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Готові списки джерел за темами / Analysis of biological data

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Добірка наукової літератури з теми "Analysis of biological data"

Автор: Grafiati

Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Analysis of biological data"

1

Dwivedi, Vivek Dhar, Indra Prasad Tripathi, Aman Chandra Kaushik, Shiv Bharadwaj, and Sarad Kumar Mishra. "Biological Data Analysis Program (BDAP): a multitasking biological sequence analysis program." Neural Computing and Applications 30, no. 5 (December 17, 2016): 1493–501. http://dx.doi.org/10.1007/s00521-016-2772-z.

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2

Srivastava, Chandan. "Biological Data Analysis: Error and Uncertainty." World Journal of Computer Application and Technology 1, no. 3 (November 2013): 67–74. http://dx.doi.org/10.13189/wjcat.2013.010302.

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3

Eliceiri, K. W., C. Rueden, W. A. Mohler, W. L. Hibbard, and J. G. White. "Analysis of Multidimensional Biological Image Data." BioTechniques 33, no. 6 (December 2002): 1268–73. http://dx.doi.org/10.2144/02336bt01.

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4

Grewal, Rumdeep Kaur, and Sampa Das. "Microarray data analysis: Gaining biological insights." Journal of Biomedical Science and Engineering 06, no. 10 (2013): 996–1005. http://dx.doi.org/10.4236/jbise.2013.610124.

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5

El-Bayomi, Kh M., El A. Rady, M. S. El-Tarabany, and Fatma D. Mohammed. "Statistical Analysis of Biological Survival Data." Zagazig Veterinary Journal 42, no. 1 (March 1, 2014): 129–39. http://dx.doi.org/10.21608/zvjz.2014.59478.

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6

Fry, J. C. "Biological Data Analysis: A Practical Approach." Biometrics 50, no. 1 (March 1994): 318. http://dx.doi.org/10.2307/2533236.

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7

Johnson, Michael L. "Review of Fry, Biological Data Analysis." Biophysical Journal 67, no. 2 (August 1994): 937. http://dx.doi.org/10.1016/s0006-3495(94)80557-0.

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8

Sung, Wing-Kin. "Pan-omics analysis of biological data." Methods 102 (June 2016): 1–2. http://dx.doi.org/10.1016/j.ymeth.2016.05.004.

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9

Stansfield, William D., and Matthew A. Carlton. "Bayesian Statistics for Biological Data: Pedigree Analysis." American Biology Teacher 66, no. 3 (March 1, 2004): 177–82. http://dx.doi.org/10.2307/4451651.

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10

Topaz, Chad M., Lori Ziegelmeier, and Tom Halverson. "Topological Data Analysis of Biological Aggregation Models." PLOS ONE 10, no. 5 (May 13, 2015): e0126383. http://dx.doi.org/10.1371/journal.pone.0126383.

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Дисертації з теми "Analysis of biological data"

1

Droop, Alastair Philip. "Correlation Analysis of Multivariate Biological Data." Thesis, University of York, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507622.

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2

McCormick, Paul Stephen. "Statistical analysis of biological expression data." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613819.

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3

Hasegawa, Takanori. "Reconstructing Biological Systems Incorporating Multi-Source Biological Data via Data Assimilation Techniques." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/195985.

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4

Waterworth, Alan Richard. "Data analysis techniques of measured biological impedance." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340146.

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5

Becker, Katinka [Verfasser]. "Logical Analysis of Biological Data / Katinka Becker." Berlin : Freie Universität Berlin, 2021. http://d-nb.info/1241541779/34.

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6

REHMAN, HAFEEZ UR. "Integration and Analysis of Heterogeneous Biological Data." Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2537092.

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Анотація:

We live in the era of networks. The power of networks is the most fundamental driving force behind the machinery of life. Living bodies stay alive through complex inter-regulations of biochemical networks and information flows through these networks with such a great intensity and complexity that it exceeds anything that the human ingenuity has been able to spawn so far. Due to this overwhelming complexity we have begun to see a rapid rise in studies aimed at explaining the fundamental concepts and hidden properties of such complex systems. This thesis provides a strong foundation of using networks to understand complex biological phenomenon like protein functions, as well as more accurate method of modeling gene regulatory networks. In the first part we presented a methodology that uses existing biological data with gene ontology functional dependencies to infer functions of uncharacterized proteins. We combined different sources of structural and functional information along with gene ontology based term-specific relationships to predict precise functions of unannotated proteins. Such term-specific relationships, defined to clearly identify the functional contexts of each activity among the interacting proteins, which enables a dramatical improvement of the annotation accuracy with respect to previous approaches. The presented methodology may be easily extended to integrate more sources of biological information to further improve the function prediction confidence. In the second part of this thesis we discussed an extended BN model to account for post-transcriptional regulation in GRN simulation. Thanks to this extended model, we discussed the set of attractors of two biologically confirmed networks, focusing on the regulatory role of miR-7. Attractors have been compared with networks in which the miRNA was removed. The central role of the miRNA for increasing the network stability has been highlighted in both the networks, confirming the cooperative stabilizing role of miR-7. The enhanced BN model presented in this thesis is only a first step towards a more realistic analysis of the high-level functional and topological characteristics of GRNs. Resorting to the tool facilities, the dynamics of real networks can be analyzed. Thanks to the extended model that includes post-transcriptional regulations, not only the network simulation can be more reliable, but also it can offer new insights on the role of miRNAs from a functional perspective, and this improves the current state-of-the-art, which mostly focuses on high-level gene/gene or gene/protein interactions, neglecting post-transcriptional regulations. Due to its discrete nature, the BN model may still neglect some regulatory fine adjustments. However, the largest number of the computed attractors, now including miRNAs, still represents meaningful states of the network. The simple glimpse into the complexity of the network dynamics, that the toolkit is able to provide, could be used not only as a validation of in vitro experiments, but as a real System Biology tool able to rise new questions and drive new experiments.

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Li, Yehua. "Topics in functional data analysis with biological applications." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1867.

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8

Chen, Li. "Integrative Modeling and Analysis of High-throughput Biological Data." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/30192.

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Анотація:

Computational biology is an interdisciplinary field that focuses on developing mathematical models and algorithms to interpret biological data so as to understand biological problems. With current high-throughput technology development, different types of biological data can be measured in a large scale, which calls for more sophisticated computational methods to analyze and interpret the data. In this dissertation research work, we propose novel methods to integrate, model and analyze multiple biological data, including microarray gene expression data, protein-DNA interaction data and protein-protein interaction data. These methods will help improve our understanding of biological systems. First, we propose a knowledge-guided multi-scale independent component analysis (ICA) method for biomarker identification on time course microarray data. Guided by a knowledge gene pool related to a specific disease under study, the method can determine disease relevant biological components from ICA modes and then identify biologically meaningful markers related to the specific disease. We have applied the proposed method to yeast cell cycle microarray data and Rsf-1-induced ovarian cancer microarray data. The results show that our knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification. Second, we propose a novel method for transcriptional regulatory network identification by integrating gene expression data and protein-DNA binding data. The approach is built upon a multi-level analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes increasingly significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to reduce false positive predictions by integrating binding motif information and gene expression data; a significance analysis procedure is followed to assess the significance of each regulatory module. The resulting performance on simulation data and yeast cell cycle data shows that the multi-level SVR approach outperforms other existing methods in the identification of both regulators and their target genes. We have further applied the proposed method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. Third, we propose a bootstrapping Markov Random Filed (MRF)-based method for subnetwork identification on microarray data by incorporating protein-protein interaction data. Methodologically, an MRF-based network score is first derived by considering the dependency among genes to increase the chance of selecting hub genes. A modified simulated annealing search algorithm is then utilized to find the optimal/suboptimal subnetworks with maximal network score. A bootstrapping scheme is finally implemented to generate confident subnetworks. Experimentally, we have compared the proposed method with other existing methods, and the resulting performance on simulation data shows that the bootstrapping MRF-based method outperforms other methods in identifying ground truth subnetwork and hub genes. We have then applied our method to breast cancer data to identify significant subnetworks associated with drug resistance. The identified subnetworks not only show good reproducibility across different data sets, but indicate several pathways and biological functions potentially associated with the development of breast cancer and drug resistance. In addition, we propose to develop network-constrained support vector machines (SVM) for cancer classification and prediction, by taking into account the network structure to construct classification hyperplanes. The simulation study demonstrates the effectiveness of our proposed method. The study on the real microarray data sets shows that our network-constrained SVM, together with the bootstrapping MRF-based subnetwork identification approach, can achieve better classification performance compared with conventional biomarker selection approaches and SVMs. We believe that the research presented in this dissertation not only provides novel and effective methods to model and analyze different types of biological data, the extensive experiments on several real microarray data sets and results also show the potential to improve the understanding of biological mechanisms related to cancers by generating novel hypotheses for further study.
Ph. D.

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9

Causey, Jason L. "Studying Low Complexity Structures in Bioinformatics Data Analysis of Biological and Biomedical Data." Thesis, University of Arkansas at Little Rock, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10750808.

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Biological, biomedical, and radiological data tend to be large, complex, and noisy. Gene expression studies contain expression levels for thousands of genes and hundreds or thousands of patients. Chest Computed Tomography images used for diagnosing lung cancer consist of hundreds of 2-D image ”slices”, each containing hundreds of thousands of pixels. Beneath the size and apparent complexity of many of these data are simple and sparse structures. These low complexity structures can be leveraged into new approaches to biological, biomedical, and radiological data analyses. Two examples are presented here. First, a new framework SparRec (Sparse Recovery) for imputation of GWAS data, based on a matrix completion (MC) model taking advantage of the low-rank and low number of co-clusters of GWAS matrices. SparRec is flexible enough to impute meta-analyses with multiple cohorts genotyped on different sets of SNPs, even without a reference panel. Compared with Mendel-Impute, another MC method, our low-rank based method achieves similar accuracy and efficiency even with up to 90% missing data; our co-clustering based method has advantages in running time. MC methods are shown to have advantages over statistics-based methods, including Beagle and fastPhase. Second, we demonstrate NoduleX, a method for predicting lung nodule malignancy from chest Computed Tomography (CT) data, based on deep convolutional neural networks. For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort and compare our results with classifications provided by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of up to 0.99, commensurate with the radiologists’ analysis. Whether they are leveraged directly or extracted using mathematical optimization and machine learning techniques, low complexity structures provide researchers with powerful tools for taming complex data.

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10

Zandegiacomo, Cella Alice. "Multiplex network analysis with application to biological high-throughput data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10495/.

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In questa tesi vengono studiate alcune caratteristiche dei network a multiplex; in particolare l'analisi verte sulla quantificazione delle differenze fra i layer del multiplex. Le dissimilarita sono valutate sia osservando le connessioni di singoli nodi in layer diversi, sia stimando le diverse partizioni dei layer. Sono quindi introdotte alcune importanti misure per la caratterizzazione dei multiplex, che vengono poi usate per la costruzione di metodi di community detection . La quantificazione delle differenze tra le partizioni di due layer viene stimata utilizzando una misura di mutua informazione. Viene inoltre approfondito l'uso del test dell'ipergeometrica per la determinazione di nodi sovra-rappresentati in un layer, mostrando l'efficacia del test in funzione della similarita dei layer. Questi metodi per la caratterizzazione delle proprieta dei network a multiplex vengono applicati a dati biologici reali. I dati utilizzati sono stati raccolti dallo studio DILGOM con l'obiettivo di determinare le implicazioni genetiche, trascrittomiche e metaboliche dell'obesita e della sindrome metabolica. Questi dati sono utilizzati dal progetto Mimomics per la determinazione di relazioni fra diverse omiche. Nella tesi sono analizzati i dati metabolici utilizzando un approccio a multiplex network per verificare la presenza di differenze fra le relazioni di composti sanguigni di persone obese e normopeso.

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Книги з теми "Analysis of biological data"

1

Maglaveras, Nicos, Ioanna Chouvarda, Vassilis Koutkias, and Rüdiger Brause, eds. Biological and Medical Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11946465.

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2

Oliveira, José Luís, Víctor Maojo, Fernando Martín-Sánchez, and António Sousa Pereira, eds. Biological and Medical Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573067.

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Barreiro, José María, Fernando Martín-Sánchez, Víctor Maojo, and Ferran Sanz, eds. Biological and Medical Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b104033.

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4

Dolph, Schluter, ed. The analysis of biological data. Greenwood Village, Colo: Roberts and Co. Publishers, 2009.

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5

C, Fry John, ed. Biological data analysis: A practical approach. Oxford: IRL Press at Oxford University Press, 1993.

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6

Glasbey, C. A. Image analysis for the biological sciences. Chichester: J. Wiley, 1995.

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7

Analysis of infectious disease data. London: Chapman and Hall, 1989.

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8

R, Margules C., Austin M. P, and CSIRO (Australia), eds. Nature conservation: Cost effective biological surveys and data analysis. [Canberra]: CSIRO Australia, 1991.

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9

Ophir, Frieder, and Martino Robert L, eds. High performance computational methods for biological sequence analysis. Boston: Kluwer Academic Publishers, 1996.

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10

Podani, János. Introduction to the exploration of multivariate biological data. Leiden: Backhuys Publishers, 2000.

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Частини книг з теми "Analysis of biological data"

1

Kim, Ju Han. "Biological Network Analysis." In Genome Data Analysis, 233–46. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_13.

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2

Rieger, Josef, Karel Kosar, Lenka Lhotska, and Vladimir Krajca. "EEG Data and Data Analysis Visualization." In Biological and Medical Data Analysis, 39–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30547-7_5.

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3

Kim, Ju Han. "Gene Ontology and Biological Pathway-Based Analysis." In Genome Data Analysis, 121–34. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1942-6_7.

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4

Barah, Pankaj, Dhruba Kumar Bhattacharyya, and Jugal Kumar Kalita. "Information Flow in Biological Systems." In Gene Expression Data Analysis, 27–38. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429322655-2.

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5

O'Hara, Timothy D., Thomas A. Schlacher, Ashley A. Rowden, and Derek P. Tittensor. "Data Analysis Considerations." In Biological Sampling in the Deep Sea, 386–403. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118332535.ch17.

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6

Ino, Fumihiko, Katsunori Matsuo, Yasuharu Mizutani, and Kenichi Hagihara. "Minimizing Data Size for Efficient Data Reuse in Grid-Enabled Medical Applications." In Biological and Medical Data Analysis, 195–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11946465_18.

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Potamias, George. "Knowledgeable Clustering of Microarray Data." In Biological and Medical Data Analysis, 491–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30547-7_49.

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8

Polaillon, Géraldine, Laure Vescovo, Magali Michaut, and Jean-Christophe Aude. "Mining Biological Data Using Pyramids." In Selected Contributions in Data Analysis and Classification, 397–408. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73560-1_37.

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9

Hernández, Juan A., Martha L. Mora, Emanuele Schiavi, and Pablo Toharia. "RF Inhomogeneity Correction Algorithm in Magnetic Resonance Imaging." In Biological and Medical Data Analysis, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30547-7_1.

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10

Diez, Raquel Montes, Juan M. Marin, and David Rios Insua. "Bayesian Prediction of Down Syndrome Based on Maternal Age and Four Serum Markers." In Biological and Medical Data Analysis, 85–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30547-7_10.

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Тези доповідей конференцій з теми "Analysis of biological data"

1

Soetaert, Karline, Dick van Oevelen, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Modelling Marine Biological and Biogeochemical Data." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3636664.

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2

Ogiela, Lidia. "Biological Modelling in Semantic Data Analysis Systems." In 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS). IEEE, 2012. http://dx.doi.org/10.1109/imis.2012.81.

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3

Kim, Christine, Peggy Yin, Carlos X. Soto, Ian K. Blaby, and Shinjae Yoo. "Multimodal biological analysis using NLP and expression profile." In 2018 New York Scientific Data Summit (NYSDS). IEEE, 2018. http://dx.doi.org/10.1109/nysds.2018.8538944.

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Livengood, Philip, Ross Maciejewski, Wei Chen, and David S. Ebert. "A visual analysis system for metabolomics data." In 2011 IEEE Symposium on Biological Data Visualization (BioVis). IEEE, 2011. http://dx.doi.org/10.1109/biovis.2011.6094050.

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5

Thai, My T., Ping Deng, Weili Wu, Taieb Znati, Onur Seref, O. Erhun Kundakcioglu, and Panos Pardalos. "Approximation algorithms of non-unique probes selection for biological target identification." In DATA MINING, SYSTEMS ANALYSIS AND OPTIMIZATION IN BIOMEDICINE. AIP, 2007. http://dx.doi.org/10.1063/1.2817340.

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6

Jager, Gunter, Florian Battke, and Kay Nieselt. "TIALA — Time series alignment analysis." In 2011 IEEE Symposium on Biological Data Visualization (BioVis). IEEE, 2011. http://dx.doi.org/10.1109/biovis.2011.6094048.

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7

Pedersen, Edvard, Inge Alexander Raknes, Martin Ernstsen, and Lars Ailo Bongo. "Integrating Data-Intensive Computing Systems with Biological Data Analysis Frameworks." In 2015 23rd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE, 2015. http://dx.doi.org/10.1109/pdp.2015.106.

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8

Nowke, Christian, Maximilian Schmidt, Sacha J. van Albada, Jochen M. Eppler, Rembrandt Bakker, Markus Diesrnann, Bernd Hentschel, and Torsten Kuhlen. "VisNEST — Interactive analysis of neural activity data." In 2013 IEEE Symposium on Biological Data Visualization (BioVis). IEEE, 2013. http://dx.doi.org/10.1109/biovis.2013.6664348.

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9

Cui, Guangzhao, Xianghong Cao, and Xuncai Zhang. "Analysis of Biological Data with Digital Signal Processing." In 2005 IEEE 7th Workshop on Multimedia Signal Processing. IEEE, 2005. http://dx.doi.org/10.1109/mmsp.2005.248561.

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10

Majid Rastegar-Mojarad, Saeed Talatian-Azad, and Behrouz Minaei-Bidgoli. "A survey on biological data analysis by biclustering." In 2010 International Conference on Educational and Information Technology (ICEIT). IEEE, 2010. http://dx.doi.org/10.1109/iceit.2010.5607792.

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Звіти організацій з теми "Analysis of biological data"

1

Langston, Michael A. Scalable Computational Methods for the Analysis of High-Throughput Biological Data. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1050046.

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2

Ratnarajah, Lavenia. Map of BioEco Observing networks/capability. EuroSea, October 2021. http://dx.doi.org/10.3289/eurosea_d1.2.

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Анотація:

This deliverable maps the locations and properties of sustained biological observing networks through Europe including identifying coordinating groups and data aggregators. Data come from a global survey of networks, supplemented by an analysis of sustained observations in OBIS (that receives all biological data from EMODNet).

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Reilly-Collette, Marina, Brandon Booker, Kathryn Trubac, Tyler Elliott, Andrew Reichert, Charles Woodruff, and Lien Senchak. Testing of dry decontamination technologies for chemical, biological, radiological, and nuclear (CBRN) response. Engineer Research and Development Center (U.S.), May 2023. http://dx.doi.org/10.21079/11681/47032.

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This report provides a summary of the results obtained in laboratory-scale testing of dry-decontamination technologies. The purpose of the experiment is to assess nonaqueous technologies to determine the viability of a solution to mitigate chemical, biological, radiological, and nuclear (CBRN) defense, CBRN Response Enterprise, medical casualty care, and cold-weather operational gaps. The Cold Regions Research and Engineering Laboratory (CRREL) assessed the efficacy, via percentage reduction, of four nonaqueous technologies to decontaminate particulate contamination, at three operational temperatures, from three starting challenges. Testing was conducted by CRREL personnel according to protocols developed in conjunction with the Homeland Defense/Civil Support Office Maneuver Support Center of Excellence and the Armed Forces Radiobiology Research Institute (AFRRI) and approved by Joint Program Executive Office CBRN Protection. CRREL subsequently collected data and conducted statistical measures of significance and explored additional questions about the technology capabilities. CRREL personnel then deployed with AFRRI support to Arctic Eagle/Patriot 22 (AE/P-22) for field testing of the technologies and their evaluation from an operational perspective. AE/P-22 allowed for direct, full-scale testing of the technology in conditions approximating a use-case scenario. This report documents the culmination of analysis performed on CRREL- and AFRRI-collected test data results, operational factors, and user inputs.

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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, February 2022. http://dx.doi.org/10.18235/0003982.

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The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.

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Matthew, Gray. Data from "Winter is Coming – Temperature Affects Immune Defenses and Susceptibility to Batrachochytrium salamandrivorans". University of Tennessee, Knoxville Libraries, January 2021. http://dx.doi.org/10.7290/t7sallfxxe.

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Environmental temperature is a key factor driving various biological processes, including immune defenses and host-pathogen interactions. Here, we evaluated the effects of environmental temperature on the pathogenicity of the emerging fungus, Batrachochytrium salamandrivorans (Bsal), using controlled laboratory experiments, and measured components of host immune defense to identify regulating mechanisms. We found that adult and juvenile Notophthalmus viridescens died faster due to Bsal chytridiomycosis at 14 ºC than at 6 and 22 ºC. Pathogen replication rates, total available proteins on the skin, and microbiome composition likely drove these relationships. Temperature-dependent skin microbiome composition in our laboratory experiments matched seasonal trends in wild N. viridescens, adding validity to these results. We also found that hydrophobic peptide production after two months post-exposure to Bsal was reduced in infected animals compared to controls, perhaps due to peptide release earlier in infection or impaired granular gland function in diseased animals. Using our temperature-dependent infection results, we performed a geographic analysis that suggested that N. viridescens populations in the northeastern United States and southeastern Canada are at greatest risk for Bsal invasion. Our results indicate that environmental temperature will play a key role in the epidemiology of Bsal and provide evidence that temperature manipulations may be a viable Bsal management strategy.

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Cao, Siyang, Yihao Wei, Tiantian Qi, Peng Liu, Yingqi Chen, Fei Yu, Hui Zeng, and Jian Weng. Stem cell therapy for peripheral nerve injury: An up-to-date meta-analysis of 55 preclinical researches. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2022. http://dx.doi.org/10.37766/inplasy2022.10.0083.

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Review question / Objective: It has been the gold standard for decades to reconstruct a large peripheral nerve injury with a nerve autograft, and this remains true today as well. In addition to nerve autografts, biological conduits and vessels can also be applied. A fair amount of studies have examined the benefits of adding stem cells to the lumen of a nerve conduit. The aim of this meta-analysis was to summarize animal experiments related to the utilization of stem cells as a luminal additive when rebuilding a peripheral nerve injury using nerve grafts. Eligibility criteria: The inclusion criteria were as following: 1.Reconstruction of peripheral nerve injury; 2.Complete nerve transection with gap defect created; 3.Animal in-vivo models; 4.Experimental comparisons between nerve conduits containing and not containing one type of stem cell; 5.Functional testing and electrophysiology evaluations are performed. The exclusion criteria were as following: 1.Repair of central nervous system; 2.Nerve repair is accomplished by end-to-end anastomosis; 3.Animal models of entrapment injuries, frostbite, traction injuries and electric injuries; 4.Nerve conduits made from autologous epineurium; 5.Clinical trials, reviews, letters, conference papers, meta-analyses or commentaries; 6.Same studies have been published in different journals under the same or a different title.

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Nachtrieb, Julie. Field site analysis of giant salvinia nitrogen content and salvinia weevil density. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42060.

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In 2012, a giant salvinia (Salvinia molesta Mitchell) biological control project was initiated in Louisiana. Although similar quantities of salvinia weevils (Cyrtobagous salviniae Calder and Sands) were released at all sites, weevil densities were highly variable among sites. Additionally, signs of plant nitrogen depletion (yellowing plants) were observed at some sites. Because it is well known that plant nutrition can affect the success of a biocontrol agent because of slowed development and/or reduced fecundity, the correlation between giant salvinia nitrogen content and Salvinia weevil density was investigated during the growing seasons of the second and fourth years. During 2013, weevils were reintroduced to sites, and the magnitude of adult weevil density increase varied by site. Giant salvinia nitrogen content varied among sites and sampling dates. Upper Big Break plants had greater nitrogen than all other sites during 75% of sampling dates. Additionally, adult and larval densities were significantly correlated to plant nitrogen content. During 2015, trends were less distinct and weevil densities and nitrogen content varied based on the interaction between sampling date and site, but a significant correlation was not detected. Results from 1-yr of a 2-yr study confirmed published reports of the importance of plant nitrogen content to salvinia weevil productivity. Additional studies are warranted to evaluate and understand the role of nitrogen at giant salvinia biocontrol field sites.

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Torney, D. C., W. Bruno, and V. Detours. Nonlinear analysis of biological sequences. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/674921.

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McMinn, James W. Biological Diversity Research: An Analysis. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station, 1991. http://dx.doi.org/10.2737/se-gtr-071.

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McMinn, James W. Biological Diversity Research: An Analysis. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station, 1991. http://dx.doi.org/10.2737/se-gtr-71.

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