The classification of psychiatric disorders has always been a problem in clinical settings. The present debate about the major systems in clinical practice, DSM-IV and ICD-10, has resulted in attempts to improve and replace those schemes by some that include more endophenotypic and molecular features. However, these disorders not only require more precise diagnostic tools, but also have to be viewed more extensively in their dynamic behaviors, which require more precise data sets related to their origins and developments. This enormous challenge in brain research has to be approached on different levels of the biological system by new methods, including improvements in electroencephalography, brain imaging, and molecular biology. All these methods entail accumulations of large data sets that become more and more difficult to interpret. In particular, on the molecular level, there is an apparent need to use highly sophisticated computer programs to tackle these problems. Evidently, only interdisciplinary work among mathematicians, physicists, biologists, and clinicians can further improve our understanding of complex diseases of the brain.
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http://dx.doi.org/10.1007/978-1-61779-458-2_36 | DOI Listing |
ACS Cent Sci
December 2024
Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30172 Mestre, Italy.
Computational generation of cyclic peptide inhibitors using machine learning models requires large size training data sets often difficult to generate experimentally. Here we demonstrated that sequential combination of Random Forest Regression with the pseudolikelihood maximization Direct Coupling Analysis method and Monte Carlo simulation can effectively enhance the design pipeline of cyclic peptide inhibitors of a tumor-associated protease even for small experimental data sets. Further studies showed that such -evolved cyclic peptides are more potent than the best peptide inhibitors previously developed to this target.
View Article and Find Full Text PDFACS ES T Water
November 2024
Waterborne Disease Prevention Branch, Centers for Disease Control and Prevention, Atlanta, Georgia 30333, United States.
Irrigating fresh produce with contaminated water contributes to the burden of foodborne illness. Identifying fecal contamination of irrigation waters and characterizing fecal sources and associated environmental factors can help inform fresh produce safety and health hazard management. Using two previously collected data sets, we developed and evaluated the performance of logistic regression and conditional random forest models for predicting general and human-specific fecal contamination of ponds in southwest Georgia used for fresh produce irrigation.
View Article and Find Full Text PDFPain Rep
February 2025
Pain Research Institute, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.
Introduction: Pain phenomenology in patients with fibromyalgia syndrome (FMS) shows considerable overlap with neuropathic pain. Altered neural processing leading to symptoms of neuropathic pain can occur at the level of the spinal cord, and 1 potential mechanism is spinal disinhibition. A biomarker of spinal disinhibition is impaired H-reflex rate-dependent depression (HRDD).
View Article and Find Full Text PDFSurv Geophys
April 2024
Department of Atmospheric and Oceanic Science, University of Wisconsin, Madison, WI 53706 USA.
Accurate diagnosis of regional atmospheric and surface energy budgets is critical for understanding the spatial distribution of heat uptake associated with the Earth's energy imbalance (EEI). This contribution discusses frameworks and methods for consistent evaluation of key quantities of those budgets using observationally constrained data sets. It thereby touches upon assumptions made in data products which have implications for these evaluations.
View Article and Find Full Text PDFVet Rec
December 2024
School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Loughborough, UK.
Background: Negative veterinary client complaint behaviour poses wellbeing and reputational risks. Adverse events are one source of complaint. Identifying factors that influence adverse event-related complaint behaviour is key to mitigating detrimental consequences and harnessing information that can be used to improve service quality, patient safety and business sustainability.
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