RM10793 were used to screen the germplasm for salt tolerance. SSR analysis revealed that the number of alleles per locus ranged from 3 to 5 with an average of 4.25 alleles per locus. Polymorphism Information Content (PIC) values ranged from 0.4762 (RM7075) to 0.7524 (AP3206f) with an average of 0.61 per locus. The highest genetic diversity (0.7810) was observed in loci AP3206f, and the lowest genetic diversity (0.5620) was observed in loci RM7075 with a mean diversity of 0.6663. The genotypes with genetic similarity clustered together in the dendrogram based on UPGMA method and we observed seven major clusters where cluster I contained most of the genotypes. Cluster I, II, III, IV, V, VI, and VII contained 6, 1, 2, 4, 4, 4 and 1 genotypes, respectively. These results revealed that marker AP3206f would be best in screening 22 rice genotypes followed by RM1287, RM7075, and RM10793 according to PIC values. These findings can have the potential role for further improvement of salinity tolerance rice genotypes through marker-assisted breeding. Asian J. Med. Biol. Res. 2021, 7 (2), 214-221
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Sharma, Abhinav, Yinggan Zheng, Justin A. Ezekowitz, et al. "Cluster Analysis of Cardiovascular Phenotypes in Patients With Type 2 Diabetes and Established Atherosclerotic Cardiovascular Disease: A Potential Approach to Precision Medicine." Diabetes Care 45, no. 1 (2021): 204–12. http://dx.doi.org/10.2337/dc20-2806.
Texte intégralRésumé :
OBJECTIVE Phenotypic heterogeneity among patients with type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD) is ill defined. We used cluster analysis machine-learning algorithms to identify phenotypes among trial participants with T2DM and ASCVD. RESEARCH DESIGN AND METHODS We used data from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS) study (n = 14,671), a cardiovascular outcome safety trial comparing sitagliptin with placebo in patients with T2DM and ASCVD (median follow-up 3.0 years). Cluster analysis using 40 baseline variables was conducted, with associations between clusters and the primary composite outcome (cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina) assessed by Cox proportional hazards models. We replicated the results using the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial. RESULTS Four distinct phenotypes were identified: cluster I included Caucasian men with a high prevalence of coronary artery disease; cluster II included Asian patients with a low BMI; cluster III included women with noncoronary ASCVD disease; and cluster IV included patients with heart failure and kidney dysfunction. The primary outcome occurred, respectively, in 11.6%, 8.6%, 10.3%, and 16.8% of patients in clusters I to IV. The crude difference in cardiovascular risk for the highest versus lowest risk cluster (cluster IV vs. II) was statistically significant (hazard ratio 2.74 [95% CI 2.29–3.29]). Similar phenotypes and outcomes were identified in EXSCEL. CONCLUSIONS In patients with T2DM and ASCVD, cluster analysis identified four clinically distinct groups. Further cardiovascular phenotyping is warranted to inform patient care and optimize clinical trial designs.
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Pachauri, Atul Kumar, Deepak Gauraha, Sant Ram Sahu, Praveen Pujari, and Deepak Saran. "Molecular Diversity and Population Structure Analysis in Rice Genotype using SSR Markers." Environment and Ecology 41, no. 3C (2023): 1883–90. http://dx.doi.org/10.60151/envec/jwhx9999.
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The use of advanced molecular technologies is one possible approach to understand their diversity. A study was undertaken to evaluate forty two rice genotype including six checks during kharif 2019. The result of the investigation was allele number was recorded for the five markers with an average of 2.6 allele per locus. Out of twenty five markers 12 markers showed highly PIC value. However, UPGMA cluster diagram mean of 67% level of similarity showed forty two accessions into 10 distinct clusters. Maximum cluster II had 13 genotypes followed by cluster IV, X, III and IX consisting of eleven, six three and two genotype respectively. Population structure analysis population inferred ancestry, 10 pure accessions were assigned to subgroup SG1 whereas 30 pure accessions were assigned to subgroup SG2 and two lines were assigned to admixture (AD). It was observed that the genotypes of subgroup 1 have brown planthopper resistant, whereas the genotypes of subgroup 2 have high yielding.
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Nasir, Umar, Haseeb Ahmad, Sahibzadi Fatima Tariq, Mahwish zeb, Fiza Shafiq, and Asfandyar Qureshi. "An Investigation to Assess Occlusal and Psychological Parameters in Bruxism." Pakistan Journal of Medical and Health Sciences 16, no. 2 (2022): 145–47. http://dx.doi.org/10.53350/pjmhs22162145.
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Aim: To assess the anxiety and occlusal features in bruxism by means of T-Scan III and Hospital Anxiety and Depression Scale correspondingly. Study design: Case control study Place and duration of study: Department of Oral Medicine, Khyber Medical University-Institute of Dental Sciences, Kohat from 1st December 2020 to 30th November 2021. Methodology: This study comprised of a cluster of fifty patients with bruxism (Cluster Bxm) and fifty healthy persons as control cluster (Cluster NBxm). Patients were nominated from outdoor patients coming to Private Dental Teaching Hospital in Peshawar with the principal grievance of sensitivity of the teeth due to routine crushing. For the selection of cases, American Academy of Sleep Medicine (AASM) was followed. Supplementary grounded on assessment of era and sex, controls were nominated. Hospital Anxiety and Depression Scale (HADS) survey was asked equally from the clusters to assess the depression and anxiety. Record of occlusal strictures in both the clusters was completed numerically by using T-Scan III. Results: Cluster Bxm had expressively superior mean tooth wear index (20.35±9.7) than cluster NBxm (10.20±7.29). Cluster Bxm had ominously advanced anxiety (13.33±3.97/9.17±1.92) and depression scores (9±1.83/7.17±2.34) as equated to NBxm. The disclusion period of cluster Bxm was 0.953±0.860 and that of cluster NBxm was 0.358±0.390 (p=0.009). Conclusions: Patients with advanced stage of depression, anxiety and amplified disclusion period may have more fondness to misery from bruxism (p<0.05). Keywords: Bruxism, Depression, American Academy of Sleep Medicine (AASM), Tooth wear, Anxiety, Digital occlusal analysis
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JAVED, A., N. N. NAWAB, S. GOHAR, et al. "GENETIC ANALYSIS AND HETEROTIC STUDIES IN TOMATO (SOLANUM LYCOPERSICUM L.) HYBRIDS FOR FRUIT YIELD AND ITS RELATED TRAITS." SABRAO Journal of Breeding and Genetics 54, no. 3 (2022): 492–501. http://dx.doi.org/10.54910/sabrao2022.54.3.3.
Texte intégralRésumé :
A study was conducted to determine the types of gene action of different yield-related traits in tomato and the combining ability in four advanced lines. The heterotic response in tomato hybrids was also assessed. Analysis of variance (ANOVA) revealed significant differences (P ≤ 0.05) among all the traits. General combining ability (GCA) and specific combining ability (SCA) for all chosen traits were computed using Griffing’s approach of diallel. Combining ability revealed the additive and non-additive genetic effects for all selected traits of advanced lines. T-1360 was found as a good general combiner for the number of cluster plant-1, average fruit weight, number of flowers cluster-1, fruit length, number of fruit cluster-1, and yield. The variance of the GCA to SCA ratio was found less than 0.5 for each trait, which confirmed the presence of non-additive gene action. The results revealed higher magnitudes of phenotypic coefficient of variance (PCV) than the genotypic coefficient of variance (GCV). The high magnitudes of heritability (72% to 92%) and genetic advance (36.63% to 139.72%) were found for the number of cluster plant-1, average fruit weight (g), the number of fruits cluster-1, and yield. Among all crosses, the cross ST-100 × T-1360 showed maximum positive heterosis over the mid parent (566.6%) and the better parent (455.5%). The identified tomato genotypes can be used further in different tomato breeding programs to improve fruit yield and other yield-related traits.
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Kumar, Vikas, Olga Smirnova, and Lyudmila Chesnyukova. "Technological development of smart industrial regions in Russia: A cluster analysis." E3S Web of Conferences 435 (2023): 03001. http://dx.doi.org/10.1051/e3sconf/202343503001.
Texte intégralRésumé :
The article is intended to assess the level of technological development of Russian industrial regions using the method of statistical cluster analysis and discusses some special features associated with the formation of smart (promising) regional clusters. The work is based on the statistical data on production capacity, investments in scientific and technological progress, the level of production automation and other indicators characterizing the technological development of industry sectors. We evaluate these indicators and identify the regions with the highest level of technological development as well as those lagging behind the average values. The authors prove the importance of enhancing the industry’s technological base to increase the competitiveness of the regions and the entire country. The positive impact of advanced technologies on the development of smart (promising) regional clusters is emphasized.
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Zhou, Jian, Alexandra Vorobyeva, Qiyue Luan, and Ian Papautsky. "Single Cell Analysis of Inertial Migration by Circulating Tumor Cells and Clusters." Micromachines 14, no. 4 (2023): 787. http://dx.doi.org/10.3390/mi14040787.
Texte intégralRésumé :
Single-cell analysis provides a wealth of information regarding the molecular landscape of the tumor cells responding to extracellular stimulations, which has greatly advanced the research in cancer biology. In this work, we adapt such a concept for the analysis of inertial migration of cells and clusters, which is promising for cancer liquid biopsy, by isolation and detection of circulating tumor cells (CTCs) and CTC clusters. Using high-speed camera tracking live individual tumor cells and cell clusters, the behavior of inertial migration was profiled in unprecedented detail. We found that inertial migration is heterogeneous spatially, depending on the initial cross-sectional location. The lateral migration velocity peaks at about 25% of the channel width away from the sidewalls for both single cells and clusters. More importantly, while the doublets of the cell clusters migrate significantly faster than single cells (~two times faster), cell triplets unexpectedly have similar migration velocities to doublets, which seemingly disagrees with the size-dependent nature of inertial migration. Further analysis indicates that the cluster shape or format (for example, triplets can be in string format or triangle format) plays a significant role in the migration of more complex cell clusters. We found that the migration velocity of a string triplet is statistically comparable to that of a single cell while the triangle triplets can migrate slightly faster than doublets, suggesting that size-based sorting of cells and clusters can be challenging depending on the cluster format. Undoubtedly, these new findings need to be considered in the translation of inertial microfluidic technology for CTC cluster detection.
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., Naresh, Mohinder Singh Dalal, Lalit Kumar, Suman Devi, Amit ., and Rukoo Chawla. "Assessment of genetic diversity for heat tolerance in advanced breeding lines of bread wheat (Triticum aestivum L. em.Thell.)." Environment and Ecology 41, no. 4B (2023): 2679–87. http://dx.doi.org/10.60151/envec/mcsm4521.
Texte intégralRésumé :
Food security and public health are becoming major concerns for the global leaders due to climate change. The uneven distribution of rainfall and temperature has increased the global food demand with quality food. The current study was carried out for analysis of genetic diversity among 64 bread wheat genotypes for heat tolerance based on 21 morpho-physiological traits. The sixty four genotypes were grouped into five different clusters. Maximum number of genotypes was in cluster V (17) with lowest intra cluster distance (3.866) followed by cluster II (15), I (14), III and IV (each having 9 genotypes). Genotypes of cluster IV and I were more genetically diverse due to maximum inter cluster distance between them (8.873). The cluster II was designated as “highly tolerant” while cluster I and V as “moderately tolerant” and “highly sensitive” respectively, to heat stress on the basis of comparison of cluster mean values for yield and its major contributing traits like, peduncle length, flag leaf length, grain filling duration and so on. By collating their mean performance, the genotypes P-13348, P-13676, P-13820 and P- 14114 were found to be more heat tolerant in cluster II. Similarly, genotypes P-13808, P-13638 and P-14050 were found more moderately tolerant among cluster I genotypes and genotypes P-14106, P-14112 and P-14121 were most sensitive among the cluster V genotypes to terminal heat stress.
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Boshev, Dane, Mirjana Jankulovska, Sonja Ivanovska, and Ljupcho Jankuloski. "Assesment of winter wheat advanced lines by use of multivariate statistical analyses." Genetika 48, no. 3 (2016): 991–1001. http://dx.doi.org/10.2298/gensr1603991b.
Texte intégralRésumé :
This study was conducted to evaluate 49 advanced lines of winter wheat (Triticum aestivum L.) for their morphoagronomic traits and to determine best criteria for selection of lines to be included in future breeding program. The material was assessed in two years experiment at two locations, using RCBD design with three replications. Ten quantitative traits: plant height, number of fertile tillers, spike length, number of spikelets per spike, number of grains per spike, weight of grain per spike and per plant, fertility, biological yield and harvest index were evaluated by PCA and two-way cluster analysis. Three main principal components were determined explaining 71.391% of the total variation among the genotypes. One third of the variation is explained by PC1 which reflects the genotype yield potential. PC2 and PC3 explained 25.22% and 15.49% of the total variance, mostly in relation to the plant height and spike components, respectively. Biplot graph revealed strongest positive association between spike length, number of spikelets and biological yield and between number of tillers, weight of grains per spike and per plant. Two-way cluster analysis resulted with a dendrogram with one solely separated genotype, superior for all traits and two main clusters of genotypes defined with wide genetic diversity especially between the groups within the second cluster. Genotypes with high values for specific traits will be included in the future breeding programmes. Classification of genotypes and the extend of variation among them illustrated on the heatmap has proved to be practical tool for selecting genotypes with desired traits in the early stages of the breeding process.
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Hermansyah, Hermansyah, Agung Riyadi, and Rina Delfina. "Cluster Analysis of the Productivity of Nurses’ Work." Jurnal Keperawatan Indonesia 25, no. 3 (2022): 136–44. http://dx.doi.org/10.7454/jki.v25i3.1411.
Texte intégralRésumé :
Productivity is a measure of performance, including effectiveness and efficiency. The importance of work productivity for nurses includes its evaluation role in contributing to continuous improvement. The purpose of this study is to determine the classification of nurses in clusters based on work productivity in the inpatient room. It is an analytic study with a cross-sectional design. The study sample were 130 nurses in the inpatient room at the Bengkulu Provincial Hospital, selected using the proportional random sampling technique. A questionnaire was employed for the data collection. Data analysis was performed univariately, and multivariately with cluster analysis. The study results involved clusters I-III, which comprised nurses with high, medium and low work productivity. The variables of motivation, management, work environment, achievement opportunities, work climate, income, workload, work ethic, and work discipline have a significant effect on the formation of the cluster (p < 0.001). Cluster I comprised 69 nurses, cluster II 53 nurses and cluster III eight. A need is shown for clarity of organizational structure, job descriptions, the granting of authority and responsibility, creation of a work system that encourages innovation, provision of facilities, clarity of Nursing Care Standard (NCS), work guidelines, and Standard Operational Procedure (SOP). Abstrak Analisis Klaster Produktivitas Kinerja Perawat. Produktivitas merupakan salah satu alat ukur kinerja, termasuk efektivitas dan efisiensi. Produktivitas menjadi penting bagi perawat karena menjadi tolak ukur dalam evaluasi untuk perbaikan yang berkelanjutan. Tujuan penelitian ini adalah untuk mengetahui klasifikasi perawat dalam klaster berdasarkan produktivitas kerja di ruang rawat inap dengan menggunakan jenis penelitian analitik dan desain studi cross-sectional. Sampel pada penelitian adalah 130 perawat pelaksana di ruangan rawat inap di Rumah Sakit Provinsi Bengkulu, diambil dengan teknik proportional random sampling. Pengumpulan data menggunakan kuesioner. Analisis data dilakukan secara univariat dan multivariat dengan analisis klaster. Hasil penelitian terdiri dari klaster I-III yang menunjukkan perawat dengan produktivitas kerja tinggi, sedang, dan rendah. Variabel motivasi, manajemen, lingkungan kerja, kesempatan berprestasi, iklim kerja, penghasilan, beban kerja, etos kerja, dan disiplin kerja berpengaruh signifikan terhadap terbentuknya klaster (p < 0,001), dan jumlah anggota klaster I adalah 69 perawat pelaksana, jumlah anggota klaster II adalah 53 perawat pelaksana, sedangkan jumlah anggota klaster III adalah 8 perawat pelaksana. Perlunya kejelasan struktur organisasi, uraian tugas, pemberian wewenang, dan tanggung jawab, dapat menciptakan sistem kerja yang mendorong inovasi, penyediaan fasilitas yang mendukung kinerja, kejelasan standar asuhan keperawatan, pedoman kerja, dan standar operasional prosedur. Kata Kunci: analisis klaster, perawat, produktivitas kerja
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GIACOMIN, VALERIA. "The Emergence of an Export Cluster: Traders and Palm Oil in Early Twentieth-Century Southeast Asia." Enterprise & Society 19, no. 2 (2017): 272–308. http://dx.doi.org/10.1017/eso.2017.10.
Texte intégralRésumé :
Malaysia and Indonesia account for 90 percent of global exports of palm oil, forming one of the largest agricultural clusters in the world. This article uses archival sources to trace how this cluster emerged from the rubber business in the era of British and Dutch colonialism. Specifically, the rise of palm oil in this region was due to three interrelated factors: (1) the institutional environment of the existing rubber cluster; (2) an established community of foreign traders; and (3) a trading hub in Singapore that offered a multitude of advanced services. This analysis stresses the historical dimension of clusters, which has been neglected in the previous management and strategy works, by connecting cluster emergence to the business history of trading firms. The article also extends the current literature on cluster emergence by showing that the rise of this cluster occurred parallel, and intimately related, to the product specialization within international trading houses.
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Perren, Gabriel I., Ruben A. Vázquez, Andrés E. Piatti, and André Moitinho. "OCAAT: automated analysis of star cluster colour-magnitude diagrams for gauging the local distance scale." Proceedings of the International Astronomical Union 10, S306 (2014): 298–300. http://dx.doi.org/10.1017/s1743921314011077.
Texte intégralRésumé :
AbstractStar clusters are among the fundamental astrophysical objects used in setting the local distance scale. Despite its crucial importance, the accurate determination of the distances to the Magellanic Clouds (SMC/LMC) remains a fuzzy step in the cosmological distance ladder. The exquisite astrometry of the recently launched ESA Gaia mission is expected to deliver extremely accurate statistical parallaxes, and thus distances, to the SMC/LMC. However, an independent SMC/LMC distance determination via main sequence fitting of star clusters provides an important validation check point for the Gaia distances. This has been a valuable lesson learnt from the famous Hipparcos Pleiades distance discrepancy problem. Current observations will allow hundreds of LMC/SMC clusters to be analyzed in this light.Today, the most common approach for star cluster main sequence fitting is still by eye. The process is intrinsically subjective and affected by large uncertainties, especially when applied to poorly populated clusters. It is also, clearly, not an efficient route for addressing the analysis of hundreds, or thousands, of star clusters. These concerns, together with a new attitude towards advanced statistical techniques in astronomy and the availability of powerful computers, have led to the emergence of software packages designed for analyzing star cluster photometry. With a few rare exceptions, those packages are not publicly available.Here we present OCAAT (Open Cluster Automated Analysis Tool), a suite of publicly available open source tools that fully automatises cluster isochrone fitting. The code will be applied to a large set of hundreds of open clusters observed in the Washington system, located in the Milky Way and the Magellanic Clouds. This will allow us to generate an objective and homogeneous catalog of distances up to ~ 60 kpc along with its associated reddening, ages and metallicities and uncertainty estimates.
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Koziol, J. A. "Cluster Analysis of Antigenic Profiles of Tumors: Selection of Number of Clusters Using Akaike’s Information Criterion." Methods of Information in Medicine 29, no. 03 (1990): 200–204. http://dx.doi.org/10.1055/s-0038-1634783.
Texte intégralRésumé :
AbstractA basic problem of cluster analysis is the determination or selection of the number of clusters evinced in any set of data. We address this issue with multinomial data using Akaike’s information criterion and demonstrate its utility in identifying an appropriate number of clusters of tumor types with similar profiles of cell surface antigens.
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Mensen, Armand, and Ramin Khatami. "Advanced EEG analysis using threshold-free cluster-enhancement and non-parametric statistics." NeuroImage 67 (February 2013): 111–18. http://dx.doi.org/10.1016/j.neuroimage.2012.10.027.
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Jeong, Sona, and Ji Na Jeong. "Analysis of Research Trends in Korean Dentistry Journals by Assigning MeSH to Author Keywords." Journal of Korean Medical Library Association 47, no. 1_2 (2020): 1–20. http://dx.doi.org/10.69528/jkmla.2020.47.1_2.1.
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Researchers seek to identify optimal journals for submission based on their studies but tend to rely on journal impact factors or scientific journal rankings. We investigated research trends by selecting highfrequency words from author keywords (AKs), analyzing subject areas, and performing quantitative data analysis of Korean dental journals. Consequently, we suggest a method for choosing journals that fit a specific subject area. We used a corpus of 9 Korean dentistry journals regarded in Korea as quality internationally approved journals. AKs occurring more than 10 times were assigned to Medical Subject Headings (MeSH) terms and subcategories, which were then categorized using the MeSH tree structure. Knowledge- Matrix Plus and VOSviewer were used to analyze network relationships, density, and clustering. The AKs were of 7527 types, 15,960 terms, and formed 54 clusters. The AKs with 10+ occurrence were 199 types, 4289 terms, and formed 9 clusters. Assigning the AKs with 10+ occurrence to MeSH terms led to expanding 732 types of AK terms into 249 types with 9 clusters and 4268 links. Core study areas over the past 10 years were facial asymmetry, a topic under oral surgery and medicine, and orthognathic surgery focused on mandibular fractures, followed by shear bond strength of zirconia. Analyzing 16 MeSH subject categories, we found that the “analytical, diagnostic and therapeutic techniques and equipment” category had the largest distribution of AKs (40.7%). This was followed by “diseases” (21.2%) and “anatomy” (14.90%). The orthognathic surgery cluster was the largest, followed by the shear bond strength cluster. Dental implants is a core area with strong links to highoccurrence words, such as cone-beam computed tomography and mandible, which were distributed in the order of The Journal of Advanced Prosthodontics (37.8%) and Journal of Periodontal & Implant Science (30.6%). Five clusters were closely packed in the center, 2 clusters were formed above the center, 1 cluster was formed below the center, and a cluster on the right was widespread. Cluster analysis using AKs and MeSH may be a good analytic method for researchers to determine expanding research areas and select optimal journals for paper submission.
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Pant, Khem Raj, Deepak Pandey, Upama Adhikari, et al. "Performance evaluation of advanced durum wheat genotypes under irrigated condition at Bhairahawa, Nepal." Archives of Agriculture and Environmental Science 9, no. 2 (2024): 206–15. http://dx.doi.org/10.26832/24566632.2024.090202.
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A field research was carried out at the National Wheat Research Program (NWRP) in Bhairahawa, Nepal in 2022 to investigate elite durum wheat genotypes and key traits contributing to grain yield. The experiment was performed in an alpha lattice design with two replications. Thirty distinct durum wheat genotypes were assessed, focusing on fourteen quantitative traits including days to booting, days to heading, days to maturity, plant height, spike length, peduncle length, number of tillers per square meter, number of spikes per square meter, number of grains per spike, grain weight per spike, thousand kernel weight, grain yield, biomass yield, chlorophyll content. The studied genotypes were grown under irrigated condition. Genotype NL1779 attained the highest grain yield of 3828 kg/ha, followed by NL1769 (3784 kg/ha), NL1772 (3726 kg/ha), NL1789 (3640 kg/ha) and NL1784 (3570 kg/ha). Principal components analysis revealed that eight traits were the major loadings on the first two principal components that describe 53.4% of the total morphological variance at irrigated condition. Cluster analysis grouped the different genotypes into four clusters, with each cluster showing variation in performance for different traits under irrigated conditions. Cluster III is characterized by genotypes exhibiting the highest grain yield, biomass yield, spike length, number of grains per spike, and number of spikes per square meter. Notably, the high-yielding genotypes NL1779, NL1769, NL1772, NL1789, NL1784, and NL1773 identified within this cluster could serve as potential candidates for inclusion in the national breeding program. These superior genotypes could be recommended for irrigated environment after further evaluation. Integrating them into national breeding programs offers an opportunity for genetic improvement, contributing to establishing a robust durum wheat production system in Nepal, meeting the growing demand for durum wheat products while promoting dietary diversity and sustainable agriculture.
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Maclean, Rory H., Peter Jacob, Pratik Choudhary, et al. "Hypoglycemia Subtypes in Type 1 Diabetes: An Exploration of the Hypoglycemia Fear Survey-II." Diabetes Care 45, no. 3 (2022): 538–46. http://dx.doi.org/10.2337/dc21-1120.
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OBJECTIVE The Hypoglycemia Fear Survey-II (HFS-II) is a well-validated measure of fear of hypoglycemia in people with type 1 diabetes. The aim of this study was to explore the relationships between hypoglycemia worries, behaviors, and cognitive barriers to hypoglycemia avoidance and hypoglycemia awareness status, severe hypoglycemia, and HbA1c. RESEARCH DESIGN AND METHODS Participants with type 1 diabetes (n = 178), with the study population enriched for people at risk for severe hypoglycemia (49%), completed questionnaires for assessing hypoglycemia fear (HFS-II), hyperglycemia avoidance (Hyperglycemia Avoidance Scale [HAS]), diabetes distress (Problem Areas In Diabetes [PAID]), and cognitive barriers to hypoglycemia avoidance (Attitudes to Awareness of Hypoglycemia [A2A]). Exploratory factor analysis was applied to the HFS-II. We sought to establish clusters based on HFS-II, A2A, Gold, HAS, and PAID using k-means clustering. RESULTS Four HFS-II factors were identified: Sought Safety, Restricted Activity, Ran High, and Worry. While Sought Safety, Restricted Activity, and Worry increased with progressively impaired awareness and recurrent severe hypoglycemia, Ran High did not. With cluster analysis we outlined four clusters: two clusters with preserved hypoglycemia awareness were differentiated by low fear/low cognitive barriers to hypoglycemia avoidance (cluster 1) versus high fear and distress and increased Ran High behaviors (cluster 2). Two clusters with impaired hypoglycemia awareness were differentiated by low fear/high cognitive barriers (cluster 3) as well as high fear/low cognitive barriers (cluster 4). CONCLUSIONS This is the first study to define clusters of hypoglycemia experience by worry, behaviors, and cognitive barriers to hypoglycemia avoidance. The resulting subtypes may be important in understanding and treating problematic hypoglycemia.
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Kwekkeboom, Kristine L., Erin S. Costanzo, and Toby Christopher Campbell. "Stress biomarkers in advanced cancer patients experiencing the pain, fatigue, sleep disturbance symptom cluster." Journal of Clinical Oncology 33, no. 29_suppl (2015): 5. http://dx.doi.org/10.1200/jco.2015.33.29_suppl.5.
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5 Background: Cancer symptom clusters such as co-occurring pain, fatigue, and sleep disturbance, are common and debilitating for patients with advanced disease. Stress-related neuorendocrine system alterations are thought to play a significant role in symptom co-occurrence. While studies have documented relationships between stress biomarkers and symptoms in persons with cancer, few have done so in the context of a specific symptom cluster or among persons receiving treatment for advanced disease. Objectives: This preliminary analysis describes biomarkers of neuroendocrine stress systems – salivary cortisol and salivary alpha amylase (sAA) – and their relationship with the pain, fatigue, sleep disturbance symptom cluster in cancer. Methods: We analyzed baseline data from 14 participants of a RCT of a cognitive-behavioral symptom cluster intervention. Participants were receiving chemotherapy for recurrent or metastatic cancer. The sample was largely female (93%), aged 50-74 years old (M=63.57), with lung (57%), breast (14%), GYN (22%) or prostate (7%) cancer. Participants reported symptom cluster severity and collected saliva over two days prior to a new chemotherapy cycle. Cortisol concentrations were determined by luminescence immunoassay and salivary alpha-amylase by enzyme kinetic reaction assay using standardized kits (Salimetrics, State College, PA). Results: Mean (SD) cortisol and sAA levels were within normal range and followed typical diurnal patterns (Table 1); although evening levels of cortisol appeared higher in this sample compared to those of healthy adults. Low to moderate observed correlations between symptom cluster severity and stress biomarkers (evening cortisol r = -.204; evening sAA r = .326) were not significant in this small sample. Conclusions: Elevated evening cortisol levels may suggest dysregulation of the stress response in this population. The ongoing study will further evaluate if alterations in neuroendocrine function contribute to the symptom cluster experience. Clinical trial information: NCT01954420. [Table: see text]
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Moss, Maxamillian A. N., Dagen D. Hughes, Ian Crawford, Martin W. Gallagher, Michael J. Flynn, and David O. Topping. "Comparative Analysis of Traditional and Advanced Clustering Techniques in Bioaerosol Data: Evaluating the Efficacy of K-Means, HCA, and GenieClust with and without Autoencoder Integration." Atmosphere 14, no. 9 (2023): 1416. http://dx.doi.org/10.3390/atmos14091416.
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In a comparative study contrasting new and traditional clustering techniques, the capabilities of K-means, the hierarchal clustering algorithm (HCA), and GenieClust were examined. Both K-means and HCA demonstrated strong consistency in cluster profiles and sizes, emphasizing their effectiveness in differentiating particle types and confirming that the fundamental patterns within the data were captured reliably. An added dimension to the study was the integration of an autoencoder (AE). When coupled with K-means, the AE enhanced outlier detection, particularly in identifying compositional loadings of each cluster. Conversely, whilst the AE’s application to all methods revealed a potential for noise reduction by removing infrequent, larger particles, in the case of HCA, this information distortion during the encoding process may have affected the clustering outcomes by reducing the number of observably distinct clusters. The findings from this study indicate that GenieClust, when applied both with and without an AE, was effective in delineating a notable number of distinct clusters. Furthermore, each cluster’s compositional loadings exhibited greater internal variability, distinguishing up to 3× more particle types per cluster compared to traditional means, and thus underscoring the algorithms’ ability to differentiate subtle data patterns. The work here postulates that the application of GenieClust both with and without an AE may provide important information through initial outlier detection and enriched speciation with an AE applied, evidenced by a greater number of distinct clusters within the main body of the data.
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Brasó-Maristany, Fara, Javier Cortés, José Manuel Pérez-García, et al. "Abstract PO2-15-06: ctDNA-based DNADX in hormone receptor-positive and HER2-negative (HR+/HER2-) advanced breast cancer following endocrine therapy and CDK4/6 inhibition: a correlative analysis from the randomized phase 2 PARSIFAL trial." Cancer Research 84, no. 9_Supplement (2024): PO2–15–06—PO2–15–06. http://dx.doi.org/10.1158/1538-7445.sabcs23-po2-15-06.
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Abstract ctDNA-based DNADX in hormone receptor-positive and HER2-negative (HR+/HER2-) advanced breast cancer following endocrine therapy and CDK4/6 inhibition: a correlative analysis from the randomized phase 2 PARSIFAL trial Background: DNADX, a novel machine learning-based approach, utilizes DNA from tumor tissue or plasma ctDNA to identify clinically relevant phenotypic tumor features and classify breast cancer into 4 subtypes (Nat Comm 2023). Here, we evaluated DNADX's ability to predict prognosis and treatment benefit in HR+/HER2- advanced breast cancer following endocrine therapy and a CDK4/6 inhibitor. Methods: DNADX was evaluated centrally in available baseline plasma ctDNA samples from PARSIFAL trial (NCT02491983) which randomized 486 patients (pts) with HR+/HER2- advanced breast cancer to receive (1:1 ratio) first line palbociclib with either fulvestrant or letrozole. Shallow whole genome sequencing was performed on ctDNA, and the 4 DNA-based subtypes (Clusters-1, -2, -3, and -4) were identified if the ctDNA tumor fraction (TF)≥3%. The main objective was to evaluate the association of DNADX subtypes with progression-free survival (PFS) and overall survival (OS). Secondary objective was to identify the subgroup of pts who benefit more from each endocrine treatment. Uni- and multi-variable Cox regression models were used after adjusting for TF, menopausal status, ECOG status, de novo metastasis (vs. recurrence), visceral disease and number of metastatic sites. Results: DNADX was evaluated in plasma ctDNA samples from 122 pts (25.1%). Clinical variables and median PFS (27.6 months) were similar as the overall PARSIFAL population. DNADX identified 56.6% pts with TF of &lt; 3%, 14.8% with Cluster-1, 19.7% with Cluster-2, 5.7% with Cluster-3 and 3.3% with Cluster-4. In terms of PFS, pts classified with TF&lt; 3% had a lower risk of progression compared to Cluster-1, Cluster-2, Cluster-3, Cluster-4 subtypes (pairwise PFS hazard ratios [HRs] of 1.88, 2.02, 3.15, and 5.62, respectively, with a global log-rank test of p=0.010). Similar results were obtained after adjusting for other clinical-pathologic variables. A numerical benefit of fulvestrant in comparison with letrozole was observed in pts classified in Cluster-1 and Cluster-4 (HR=0.42, 95% CI 0.14-1.24) in contrast to the other groups (HR=1.21, 95% CI 0.68-2.18), and the interaction test was statistically significant after adjusting for clinical-pathologic variables (anova p-value=0.037). In terms of OS, pts classified with TF&lt; 3% had a lower risk of death compared to Cluster-1, Cluster-2, Cluster-3, and Cluster-4 subtypes (pairwise OS HRs of 1.90, 4.23, 11.13, and 6.80, respectively, with a global log-rank test of p=0.003). In the multivariable analysis, results were consistent after adjusting for clinical-pathologic variables and TF. Conclusions: Liquid biopsy-based DNADX subtypes predict outcomes in pts with HR+/HER2- advanced breast cancer on endocrine therapy and CDK4/6 inhibitors, potentially identifying the most optimal endocrine treatment for each pt. Citation Format: Fara Brasó-Maristany, Javier Cortés, José Manuel Pérez-García, Rosario Vega-León, Laia Paré, Guillermo Villacampa, Judit Matito, Francisco Pardo, Marina Gomez-Rey, Mario Mancino, Elena Martínez-García, Carmen Mora Gallardo, Leonardo Mina, Florence Dalenc, Meritxell Bellet- Ezquerra, Manuel Ruíz - Borrego, Miguel Gil-Gil, Peter Schmid, Charles M. Perou, Joel S. Parker, Patricia Villagrasa, Ana Vivancos, Aleix Prat, Antonio Llombart-Cussac. ctDNA-based DNADX in hormone receptor-positive and HER2-negative (HR+/HER2-) advanced breast cancer following endocrine therapy and CDK4/6 inhibition: a correlative analysis from the randomized phase 2 PARSIFAL trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-15-06.
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Chen, Yu, Regina K. Irwin, Gregory W. Williams, Mary Smithson, Karin M. Hardiman, and Zechen Chong. "Abstract 4079: Single-cell RNA-seq revealed important pathways in response to neoadjuvant therapy resistance in rectal cancer." Cancer Research 82, no. 12_Supplement (2022): 4079. http://dx.doi.org/10.1158/1538-7445.am2022-4079.
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Abstract Colorectal cancer (CRC) is the second leading cause of cancer-related death in Europe and United States and is often diagnosed at advanced stage. Rectal cancer (RC) accounts for one third of all CRCs. The treatment options of RC include surgery, chemotherapy, and radiation. Locally advanced RC is treated with neoadjuvant chemoradiotherapy (nCRT) and subsequent surgery. Less than 20% of RC patients have a complete response to nCRT. Our previous work demonstrated that RC subclones respond differently to nCRT. However, the resistance mechanisms remain elusive. To explore the mechanisms underlying therapy resistance in RC, we performed single-cell RNA sequencing (scRNA-Seq) to identify and characterize the therapy-resistant RC cells. We generated patient-derived xenograft model in athymic nude mice with primary tumor cells collected from pre-treatment advanced RC patients. The compete treatment for xenograft animals includes 2 weeks of chemoradiotherapy and 1 week off. Tumor tissues were collected from mice for scRNA-Seq at a series of time points: no treatment, 2 days, 1 week, and 3 weeks after treatment. Single-cell analysis revealed five tumor cellular clusters with distinct gene expression profiles. The marker genes of cluster 1 were related to cell cycle, DNA replication, and p53 signaling pathway based on KEGG pathway enrichment analysis, which suggests that this cluster of cells are actively proliferating. Marker genes of cluster 3 were related to mineral absorption, PPAR signaling pathway, and HIF-1 signaling pathway. We compared the composition of the five cell clusters at each time point and found that while the cellular fractions of cluster 1 and cluster 3 initially dropped (2 days), they later increased (1 week and 3 weeks), suggesting that these two clusters were therapy-resistant and continued proliferating after the nCRT. Consistent with our previous DNA sequencing results, different cell populations also demonstrated tumor heterogeneity based on scRNA-seq clustering analysis of the RC xenograft model. We further compared the gene expression levels before and after treatment and identified 147 differently expressed genes (DEGs). Pathway enrichment analysis of these DEGs highlighted that signaling pathways of IL-17, TNF, MAPK, and ErbB were up-regulated posttreatment, suggesting that they may be responsible for the therapy resistance of the tumor cell clusters. These results shed light on the importance of these signaling pathways on the mechanisms of rectal cancer therapy resistance. Further work is needed to validate this discovery. Citation Format: Yu Chen, Regina K. Irwin, Gregory W. Williams, Mary Smithson, Karin M. Hardiman, Zechen Chong. Single-cell RNA-seq revealed important pathways in response to neoadjuvant therapy resistance in rectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4079.
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HASAN, NORAISHAH, Mohd Rafii Yusop, Abdul Rahim Harun, Norida Mazlan, Nusaibah Syd Ali, and Shamsiah Abdullah. "Assessment of Variability and Genetic Diversity Study in an Advanced Segregating Population in Rice with Blast Resistance Genes Introgression." Journal of Experimental Biology and Agricultural Sciences 10, no. 2 (2022): 306–17. http://dx.doi.org/10.18006/2022.10(2).306.317.
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Blast disease caused by a pathogenic fungus, Magnaphorthe oryzae, is the most destructive disease and has resulted in more than 50% of crop losses worldwide, including in Malaysia. The present study was conducted to investigate genetic variability among 36 advanced lines of MR264 × PS2 rice with blast resistance genes introduced at the Faculty of Applied Sciences, Universiti Teknologi MARA, Malaysia. Traits such as days of maturity, plant height, grain width, and seed setting rate exhibited negative skewness in this study, indicating a doubling of gene effects. Seed setting rate and 1000 grain weight showed positive kurtosis, indicating gene interactions. The phenotypic coefficient of variation (PCV) was slightly higher than the genotypic coefficient of variation (GCV) for all traits, indicating that environmental influences affect the expression of these traits. High heritability associated with high genetic advance as a percentage of the mean was observed for filled grains per panicle. In addition, the second-highest value for high heritability and the high genetic advance was observed for the number of tillers. Cluster and principal component analysis revealed that 36 advanced lines were grouped into four clusters based on ten agromorphological traits. Clusters A and C had higher mean values for most of the traits studied than clusters B and D. Desirable recombinants for higher yields with a broad genetic base can be generated by using cross lines from different clusters.
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Han, Jing, Wenjiu Yang, Dabo Wang, and Haiqing Bai. "Structure-Function Relationship between Cluster Mean Defect and Sector Peripapillary Retinal Nerve Fiber Layer Thickness in Primary Open Angle Glaucoma." Journal of Ophthalmology 2022 (July 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/5231545.
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Purpose. To determine the structure–function relationship between cluster mean defect (MD) offered by standard automated perimetry and corresponding sector peripapillary retinal nerve fiber layer thickness (pRNFLT) measured with optical coherence tomography (OCT) in primary open angle glaucoma (POAG). Method. 39 healthy eyes (control group), 43 early POAG eyes (global MD ≤ 6 dB, early group), 30 moderate POAG eyes (global MD between 6 and 12 dB, moderate group), and 53 advanced POAG eyes (global MD > 12 dB, advanced group) underwent visual field (VF) examination with Octopus perimeter (dynamic strategy/G2 pattern) and peripapillary retinal nerve fiber layer thickness measurements with RTVue-100 FD-OCT. Spearman analysis was used to investigate the correlation between cluster MDs provided by Octopus perimeter and corresponding sector pRNFLT for the total sample and each subgroup, respectively. Then, linear (y = a+ bx) and curvilinear (quadratic, y = a+bx + cx2) regression analyses were employed to investigate the model for the cluster MD-sector pRNFLT pair with significant correlation. The strength of the relationship was characterized with correlation coefficient (ρ) and coefficient of determination (R2). For the cluster–sector pair that could be fitted by both models, Wilcoxon signed rank test of absolute residuals was used to compare the goodness of fit. Results. Correlation between cluster MDs and corresponding sector pRNFLT was significant for all clusters in the total sample (ρ values: −0.572 to 0.832, P < 0.001 ) and in the POAG group (ρ values: −0.551 to −0.777, P < 0.001 ). The highest ρ values were found for cluster-sector pair 9 and pair 3, respectively. The curvilinear (quadratic) model provided better fit for all 10 cluster-sector pairs in the total sample (R2 values: 0.431–0.687, P < 0.001 ) and in the POAG group (R2 values: 0.364–0.594, P < 0.01 ). The highest R2 values were found also for cluster–sector pair 9 and pair 3, respectively. In the control group, no significant correlation was found for any cluster–sector pair ( P > 0.01 ). In the early group, correlation was significant for cluster–sector pairs 3, 8, and 9 (ρ values: −0.449, −0.627, and −0.815, resp., P < 0.01 ). In the moderate group, correlation was significant for pairs 2, 3, 8, and 9 (ρ values: −0.703, −0.556, −0.680, and −0.637, resp., P < 0.01 ). In the advanced group, correlation was significant ( P < 0.01 ) for all 10 pairs (ρ values: −0.395 to −0.699, P < 0.001 ) except for pairs 2, 3, and 8, and the highest ρ value was found for pair 1. For all cluster–sector pairs with significant correlation in the early, moderate, and advanced groups, only linear model could be fitted ( P < 0.01 ), except for pair 9 in the early group and pair 5 in the advanced group. Conclusions. Cluster MD of the Octopus visual field showed significant moderate-to-strong negative correlation and curvilinear (quadratic) relationship with the corresponding sector pRNFLT for POAG. This type of regional structure–function relationship varied according to the severity of POAG, and at each stage, the significantly correlated cluster–sector pairs mainly showed linear relationship. The results could provide guidance for better utilization of this regional structure–function method in the management of different stages of POAG.
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MASHKANTSEVA, Svetlana. "Development of transport and logistics clusters in the region transport system." Actual problems of innovative economy, no. 4 (June 27, 2019): 5–9. http://dx.doi.org/10.36887/2524-0455-2019-4-1.
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Transport and logistics clusters are aimed at optimizing the movement of freight flow in regional supply chains and concentrate the transport and logistics infrastructure of the territory, transport and logistics companies, manufacturing enterprises. The purpose of the article is to study the structure, factors and conditions of creation of transport and logistics clusters at the regional level in accordance with this approach. An example of Germany’s transport and logistics cluster «Frankfurt am Main» is considered. The content of the cluster approach is considered. Cluster benefits are provided. The main participants of the transport and logistics cluster are considered. The model of formation of transport and logistics cluster at regional level is presented. The main tasks of forming a transport and logistic cluster are determined. In-fluence factors on the formation of transport and logistics clusters are generalized: consistent state eco-nomic policy, globalization of transport and logistics infrastructure, horizontal integration of the logistics infrastructure complex and the companies that serve it, outsourcing of transport and logistics services, forecasting and minimizing the level of risk. The directions of the analysis for understanding the weak-nesses and strengths of the region for the formation of cluster structures are determined. Prospects of crea-tion of transport and logistics cluster in Kherson region are considered and its structural elements are defined. The content of the cluster is considered. The activity of the transport and logistics cluster of the region promotes the use of innovative poten-tial through the creation of an efficient transport complex. The cluster maximally takes into account the effect of the market mechanism. The cluster approach is one of the most advanced industry management technologies. Keywords: transport and logistics system; transport and logistics cluster; region; transport infra-structure; cluster approach.
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Nair B J, Bipin, and Sarath M S. "Evolutionary clustering annotation of ortho-paralogous gene in a multi species using Venn diagram visualization." International Journal of Engineering & Technology 7, no. 1.9 (2018): 162. http://dx.doi.org/10.14419/ijet.v7i1.9.9755.
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The evolutionary analysis of the genome of the immediate cluster is an important part of comparative genomics research. Identifying the overlap between immediate homologous clusters allows us to elucidate the function and evolution of proteins between species. Here, we report a network platform called Ortho-paralogous Venn-diagram representation that can be used to compare and visualize a wide range of ortho-paralogous clustering of genomes. In our work Ortho-paralogous Venn-diagram results show a functional summary of interactive Venn diagrams, summary counts, and interspecies shared cluster separations and intersections. Ortho-paralogous Venn-diagram also uses a variety of sequence analysis tools to gain an in-depth understanding of the cluster. In addition, Ortho-paralogous Venn identifies direct homologous clusters of single copy genes and allows custom search of specific gene clusters. It enables us in wide analysis of the genes and protein by comparing the genes using Venn diagram .Here the user can upload our own gene sequences into the application ,using three clustering approach to check the best clustering approches like SOM,K-means and advanced clustering after that we are using the Venn diagram repersentator to evolutionary cluster the genes having similar functionality and structural similarity from the uploaded data.Here we are using a venn diagram representation as an application which used to cluster the orthologous and paralogous gene on basics of their evolution and functional aspects.it enables us in wide analysis of the genes and protein bycomparing the genes using venn diagram representation.here the user can upload our own gene sequences into the application where the venn diagram representatorclusters.the genes having similar functionality and structural similarity from the uploaded data.
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Garncarz, M., M. Hulanicka, H. Maciejewski, M. Parzeniecka-Jaworska, and M. Jank. "Correlation between peripheral blood cell transcriptomic profile and clinical parameters of chronic mitral valve disease in Dachshunds." Polish Journal of Veterinary Sciences 19, no. 4 (2016): 849–57. http://dx.doi.org/10.1515/pjvs-2016-0106.
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Abstract Studies identifying specific pathologically expressed genes have been performed on diseased myocardial tissue samples, however less invasive studies on gene expression of peripheral blood mononucleated cells give promising results. This study assessed transcriptomic data that may be used to evaluate Dachshunds with chronic mitral valve disease. Dachshunds with different stages of heart disease were compared to a control, healthy group. Microarray data analysis revealed clusters of patients with similar expression profiles. The clusters were compared to the clinical classification scheme. Unsupervised classification of the studied groups showed three clusters. Clinical and laboratory parameters of patients from the cluster 1 were in accordance with those found in patients without heart disease. Data obtained from patients from the cluster 3 were typical of advanced heart failure patients. Comparison of the cluster 1 and 3 groups revealed 1133 differentially expressed probes, 7 significantly regulated process pathways and 2 significantly regulated Ariadne Metabolic Pathways. This study may serve as a guideline for directing future research on gene expression in chronic mitral valve disease.
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De B. Harrington, Peter, Eric S. Reese, Paùl J. Rauch, Lijuan Hu, and Dennis M. Davis. "Interactive Self-Modeling Mixture Analysis of Ion Mobility Spectra." Applied Spectroscopy 51, no. 6 (1997): 808–16. http://dx.doi.org/10.1366/00037029760563499.
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Ion mobility spectrometry (IMS) has been successfully developed to yield an advanced portable instrument. However, the formation of pure or heterogeneous cluster ions introduces nonlinear variances into the data. Cluster ions may arise from the sample in addition, and competition to the standard anticipated product ions and may deleteriously affect quantitative determinations. The SIMPLISMA (simple-to-use interactive self-modeling mixture analysis) method is demonstrated for detecting and modeling these nonlinear variances in IMS data, which is especially useful when vapor mixtures are encountered. Furthermore, SIMPLISMA may assist in the resolution of overlapping peaks that are characteristic of low-resolution IMS drift tubes. The synergistic combination of IMS and SIMPLISMA is shown for the detection of heterogeneous cluster ions produced from vapor mixtures of 1-pentanol and 1-octanol.
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Li, Chuan, Yee Peng Phoon, Keaton Karlinsey, et al. "P853 Single cell transcriptome analysis identifies unique features in circulating CD8+ T cells that can predict immunotherapy response in melanoma patients." Journal for ImmunoTherapy of Cancer 8, Suppl 1 (2020): A5.1—A5. http://dx.doi.org/10.1136/lba2019.7.
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BackgroundImmune checkpoint blockade (ICB) has greatly advanced the treatment of melanoma. A key component of ICB is the stimulation of CD8+ T cells in the tumor. However, ICB therapy only benefits a subset of patients and a reliable prediction method that does not require invasive biopsies is still a major challenge in the field.MethodsWe conducted a set of comprehensive single-cell transcriptomic analyses of CD8+ T cells in the peripheral blood (mPBL) and tumors (mTIL) from 8 patients with metastatic melanoma.ResultsCompared to circulating CD8+ T cells from healthy donors (hPBL), mPBLs contained subsets resembling certain features of mTIL. More importantly, three clusters (2, 6 and 15) were represented in both mPBL and mTIL. Cluster 2 was the major subset of the majority of hPBL, which phenocopied hallmark parameters of resting T cells. Cluster 6 and 15 were uniquely presented in melanoma patients. Cluster 15 had the highest PD-1 levels, with elevated markers of both activation and dysfunction/exhaustion; while Cluster 6 was enriched for ‘dormant’ cells with overall toned-down transcriptional activity except PPAR signaling, a known suppressor for T cell activation. Interestingly, unlike other mTIL clusters that would classically be defined as exhausted, Cluster 15 exhibited the highest metabolic activity (oxidative-phosphorylation and glycolysis). We further analyzed total sc-transcriptomics using cell trajectory algorithms and identified that these three clusters were the most distinct subtypes of CD8 T cells from each other, representing: resting (cluster 2), metabolically active-dysfunctional (cluster 15), and dormant phenotypes (cluster 6). Further, three unique intracellular programs in melanoma drive the transition of resting CD8+ T cells (cluster 2) to both metabolic/dysfunctional (cluster 15) and dormant states (cluster 6) that are unique to tumor bearing conditions. Based on these high-resolution analyses, we developed original algorithms to build a novel ICB response predictive model using immune-blockade co-expression gene patterns. The model was trained and tested using previously published GEO datasets containing CD8 T cells from anti-PD-1 treated patients and presented an AUC of 0.82, with 92% and 89% accuracy of ICB response in the two datasets.ConclusionsWe identified and analyzed unique populations of CD8+ T cells in circulation and tumor using high-resolution single-cell transcriptomics to define the landscape of CD8+ T cell states, revealing critical subsets with shared features in PBLs and TILs. Most importantly, we established an innovative model for ICB response prediction by using peripheral blood lymphocytes.Ethics ApprovalThis study was performed under an IRB approved protocol.
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Gracey, Fergus, Donna Malley, Adam P. Wagner, and Isabel Clare. "Characterising neuropsychological rehabilitation service users for service design." Social Care and Neurodisability 5, no. 1 (2014): 16–28. http://dx.doi.org/10.1108/scn-09-2013-0034.
Texte intégralRésumé :
Purpose – Needs of people following acquired brain injury vary over their life-course presenting challenges for community services, especially for those with “hidden” neuropsychological needs. Characterisation of subtypes of rehabilitation service user may help improve service design towards optimal targeting of resources. This paper aims to characterise a neuropsychologically complex group of service users. Design/methodology/approach – Preliminary data from 35 participants accepted for a holistic neuropsychological rehabilitation day programme were subject to cluster analysis using self-ratings of mood, executive function and brain injury symptomatology. Findings – Analysis identified three clusters significantly differentiated in terms of symptom severity (Cluster 1 least and Cluster 2 most severe), self-esteem (Clusters 2 and 3 low self-esteem) and mood (Cluster 2 more anxious and depressed). The three clusters were then compared on characteristics including age at injury, type of injury, chronicity of problems, presence of pre-injury problems and completion of rehabilitation. Cluster 2 were significantly younger at time of injury, and all had head injury. Research limitations/implications – Results suggest different subgroups of neuropsychological rehabilitation service user, highlighting the importance of early identification and provision of rehabilitation to prevent deterioration, especially for those injured when young. Implications for design of, and research into, community rehabilitation service design for those with “hidden disability” are considered. Originality/value – The paper findings suggests that innovative conceptual frameworks for understanding potentially complex longer term outcomes are required to enable development of tools for triaging and efficient allocation of community service resources.