Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models.

Compute-in-memory based on resistive random-access memory has emerged as a promising technology for accelerating neural networks on edge devices. It can reduce frequent data transfers and improve energy efficiency. However, the nonvolatile nature of resistive memory raises concerns that stored weights can be easily extracted during computation.

Exploring the Active Constituents of in Protecting the Skin Barrier and the Synergistic Effects with Collagen XVII.

is mainly used to treat skin inflammations, wounds, and infections. In this study, Andrographis Herba, the aerial part of the plant, was proven to increase the viability of UVB-damaged HaCat cells and reduce reactive oxygen species levels. The chemical composition of Andrographis Herba extract (AHE) was analyzed using UPLC-Q-TOF-MS, and diterpene lactones were identified as its primary constituents.

Skeletal Site-Specific Lipid Profile and Hematopoietic Progenitors of Bone Marrow Adipose Tissue in Patients Undergoing Primary Hip Arthroplasty.

Background/objectives: Bone marrow adipose tissue (BMAT) has been described as an important biomechanic and lipotoxic factor with negative impacts on skeletal and hematopoietic system regeneration. BMAT undergoes metabolic and cellular adaptations with age and disease, being a source of potential biomarkers. However, there is no evidence on the lipid profile and cellularity at different skeletal locations in osteoarthritis patients undergoing primary hip arthroplasty.

The association between total social exposure and incident multimorbidity: A population-based cohort study.

Background: Multimorbidity, the co-occurrence of two or more chronic conditions, is associated with the social determinants of health. Using comprehensive linked population-representative data, we sought to understand the combined effect of multiple social determinants on multimorbidity incidence in Ontario, Canada.

Methods: Ontario respondents aged 20-55 in 2001-2011 cycles of the Canadian Community Health Survey were linked to administrative health data ascertain multimorbidity status until 2022.

The impact of solute carrier proteins on disrupting substance regulation in metabolic disorders: insights and clinical applications.

Carbohydrates, lipids, bile acids, various inorganic salt ions and organic acids are the main nutrients or indispensable components of the human body. Dysregulation in the processes of absorption, transport, metabolism, and excretion of these metabolites can lead to the onset of severe metabolic disorders, such as type 2 diabetes, non-alcoholic fatty liver disease, gout and hyperbilirubinemia. As the second largest membrane receptor supergroup, several major families in the solute carrier (SLC) supergroup have been found to play key roles in the transport of substances such as carbohydrates, lipids, urate, bile acids, monocarboxylates and zinc ions.

Associations of maternal night shift work during pregnancy with DNA methylation in offspring: a meta-analysis in the PACE consortium.

Background: Night shift work during pregnancy has been associated with differential DNA methylation in placental tissue, but no studies have explored this association in cord blood. We aimed to examine associations of maternal night shift work with cord blood DNA methylation.

Methods: A total of 4487 mother-newborn pairs from 7 studies were included.

Neural processing of naturalistic audiovisual events in space and time.

Our brain seamlessly integrates distinct sensory information to form a coherent percept. However, when real-world audiovisual events are perceived, the specific brain regions and timings for processing different levels of information remain less investigated. To address that, we curated naturalistic videos and recorded functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data when participants viewed videos with accompanying sounds.

Genomics yields biological and phenotypic insights into bipolar disorder.

Bipolar disorder is a leading contributor to the global burden of disease. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.

Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors.

We introduce a quantum-classical generative model for small-molecule design, specifically targeting KRAS inhibitors for cancer therapy. We apply the method to design, select and synthesize 15 proposed molecules that could notably engage with KRAS for cancer therapy, with two holding promise for future development as inhibitors. This work showcases the potential of quantum computing to generate experimentally validated hits that compare favorably against classical models.

Use of corn carbon as an additive to enhance magnesium metal self-corrosion and recover phosphorus from swine wastewater in the form of struvite.

Recovery of phosphate from swine wastewater is significant for alleviating eutrophication in aquatic ecosystems and addressing the increasing scarcity of phosphorus resources. In this study, a method for phosphate recovery from swine wastewater using corn carbon as an additive and non-dynamic magnesium metal self-corrosion was studied. The effects of reaction time, C:Mg mass ratio, stirring rate, and aeration rate on phosphate recovery were discussed, and eight experimental models were explored.

Loneliness is associated with different structural brain changes in schizophrenia spectrum disorders and major depression.

Background: Loneliness, distress from having fewer social contacts than desired, has been recognized as a significant public health crisis. Although a substantial body of research has established connections between loneliness and various forms of psychopathology, our understanding of the neural underpinnings of loneliness in schizophrenia spectrum disorders (SSD) and major depressive disorder (MDD) remains limited.

Methods: In this study, structural magnetic resonance imaging (sMRI) data were collected from 57 SSD and 45 MDD patients as well as 41 healthy controls (HC).

Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for Δ-machine learning.

We introduce the alchemical harmonic approximation (AHA) of the absolute electronic energy for charge-neutral iso-electronic diatomics at fixed interatomic distance d0. To account for variations in distance, we combine AHA with this ansatz for the electronic binding potential, E(d)=(Eu-Es)Ec-EsEu-Esd/d0+Es, where Eu, Ec, Es correspond to the energies of the united atom, calibration at d0, and the sum of infinitely separated atoms, respectively. Our model covers the two-dimensional electronic potential energy surface spanned by distances of 0.

Optimizing personalized psychological well-being interventions through digital phenotyping: results from a randomized non-clinical trial.

Background: Digital technologies, including smartphones, hold great promise for expanding mental health services and improving access to care. Digital phenotyping, which involves the collection of behavioral and physiological data using smartphones, offers a novel way to understand and monitor mental health. This study examines the feasibility of a psychological well-being program using a telegram-integrated chatbot for digital phenotyping.

Machine learning recognition of hybrid lead halide perovskites and perovskite-related structures from X-ray diffraction patterns.

Identification of crystal structures is a crucial stage in the exploration of novel functional materials. This procedure is usually time-consuming and can be false-positive or false-negative. This necessitates a significant level of expert proficiency in the field of crystallography and, especially, requires deep experience in perovskite-related structures of hybrid perovskites.

Resource-Rational Virtual Bargaining for Moral Judgment: Toward a Probabilistic Cognitive Model.

Recent theoretical work has argued that moral psychology can be understood through the lens of "resource rational contractualism." The view posits that the best way of making a decision that affects other people is to get everyone together to negotiate under idealized conditions. The outcome of that negotiation is an arrangement (or "contract") that would lead to mutual benefit.

Lipids, lipid-lowering drug target genes and pancreatic cancer: a Mendelian randomization study.

Background: Pancreatic cancer (PC) is a malignant tumor with a low survival rate. Lipid modifiers show potential for PC therapy, but evidence is lacking.

Aim: This Mendelian randomization (MR) study aimed to explore the relationship between lipid traits, and lipid-lowering drug target genes with PC risk.

Inhibitory Activity of - and -Functionalized Monoterpene Diols Towards Monoamine Oxidases A and B.

Monoamine oxidase B (MAO-B) inhibitors are widely used as part of combination drug therapy for Parkinson's disease. As demonstrated in both in vitro and in vivo experiments, the monoterpenoid Prottremine and some of its derivatives exhibit high antiparkinsonian activity. In this study, the inhibitory activity of Prottremine and its derivatives (including 14 new 9-- and -derivatives) against MAO-A and MAO-B enzymes has been investigated for the first time.

ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction.

The Caco-2 cell model has been widely used to assess the intestinal permeability of drug candidates in vitro, owing to its morphological and functional similarity to human enterocytes. While Caco-2 cell assay is considered safe and cost-effective, it is also characterized by being time-consuming. Therefore, computational models that achieve high accuracies in predicting Caco-2 permeability are crucial for enhancing the efficiency of oral drug development.

Staging of prostate Cancer with ultra-fast PSMA-PET scans enhanced by AI.

Purpose: PSMA-PET is a reference standard examination for patients with prostate cancer, but even using recently introduced digital PET detectors image acquisition with standard field-of-view scanners is still in the range of 20 min. This may cause limited access to examination slots because of the growing demand for PSMA-PET. Ultra-fast PSMA-PET may enhance throughput but comes at the cost of poor image quality.

JOSD2 promotes breast cancer metastasis by deubiquitinating and stabilizing SMAD4.

Breast cancer is one of the most common malignant tumors among women worldwide, and its high degree of metastasis significantly impacts treatment effectiveness leading to poor prognosis. The potential molecular mechanisms underlying breast cancer metastasis remain to be further elucidated. In this study, via database analysis, we revealed that the deubiquitinase josephin domain containing 2 (JOSD2) was abnormally amplified in patients with metastatic breast cancer, and was significantly negatively correlated with patient prognosis.

A survey of open-access datasets for computer vision in precision poultry farming.

Computer vision has progressively advanced precision poultry farming. Despite this substantial increase in research activity, computer vision in precision poultry farming still lacks large-scale, open-access datasets with consistent evaluation metrics and baselines, which makes it challenging to reproduce and validate comparisons of different approaches. Since 2019, several image/video datasets have been published and open-accessed to alleviate the issue of dataset scarcity.

Leveraging Transformers-based models and linked data for deep phenotyping in radiology.

Background And Objective: Despite significant investments in the normalization and the standardization of Electronic Health Records (EHRs), free text is still the rule rather than the exception in clinical notes. The use of free text has implications in data reuse methods used for supporting clinical research since the query mechanisms used in cohort definition and patient matching are mainly based on structured data and clinical terminologies. This study aims to develop a method for the secondary use of clinical text by: (a) using Natural Language Processing (NLP) for tagging clinical notes with biomedical terminology; and (b) designing an ontology that maps and classifies all the identified tags to various terminologies and allows for running phenotyping queries.

A Transformer-Based Pipeline for German Clinical Document De-Identification.

Objective:  Commercially available large language models such as Chat Generative Pre-Trained Transformer (ChatGPT) cannot be applied to real patient data for data protection reasons. At the same time, de-identification of clinical unstructured data is a tedious and time-consuming task when done manually. Since transformer models can efficiently process and analyze large amounts of text data, our study aims to explore the impact of a large training dataset on the performance of this task.