Cross-sectional and longitudinal changes in category-selectivity in visual cortex following pediatric cortical resection.

The topographic organization of category-selective responses in human ventral occipitotemporal cortex (VOTC) and its relationship to regions subserving language functions is remarkably uniform across individuals. This arrangement is thought to result from the clustering of neurons responding to similar inputs, constrained by intrinsic architecture and tuned by experience. We examined the malleability of this organization in individuals with unilateral resection of VOTC during childhood for the management of drug-resistant epilepsy.

L2G: Repurposing Language Models for Genomics Tasks.

Pre-trained language models have transformed the field of natural language processing (NLP), and their success has inspired efforts in genomics to develop domain-specific foundation models (FMs). However, creating high-quality genomic FMs from scratch is resource-intensive, requiring significant computational power and high-quality pre-training data. The success of large language models (LLMs) in NLP has largely been driven by industrial-scale efforts leveraging vast, diverse corpora and massive computing infrastructure.

Space- and object-based attention in patients with a single hemisphere following childhood resection.

The neural processes underlying attentional processing are typically lateralized in adults, with spatial attention associated with the right hemisphere (RH) and object-based attention with the left hemisphere (LH). Using a modified two-rectangle attention paradigm, we compared the lateralization profiles of individuals with childhood hemispherectomy (either LH or RH) and age-matched, typically developing controls. Although patients exhibited slower reaction times (RTs) compared to controls, both groups benefited from valid attentional cueing.

Engineering the 3D structure of organoids.

Organoids form through the sel f-organizing capabilities of stem cells to produce a variety of differentiated cell and tissue types. Most organoid models, however, are limited in terms of the structure and function of the tissues that form, in part because it is difficult to regulate the cell type, arrangement, and cell-cell/cell-matrix interactions within these systems. In this article, we will discuss the engineering approaches to generate more complex organoids with improved function and translational relevance, as well as their advantages and disadvantages.

Balance correlations, agentic zeros, and networks: The structure of 192 years of war and peace.

Social network extensions of Heider's balance theory have led to a plethora of adaptations, often inconsistent with Heider and each other. We present a general model that permits the description and testing of specific balance theoretic predictions as Heider had originally proposed them. We formulate balance statements as a comparison of two conditional probabilities of a tie: [Formula: see text], conditioned on 2-path relations [Formula: see text] vs.

Improving localization and measurements of M-waves using high-density surface electromyography.

Surface electromyography () is useful for studying muscle function and controlling prosthetics, but crosstalk from nearby muscles often limits its effectiveness. High-density surface EMG () improves spatial resolution, allowing for the isolation of in the densely packed forearm muscles. This study assessed for localizing and evaluated the impact of spatial filters on crosstalk reduction.

Expanding PFAS Identification with Transformation Product Libraries: Nontargeted Analysis Reveals Biotransformation Products in Mice.

Per- and polyfluoroalkyl substances (PFAS) are widely used persistent synthetic chemicals that have been linked to adverse health effects. While the behavior of PFAS has been evaluated in the environment, our understanding of reaction products in mammalian systems is limited. This study identified biological PFAS transformation products and generated mass spectral libraries to facilitate an automated search and identification.

Psychosocial predictors of short-term glucose among people with diabetes: A narrative review.

Type 1 and type 2 diabetes are metabolic disorders that require one to manage one's blood glucose levels on a daily basis through a series of behaviorally complex tasks. Research shows that psychosocial factors, including mood, stress, and social relationships, have a significant influence on one's ability to maintain these disease management routines and achieve healthy blood glucose levels. However, researchers have typically approached these questions from a between-person perspective.

The EstroGene2.0 database for endocrine therapy response and resistance in breast cancer.

Endocrine therapies targeting the estrogen receptor (ER/ESR1) are the cornerstone to treat ER-positive breast cancers patients, but resistance often limits their effectiveness. Notable progress has been made although the fragmented way data is reported has reduced their potential impact. Here, we introduce EstroGene2.

Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans.

Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics and designing personalized radiotherapy treatment plans. Mathematical models of GBM growth can complement the data in the prediction of spatial distributions of tumor cells. However, this requires estimating patient-specific parameters of the model from clinical data, which is a challenging inverse problem due to limited temporal data and the limited time between imaging and diagnosis.

Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties.

Peptides are crucial in biological processes and therapeutic applications. Given their importance, advancing our ability to predict peptide properties is essential. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models with graph neural networks (GNNs) to predict peptide properties.

Predicting lung aging using scRNA-Seq data.

Age prediction based on single cell RNA-Sequencing data (scRNA-Seq) can provide information for patients' susceptibility to various diseases and conditions. In addition, such analysis can be used to identify aging related genes and pathways. To enable age prediction based on scRNA-Seq data, we developed PolyEN, a new regression model which learns continuous representation for expression over time.

10-Fold Increase in Hydrogen Atom Transfer Reactivity for a Series of = 1 Fe═O Complexes Over the = 2 [(TQA)Fe═O] Complex via Entropic Lowering of Reaction Barriers by Secondary Sphere Cycloalkyl Substitution.

Nonheme iron enzymes utilize = 2 iron(IV)-oxo intermediates as oxidants in biological oxygenations. In contrast, corresponding synthetic nonheme Fe═O complexes characterized to date favor the = 1 ground state that generally shows much poorer oxidative reactivity than their = 2 counterparts. However, one intriguing exception found by Nam a decade ago is the = 1 [Fe(O)(MeNTB)] complex (MeNTB = [tris((-methyl-benzimidazol-2-yl)methyl)amine], ) with a hydrogen atom transfer (HAT) reactivity that is 70% that of the = 2 [Fe(O)(TQA)] complex (TQA = tris(2-quinolylmethyl)amine, ).

Dynamic relations between longitudinal morphological, behavioral, and emotional indicators and cognitive impairment: evidence from the Chinese Longitudinal Healthy Longevity Survey.

Background: We aimed to assess the effects of body mass index (BMI), activities of daily living (ADL), and subjective well-being (SWB) on cognitive impairment and propose dynamic risk prediction models for aging cognitive decline.

Methods: We leveraged the Chinese Longitudinal Healthy Longevity Survey from 1998 to 2018. Cognitive status was measured using the Chinese Mini-Mental State Examination.

Longitudinal changes in electrophysiology and widefield calcium imaging following electrode implantation.

. Intracortical microelectrode arrays often fail to deliver reliable signal quality over chronic recordings, and the effect of an implanted recording array on local neural circuits is not completely understood..

Ligand binding kinetics to evaluate the function and stability of AR in nanodiscs.

G-protein-coupled receptors (GPCRs) represent one of the largest classes of therapeutic targets. However, developing successful therapeutics to target GPCRs is a challenging endeavor, with many molecules failing during in vivo clinical trials due to a lack of efficacy. The in vitro identification of drug-target residence time (1/k) has been suggested to improve predictions of in vivo success.

Catalysts for sulfur: understanding the intricacies of enzymes orchestrating plant sulfur anabolism.

This review highlights the sulfur transporters, key enzymes and their encoding genes involved in plant sulfur anabolism, focusing on their occurrence, chemistry, location, function, and regulation within sulfur assimilation pathways. Sulfur, a vital element for plant life, plays diverse roles in metabolism and stress response. This review provides a comprehensive overview of the sulfur assimilation pathway in plants, highlighting the intricate network of enzymes and their regulatory mechanisms.

Contrastive Clustering-Based Patient Normalization to Improve Automated In Vivo Oral Cancer Diagnosis from Multispectral Autofluorescence Lifetime Images.

Multispectral autofluorescence lifetime imaging systems have recently been developed to quickly and non-invasively assess tissue properties for applications in oral cancer diagnosis. As a non-traditional imaging modality, the autofluorescence signal collected from the system cannot be directly visually assessed by a clinician and a model is needed to generate a diagnosis for each image. However, training a deep learning model from scratch on small multispectral autofluorescence datasets can fail due to inter-patient variability, poor initialization, and overfitting.

Enhanced Proton-Coupled Electron-Transfer Reactivity by a Mononuclear Nickel(II) Hydroxide Radical Complex.

The synthesis, characterization, and reactivity of a NiOH core bearing a tridentate redox-active ligand capable of reaching three molecular oxidation states is presented in this paper. The reduced complex [LNiOH] was characterized by single-crystal X-ray diffraction analysis, depicting a square-planar NiOH core stabilized by intramolecular H-bonding interactions. Cyclic voltammetry measurements indicated that [LNiOH] can be reversibly oxidized to [LNiOH] and [LNiOH] at very negative reduction potentials (-1.

The ISCB competency framework v. 3: a revised and extended standard for bioinformatics education and training.

Motivation: Developing competency in the broad area of bioinformatics is challenging globally, owing to the breadth of the field and the diversity of its audiences for education and training. Course design can be facilitated by the use of a competency framework-a set of competency requirements that define the knowledge, skills and attitudes needed by individuals in (or aspiring to be in) a particular profession or role. These competency requirements can help to define curricula as they can inform both the content and level to which competency needs to be developed.

Optimizing design of genomics studies for clonal evolution analysis.

Motivation: Genomic biotechnology has rapidly advanced, allowing for the inference and modification of genetic and epigenetic information at the single-cell level. While these tools hold enormous potential for basic and clinical research, they also raise difficult issues of how to design studies to deploy them most effectively. In designing a genomic study, a modern researcher might combine many sequencing modalities and sampling protocols, each with different utility, costs, and other tradeoffs.

Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments.

Background: Longitudinal tracking of multiple sclerosis (MS) symptoms in an individual's own environment may improve self-monitoring and clinical management for people with MS (pwMS).

Objective: We present a machine learning approach that enables longitudinal monitoring of clinically relevant patient-reported symptoms for pwMS by harnessing passively collected data from sensors in smartphones and fitness trackers.

Methods: We divide the collected data into discrete periods for each patient.

Ultrasound-based geometric modeling of the human ovary with applications to cryopreservation.

Successful cryopreservation of the whole ovary outside of the body, while a woman undergoes cancer treatments, may help preserving fertility and regaining hormone balance during recovery. One of the key challenges in whole ovary cryopreservation is adequately loading the organ with cryoprotective agents (CPAs). Another notable challenge in developing the application is the lack of geometric data needed for designing matching thermal protocols.