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Supernumerary B chromosomes (Bs) are dispensable genetic elements found in thousands of species of plants and animals, and some fungi. Since their discovery more than a century ago, they have been a source of puzzlement, as they only occur in some members of a population and are absent from others. When they do occur, they are often harmful, and in the absence of “selfishness”, based on mechanisms of mitotic and meiotic drive, there appears to be no obvious reason for their existence. Cytogeneticists have long wrestled with questions about the biological existence of these enigmatic elements, including their lack of any adaptive properties, apparent absence of functional genes, their origin, sequence organization, and co-evolution as nuclear parasites. Emerging new technologies are now enabling researchers to step up a gear, to look enthusiastically beyond the previous limits of the horizon, and to uncover the secrets of these “silent” chromosomes. This book provides a comprehensive guide to theoretical advancements in the field of B chromosome research in both animal and plant systems.

parent-of-origin effects --- fluorescent in situ hybridization --- coverage ratio analysis --- n/a --- ribosomal DNA --- reactivation --- cytogenetics --- epigenetics --- heterochromatin --- interphase nucleus --- whole genome resequencing --- transmission --- grasshoppers --- genome instability --- dot-like (micro) Bs --- ?s --- B chromosome --- supernumerary elements --- transcription of heterochromatin --- maternal X chromosome --- supernumerary chromosome --- population analysis --- supernumerary --- repeat clusters --- extra chromosomes --- genes --- tandem repeats --- B morphotypes --- repetitive DNA --- repetitive elements --- DNA copy number variation --- chromosome polymorphism --- satellite DNA --- mammals --- maize B chromosome --- additional chromosomes --- inactivation --- drive --- B chromosomes --- FISH (fluorescence in situ hybridisation) --- organelle DNA --- Orthoptera --- origin --- supernumerary chromosomes --- karyotype evolution --- GISH (genomic in situ hybridisation) --- DNA composition --- de novo centromere formation --- genomics --- paternal X chromosome --- euchromatin degradation --- supernumerary chromosomal segments (SCS) evolution --- centromere --- sSMC --- Prospero autumnale complex --- next-generation sequencing --- Drosophila --- host/parasite interaction --- Apodemus peninsulae --- genome evolution --- evolution --- teleost --- chromosome evolution --- microdissected DNA probes --- controlling element --- mobile element --- RNA-Seq --- karyotypes --- karyotypic characteristics --- RepeatExplorer

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Wood surface attributes can be established by examining its several different physical or chemical properties. Differences in the wood surfaces occur between the manufacturing and post-treatment processes as well. Understanding how their unique anisotropic molecular organization, chemical linkages, branching, and other molecular features govern micro- and macroscale accessibility is essential for coating and complex modification processes. It is therefore important for scientific as well as practical reasons to qualify and quantify the effects of wood surface treatments and modifications. Challenges still exist to fully understanding the effect of the numerous applied chemicals and the wide range of treatment processes on wood surfaces.

broiler --- thermal manipulation --- antioxidant --- heat stress --- cold stress --- Bovine Viral Diarrhea Virus --- RNA-Seq --- Transcriptome analysis --- Holstein cattle --- sheep --- intersex --- whole-genome resequencing --- copy number variation --- forming mechanism --- dairy cattle diseases --- innate immune system --- metabolic stress --- microbiome --- mastitis --- bovine mammary epithelial cells --- inflammatory cytokines --- NF-κB signaling --- PRRs --- TLRs --- Piemontese breed --- arthrogryposis --- macroglossia --- genetic model --- TLR3 --- TLR4 --- TLR7 --- foals --- immunostimulation --- gene expression --- bovine mastitis --- JAK-STAT pathway --- JAK2 --- STATs --- SOCS3 --- immunity --- milk production --- DNA methylation --- high-fat diet --- rabbits --- next generation sequencing --- transcriptomics --- bioinformatics --- genome editing --- disease resistance --- livestock --- dairy cattle --- teat-end hyperkeratosis --- udder health --- somatic cell --- genetic correlation --- selection response --- Holstein Friesian cattle --- mastitis resistance --- candidate genes --- SNP selection --- next-generation sequencing

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Polypharmacy is a necessary and important aspect of drug treatment; however, it becomes a challenge when the medication risks outweigh the benefits for an individual patient. Drug–drug interactions and the introduction of prescribing cascades are common features of polypharmacy, which can lead to ineffectiveness and increased risk of adverse drug reactions (ADR). Genes encoding CYP450 isozymes and other drug-related biomarkers have attracted considerable attention as targets for pharmacogenetic (PGx) testing due to their impact on drug metabolism and response. This Special Issue is devoted to explore the status and initiatives taken to circumvent ineffectiveness and to improve medication safety for polypharmacy patients. Specific areas include drug–drug interactions and consequences thereof in therapeutic management, including PK- and PD-profiling; the application of PGx-based guidance and/or decision tools for drug–gene and drug–drug gene interactions; medication reviews; development and application of deprescribing tools; and drivers and barriers to overcome for successful implementation in the healthcare system.

Medicine --- Pharmaceutical industries --- acute kidney injury --- early biomarker --- plasma neutrophil gelatinase-associated lipocalin --- soluble urokinase plasminogen activator receptor --- medication optimization --- older patients --- emergency department --- multimorbidity --- polypharmacy --- potentially inappropriate medication use --- older adults --- prevalence --- determinants --- chronic --- outpatient --- 2019 Beers criteria --- Ethiopia --- pharmacogenomics --- persons with diabetes --- drug–drug interactions --- drug–gene interactions --- cytochrome P450 --- SLCO1B1 --- drug interaction checkers --- adverse drug reactions --- pharmacogenetics --- personalized medicine --- phenprocoumon --- DOACs --- bleeding --- thromboembolism --- HLA --- drug hypersensitivity --- abacavir --- allopurinol --- flucloxacillin --- antiepileptic drugs --- cost-effectiveness --- shared medication record --- medication reconciliation --- drug information service --- hospital pharmacy service --- electronic prescribing --- electronic medical record --- clinical pharmacist --- CYP2D6 --- CYP2D7P --- CYP2D8P --- copy number variation --- CNV --- genotyping --- 5’nuclease assay --- HRM --- high resolution melting --- drug metabolization --- extracellular vesicles --- exosomes --- microvesicles --- pharmacogene expression --- medication review --- deprescriptions --- quality of life --- aged --- aged, 80 and over --- nursing homes --- deprescribing --- medication-based risk score --- health outcomes --- cytochromes --- CYP1A2 --- adverse drug reaction --- antipsychotics --- olanzapine --- clozapine --- loxapine --- children --- youth --- digital decision-support --- health services research --- general practice --- process evaluation --- antidepressants --- utility --- population-based --- appropriateness --- medication adherence --- digital health

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The book highlights important aspects of Molecular Psychiatry, including molecular mechanisms, animal models, biomarkers, advanced methods, drugs and antidepressant response, as well as genetics and epigenetics. Molecular mechanisms are a vital part of the search for the biological basis of psychiatric disorders, providing molecular hints that can later be tested as biomarkers or targets for drug development. Animal models represent a commonly used approach to aid in this bench-to-bed translation; the examples here are social defeat stress and the Roman High-Avoidance (RHA) and the Roman Low-Avoidance (RLA) rats. For biomarkers, psychiatric disorders pose a particular challenge due to the tissue specificity of many currently investigated biomarkers; i.e., not all blood-based measures directly represent changes in the brain. The Ebook includes five articles focused on the challenges of identifying clinically and biologically relevant biomarkers for psychiatric disorders. Scientific progress typically is fostered by the development of new methods. The application of machine learning methods for the proper analysis of Big Data and induced pluripotent stem cells are examples outlined in this Ebook. Furthermore, three articles are devoted to the understanding of the mechanisms of actions of existing drugs with the ultimate goal of identifying ways to predict treatment response in patients. Finally, three articles deepen the insight into the genetics and epigenetics of psychiatric disorders.

cardiovascular disease --- cell adhesion molecules --- immunology --- inflammation --- nervous system --- schizophrenia --- bipolar disorder --- major depressive disorder --- DNA methylation --- response variability --- antipsychotics --- drug design --- multi-target drugs --- polypharmacology --- multi-task learning --- machine learning --- biomarker discovery --- psychiatry --- serotonin --- 5-HT 4 receptor --- 5-HT4R --- depression --- mood disorder --- expression --- Alzheimer’s disease --- cognition --- Parkinson’s disease --- forced swimming --- Roman rat lines --- stress --- hippocampus --- BDNF --- trkB --- PSA-NCAM --- western blot --- immunohistochemistry --- general cognitive function --- intelligence --- GWAS --- genetic correlation --- childhood-onset schizophrenia (COS) --- induced pluripotent stem cell (iPSC) --- copy number variation (CNV) --- early neurodevelopment --- neuronal differentiation --- synapse --- dendritic arborization --- miRNAs --- stress physiology --- cytoskeleton --- actin dynamics --- DRR1 --- TU3A --- FAM107A --- acid sphingomyelinase --- alcohol dependence --- liver enzymes --- sphingolipid metabolism --- withdrawal --- Hsp90 --- GR --- stress response --- steroid hormones --- molecular chaperones --- psychiatric disease --- circadian rhythms --- FKBP51 --- FKBP52 --- CyP40 --- PP5 --- DISC1 --- neurodevelopment --- CRMP-2 --- proteomics --- antidepressant treatment --- HPA axis --- gene expression --- FKBP5 --- sleep --- sleep EEG --- biomarkers --- antidepressants --- cordance --- gender --- sex difference --- antidepressant --- rapid-acting --- Ketamine --- endocrinology --- (2R,6R)-Hydroxynorketamine --- electroconvulsive therapy --- basic-helix-loop-helix --- brain --- coactivator --- glucocorticoids --- mineralocorticoid receptor knockout --- transcription biology --- dopaminergic gene polymorphisms --- affective temperament --- obesity --- alpha-synuclein --- SNCA --- major depression --- Hamilton Scale of Depression --- chemokines --- neuroinflammation --- social defeat --- Immune response --- T cells --- susceptibility --- resilience --- Treg cells --- Th17 cells --- behavior --- PPARγ --- n/a --- Alzheimer's disease --- Parkinson's disease

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This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible.

cancer treatment --- extreme learning --- independent prognostic power --- AID/APOBEC --- HP --- gene inactivation biomarkers --- biomarker discovery --- chemotherapy --- artificial intelligence --- epigenetics --- comorbidity score --- denoising autoencoders --- protein --- single-biomarkers --- gene signature extraction --- high-throughput analysis --- concatenated deep feature --- feature selection --- differential gene expression analysis --- colorectal cancer --- ovarian cancer --- multiple-biomarkers --- gefitinib --- cancer biomarkers --- classification --- cancer biomarker --- mutation --- hierarchical clustering analysis --- HNSCC --- cell-free DNA --- network analysis --- drug resistance --- hTERT --- variable selection --- KRAS mutation --- single-cell sequencing --- network target --- skin cutaneous melanoma --- telomeres --- Neoantigen Prediction --- datasets --- clinical/environmental factors --- StAR --- PD-L1 --- miRNA --- circulating tumor DNA (ctDNA) --- false discovery rate --- predictive model --- Computational Immunology --- brain metastases --- observed survival interval --- next generation sequencing --- brain --- machine learning --- cancer prognosis --- copy number aberration --- mutable motif --- steroidogenic enzymes --- tumor --- mortality --- tumor microenvironment --- somatic mutation --- transcriptional signatures --- omics profiles --- mitochondrial metabolism --- Bufadienolide-like chemicals --- cancer-related pathways --- intratumor heterogeneity --- estrogen --- locoregionally advanced --- RNA --- feature extraction and interpretation --- treatment de-escalation --- activation induced deaminase --- knockoffs --- R package --- copy number variation --- gene loss biomarkers --- cancer CRISPR --- overall survival --- histopathological imaging --- self-organizing map --- Network Analysis --- oral cancer --- biostatistics --- firehose --- Bioinformatics tool --- alternative splicing --- biomarkers --- diseases genes --- histopathological imaging features --- imaging --- TCGA --- decision support systems --- The Cancer Genome Atlas --- molecular subtypes --- molecular mechanism --- omics --- curative surgery --- network pharmacology --- methylation --- bioinformatics --- neurological disorders --- precision medicine --- cancer modeling --- miRNAs --- breast cancer detection --- functional analysis --- biomarker signature --- anti-cancer --- hormone sensitive cancers --- deep learning --- DNA sequence profile --- pancreatic cancer --- telomerase --- Monte Carlo --- mixture of normal distributions --- survival analysis --- tumor infiltrating lymphocytes --- curation --- pathophysiology --- GEO DataSets --- head and neck cancer --- gene expression analysis --- erlotinib --- meta-analysis --- traditional Chinese medicine --- breast cancer --- TCGA mining --- breast cancer prognosis --- microarray --- DNA --- interaction --- health strengthening herb --- cancer --- genomic instability