Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis.

Spatially resolved transcriptomics enable comprehensive measurement of gene expression at subcellular resolution while preserving the spatial context of the tissue microenvironment. While deep learning has shown promise in analyzing SCST datasets, most efforts have focused on sequence data and spatial localization, with limited emphasis on leveraging rich histopathological insights from staining images. We introduce GIST, a deep learning-enabled gene expression and histology integration for spatial cellular profiling.

Identify structures underlying out-of-equilibrium reaction networks with random graph analysis.

Network measures have proven very successful in identifying structural patterns in complex systems (, a living cell, a neural network, the Internet). How such measures can be applied to understand the rational and experimental design of chemical reaction networks (CRNs) is unknown. Here, we develop a procedure to model CRNs as a mathematical graph on which network measures and a random graph analysis can be applied.

Schedule optimization for chemical library synthesis.

Automated chemistry platforms hold the potential to enable large-scale organic synthesis campaigns, such as producing a library of compounds for biological evaluation. The efficiency of such platforms will depend on the schedule according to which the synthesis operations are executed. In this work, we study the scheduling problem for chemical library synthesis, where operations from interdependent synthetic routes are scheduled to minimize the makespan-the total duration of the synthesis campaign.

A deep-learning-based consistency test approach for Earth system models on HPC systems.

Evaluating climate consistency is a critically important step in the development and optimization of Earth system models (ESMs) on the high-performance computing (HPC) systems. We have developed an Earth system model deep-learning consistency test, referred to as ESM-DCT. The ESM-DCT is based on the unsupervised bidirectional gate recurrent unit-autoencoder (BGRU-AE) model to study the features from the ESM simulation ensembles and adopts the reconstruction errors to evaluate the consistency.

Exploring the therapeutic potential of acorn extract in papillomavirus-induced lesions.

Background And Aim: Papillomaviruses (PVs) infections have been documented in numerous animal species across different regions worldwide. They often exert significant impacts on animal health and livestock production. Scientists have studied natural products for over half a century due to their diverse chemical composition, acknowledging their value in fighting cancer.

Navigating the Complex Terrain of Methane Synthesis: Multienzyme Control Points and Data-Driven Strategies.

Anaerobic digestion is a crucial process in wastewater treatment, renowned for its sustainable biogas production capabilities and the simultaneous reduction of environmental pollution. However, dysregulation of vital biological processes and pathways can lead to reduced efficiency and suboptimal biogas output, which can be seen through low counts per million of sequences related to three critical control points for methane synthesis. Namely, tetrahydromethanopterin S-methyltransferase (MTR), methyl-coenzyme reductase M (MCR), and CoB/CoM heterodisulfide oxidoreductase (HDR) are the last reactions that must occur.

Synaptic Density Reductions in MSA: A Potential Biomarker Identified Through [F]SynVesT-1 PET Imaging.

Objective: The objective of this study was to delineate synaptic density alterations in multiple system atrophy (MSA) and explore its potential role as a biomarker for MSA diagnosis and disease severity monitoring using [F]SynVesT-1 positron emission tomography / computed tomography (PET CT).

Methods: In this prospective study, 60 patients with MSA (30 patients with MSA-parkinsonian [MSA-P] subtype and 30 patients with MSA-cerebellar [MSA-C] subtype), 30 patients with Parkinson's disease (PD), and 30 age-matched healthy controls (HCs) underwent [F]SynVesT-1 PET/CT for synaptic density assessment. Visual, voxel, and volumetric region of interest (VOI) analyses were used to elucidate synaptic density patterns in the MSA brain and establish diagnostic criteria.

FDG-PET-based brain network analysis: a brief review of metabolic connectivity.

Background: Over the past decades, the analysis metabolic connectivity patterns has received significant attention in exploring the underlying mechanism of human behaviors, and the neural underpinnings of brain neurological disorders. Brain network can be considered a powerful tool and play an important role in the analysis and understanding of brain metabolic patterns. With the advantages and emergence of metabolic-based network analysis, this study aims to systematically review how brain properties, under various conditions, can be studied using Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images and graph theory, as well as applications of this approach.

Gene function revealed at the moment of sitochastic gene silencing.

Gene expression is a dynamic and stochastic process characterized by transcriptional bursting followed by periods of silence. Single-cell RNA sequencing (scRNA-seq) is a powerful tool to measure transcriptional bursting and silencing at the individual cell level. In this study, we introduce the single-cell Stochastic Gene Silencing (scSGS) method, which leverages the natural variability in single-cell gene expression to decipher gene function.

Enhancing predictive accuracy for urinary tract infections post-pediatric pyeloplasty with explainable AI: an ensemble TabNet approach.

Ureteropelvic junction obstruction (UPJO) is a common pediatric condition often treated with pyeloplasty. Despite the surgical intervention, postoperative urinary tract infections (UTIs) occur in over 30% of cases within six months, adversely affecting recovery and increasing both clinical and economic burdens. Current prediction methods for postoperative UTIs rely on empirical judgment and limited clinical parameters, underscoring the need for a robust, multifactorial predictive model.

Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model.

Enhancement of security, personalization, and safety in advanced transportation systems depends on driver identification. In this context, this work suggests a new method to find drivers by means of a Random Forest model optimized using the osprey optimization algorithm (OOA) for feature selection and the salp swarm optimization (SSO) for hyperparameter tuning based on driving behavior. The proposed model achieves an accuracy of 92%, a precision of 91%, a recall of 93%, and an F1-score of 92%, significantly outperforming traditional machine learning models such as XGBoost, CatBoost, and Support Vector Machines.

[Statistical methods for extremely unbalanced data in genome-wide association study (2)].

Extremely unbalanced data refers to datasets with independent or dependent variables showing severe imbalances in proportions, which might lead to deviation of classical test statistics from theoretical distribution and difficulties in controlling type Ⅰ error. The increased availability of genome-wide resources from large population cohorts has highlighted the growing demand for efficient and accurate statistical methods for the process of extremely unbalanced data to improve the development of genetic statistical methods. This paper introduces two widely used correction methods in current genome-wide association study for extremely unbalanced data, i.

Towards molecular structure discovery from cryo-ET density volumes via modelling auxiliary semantic prototypes.

Cryo-electron tomography (cryo-ET) is confronted with the intricate task of unveiling novel structures. General class discovery (GCD) seeks to identify new classes by learning a model that can pseudo-label unannotated (novel) instances solely using supervision from labeled (base) classes. While 2D GCD for image data has made strides, its 3D counterpart remains unexplored.

Tracheal, bronchus, and lung cancer mortality and air pollution exposure in Tuscany, Italy: Bayesian Health Impact Assessment and Global Sensitivity Analysis on a sub-regional scale.

Outdoor air pollution is a significant risk factor for tracheal, bronchus, and lung (TBL) cancer. This study employs a Bayesian approach to evaluate TBL cancer mortality due to air pollution in Tuscany, Central Italy, in 2023. Using locally validated data, we assessed the impact of fine particulate matter (PM and PM) and nitrogen dioxide (NO) in terms of attributable deaths and years of life lost (YLL).

Supervised learning approaches for predicting Ebola-Human Protein-Protein interactions.

The goal of this research work is to predict protein-protein interactions (PPIs) between the Ebola virus and the host who is at risk of infection. Since there are very limited databases available on the Ebola virus; we have prepared a comprehensive database of all the PPIs between the Ebola virus and human proteins (EbolaInt). Our work focuses on the finding of some new protein-protein interactions between humans and the Ebola virus using some state- of-the-arts machine learning techniques.

Valley-Forecast: Forecasting Coccidioidomycosis incidence via enhanced LSTM models trained on comprehensive meteorological data.

Coccidioidomycosis (cocci), or more commonly known as Valley Fever, is a fungal infection caused by Coccidioides species that poses a significant public health challenge, particularly in the semi-arid regions of the Americas, with notable prevalence in California and Arizona. Previous epidemiological studies have established a correlation between cocci incidence and regional weather patterns, indicating that climatic factors influence the fungus's life cycle and subsequent disease transmission. This study hypothesizes that Long Short-Term Memory (LSTM) and extended Long Short-Term Memory (xLSTM) models, known for their ability to capture long-term dependencies in time-series data, can outperform traditional statistical methods in predicting cocci outbreak cases.

Deep learning-based free-water correction for single-shell diffusion MRI.

Free-water elimination (FWE) modeling in diffusion magnetic resonance imaging (dMRI) is crucial for accurate estimation of diffusion properties by mitigating the partial volume effects caused by free water, particularly at the interface between white matter and cerebrospinal fluid. The presence of free water partial volume effects leads to biases in estimating diffusion properties. Additionally, the existing mathematical FWE model is a two-compartment model, which can be well posed for multi-shell data.

Addressing data uncertainty of Caulobacter crescentus cell cycles using hybrid Petri nets with fuzzy kinetics.

Studying and analysing the various phases and key proteins of cell cycles is essential for the understanding of cell development and differentiation. To this end, mechanistic models play an important role towards a system level understanding of the interactions between cell cycle components. Many quantitative models of cell cycles have been previously constructed using either stochastic or deterministic approaches.

Multi-region nomogram for predicting central lymph node metastasis in papillary thyroid carcinoma using multimodal imaging: A multicenter study.

Background And Objective: Central lymph node metastasis (CLNM) is associated with high recurrence rate and low survival in patients with papillary thyroid carcinoma (PTC). However, there is no satisfactory model to predict CLNM in PTC. This study aimed to integrate PTC deep learning feature based on ultrasound (US) images, fat radiomics features based on computed tomography (CT) images and clinical characteristics to construct a multimodal and multi-region nomogram (MMRN) for predicting the CLNM in PTC.

Technology ownership, use, and perceptions of web-based program design features for older adults prescribed oral anticancer medication.

Introduction: Older adults are often prescribed oral anticancer agents (OAAs). Technology-based interventions may offer medication and symptom support. We aimed to evaluate technology ownership, use, and preferred design features of a supportive web-based program using a multimethod design utilizing surveys and semi-structured interviews.

AutoCOR: Autonomous condylar offset ratio calculator for post-operative total knee arthroplasty radiographs.

Background: This study aims to automate the measurement process of posterior condylar offset ratio (PCOR) and anterior condylar offset ratio (ACOR) to improve the Total Knee Arthroplasty (TKA) evaluation. Accurate calculation of PCOR and ACOR, performed manually by orthopedic surgeons, is crucial for assessing postoperative range of motion and implant positioning. Manual measurements, however, are time-consuming, prone to human error, and subject to variability.

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.

Inferring single-cell and spatial microRNA activity from transcriptomics data.

The activity of miRNA varies across different cell populations and systems, as part of the mechanisms that distinguish cell types and roles in living organisms and in human health and disease. Typically, miRNA regulation drives changes in the composition and levels of protein-coding RNA and of lncRNA, with targets being down-regulated when miRNAs are active. The term "miRNA activity" is used to refer to this transcriptional effect of miRNAs.