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Screening Targets and Therapeutic Drugs for Alzheimer's Disease Based on Deep Learning Model and Molecular Docking

Background: Alzheimer's disease (AD) is a neurodegenerative disorder caused by a complex interplay of various factors. However, a satisfactory cure for AD remains elusive. Pharmacological interventions based on drug targets are considered the most cost-effective therapeutic strategy. Therefore, it is paramount to search potential drug targets and drugs for AD. Objective: We aimed to provide novel

Disability and long-term breathlessness : A cross-sectional, population study

Introduction Disability, resulting from altered interactions between individuals and their environment, is a worldwide issue causing inequities and suffering. Many diseases associated with breathlessness cause disability but the relationship between disability and the severity of breathlessness itself is unknown. This study evaluated associations between disability using the WHO's Disability Asses

Bee-inspired insights : Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction

Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in

Early Prostate Cancer Deaths among Men with Higher vs Lower Genetic Risk

Importance: Prostate cancer, a leading cause of cancer death among men, urgently requires new prevention strategies, which may involve targeting men with an underlying genetic susceptibility. Objective: To explore differences in risk of early prostate cancer death among men with higher vs lower genetic risk to inform prevention efforts. Design, Setting, and Participants: This cohort study used a c

Enhancing damage prediction in bulk metal forming through machine learning-assisted parameter identification

The open-source parameter identification tool ADAPT (A diversely applicable parameter identification Tool) is integrated with a machine learning-based approach for start value prediction in order to calibrate a Gurson–Tvergaard–Needleman (GTN) and a Lemaitre damage model. As representative example case-hardened steel 16MnCrS5 is elaborated. An artificial neural network (ANN) is initially trained b

Aqueous Ammonium Nitrate Investigated Using Photoelectron Spectroscopy of Cylindrical and Flat Liquid Jets

Ammonium nitrate in aqueous solution was investigated with synchrotron radiation based photoelectron spectroscopy using two types of liquid jet nozzles. Electron emission from a cylindrical microjet of aqueous ammonium nitrate solution was measured at two different angles relative to the horizontal polarization of the incident synchrotron radiation, 90° and 54.7° (the “magic angle”), for a range o

COCONUT SHELLS, WATER HYACINTH, AND RICE IN IMPROVING THE QUALITY OF DUG WELL WATER IN FLOOD AREAS

Introduction: Ensuring access to clean and safe drinking water is crucial, especially in flood-prone regions where the water quality in dug wells can deteriorate due to various physicochemical factors. This research was aimed to measure the effectiveness of natural materials in improving water quality based on physicochemical parameters and to compare water quality before and after treatment in As

Photofragmentation laser-induced fluorescence imaging of CH3 by structured illumination in a plasma discharge

Methyl is crucial in plasma-assisted hydrocarbon chemistry, making precise in situ imaging essential for understanding various plasma applications. Its importance in methane chemistry arises from its role as a primary byproduct during the initial phase of methane dehydrogenation. Detecting the CH3 radical is challenging due to its high reactivity and the prevalence of strongly pre-dissociative ele

Data on the saturation behaviour of the 63-90 µm quartz from the Carpathian Basin

This dataset offers valuable insights into the luminescence saturation behaviour of 63–90 µm quartz grains sourced from the Carpathian Basin, as examined under controlled laboratory conditions. Its significance lies not only in shedding light on the luminescence properties specific to this region but also in facilitating comparative analyses with quartz samples from other geographic areas. Moreove

Aging enacted in practice : How unloved objects thrive in the shadows of care

In this paper, we explore the seeming stability of aging. More precisely, we offer an empirical account of how aging – images of aging, embodiments of aging, feelings about aging – is enacted in company practice, both in place and across time. Drawing on ethnographic fieldwork conducted at SMCare, a small-to-medium sized company active in the care technology sector, we show how aging achieves its

The SAM-Krom biomonitoring study shows occupational exposure to hexavalent chromium and increased genotoxicity in Denmark

Background Hexavalent chromium (Cr(VI)) is a carcinogen. Exposure to Cr(VI) may occur in different industrial processes such as chrome plating and stainless steel welding. The aim of this study was to assess occupational exposure to Cr(VI) in Denmark. Methods This cross-sectional study included 28 workers and 8 apprentices with potential Cr(VI) exposure and 24 within company controls, all recruite

Extreme overall mushroom genome expansion in Mycena s.s. irrespective of plant hosts or substrate specializations

Mycena s.s. is a ubiquitous mushroom genus whose members degrade multiple dead plant substrates and opportunistically invade living plant roots. Having sequenced the nuclear genomes of 24 Mycena species, we find them to defy the expected patterns for fungi based on both their traditionally perceived saprotrophic ecology and substrate specializations. Mycena displayed massive genome expansions over

Tailoring Auger Recombination Dynamics in CsPbI3 Perovskite Nanocrystals via Transition Metal Doping

Auger recombination is a pivotal process for semiconductor nanocrystals (NCs), significantly affecting charge carrier generation and collection in optoelectronic devices. This process depends mainly on the NCs’ electronic structures. In our study, we investigated Auger recombination dynamics in manganese (Mn2+)-doped CsPbI3 NCs using transient absorption (TA) spectroscopy combined with theoretical