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Today, a single laboratory can generate a vast amount of biological data. There is a wealth of data already available in public databases, which makes the modern life sciences almost dependent on bioinformatics. This book brings together an international team of experts to discuss the state-of-the-art from several fields of bioinformatics, from the automatic identification and classification of viruses to the analysis of the transcriptome of single cells and plants, including artificial intelligence algorithms to discover biomarkers and text mining approaches to help in the interpretation of the findings. Machine learning, pattern discovery and analysis, error correction, Bayesian inference and novel computational techniques to discover chromosomal rearrangements continue to play crucial roles in biological discovery, and all of them are explored in chapters of this book. In sum, this book contains high-quality chapters that provide excellent views into key topics of current bioinformatics research, topics that should remain important for the next several years.

Bioinformatics. --- Text Mining Gene Selection; Biological Big Data; Single-Cell RNA Sequencing; Large-Scale Structural Rearrangements in Chromosomes; Machine Learning Approaches; Biomarker Discovery; Gene Expression Data; Bayesian Inference of Gene Expression; Error-Correction Methodologies; Genome Sequencing Data; Plant Transcriptome Assembly; Aligned Pattern Clustering System; Pattern Analysis; Hidden Markov Models; Viral Classification and Discovery; Pattern Discovery and Disentanglement; Aligned Pattern Cluster Analysis; Protein Binding Complexes Detection

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Extreme climatic events, such as intense and prolonged droughts and heat waves, are occurring with increasing frequency and with pronounced impacts on forests. Forest trees, as long-lived organisms, need to develop adaptation mechanisms to successfully respond to such climatic extremes. Whether physiological adaptations on the tree level result in ecophysiological responses that ensure plasticity of forest ecosystems to climate change is currently in the core forest research. Within this Special Issue, forest species’ responses to climatic variability were reported from diverse climatic zones and ecosystem types: from near-desert mountains in western USA to tropical forests in central America and Asia, and from Mediterranean ecosystems to temperate European forests. The clear effects of constraints related to climate change were evidenced on the tree level, such as in differentiated gene expression, metabolite abundance, sap flow rates, photosynthetic performance, seed germination, survival and growth, while on the ecosystem level, tree line shifts, temporal shifts in allocation of resources and species shifts were identified. Experimental schemes such as common gardens and provenance trails also provided long-term indications on the tolerance of forest species against drought and warming and serve to evaluate their performance under the predicted climate in near future. These findings enhance our knowledge on the potential resilience of forest species and ecosystems to climate change and provide an updated basis for continuing research on this topic.

Cedrela odorata --- seeds --- germination --- cardinal temperatures --- thermal time --- climate change --- dendrochronology --- ecology --- moving window analysis --- Pinaceae --- Pinus arizonica Engelm. --- Pinus ponderosa var. brachyptera (Engelm.) --- Ponderosae --- response function --- tree rings --- global climate change --- forest ecology --- trees adaptation --- phenotypic plasticity --- Phoebe bournei --- nitrogen --- carbon dioxide --- photosynthesis --- leaf anatomy --- National Park --- tree line shift --- acclimation --- adaptation --- common garden --- drought --- ecodistance --- mortality --- stomatal frequency --- stomatal size --- sap flux --- radial profile --- sapwood depth --- Aleppo pine --- diurnal variation --- seasonal variation --- climate --- basal area increment --- forest dieback --- Mediterranean forest --- stem growth --- water availability --- Quercus --- morphology evaluation --- survival rate --- extreme frost --- heat and drought --- open-top chamber --- RNA sequencing --- gene expression analysis --- Populus --- n/a

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p38 Mitogen activated protein kinases (p38MAPK) are a group of evolutionary conserved protein kinases which are central for cell adaptation to environmental changes as well as for immune response, inflammation, tissue regeneration and tumour formation. The interest in this group of protein kinases has grown continually since their discovery. Recent studies using new genetic and pharmacological tools are providing helpful information on the function of these stress-activated protein kinases and show that they have an acute impact on the development of prevalent diseases related to inflammation, diabetes, neurodegeneration, and cancer. In this Special Issue we present novel advances and review the knowledge on the identification of p38MAPK substrates, functions, and regulation; mechanisms underlying the role of p38MAPK in malignant transformation and other pathologies; and therapeutic opportunities associated with regulation of p38MAPK activity.

arginine methylation --- erythroid differentiation --- MKK3 --- phosphorylation, PRMT1 --- p38 MAPK --- cocaine --- conditioned place preference --- reward --- stress --- anxiety --- depression --- nucleus accumbens --- social interaction --- k opioid receptors --- p38α --- Rab5 --- endosome --- Alzheimer’s --- Lewy Bodies --- amyloid-β --- tau --- α-synuclein --- p38-MAPK α inhibitor --- Alzheimer’s disease --- synaptic plasticity --- neuroinflammation --- β-amyloid --- Tau --- Kv4.2 --- seizure --- temporal lobe epilepsy --- hippocampus --- neuronal firing and excitability --- p38MAPK --- nuclear translocation --- β-like importins --- inflammation --- cancer --- skeletal muscle --- energy metabolism --- signal transduction --- exercise --- type 2 diabetes --- p38 mitogen-activated protein kinase --- bleomycin-induced pulmonary fibrosis --- idiopathic pulmonary fibrosis --- RNA sequencing --- alveolar epithelial type II cells --- MAPK --- p38 --- physiology --- metabolism --- signaling --- hypoxia --- arrhythmia --- MAPK11 --- p38β --- n/a --- Alzheimer's --- Alzheimer's disease

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The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions.

Medicine --- Pharmaceutical industries --- COVID-19 --- drug repurposing --- topological data analysis --- persistent Betti function --- SARS-CoV-2 --- network-based pharmacology --- combination therapy --- nucleoside GS-441524 --- fluoxetine --- synergy --- antidepressant --- natural compounds --- QSAR --- molecular docking --- drug repositioning --- UK Biobank --- vaccine --- LC-2/ad cell line --- drug discovery --- docking --- MM-GBSA calculation --- molecular dynamics --- cytotoxicity assay --- GWAS --- multiple sclerosis --- oxidative stress --- repurposing --- ADME-Tox --- bioinformatics --- complex network analysis --- modularity clustering --- ATC code --- hidradenitis suppurativa --- acne inversa --- transcriptome --- proteome --- comorbid disorder --- biomarker --- signaling pathway --- druggable gene --- drug-repositioning --- MEK inhibitor --- MM/GBSA --- Glide docking --- MD simulation --- MM/PBSA --- single-cell RNA sequencing --- pulmonary fibrosis --- biological networks --- p38α MAPK --- allosteric inhibitors --- in silico screening --- computer-aided drug discovery --- network analysis --- psychiatric disorders --- medications --- psychiatry --- mental disorders --- toxoplasmosis --- Toxoplasma gondii --- in vitro screening --- drug targets --- drug-disease interaction --- target-disease interaction --- DPP4 inhibitors --- lipid rafts

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Cells are the most fundamental building block of all living organisms. The investigation of any type of disease mechanism and its progression still remains challenging due to cellular heterogeneity characteristics and physiological state of cells in a given population. The bulk measurement of millions of cells together can provide some general information on cells, but it cannot evolve the cellular heterogeneity and molecular dynamics in a certain cell population. Compared to this bulk or the average measurement of a large number of cells together, single-cell analysis can provide detailed information on each cell, which could assist in developing an understanding of the specific biological context of cells, such as tumor progression or issues around stem cells. Single-cell omics can provide valuable information about functional mutation and a copy number of variations of cells. Information from single-cell investigations can help to produce a better understanding of intracellular interactions and environmental responses of cellular organelles, which can be beneficial for therapeutics development and diagnostics purposes. This Special Issue is inviting articles related to single-cell analysis and its advantages, limitations, and future prospects regarding health benefits.

single-cell RNA sequencing --- cholestatic liver injury --- hepatocyte heterogeneity --- inflammation --- liver tissue repair --- single cell mass cytometry --- single cell proteomics --- non-small cell lung cancer --- three-dimensional tissue culture --- snRNA-seq --- RNA velocity --- cluster analysis --- cardiomyocytes --- seurat --- cell heterogeneity --- sarcoma --- single-cell analysis --- total mRNA level --- transcriptome size --- proteomics --- immunofluorescence --- immunohistochemistry --- protein --- genome --- biomedical applications --- commercialization --- protein characterization --- conventional approaches --- microfluidic technologies --- single cell --- infectious disease --- pathophysiology --- therapeutics --- diagnostics --- microfluidics --- single-cell cloning --- monoclonal cell lines --- single-neuron models --- mapping --- electrophysiological recording --- isolation --- therapy --- micro/nanofluidic devices --- microelectrode array --- transfection --- artificial intelligence --- localized high-risk prostate cancer --- circulating tumor cells --- three-dimensional (3-D) telomere profiling --- laser microdissection --- whole-exome genome sequencing --- somatic single nucleotide variants --- copy number alterations --- precision medicine --- n/a