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Journal articles on the topic 'Post-clustering inference'
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Balabaeva, Ksenia, and Sergey Kovalchuk. "Post-hoc Interpretation of Clinical Pathways Clustering using Bayesian Inference." Procedia Computer Science 178 (2020): 264–73. http://dx.doi.org/10.1016/j.procs.2020.11.028.
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Hessami, Masoud, François Anctil, and Alain A. Viau. "An adaptive neuro-fuzzy inference system for the post-calibration of weather radar rainfall estimation." Journal of Hydroinformatics 5, no. 1 (January 1, 2003): 63–70. http://dx.doi.org/10.2166/hydro.2003.0005.
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An Adaptive Neuro-Fuzzy Inference System, based on a jack-knife approach, is proposed for the post-calibration of weather radar rainfall estimation exploiting available raingauge observations. The methodology relies on the construction of a fuzzy inference system with three inputs (radar x coordinate, y coordinate and rainfall estimation at raingauge locations) and one output (raingauge observations). Subtractive clustering is used to generate the initial fuzzy inference system. Artificial neural network learning provides a fast way to automatically generate additional fuzzy rules and membership functions for the fuzzy inference system. Fuzzy logic enhances the generalisation of the artificial neural network system. In order to demonstrate the steps of the radar rainfall post-calibration using the Adaptive Neuro-Fuzzy Inference System, CAPPIs of one-hour rainfall accumulation and corresponding raingauge observations have been selected. Results show that the proposed approach looks for a response that is a compromise between radar rainfall estimations and raingauge observations and does not necessarily consider the raingauge observations as ground truth. The algorithm is very fast and can be implemented for real time post-calibration. This algorithm makes use of all available data—raingauge observations are usually scarce—for training and checking the neuro-fuzzy inference system. It also provides a degree of reliability of the post-calibration.
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Hivert, Benjamin, Denis Agniel, Rodolphe Thiébaut, and Boris P. Hejblum. "Post-clustering difference testing: Valid inference and practical considerations with applications to ecological and biological data." Computational Statistics & Data Analysis 193 (May 2024): 107916. http://dx.doi.org/10.1016/j.csda.2023.107916.
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Moreno, Elías, Francisco-José Vázquez-Polo, and Miguel A. Negrín. "Bayesian meta-analysis: The role of the between-sample heterogeneity." Statistical Methods in Medical Research 27, no. 12 (May 16, 2017): 3643–57. http://dx.doi.org/10.1177/0962280217709837.
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The random effect approach for meta-analysis was motivated by a lack of consistent assessment of homogeneity of treatment effect before pooling. The random effect model assumes that the distribution of the treatment effect is fully heterogenous across the experiments. However, other models arising by grouping some of the experiments are plausible. We illustrate on simulated binary experiments that the fully heterogenous model gives a poor meta-inference when fully heterogeneity is not the true model and that the knowledge of the true cluster model considerably improves the inference. We propose the use of a Bayesian model selection procedure for estimating the true cluster model, and Bayesian model averaging to incorporate into the meta-analysis the clustering estimation. A well-known meta-analysis for six major multicentre trials to assess the efficacy of a given dose of aspirin in post-myocardial infarction patients is reanalysed.
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Mousavi Fard, Zahra Sadat, Hassan Asilian Mahabadi, Farahnaz Khajehnasiri, and Mohammad Amin Rashidi. "Modeling the concentration of suspended particles by fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) techniques: A case study in the metro stations." Environmental Health Engineering and Management 10, no. 3 (August 20, 2023): 311–19. http://dx.doi.org/10.34172/ehem.2023.35.
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Background: Today, the usage of artificial intelligence systems and computational intelligence is increasing. This study aimed to determine the fuzzy system algorithms to model and predict the amount of air pollution based on the measured data in subway stations. Methods: In this study, first, the effective variables on the concentration of particulate matter were determined in metro stations. Then, PM2.5, PM10, and total size particle (TSP) concentrations were measured. Finally, the particles’ concentration was modeled using fuzzy systems, including the fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS). Results: It was revealed that FIS with modes gradient segmentation (FIS-GS) could predict 76% and ANFIS-FCM with modes of clustering and post-diffusion training algorithm (CPDTA) could predict 85% of PM2.5, PM10, and TSP particle concentrations. Conclusion: According to the results, among the models studied in this work, ANFIS-FCM-CPDTA, due to its better ability to extract knowledge and ambiguous rules of the fuzzy system, was considered a suitable model.
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Muhammad, Fadel, Changkun Xie, Julian Vogel, and Afshin Afshari. "Inference of Local Climate Zones from GIS Data, and Comparison to WUDAPT Classification and Custom-Fit Clusters." Land 11, no. 5 (May 18, 2022): 747. http://dx.doi.org/10.3390/land11050747.
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A GIS-based approach is used in this study to obtain a better LCZ map of Berlin in comparison to the remote-sensing-based WUDAPT L0 approach. The LCZ classification of land use/cover can be used, among other applications, to characterize the urban heat island. An improved fuzzy logic method is employed for the purpose of classification of the zone properties to yield the GIS-LCZ map over 100 m × 100 m grid tiles covering the Berlin region. The zone properties are calculated from raster and vector datasets with the aids of the urban multi-scale environmental predictor (UMEP), QGIS and Python scripts. The standard framework is modified by reducing the threshold for the zone property impervious fraction for LCZ E to better detect paved surfaces in urban areas. Another modification is the reduction in the window size in the majority filter during post-processing, compared to the WUDAPT L0 method, to retain more details in the GIS-LCZ map. Moreover, new training areas are generated considering building height information. The result of the GIS-LCZ approach is compared to the new training areas for accuracy assessment, which shows better overall accuracy compared to that of the WUDAPT L0 method. The new training areas are also submitted to the LCZ generator and the resulting LCZ-map gives a better overall accuracy value compared to the previous (WUDAPT) submission. This study shows one shortcoming of the WUDAPT L0 method: it does not explicitly use building height information and that leads to misclassification of LCZs in several cases. The GIS-LCZ method addresses this shortcoming effectively. Finally, an unsupervised machine learning method, k-means clustering, is applied to cluster the grid tiles according to their zone properties into custom classes. The custom clusters are compared to the GIS-LCZ classes and the results indicate that k-means clustering can identify more complex city-specific classes or LCZ transition types, while the GIS-LCZ method always divides regions into the standard LCZ classes.
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Chen, Qipeng, Qiaoqiao Xiong, Haisong Huang, Saihong Tang, and Zhenghong Liu. "Research on the Construction of an Efficient and Lightweight Online Detection Method for Tiny Surface Defects through Model Compression and Knowledge Distillation." Electronics 13, no. 2 (January 5, 2024): 253. http://dx.doi.org/10.3390/electronics13020253.
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In response to the current issues of poor real-time performance, high computational costs, and excessive memory usage of object detection algorithms based on deep convolutional neural networks in embedded devices, a method for improving deep convolutional neural networks based on model compression and knowledge distillation is proposed. Firstly, data augmentation is employed in the preprocessing stage to increase the diversity of training samples, thereby improving the model’s robustness and generalization capability. The K-means++ clustering algorithm generates candidate bounding boxes, adapting to defects of different sizes and selecting finer features earlier. Secondly, the cross stage partial (CSP) Darknet53 network and spatial pyramid pooling (SPP) module extract features from the input raw images, enhancing the accuracy of defect location detection and recognition in YOLO. Finally, the concept of model compression is integrated, utilizing scaling factors in the batch normalization (BN) layer, and introducing sparse factors to perform sparse training on the network. Channel pruning and layer pruning are applied to the sparse model, and post-processing methods using knowledge distillation are used to effectively reduce the model size and forward inference time while maintaining model accuracy. The improved model size decreases from 244 M to 4.19 M, the detection speed increases from 32.8 f/s to 68 f/s, and mAP reaches 97.41. Experimental results demonstrate that this method is conducive to deploying network models on embedded devices with limited GPU computing and storage resources. It can be applied in distributed service architectures for edge computing, providing new technological references for deploying deep learning models in the industrial sector.
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Černa Bolfíková, Barbora, Kristýna Eliášová, Miroslava Loudová, Boris Kryštufek, Petros Lymberakis, Attila D. Sándor, and Pavel Hulva. "Glacial allopatry vs. postglacial parapatry and peripatry: the case of hedgehogs." PeerJ 5 (April 25, 2017): e3163. http://dx.doi.org/10.7717/peerj.3163.
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Although hedgehogs are well-known examples of postglacial recolonisation, the specific processes that shape their population structures have not been examined by detailed sampling and fast-evolving genetic markers in combination with model based clustering methods. This study aims to analyse the impacts of isolation within glacial refugia and of postglacial expansion on the population structure of the Northern White-breasted hedgehog (Erinaceus roumanicus). It also discusses the role of the processes at edges of species distribution in its evolutionary history. The maternally inherited mitochondrial control region and the bi-parentally inherited nuclear microsatellites were used to examine samples within the Central Europe, Balkan Peninsula and adjacent islands. Bayesian coalescent inference and neutrality tests proposed a recent increase in the population size. The most pronounced pattern of population structure involved differentiation of the insular populations in the Mediterranean Sea and the population within the contact zone with E. europaeus in Central Europe. An interspecies hybrid was detected for the first time in Central Europe. A low genetic diversity was observed in Crete, while the highest genetic distances among individuals were found in Romania. The recent population in the post-refugial area related to the Balkan Peninsula shows a complex pattern with pronounced subpopulations located mainly in the Pannonian Basin and at the Adriatic and Pontic coasts. Detailed analyses indicate that parapatry and peripatry may not be the only factors that limit range expansion, but also strong microevolutionary forces that may change the genetic structure of the species. Here we present evidence showing that population differentiation may occur not only during the glacial restriction of the range into the refugia, but also during the interglacial range expansion. Population differentiation at the Balkan Peninsula and adjacent regions could be ascribed to diversification in steppe/forest biomes and complicated geomorphology, including pronounced geographic barriers as Carpathians.
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Shi, Wenkai, Wenbin An, Feng Tian, Yan Chen, Yaqiang Wu, Qianying Wang, and Ping Chen. "A Unified Knowledge Transfer Network for Generalized Category Discovery." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (March 24, 2024): 18961–69. http://dx.doi.org/10.1609/aaai.v38i17.29862.
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Generalized Category Discovery (GCD) aims to recognize both known and novel categories in an unlabeled dataset by leveraging another labeled dataset with only known categories. Without considering knowledge transfer from known to novel categories, current methods usually perform poorly on novel categories due to the lack of corresponding supervision. To mitigate this issue, we propose a unified Knowledge Transfer Network (KTN), which solves two obstacles to knowledge transfer in GCD. First, the mixture of known and novel categories in unlabeled data makes it difficult to identify transfer candidates (i.e., samples with novel categories). For this, we propose an entropy-based method that leverages knowledge in the pre-trained classifier to differentiate known and novel categories without requiring extra data or parameters. Second, the lack of prior knowledge of novel categories presents challenges in quantifying semantic relationships between categories to decide the transfer weights. For this, we model different categories with prototypes and treat their similarities as transfer weights to measure the semantic similarities between categories. On the basis of two treatments, we transfer knowledge from known to novel categories by conducting pre-adjustment of logits and post-adjustment of labels for transfer candidates based on the transfer weights between different categories. With the weighted adjustment, KTN can generate more accurate pseudo-labels for unlabeled data, which helps to learn more discriminative features and boost model performance on novel categories. Extensive experiments show that our method outperforms state-of-the-art models on all evaluation metrics across multiple benchmark datasets. Furthermore, different from previous clustering-based methods that can only work offline with abundant data, KTN can be deployed online conveniently with faster inference speed. Code and data are available at https://github.com/yibai-shi/KTN.
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van de Stolpe, A., W. Verhaegh, and L. Holtzer. "OS5.3 Quantitative signaling pathway analysis of Diffuse Intrinsic Pontine Glioma identifies two subtypes, respectively high TGFβ/MAPK-AP1 and high PI3K/HH pathway activity, which are potentially clinically actionable." Neuro-Oncology 21, Supplement_3 (August 2019): iii11. http://dx.doi.org/10.1093/neuonc/noz126.036.
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Abstract BACKGROUND Diffuse Intrinsic Pontine Glioma (DIPG) is a pediatric brain tumor (glioma), resistant to chemotherapy, with only a temporary response to radiotherapy and an extremely bad prognosis. Genomic abnormalities have been found, indicating abnormal activation of certain growth factor signaling pathways, while expression analysis suggests involvement of developmental signaling pathways.10–15 signal transduction pathways can drive cancer growth and metastasis. We have developed, and biologically validated, a method which enables quantitative measurements of functional activity of signal transduction pathways in individual cell/tissue samples, based on Bayesian computational model inference of pathway activity from measurements of mRNA levels of target genes of the transcription factor associated with the respective signalling pathway. A major envisioned clinical utility is prediction of therapy response. MATERIAL AND METHODS For signaling pathway analysis, Affymetrix expression microarray data were available (GEO dataset GSE26576) from 2 normal brain stem samples and from 6 low grade glioma and 26 DIPG samples (post-mortem after therapy). Of one DIPG patient samples were available before and after therapy. Signaling pathway activity scores were calculated for estrogen and androgen receptor, PI3K-FOXO, MAPK-AP1, JAK-STAT, NFκB, Hedgehog (HH), TGFβ, NOTCH and Wnt pathways. PI3K pathway activity is the reverse of FOXO activity, in the absence of oxidative stress (measured by SOD2 expression). Pathway activity scores were compared between normal tissue and low grade glioma samples and DIPG, and k-means cluster analysis was performed on the DIPG pathway activity scores. RESULTS After treatment, HH pathway activity was increased in DIPG compared to low grade glioma (p=0.0003), PI3K pathway activity scores showed large variations in activity in the DIPG group. Tumors with cell cycle (CDK4/6, CCND1-3) or Receptor Tyrosine Kinase-related gene amplifications had higher PI3K and HH pathway activity compared to tumors without identified amplifications (p<0.05) which, in contrast, had higher MAPK-AP1 pathway activity (p<0.002). Pathway-based clustering analysis revealed two DIPG clusters, C1: high TGFβ/MAPK-AP1 and low PI3K/HH pathway activity; C2: low TGFβ/MAPK-AP1, high PI3K/HH pathway activity. C1 best resembled low grade glioma. In the patient with pre/post treatment samples, a C1 pathway profile switched to a C2 profile after treatment. CONCLUSION Using our quantitative analysis of signaling pathway activity in post-treatment DIPG, two pathway activity subtypes were identified, of which the HH/PI3K high, TGFβ low activity subtype was associated with defined gene amplifications, and may have been induced by chemoradiation therapy. Clusters are supported by a clear biological rationale. Identified signaling pathways are potentially drug targetable.
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Mahajan, Nitin, Laura Arthur, Jayakumar Vadakekolathu, John Muth, Jan K. Davidson-Moncada, and Sergio Rutella. "Single-Cell Transcriptional Landscape of W-NK1, an Off-the-Shelf Natural Killer Cell Therapy." Blood 144, Supplement 1 (November 5, 2024): 7184. https://doi.org/10.1182/blood-2024-201496.
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Background Natural killer (NK) cells play a critical role in tumor eradication. W-NK1 is a non-edited, cryopreserved off-the-shelf NK cell therapy product with a ‘memory-like’ phenotype that addresses challenges faced by adoptive cell therapies, such as poor resilience to adverse and immunosuppressive tumor microenvironments (TME). W-NK1 not only overcomes nutrient deprivation, inhibitory ligands, and reduced chemokine expression in the TME, but also recruits and activates dendritic and T cells through cytokine and chemokine production, thereby enhancing the overall immune response. This study aims to elucidate the molecular underpinnings of W-NK1's activity as well as its cellular heterogeneity using bulk and single-cell RNA-sequencing (sc-RNA-seq) approaches. Methods W-NK1 were manufactured from healthy donor peripheral blood NK cells (herein referred to as conventional NK, cNK) which are activated, expanded, and cryopreserved to create an off-the-shelf memory NK-cell product. We then performed bulk RNA sequencing (RNA-seq) and scRNA-seq on both cNK (before cytokine priming) and W-NK1. Utilizing transfer learning, we compared W-NK1's single-cell transcriptomes to recently published reference maps encompassing NK cells from healthy donors (GSE197037 and https://zenodo.org/doi/10.5281/zenodo.8434223). Results We identified over 7,000 differentially expressed genes (DEGs) at a false discovery rate (FDR) <0.01. Upregulated genes in W-NK1 included IL2RA, TPX, and MKI67, indicating an activated and proliferative state post-cytokine expansion. Additionally, genes involved in NK-cell regulation, such as CSF2 encoding GM-CSF, and BIRC5 encoding survivin, were also highly expressed. We observed reduced intra-donor variance in cluster proportions in W-NK1 compared to cNK cells, with tighter clustering in principal component analysis suggesting low batch-to-batch heterogeneity of the cell therapy product. Unbiased clustering and UMAP visualization of sc-RNA-seq data from more than 49,000 cells across four donors revealed eight cellular states (c0-c7), most of which were significantly differentially abundant in W-NK1 compared to cNK cells, as supported by linear modeling of sc-RNA-seq data. W-NK1's cytolytic capacity was supported by the high expression of established (perforin and GZMB) as well as novel cytotoxicity genes (FGFBP2, SPON2). Pseudotime inference illustrated a progression of maturation from cytotoxic W-NK1 in c0 to ‘hyper-metabolic’ and proliferative states in c7, driven by cytokine priming. Transfer learning highlighted unique transcriptional features of W-NK1, including high expression of NKG2A and low relatedness with adaptive NK states characterized by human cytomegalovirus-induced inflammatory memory. Pathway analysis indicated significant enrichment in glycolysis, amino acid and fatty acid metabolism, DNA replication, and cell cycle-related gene programs in W-NK1 compared to cNK cells. Conclusion Our multi-omic analysis highlights potential advantages of W-NK1 in overcoming a hostile and nutrient-deprived TME, such as enhanced proliferation and metabolic fitness, and augmenting immune responses. These findings position W-NK1 as a promising candidate for cancer immunotherapy.
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Mazziotta, Francesco, Lauren E. Martin, Daniel Eagan, Filippo Milano, Valentin Voillet, Tzu-Hao Tang, Miranda Lahman, et al. "NK-like Skewing As the Key Driver of T Cell Dysfunction in AML: Implications for Enhancing TCR-T Cell Therapy Efficacy." Blood 144, Supplement 1 (November 5, 2024): 3438. https://doi.org/10.1182/blood-2024-208138.
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Understanding acute myeloid leukemia (AML)-specific mechanisms of T cell dysfunction is key to improving T cell therapies. In solid tumors, reduced T cell persistence commonly links to the expression of exhaustion markers (PD1, CTLA4, TIM3, LAG3, TIGIT) targetable with checkpoint blockade. In AML, the presence of T cell exhaustion has been contested (Penter et al., Blood 2023), highlighting alternative T cell dysfunction mechanisms based on terminal differentiation of CD8+ T cells expressing NK-like (NKL) markers (Abbas et al., Nat Commun. 2021; Mazziotta et al., Blood 2024). Whether AML induces this skewing of antigen-specific T cells remains an open question. Wilms' Tumor 1 (WT1) protein is overexpressed and correlates with poor prognosis in high-risk AML. In a phase I/II clinical trial, we employed T cells engineered to express a high-affinity WT1-specific T cell receptor (TCRC4) to treat 15 HLA-A2+ patients with relapsed/refractory AML after allogeneic transplant. To mitigate graft-versus-host disease (GVHD) from the endogenous TCR, we used EBV or CMV-specific CD8+ T cells obtained from HLA-matched donors. This framework enabled us to longitudinally monitor post-transfer AML-specific T cells via high-dimensional multimodal approaches. We studied the impact of virus-specificity on persistence, finding higher persistence (p < 0.05) for EBV vs. CMV-specific TTCR-C4. To directly compare CMV and EBV-specific T cells, we reanalyzed mass cytometry data (Schmidt et al, Cell Rep 2023) from healthy individuals and others with non-AML cancers (n = 143). Differential abundance analysis showed increased (FDR < 0.05) terminally differentiated (Temra) CMV vs. EBV-specific CD8+ T cells suggesting that CMV-specific TTCR-C4 cells were affected by the substrate cell's terminal differentiation, compromising post-infusion persistence. Disease status at TTCR-C4 infusion also affected persistence, with lower persistence in patients with minimal residual disease (MRD)+ or overt AML vs. MRD- patients. To study TTCR-C4 post-transfer, we built a 24-color spectral flow-cytometry panel for peripheral blood samples at ~1, ~7, ~28 days, and 4 months post-transfer. Unsupervised clustering and differential abundance analysis showed TTCR-C4's progressive differentiation from proliferative (Ki67+) effector to non-proliferative (Ki67-) Temra cells with cytotoxic/NKL markers (CD45RA, CD57, KLRG1, GNLY) (p <0.05). This shift correlated with a drop in IFNg and TNF⍺ producing TTCR-C4, associating the NKL shift with T cell dysfunction. Single-cell RNA sequencing (scRNAseq) corroborated these results at the transcriptional level. Principal component analysis, trajectory, and velocity inference methods positioned TTCR-C4 between effector memory/activated and NKL/Temra cells in the CD8+ T cell differentiation spectrum. However, when comparing timepoints with detectable (+) (n = 9) and non-detectable (-) (n = 6) AML, we found that TTCR-C4 overexpressed NKL markers in AML+, suggesting that AML accelerates NKL/terminal differentiation in antigen-specific cells. To verify, we rechallenged CD8+ WT1-specific T cells in an in-vitro model with WT1+ HLA-A2+-transduced K562 cells every 3-4 days. By day 13, T cells lost control of K562 cells and skewed to an NKL transcriptional profile (bulk-RNAseq). To further prove that T cells do not undergo T cell exhaustion in AML vs. solid tumors, we reanalyzed AML, melanoma, pancreatic and lung cancer scRNAseq independent datasets documenting exhaustion as a typical cell state in solid tumors but not in AML. Data from one AML patient suggested that azacitidine treatment can prevent TTCR-C4 terminal differentiation, likely increasing persistence. Another patient exhibited blasts with a myeloid derived suppressor cells transcriptional profile which became treatment-resistant, consistent with previous findings (Van Galen et al., Cell 2019). Killing assays showed that using a higher affinity TCR and co-targeting WT-1 with CD4+ and CD8+ T cells, can overcome this immune escape. In conclusion, we demonstrate that AML-specific T cell dysfunction is driven by NKL skewing rather than T cell exhaustion. Our findings inform strategies that can be developed to target T cell dysfunction and overcome immune escape in TCR-T cell therapies for AML patients.
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Canton, Patricia, Richeal Burns, and Konrad Mulrennan. ""Understanding variation in health-related quality of life domains for patients with end-stage liver disease- findings from the EU COPE trial."." International Journal of Integrated Care 25 (April 9, 2025): 486. https://doi.org/10.5334/ijic.icic24484.
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Introduction: Understanding the extent of patient morbidity is important for managing patient care: end-stage liver disease (ESLD) is the terminal stage of liver disease necessitating transplantation, and for which there are 11 clinical classifications with varying impacts on the patient’s symptoms and severity. Evaluating health-related quality of life (HRQoL)2 of patient groups provides insights into how specific health issues affect well-being. Given the clinical heterogeneity across the sub-group classifications, patient-reported HRQoL measures may exhibit high levels of variability. Understanding the variation is crucial to avoid misleading HRQoL reporting which may inform decision making by policy makers. The aim is to examine the extent of variation across HRQoL domains, utilising data from the EuroQol EQ-5D-5L alongside a European trial for liver transplantation. Methods: This analysis examined the responses across each of the EQ-5D-5L domains6 for HRQoL self-assessment (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) using the EU COPE liver transplantation trial data for ESLD overall and by clinical sub-group. Variation in domain response at baseline and the follow-up periods, post-transplantation (30 days, 6 months) was examined. It was investigated whether other patient-level characteristics were drivers of variation usingvariation using ordinal regression as well as the probability of higher vs. lower categories being dominant. Computation was carried out using Microsoft Excel and Matlab. Results: This analysis focused on HRQoL collected alongside a small European RCT and included UK responses only. Sub-clinical groups sample sizes varied from n=2 to n=41(total sample used was n= 145). Baseline HRQoL overall exhibited the highest domain responses i.e. ‘no issues’ for Mobility (Level 1:46%), Self-Care (Level 1:66%), Usual Activities (Level 1:29%), and Depression/Anxiety (Level 1: 41%); with Pain/Discomfort (Level 2:32%) having the highest proportion of ‘mild issues’. Clinical subgroups indicated Hepatocellular Carcinoma without Cirrhosis and Non-Alcoholic Steato-Hepatitis had the lowest domain scores comparatively, while Hepatitis B and Non-Alcoholic Fatty Liver Disease had the highest. Conclusions: Clinical sub-groups report different HRQoL profiles. A “one size fits all” approach to disease management guided by inference from descriptive summaries of outcomes, neglects to understand the heterogeneity across self-reported morbidity. It is important to provide detail on the uncertainty in patient-reported HRQoL and the drivers of variation to ensure decisions made are patient-centred and reflect individual needs due to the impact of disease severity. References: [1]Philips CA, Kedarisetty CK. Palliative Care for Patients with End-Stage Liver Disease. Clinical and Experimental Hepatology. 2022 Aug [2]Yin S, Njai R. Summarizing health-related quality of life (HRQOL): development and testing of a one-factor model Population Health Metrics . 2016 Jul 11 [3]Yang MS, Lai CY. A robust EM clustering algorithm for Gaussian mixture models. Pattern Recognition. 2012 Nov [4]Herdman M, Gudex C et al. Development and preliminary testing of the new five-level version of EQ-5D. Quality of Life Research. 2011 Apr 9 Contributorship: Eugene McCarthy4, Simon Knight5, David Nasralla6, Peter Friend5, Rutger Ploeg5 on behalf of the COPE. Department of Biopharmaceutical and Medical Science, Atlantic Technological University, Galway Nuffield Department of Surgical Sciences, University of Oxford Royal Free London, NHS Foundation Trust
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Basma, Naseer J., Alexia J. Strickson, Kristian Gurashi, Maria Kleppe, Hugh Young Rienhoff Jr., and Tim CP Somervaille. "MSC Reprogramming and Aberrant Inflammatory Signaling in Myelofibrosis: Insights from Single-Cell RNA-Sequencing of Trephine Bone Marrow Cells." Blood 142, Supplement 1 (November 28, 2023): 743. http://dx.doi.org/10.1182/blood-2023-181674.
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Myelofibrosis (MF) results from mutation-induced activation of JAK/STAT signaling in hematopoietic stem, progenitor and mature cells, and features a progressive, fibrosing stromal reaction, splenomegaly, constitutional symptoms and reduced life expectancy. Identification of disrupted cellular pathways within bone marrow (BM) is pivotal for therapeutic breakthrough, but fibrosis severely limits access to stromal elements for single-cell analysis. To overcome this, we utilized collagenase digestion of fresh BM trephine biopsy material surplus to diagnostic needs and profiled isolated cells using scRNAseq. 9 samples were obtained from 8 patients with MF (4, JAK inhibitor naïve; 4, on ruxolitinib); 4 samples were from 4 patients with MF 4, 10, 12 & 12 months after allogeneic transplant; and 7 samples (termed “normal controls”) were from 6 patients who had received an allograft for myelodysplasia or acute leukemia, and 1 who had received CAR-T cells for B-ALL. Normal controls exhibited normal or near-normal blood counts and lacked BM fibrosis. Each sample was subjected to collagenase digestion and flow sorted to isolate non-erythroid hematopoietic (CD45+GlyA-) and microenvironmental (CD45+GlyA-CD71-) cells for single cell RNA sequencing and downstream comparative analysis. 105,231 transcriptomes met quality control criteria (median 4975/sample, range 1036-13277, no significant difference between sample categories) and 43 transcriptional clusters were identified. Compositional analysis identified the non-hematopoietic stromal cell fraction as harboring the biggest differences with, surprisingly, a proportional relative depletion of mesenchymal stromal cells (MSCs) and enrichment of endothelial cells (ECs) in MF vs. normal controls. While transcriptomes of ECs did not differ substantially, there were extensive transcriptional differences between MSCs recovered from MF vs. normal control samples. Subclustering revealed two subsets, MSC1 & MSC2 asymmetrically distributed between MF and normal controls. Intriguingly, MSCs recovered from MF patients after allograft occupied an intermediate position in principal component space indicating that reversion of disease-related MSC transcriptional reprogramming post-allograft occurs over a timescale of months. The reprogrammed transcriptional state of MF MSCs is defined by reduced expression of hematopoietic support factor genes (eg, KITLG, CXCL12, ANGPTL2, NCAM2, CSF1) and increased expression of extracellular matrix (ECM) genes (eg, FN1, COL1A2, COL3A1, DCN, SPARC), features which suggest an explanation for the increased frequency of graft failure following allograft in MF versus other hematologic malignancies. Single-Cell rEgulatory Network Inference and Clustering (SCENIC) indicated transcription factors with either increased (eg, NR2F2, RUNX2, FOXP2) or decreased (eg, CEBPA, FOXC1, and PPARG) transcriptional activity as candidate master transcriptional regulators of the observed MSC reprogramming in MF. Transcriptional change in MSCs correlated strongly with histopathological assessment of BM reticulin. Within the hematopoietic fraction, and agnostic of JAK inhibitor treatment status, comparison of MF vs. control cells unexpectedly revealed significant upregulation of interferon response genes, especially in CD14 + monocytes, but also in neutrophils and their progenitors, centred on expression of interferon-stimulated gene factor 3 (ISGF3) genes IRF9 and STAT1/2. Comparison of MF samples according to ruxolitinib treatment status indicated that ruxolitinib induced downregulation of an NFκB-centered inflammatory program, particularly in neutrophils. Finally, ligand-receptor analysis using NicheNet identified TGFB1, IL1B, and TNF as top predicted ligands for induction of the MF signature in MSCs. While myeloid cells were top producers of these factors, their expression did not vary between MF vs. controls, suggesting other mechanisms, such as prolonged retention of ligand in the ECM, are important. In summary, our comparative scRNA analysis of BM cells from MF patients and controls - the largest reported to date - reveals (i) transcriptional reprogramming of MSCs leading to reduced expression of hematopoietic support factor and increased expression of ECM genes; and (ii) increased expression of interferon and NFkB inflammatory pathways in myeloid cells.
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Keith, Monica H., Mark V. Flinn, Harly J. Durbin, Troy N. Rowan, Gregory E. Blomquist, Kristen H. Taylor, Jeremy F. Taylor, and Jared E. Decker. "Genetic ancestry, admixture, and population structure in rural Dominica." PLOS ONE 16, no. 11 (November 3, 2021): e0258735. http://dx.doi.org/10.1371/journal.pone.0258735.
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The Caribbean is a genetically diverse region with heterogeneous admixture compositions influenced by local island ecologies, migrations, colonial conflicts, and demographic histories. The Commonwealth of Dominica is a mountainous island in the Lesser Antilles historically known to harbor communities with unique patterns of migration, mixture, and isolation. This community-based population genetic study adds biological evidence to inform post-colonial narrative histories in a Dominican horticultural village. High density single nucleotide polymorphism data paired with a previously compiled genealogy provide the first genome-wide insights on genetic ancestry and population structure in Dominica. We assessed family-based clustering, inferred global ancestry, and dated recent admixture by implementing the fastSTRUCTURE clustering algorithm, modeling graph-based migration with TreeMix, assessing patterns of linkage disequilibrium decay with ALDER, and visualizing data from Dominica with Human Genome Diversity Panel references. These analyses distinguish family-based genetic structure from variation in African, European, and indigenous Amerindian admixture proportions, and analyses of linkage disequilibrium decay estimate admixture dates 5–6 generations (~160 years) ago. African ancestry accounts for the largest mixture components, followed by European and then indigenous components; however, our global ancestry inferences are consistent with previous mitochondrial, Y chromosome, and ancestry marker data from Dominica that show uniquely higher proportions of indigenous ancestry and lower proportions of African ancestry relative to known admixture in other French- and English-speaking Caribbean islands. Our genetic results support local narratives about the community’s history and founding, which indicate that newly emancipated people settled in the steep, dense vegetation along Dominica’s eastern coast in the mid-19th century. Strong genetic signals of post-colonial admixture and family-based structure highlight the localized impacts of colonial forces and island ecologies in this region, and more data from other groups are needed to more broadly inform on Dominica’s complex history and present diversity.
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Guimarães Simões, Marcello, Antonio Carlos Marques, Luiz Henrique Cruz de Mello, and Renato Pirani Ghilardi. "The role of taphonomy in cladistic analysis: A case study in Permian bivalves." Spanish Journal of Palaeontology 15, no. 2 (December 19, 2021): 153. http://dx.doi.org/10.7203/sjp.15.2.22140.
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The Megadesmidae (Bivalvia, Anomalodesmata) fossil record was examined in order to assess the role of taphonomy in cladistic analysis. Megadesmids are thick-shelled, infaunal, suspension-feeding bivalves. Our data indicate that their fossil record seems biased in favor of thick-shelled, shallow-burrowing genera and/or deepburrowing forms. Consequently, there is a relation between the mode of life (shallow versus deep) and the resolution and quality of the fossil record. Deep-burrowers (Vacunella) are often preserved in life position offering a more accurate (temporal and spatial) fossil record, adequate for paleoecological inferences, while shallow-burrower shells (Plesiocyprinella), that are more prone to post-mortem transport and temporal mixing, offer a record with poor spatial and temporal resolution. The identification of homoplasy among infauna! bivalves constitutes a major challenge for their cladistic analysis. Within Megadesmidae intrinsic (bauplan limitations) and extrinsic (better preservational potential) factors favor the occurrence and preservation of homoplasy among the deep-burrowers. The implications are: a) clustering of deep-burrowing bivalves (Vacunella, Roxoa) due to parallel homoplasies, forming "adaptive", not necessarily "evolutive" taxa, and b) lower consistency indices in their cladistic analysis.
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Bachoc, Francois, Cathy Maugis-Rabusseau, and Pierre Neuvial. "Selective inference after convex clustering with l1 penalization." ESAIM: Probability and Statistics, March 4, 2025. https://doi.org/10.1051/ps/2025004.
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Classical inference methods notoriously fail when applied to data-driven test hypotheses or inference targets. Instead, dedicated methodologies are required to obtain statistical guarantees for these selective inference problems. Selective inference is particularly relevant post-clustering, typically when testing a difference in mean between two clusters. In this paper, we address convex clustering with l1 penalization, by leveraging related selective inference tools for regression, based on Gaussian vectors conditioned to polyhedral sets. In the one-dimensional case, we prove a polyhedral characterization of obtaining given clusters, that enables us to introduce a test procedure with statistical guarantees. This characterization also allows us to provide a computationally efficient regularization path algorithm. Then, we extend the above test procedure and guarantees to multi-dimensional clustering with l1 penalization, and also to more general multi-dimensional clusterings that aggregate one-dimensional ones. With various numerical experiments, we validate our statistical guarantees and study the power of our methods to detect differences in mean between clusters. Our methods are implemented in the R package poclin.
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Butzin-Dozier, Zachary, Tejas S. Athni, and Jade Benjamin-Chung. "A Review of the Ring Trial Design for Evaluating Ring Interventions for Infectious Diseases." Epidemiologic Reviews, May 19, 2022. http://dx.doi.org/10.1093/epirev/mxac003.
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Abstract In trials of infectious disease interventions, rare outcomes and unpredictable spatiotemporal variation can introduce bias, reduce statistical power, and prevent conclusive inferences. Spillover effects can complicate inference if individual randomization is used to gain efficiency. Ring trials are a type of cluster-randomized trial that may increase efficiency and minimize bias, particularly in emergency and elimination settings with strong clustering of infection. They can be used to evaluate ring interventions, which are delivered to individuals in proximity to or contact with index cases. Here we review ring trials, compare them to other trial designs for evaluating ring interventions, and describe strengths and weaknesses of each design. We conducted a systematic review to identify trials and trial protocols evaluating ring interventions. Of 849 articles and 322 protocols screened, we identified 26 ring trials, 15 cluster-randomized trials, five trials that randomized households or individuals within rings, and one individually randomized trial. The most common interventions were post-exposure prophylaxis (n = 23) and focal mass drug administration and screening and treatment (n = 7). Ring trials require robust surveillance systems and contact tracing for directly transmitted diseases. For rare diseases with strong spatiotemporal clustering, they may have higher efficiency and internal validity than cluster-randomized designs in part because they ensure that no clusters are excluded from analysis due to zero cluster incidence. Though further research is needed to compare them to other types of trials, ring trials hold promise as a design that can increase trial speed and efficiency while reducing bias.
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Rubert, Diego P., and Marília D. V. Braga. "Efficient gene orthology inference via large-scale rearrangements." Algorithms for Molecular Biology 18, no. 1 (September 28, 2023). http://dx.doi.org/10.1186/s13015-023-00238-y.
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Abstract Background Recently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space. Results In this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into $$m\ge 1$$ m ≥ 1 subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to $${\text{F}} {\textsc{ly}} {\text{B}} {\textsc{ase}}$$ F L Y B A S E families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with $${\text{F}} {\textsc{ly}} {\text{B}} {\textsc{ase}}$$ F L Y B A S E , when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the $${\textsc{mcl}}$$ M C L algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with $$\textsc {mcl}$$ M C L . Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at https://gitlab.ub.uni-bielefeld.de/gi/FFGC or as a Conda package at https://anaconda.org/bioconda/ffgc.
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Hemming, Karla, and Monica Taljaard. "Key considerations for designing, conducting and analysing a cluster randomized trial." International Journal of Epidemiology, May 18, 2023. http://dx.doi.org/10.1093/ije/dyad064.
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Abstract Not only do cluster randomized trials require a larger sample size than individually randomized trials, they also face many additional complexities. The potential for contamination is the most commonly used justification for using cluster randomization, but the risk of contamination should be carefully weighed against the more serious problem of questionable scientific validity in settings with post-randomization identification or recruitment of participants unblinded to the treatment allocation. In this paper we provide some simple guidelines to help researchers conduct cluster trials in a way that minimizes potential biases and maximizes statistical efficiency. The overarching theme of this guidance is that methods that apply to individually randomized trials rarely apply to cluster randomized trials. We recommend that cluster randomization be only used when necessary—balancing the benefits of cluster randomization with its increased risks of bias and increased sample size. Researchers should also randomize at the lowest possible level—balancing the risks of contamination with ensuring an adequate number of randomization units—as well as exploring other options for statistically efficient designs. Clustering should always be allowed for in the sample size calculation; and the use of restricted randomization (and adjustment in the analysis for covariates used in the randomization) should be considered. Where possible, participants should be recruited before randomizing clusters and, when recruiting (or identifying) participants post-randomization, recruiters should be masked to the allocation. In the analysis, the target of inference should align with the research question, and adjustment for clustering and small sample corrections should be used when the trial includes less than about 40 clusters.
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Hao, Chunlin, Joshua E. Elias, Patrick K. H. Lee, and Henry Lam. "metaSpectraST: an unsupervised and database-independent analysis workflow for metaproteomic MS/MS data using spectrum clustering." Microbiome 11, no. 1 (August 7, 2023). http://dx.doi.org/10.1186/s40168-023-01602-1.
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Abstract Background The high diversity and complexity of the microbial community make it a formidable challenge to identify and quantify the large number of proteins expressed in the community. Conventional metaproteomics approaches largely rely on accurate identification of the MS/MS spectra to their corresponding short peptides in the digested samples, followed by protein inference and subsequent taxonomic and functional analysis of the detected proteins. These approaches are dependent on the availability of protein sequence databases derived either from sample-specific metagenomic data or from public repositories. Due to the incompleteness and imperfections of these protein sequence databases, and the preponderance of homologous proteins expressed by different bacterial species in the community, this computational process of peptide identification and protein inference is challenging and error-prone, which hinders the comparison of metaproteomes across multiple samples. Results We developed metaSpectraST, an unsupervised and database-independent metaproteomics workflow, which quantitatively profiles and compares metaproteomics samples by clustering experimentally observed MS/MS spectra based on their spectral similarity. We applied metaSpectraST to fecal samples collected from littermates of two different mother mice right after weaning. Quantitative proteome profiles of the microbial communities of different mice were obtained without any peptide-spectrum identification and used to evaluate the overall similarity between samples and highlight any differentiating markers. Compared to the conventional database-dependent metaproteomics analysis, metaSpectraST is more successful in classifying the samples and detecting the subtle microbiome changes of mouse gut microbiomes post-weaning. metaSpectraST could also be used as a tool to select the suitable biological replicates from samples with wide inter-individual variation. Conclusions metaSpectraST enables rapid profiling of metaproteomic samples quantitatively, without the need for constructing the protein sequence database or identification of the MS/MS spectra. It maximally preserves information contained in the experimental MS/MS spectra by clustering all of them first and thus is able to better profile the complex microbial communities and highlight their functional changes, as compared with conventional approaches. tag the videobyte in this section as ESM4
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Farouk, Sherif, Sreepat Jain, Youssef S. Bazeen, Fayez Ahmad, Zaineb Elamri, Khaled Al‐Kahtany, and Ahmed Abdeldaim. "Palaeoenvironmental changes across the Mid‐ and latest Maastrichtian events: Planktic foraminiferal inference from the Elles section (central Tunisia)." Geological Journal, September 18, 2023. http://dx.doi.org/10.1002/gj.4881.
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Based on quantitative changes in the Maastrichtian planktic foraminiferal species distribution patterns from the Elles section (central Tunisia), δ13C, δ18O‐based palaeotemperature and inferred proxies (species diversity, ecological associations, and depth ranking), the palaeoenvironment is inferred. Based on Constrained Clustering and corroborated by Non‐metric Multidimensional Scaling (NMDS), four statistically significant intervals are identified. Interval 1 (lower–middle part of CF5 Zone) is marked by a warm, oligotrophic, stable, and well‐stratified upper water column. Interval 2 (upper part of CF5 Zone), here designated as the pre‐Mid‐Maastrichtian Event (MME) event, is marked by stressed, warmer, moderately mesotrophic, and weakly stratified surface waters with an unstable upper water column. Interval 3 (CF4 Zone) encompasses the MME and is marked by warm, stable, mesotrophic surface waters with a moderately well‐stratified upper water column. The upper part of Interval 3, designated as post‐MME, is also marked by mesotrophic conditions, but with increased surface water warming, unstable and stressed conditions. Interval 4 (CF3–CF1 zones) shows the effects of the Indian Deccan volcanism, and is marked by warmer surface waters, mesotrophic, unstable, stressed environmental conditions, with a weakly‐stratified upper water column.
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Hong, Jimin, Seung Kwan Kang, Ian Alberts, Jiaying Lu, Raphael Sznitman, Jae Sung Lee, Axel Rominger, Hongyoon Choi, and Kuangyu Shi. "Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET." European Journal of Nuclear Medicine and Molecular Imaging, February 28, 2022. http://dx.doi.org/10.1007/s00259-021-05662-z.
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Abstract Purpose Alzheimer’s disease (AD) studies revealed that abnormal deposition of tau spreads in a specific spatial pattern, namely Braak stage. However, Braak staging is based on post mortem brains, each of which represents the cross section of the tau trajectory in disease progression, and numerous studies were reported that do not conform to that model. This study thus aimed to identify the tau trajectory and quantify the tau progression in a data-driven approach with the continuous latent space learned by variational autoencoder (VAE). Methods A total of 1080 [18F]Flortaucipir brain positron emission tomography (PET) images were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. VAE was built to compress the hidden features from tau images in latent space. Hierarchical agglomerative clustering and minimum spanning tree (MST) were applied to organize the features and calibrate them to the tau progression, thus deriving pseudo-time. The image-level tau trajectory was inferred by continuously sampling across the calibrated latent features. We assessed the pseudo-time with regard to tau standardized uptake value ratio (SUVr) in AD-vulnerable regions, amyloid deposit, glucose metabolism, cognitive scores, and clinical diagnosis. Results We identified four clusters that plausibly capture certain stages of AD and organized the clusters in the latent space. The inferred tau trajectory agreed with the Braak staging. According to the derived pseudo-time, tau first deposits in the parahippocampal and amygdala, and then spreads to the fusiform, inferior temporal lobe, and posterior cingulate. Prior to the regional tau deposition, amyloid accumulates first. Conclusion The spatiotemporal trajectory of tau progression inferred in this study was consistent with Braak staging. The profile of other biomarkers in disease progression agreed well with previous findings. We addressed that this approach additionally has the potential to quantify tau progression as a continuous variable by taking a whole-brain tau image into account.
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Shen, Biyi, Haoyu Ren, Michelle Shardell, Jason Falvey, and Chixiang Chen. "Analyzing risk factors for post‐acute recovery in older adults with Alzheimer's disease and related dementia: A new semi‐parametric model for large‐scale medicare claims." Statistics in Medicine, December 27, 2023. http://dx.doi.org/10.1002/sim.9982.
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Nearly 300,000 older adults experience a hip fracture every year, the majority of which occur following a fall. Unfortunately, recovery after fall‐related trauma such as hip fracture is poor, where older adults diagnosed with Alzheimer's disease and related dementia (ADRD) spend a particularly long time in hospitals or rehabilitation facilities during the post‐operative recuperation period. Because older adults value functional recovery and spending time at home versus facilities as key outcomes after hospitalization, identifying factors that influence days spent at home after hospitalization is imperative. While several individual‐level factors have been identified, the characteristics of the treating hospital have recently been identified as contributors. However, few methodological rigorous approaches are available to help overcome potential sources of bias such as hospital‐level unmeasured confounders, informative hospital size, and loss to follow‐up due to death. This article develops a useful tool equipped with unsupervised learning to simultaneously handle statistical complexities that are often encountered in health services research, especially when using large administrative claims databases. The proposed estimator has a closed form, thus only requiring light computation load in a large‐scale study. We further develop its asymptotic properties with stabilized inference assisted by unsupervised clustering. Extensive simulation studies demonstrate superiority of the proposed estimator compared to existing estimators.
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Buen Abad Najar, Carlos F., Prakruthi Burra, Nir Yosef, and Liana F. Lareau. "Identifying cell state–associated alternative splicing events and their coregulation." Genome Research, July 20, 2022. http://dx.doi.org/10.1101/gr.276109.121.
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Alternative splicing shapes the transcriptome and contributes to each cell's unique identity, but single-cell RNA sequencing (scRNA-seq) has struggled to capture the impact of alternative splicing. We previously showed that low recovery of mRNAs from single cells led to erroneous conclusions about the cell-to-cell variability of alternative splicing. Here, we present a method, Psix, to confidently identify splicing that changes across a landscape of single cells, using a probabilistic model that is robust against the data limitations of scRNA-seq. Its autocorrelation-inspired approach finds patterns of alternative splicing that correspond to patterns of cell identity, such as cell type or developmental stage, without the need for explicit cell clustering, labeling, or trajectory inference. Applying Psix to data that follow the trajectory of mouse brain development, we identify exons whose alternative splicing patterns cluster into modules of coregulation. We show that the exons in these modules are enriched for binding by distinct neuronal splicing factors and that their changes in splicing correspond to changes in expression of these splicing factors. Thus, Psix reveals cell type–dependent splicing patterns and the wiring of the splicing regulatory networks that control them. Our new method will enable scRNA-seq analysis to go beyond transcription to understand the roles of post-transcriptional regulation in determining cell identity.
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Maia, Pedro D., Sneha Pandya, Benjamin Freeze, Justin Torok, Ajay Gupta, Yashar Zeighami, and Ashish Raj. "Origins of atrophy in Parkinson linked to early onset and local transcription patterns." Brain Communications 2, no. 2 (2020). http://dx.doi.org/10.1093/braincomms/fcaa065.
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Abstract There is enormous clinical value in inferring the brain regions initially atrophied in Parkinson disease for individual patients and understanding its relationship with clinical and genetic risk factors. The aim of this study is to leverage a new seed-inference algorithm demonstrated for Alzheimer’s disease to the Parkinsonian context and to cluster patients in meaningful subgroups based on these incipient atrophy patterns. Instead of testing brain regions separately as the likely initiation site for each patient, we solve an L1-penalized optimization problem that can return a more predictive heterogeneous, multi-locus seed patterns. A cluster analysis of the individual seed patterns reveals two distinct subgroups (S1 versus S2). The S1 subgroup is characterized by the involvement of the brainstem and ventral nuclei, and S2 by cortex and striatum. Post hoc analysis in features not included in the clustering shows significant differences between subgroups regarding age of onset and local transcriptional patterns of Parkinson-related genes. Top genes associated with regional microglial abundance are strongly associated with subgroup S1 but not with S2. Our results suggest two distinct aetiological mechanisms operative in Parkinson disease. The interplay between immune-related genes, lysosomal genes, microglial abundance and atrophy initiation sites may explain why the age of onset for patients in S1 is on average 4.5 years later than for those in S2. We highlight and compare the most prominently affected brain regions for both subgroups. Altogether, our findings may improve current screening strategies for early Parkinson onsetters.
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Khader, Karim, Candace Haroldsen, Martin Evans, Loretta Simbartl, Brian McCauley, Matthew H. Samore, and Michael Rubin. "P-329. Estimating changes in MRSA infection rates due to changes in facility MRSA precautions." Open Forum Infectious Diseases 12, Supplement_1 (January 29, 2025). https://doi.org/10.1093/ofid/ofae631.532.
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Abstract Background The role of active surveillance (AS) and contact precautions (CP) in acute care settings for preventing transmission of endemic drug resistant organisms such as methicillin-resistant Staphylococcus aureus (MRSA) remains unsettled. The emergence of SARS-CoV-2 led to a shift in VA policy in March 2020, when VA acute care facilities were allowed to suspend AS for MRSA and CP for patients with MRSA colonization or active infection, resulting in a partial and variable de-implementation of AS/CP across the VA (Figure 1). We evaluated the impact of this change on VA MRSA infection rates. Methods We modeled monthly positive MRSA culture rate at the ward-facility level using (1) all positive MRSA cultures, and (2) only positive sterile-site cultures (to more closely capture ‘true infections’). Our primary outcome was HAIs (occurring ≥ 3d after admission), including those up to 30-days post-discharge. We used Poisson, Negative Binomial, and Poisson mixed-effects regression approaches, clustering within ward type (ICU/non-ICU) and facility. With each approach, 4 different models incorporated increasing numbers of covariates to evaluate the robustness of the results. Results Using Poisson regression, we found culture rate, discontinuing AS & CP, and discontinuing CP only for colonization are associated with an increased MRSA positive culture rate (Model 1, Table 1). Addition of the year prior infection rate (Models 2–4) nullifies the association between AS & CP and MRSA positive culture rate. Results were similar when modeling positive sterile site cultures only (not shown) and using Negative Binomial regression (Table 2). Facility-level use of AS & CP is not associated with MRSA positive culture rate when using Poisson mixed-effects regression (Table 3), suggesting that accounting for facility-level clustering eliminates (or reduces) the apparent effect of AS/CP. Conclusion The impact of de-implementing AS & CP is complex and difficult to discern. The inference that de-implementation of AS & CP increased MRSA infection rates was not robust to relaxation of simplifying assumptions. These findings combined with the existence of unobservable data (e.g., use of gowns/gloves during the pandemic), imply substantial challenges surrounding statistical interpretation. Disclosures All Authors: No reported disclosures
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Wierenga, Kathryn A., Frank M. Riemers, Bart Westendorp, Jack R. Harkema, and James J. Pestka. "Single cell analysis of docosahexaenoic acid suppression of sequential LPS-induced proinflammatory and interferon-regulated gene expression in the macrophage." Frontiers in Immunology 13 (November 3, 2022). http://dx.doi.org/10.3389/fimmu.2022.993614.
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Preclinical and clinical studies suggest that consumption of long chain omega-3 polyunsaturated fatty acids (PUFAs) reduces severity of chronic inflammatory and autoimmune diseases. While these ameliorative effects are conventionally associated with downregulated expression of proinflammatory cytokine and chemokine genes, our laboratory has recently identified Type 1 interferon (IFN1)-regulated gene expression to be another key target of omega-3 PUFAs. Here we used single cell RNA sequencing (scRNAseq) to gain new mechanistic perspectives on how the omega-3 PUFA docosahexaenoic acid (DHA) influences TLR4-driven proinflammatory and IFN1-regulated gene expression in a novel self-renewing murine fetal liver-derived macrophage (FLM) model. FLMs were cultured with 25 µM DHA or vehicle for 24 h, treated with modest concentration of LPS (20 ng/ml) for 1 and 4 h, and then subjected to scRNAseq using the 10X Chromium System. At 0 h (i.e., in the absence of LPS), DHA increased expression of genes associated with the NRF2 antioxidant response (e.g. Sqstm1, Hmox1, Chchd10) and metal homeostasis (e.g.Mt1, Mt2, Ftl1, Fth1), both of which are consistent with DHA-induced polarization of FLMs to a more anti-inflammatory phenotype. At 1 h post-LPS treatment, DHA inhibited LPS-induced cholesterol synthesis genes (e.g. Scd1, Scd2, Pmvk, Cyp51, Hmgcs1, and Fdps) which potentially could contribute to interference with TLR4-mediated inflammatory signaling. At 4 h post-LPS treatment, LPS-treated FLMs reflected a more robust inflammatory response including upregulation of proinflammatory cytokine (e.g. Il1a, Il1b, Tnf) and chemokine (e.g.Ccl2, Ccl3, Ccl4, Ccl7) genes as well as IFN1-regulated genes (e.g. Irf7, Mx1, Oasl1, Ifit1), many of which were suppressed by DHA. Using single-cell regulatory network inference and clustering (SCENIC) to identify gene expression networks, we found DHA modestly downregulated LPS-induced expression of NF-κB-target genes. Importantly, LPS induced a subset of FLMs simultaneously expressing NF-κB- and IRF7/STAT1/STAT2-target genes that were conspicuously absent in DHA-pretreated FLMs. Thus, DHA potently targeted both the NF-κB and the IFN1 responses. Altogether, scRNAseq generated a valuable dataset that provides new insights into multiple overlapping mechanisms by which DHA may transcriptionally or post-transcriptionally regulate LPS-induced proinflammatory and IFN1-driven responses in macrophages.
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Pedersen, André, Erik Smistad, Tor V. Rise, Vibeke G. Dale, Henrik S. Pettersen, Tor-Arne S. Nordmo, David Bouget, Ingerid Reinertsen, and Marit Valla. "H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images." Frontiers in Medicine 9 (September 14, 2022). http://dx.doi.org/10.3389/fmed.2022.971873.
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Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering.
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Darzi, Atefe, Benedikt Halldorsson, Birgir Hrafnkelsson, Hossein Ebrahimian, Fatemeh Jalayer, and Kristín S. Vogfjörð. "Calibration of a Bayesian spatio-temporal ETAS model to the June 2000 South Iceland seismic sequence." Geophysical Journal International, October 7, 2022. http://dx.doi.org/10.1093/gji/ggac387.
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Summary The reliable forecasting of seismic sequences following a mainshock has practical implications because effective post-event response is crucial in earthquake-stricken regions, aftershocks can progressively cause increased damage, and compound economic losses. In the South Iceland Seismic Zone (SISZ), one of two large transform zones in Iceland where earthquake hazard is the highest, an intense seismic sequence took place during 17–24 June 2000, starting with a ${M_{\rm{w}}}$6.4 mainshock on 17 June 2000, followed by another ${M_{\rm{w}}}$6.5 mainshock four days later and on a different fault. Both earthquakes caused considerable damage and incurred heavy economic losses. They were immediately followed by intense aftershock activity on the causative faults and triggered earthquakes as far as 80 km away along the transform zone. To investigate the feasibility of forecasting the progression of such complex sequences, we calibrated a spatio-temporal epidemic-type aftershock sequence (ETAS) clustering model to the June 2000 seismic sequence in the framework of Bayesian statistics. Short-term seismicity forecasts were carried out for various forecasting intervals and compared with the observations, the first generated a few hours after the first mainshock and followed by daily forecasts. The reliability of the early forecasts was seen to depend on the initial model parameters. By employing an adaptive parameter inference approach where the posteriors from each preceding forecasting interval served as informative priors for the next, the fast convergence of the parametric values was ensured. As a result, the 16–84 percentile range of the forecasted number of events captured the actual number of observed events in all daily forecasts, and the model exhibited a strong spatial forecasting ability, even only a few hours after the mainshock, and over all subsequent daily forecasts. We present the spatio-temporal ETAS parameters for the June 2000 sequence as ideal candidates for future operational earthquake forecasting of other Icelandic aftershock sequences. Past seismic sequences need to be analysed retrospectively to confirm the stability of the parameters of this study, effectively enable the application of the Bayesian ETAS model as an operational earthquake forecasting system for aftershocks in Iceland.
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Jaramillo-Aguilar, Edwin, Estefania Peña-Zuñiga, Noelia Barriga-Medina, Dorian Rodríguez-González, Luz Leonor Mattos Calderon, Felipe Garces-Fiallos, and Antonio Leon-Reyes. "First Report of Lasiodiplodia theobromae Causing Fruit Crown Rot on Banana in Ecuador." Plant Disease, August 29, 2024. http://dx.doi.org/10.1094/pdis-07-24-1370-pdn.
Full textAbstract:
Post-harvest diseases like fruit crown rot (CR) on bananas (Musa spp.) worldwide are mainly attributed to Colletotrichum gloeosporioides (Berk. & Curt.) von Arx and Lasiodiplodia theobromae (Pat.) Griff. & Maubl (Sangeetha et al., 2012; Riera et al., 2019). In April 2019, at a banana farm (cultivar Williams) located in El Oro province (location at 79° 54' 05" W; 03° 17' 16" S), thirty hands were randomly collected from the postharvest process and further placed in a humid chamber at 20 ºC until signs of the disease progressed and became more evident (from 3 days to 20 days). Ten hands presented initial symptoms related to CR during the postharvest process, which included crown or peduncle rot with mycelial development on the crown's surface, leading to the blackening of tissues at the site of the wound left when the cluster was cut. Crown fruit fragments (~0.5 cm) from the edge of healthy tissue and diseased tissue underwent a series of disinfection steps, initially in ethanol (70%) for 1 min, followed by sodium hypochlorite (1%) for 1 min, rinsed three times with sterile distilled water, and dried on sterile filter paper for 10 min. The fragments were placed onto Potato dextrose agar (PDA) + chloramphenicol (100 mg L-1) and incubated at 25°C in darkness for five days. Five isolates with different colony morphologies were obtained. An initial screen of the pathogenicity of all isolates showed that only one isolate showed disease activity in banana crowns. This isolate, C1, showed grayish-white aerial mycelium in culture as described above and, after ten days, became black. We did a full pathogenicity test with C1 using ten individual banana fruits (cv. Williams Cavendish). Briefly, one disc (Ø of 5 mm) of the fungus with agar was placed on the acropetal part of the banana fruit (on the peel) and another piece in the crown without wounding. Inoculated fruit were in a humid chamber at 20 °C for 20 days. Uninoculated fruits constituted the control. Isolate C1 caused 100% of the fruit and crowns to rot, with symptoms similar to those initially observed from fruit collected at the postharvest process (Fig. S1d). The fungus was re-isolated from symptomatic tissue, and its identity was confirmed through morphological characteristics consistent with Lasiodiplodia sp. Matured conidia of all mono hyphal strains (Fig. S1b) appeared dark brown with a single septum, having an ovate shape, and displayed longitudinal striations along their thickened walls (Fig. S1c). The dimensions of the mature conidia ranged from 16.02 – 26.85 x 11.09 – 16.74 µm (n = 60). Morphological characteristics showed similarity to Lasiodiplodia sp. (Alves et al., 2008). Microscopic observations were further confirmed by sequencing three loci: the internal transcribed spacer (ITS), β-tubulin, and partial translation elongation factor-1α (TEF-1α). Fungal genomic DNA from the C1 isolate was PCR amplified using ITS5/ITS4, EF1-728F/986R, and Bt2A/Bt2B primers, respectively, according to Glass & Donaldson (1995) and Bautista-Cruz et al. (2019). The resulting amplicons were sequenced, and those sequences were deposited in GenBank with the accession numbers ITS: PP532861, TEF-1α: PP551938, and β-tubulin: PP537587. Sequence alignment was conducted using ClustalW under the MEGA 11.0 software package (Tamura et al., 2021). Subsequently, phylogenetic analysis was performed using Bayesian inference using the BEAST v1.8.4 program (Drummond & Rambaut, 2007). The concatenated sequence of the isolate revealed clustering to the Lasiodiplodia theobromae clade, confirming its identity. To our knowledge, this is the first report of this pathogen causing CR on banana fruit in Ecuador. Based on the report of CR in the country, banana exporters and the Ecuadorian government should consider developing disease management methods that include the cultivation, shipping, ripening, and storage processes of the fruit.
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Xie, Pan, Yufeng Zhang, Zhigang Fang, Shubin Zhang, Hongyue Li, Wenwen Gao, and Shuaishuai Sha. "First Report of Stem Canker Disease Caused by Neoscytalidium dimidiatum on Euphrates Poplar in Xinjiang, China." Plant Disease, May 23, 2024. http://dx.doi.org/10.1094/pdis-12-23-2780-pdn.
Full textAbstract:
Euphrates poplar (Populus euphratica Oliv.) constitutes about 61% of the global poplar population, thriving in arid regions of western China (Wu et al. 2023). It plays a crucial role in maintaining ecological balance, securing oasis agriculture, and driving socio-economic progress in the region. During a June 2023 investigation in the P. euphratica forest within the Hotan area of Xinjiang (37°20′21″N, 79°21′15″E), over 12% of the P. euphratica trees displayed branch withering symptoms. The affected trees exhibited cracked bark, trunk decay, darkened coloration, and an eventual black coal-smoke-like appearance. Fungal spores were notably present beneath peeling bark on trunks and main branches. The deep ulcers extended longitudinally into the cambium, leading to tree mortality. In some cases, lateral spread into the sapwood caused dark discoloration of vascular tissue. Twenty diseased branches from various locations were collected and 5–10 mm2 lesions were excised from the edges. These were then surface-disinfected with 75% ethanol for 30 s and 1% sodium hypochlorite for 2 min. After three rinses with sterile distilled water, excess moisture was removed using sterile filter paper, followed by incubating the samples on Potato Dextrose Agar (PDA) medium. Cultures were subsequently grown at 25 ± 1 ℃ under a 12-h photoperiod for three days, thus resulting in the isolation of 25 fungal strains with similar morphological characteristics. All strains displayed rapid colony growth (40 mm/d). On PDA medium, the mycelium initially presented as a white colony, transitioning to an olive-green to greyish color, finally turning dark-grey to black. Colonies generated mycelia that disintegrated into 0- to 1-septate, cylindrical to round, hyaline to brown arthroconidia, occurring singly or in arthric chains, averaging 8.9 ± 2.1 μm × 4.9 ± 1.3 μm, with a length/width ratio of 1.79. Based on morphological characteristics, the isolates were identified as Neoscytalidium dimidiatum (Penz.) Crous & Slippers (Crous et al. 2006). Molecular characterization involved amplifying the partial internal transcribed spacer (ITS) region and translation elongation factor 1-α (TEF1-α) and β-tubulin (TUB2) genes using ITS1/ITS4 (White et al. 1990), EF1-728F/EF1-986R (Carbone and Kohn 1999), and BT2a/BT2b primers (Glass and Donaldson 1995). Sequences, available in GenBank (ITS: PP033096, PP033097, PP033098; TUB2: PP032812, PP032813, PP032814; TEF1-α: PP032815, PP032816, PP032817), exhibited 99–100% identity with the epitype N. dimidiatum Arp2-D (ITS, MK813852; TUB2, MK816354; TEF1-α, MK816355). Phylogenetic analysis, employing maximum likelihood and Bayesian inference on concatenated ITS-TEF1-TUB, was constructed using IQ-Tree and MrBayes3.2.7, revealing isolates clustering within the N. dimidiatum clade. Three isolates (HY01, HY02, and HY05) from different collection points were chosen for pathogenic investigation. Pathogenicity testing on one-year-old healthy P. euphratica seedlings involved removing a 4-mm-diameter bark plug using a cork borer. A 3-day-cultured N. dimidiatum plug of the same size was inoculated, with a blank PDA as control. The wound was covered with moistened sterile absorbent cotton and finally sealed with parafilm for three days. Experiments were repeated thrice. Symptoms manifested by day 2 post-inoculation, resembling the original symptoms by day 7. In the control group, plants remained healthy. N. dimidiatum was exclusively re-isolated from lesions on inoculated stems, confirmed as N. dimidiatum through morphological characteristics and sequence analysis, aligning with Koch's hypothesis. To our knowledge, this is the first report of N. dimidiatum inducing stem canker on P. euphratica in China. This pathogen has been reported on many tree hosts including citrus (Alananbeh et al., 2020), common fig (Güney et al., 2022), dragon fruit (Salunkhe et al., 2023), and Almond (Nouri et al., 2018). Therefore our findings will serve as a warning for authorities to a potential threat in China's P. euphratica and other trees cultivation. Thus, further epidemiological studies are essential for devising effective management strategies.
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Michas, Georgios. "Spatiotemporal Diffusion Variability of Injection-Induced Seismicity in Enhanced Geothermal Systems." Pure and Applied Geophysics, February 18, 2025. https://doi.org/10.1007/s00024-025-03680-8.
Full textAbstract:
Abstract Injection-induced seismicity represents a major challenge for the development of Enhanced Geothermal Systems (EGS). To effectively mitigate the associated seismic hazard, a better understanding of the spatiotemporal evolution of induced seismicity and its efficient modeling are required. Towards that end, a stochastic framework within the continuous time random walk (CTRW) theory is used to make inferences regarding the diffusion properties of injection-induced seismicity in three cases of hydraulic stimulations in EGS. The analysis of seismicity within the CTRW context indicates multi-scaling variations in the waiting times distributions and in the evolution of the mean squared distance of seismicity with time, both associated with the co- and post-injection periods, respectively. During fluid-injections, an almost Poissonian waiting times distribution is followed by broad distributions during post-injection, enhancing long-term clustering effects and inter-earthquake interactions. At the same time, the rate of triggered earthquake diffusion drastically drops during the post-injection period for all the studied cases. Such properties may have implications on the main driving mechanisms of injection-induced seismicity in EGS, highlighting the transition from a dominant pressure-driven triggering mechanism during fluid-injections, to a mixed mechanism after termination of injections, where stress transfer effects and inter-earthquake interactions become more important.
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Mekonnen, Ephrem Tibebe, Luca Longo, and Pierpaolo Dondio. "A global model-agnostic rule-based XAI method based on Parameterized Event Primitives for time series classifiers." Frontiers in Artificial Intelligence 7 (September 20, 2024). http://dx.doi.org/10.3389/frai.2024.1381921.
Full textAbstract:
Time series classification is a challenging research area where machine learning and deep learning techniques have shown remarkable performance. However, often, these are seen as black boxes due to their minimal interpretability. On the one hand, there is a plethora of eXplainable AI (XAI) methods designed to elucidate the functioning of models trained on image and tabular data. On the other hand, adapting these methods to explain deep learning-based time series classifiers may not be straightforward due to the temporal nature of time series data. This research proposes a novel global post-hoc explainable method for unearthing the key time steps behind the inferences made by deep learning-based time series classifiers. This novel approach generates a decision tree graph, a specific set of rules, that can be seen as explanations, potentially enhancing interpretability. The methodology involves two major phases: (1) training and evaluating deep-learning-based time series classification models, and (2) extracting parameterized primitive events, such as increasing, decreasing, local max and local min, from each instance of the evaluation set and clustering such events to extract prototypical ones. These prototypical primitive events are then used as input to a decision-tree classifier trained to fit the model predictions of the test set rather than the ground truth data. Experiments were conducted on diverse real-world datasets sourced from the UCR archive, employing metrics such as accuracy, fidelity, robustness, number of nodes, and depth of the extracted rules. The findings indicate that this global post-hoc method can improve the global interpretability of complex time series classification models.