Revealing Fine-grained Genetically Informed Cortical Parcellation Maps of Neonates Based on Multi-view Spectral Clustering.

Genetic factors have been proven to be one of the major determinants in shaping the neonatal cerebral cortex. Previous research has demonstrated distinct genetic influences on the spatial patterns of cortical thickness (CT) and surface area (SA) in neonates, leading to their unique genetically informed parcellation maps. However, these parcellation maps were derived at coarse scales and only reliant on single cortical properties, making them unable to comprehensively characterize the fine-grained genetically regulated patterns of the neonatal cerebral cortex.

Early life phthalate exposure impacts gray matter and white matter volume in infants and young children.

Objective: Prenatal phthalate exposure is associated with adverse neurodevelopmental outcomes, yet data on impacts of early life exposure remains limited. We investigated phthalate and replacement plasticizer exposures from 2 weeks to 7 years of age in relation to brain anatomical attributes, using serial structural magnetic resonance imaging (sMRI).

Material And Methods: Children were enrolled after birth into the UNC Baby Connectome Project, a longitudinal neuroimaging study.

Erwinia aeris sp. nov., A Novel Bacterium Isolated from the Surface of an Ore in Hubei Province, China.

A novel Gram-stain-negative, phosphate-solubilizing and siderophore producing bacterium, which we designated as strain ACCC 02193, was separated from ore, collected in Zhongxiang, Hubei of China. Phylogenetic analysis based on 16S rRNA gene sequences showed strain ACCC 02193 is in the genus Erwinia and had highest similarities to Erwinia tasmaniensis DSM 17950 (99.32%) and Erwinia billingiae DSM 17872 (98.

The X chromosome's influences on the human brain.

Genes on the X chromosome are extensively expressed in the human brain. However, little is known for the X chromosome's impact on the brain anatomy, microstructure, and functional networks. We examined 1045 complex brain imaging traits from 38,529 participants in the UK Biobank.

Disentangled latent energy-based style translation: An image-level structural MRI harmonization framework.

Brain magnetic resonance imaging (MRI) has been extensively employed across clinical and research fields, but often exhibits sensitivity to site effects arising from non-biological variations such as differences in field strength and scanner vendors. Numerous retrospective MRI harmonization techniques have demonstrated encouraging outcomes in reducing the site effects at image level. However, existing methods generally suffer from high computational requirements and limited generalizability, restricting their applicability to unseen MRIs.

Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection.

Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive imaging technique to study patterns of brain activity, and is increasingly used to facilitate automated brain disorder analysis. Existing fMRI-based learning methods often rely on labeled data to construct learning models, while the data annotation process typically requires significant time and resource investment. Graph contrastive learning offers a promising solution to address the small labeled data issue, by augmenting fMRI time series for self-supervised learning.

Mapping rare protein-coding variants on multi-organ imaging traits.

Human organ structure and function are important endophenotypes for clinical outcomes. Genome-wide association studies (GWAS) have identified numerous common variants associated with phenotypes derived from magnetic resonance imaging (MRI) of the brain and body. However, the role of rare protein-coding variations affecting organ size and function is largely unknown.

ON BLOCKWISE AND REFERENCE PANEL-BASED ESTIMATORS FOR GENETIC DATA PREDICTION IN HIGH DIMENSIONS.

Genetic prediction holds immense promise for translating genetic discoveries into medical advances. As the high-dimensional covariance matrix (or the linkage disequilibrium (LD) pattern) of genetic variants often presents a block-diagonal structure, numerous methods account for the dependence among variants in predetermined local LD blocks. Moreover, due to privacy considerations and data protection concerns, genetic variant dependence in each LD block is typically estimated from external reference panels rather than the original training data set.

Estimating trans-ancestry genetic correlation with unbalanced data resources.

The aim of this paper is to propose a novel method for estimating trans-ancestry genetic correlations in genome-wide association studies (GWAS) using genetically-predicted observations. These correlations describe how genetic architecture of complex traits varies among populations. Our new estimator corrects for biases arising from prediction errors in high-dimensional weak GWAS signals, while addressing the ethnic diversity inherent in GWAS data, such as linkage disequilibrium (LD) differences.

The Interaction Effects of Sex, Age, APOE and Common Health Risk Factors on Human Brain Functions.

Importance: Nonlinear changes in brain function during aging are shaped by a complex interplay of factors, including sex, age, genetics, and modifiable health risk factors. However, the combined effects and underlying mechanisms of these factors on brain functional connectivity remain poorly understood.

Objective: To comprehensively investigate the combined associations of sex, age, genotypes, and ten common modifiable health risk factors with brain functional connectivities during aging.

Disease severity drives risk of venous thrombotic events in women with sickle cell disease in a single-center retrospective study.

Background: Estrogen-containing hormonal contraception (HC) is a well-established risk factor for venous thromboembolism (VTE). Women with sickle cell disease (SCD) also have an increased risk of VTE. However, it is unknown if exposure to HC exacerbates the risk of VTE in women with SCD.

Eye-brain connections revealed by multimodal retinal and brain imaging genetics.

The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes.

Pan-cancer subclonal mutation analysis of 7,827 tumors predicts clinical outcome.

Intra-tumor heterogeneity is an important driver of tumor evolution and therapy response. Advances in precision cancer treatment will require understanding of mutation clonality and subclonal architecture. Currently the slow computational speed of subclonal reconstruction hinders large cohort studies.

Urinary metabolite concentrations of phthalate and plasticizers in infancy and childhood in the UNC baby connectome project.

Introduction: Existing evidence suggests that exposure to phthalates is higher among younger age groups. However, limited knowledge exists on how phthalate exposure, as well as exposure to replacement plasticizers, di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH) and di-2-ethylhexyl terephthalate (DEHTP), change from infancy through early childhood.

Methods: Urine samples were collected across the first 5 years of life from typically developing infants and young children enrolled between 2017 and 2020 in the longitudinal UNC Baby Connectome Project.

Advancing microbial production through artificial intelligence-aided biology.

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis.

Multi-organ imaging-derived polygenic indexes for brain and body health.

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images.

FPLS-DC: functional partial least squares through distance covariance for imaging genetics.

Motivation: Imaging genetics integrates imaging and genetic techniques to examine how genetic variations influence the function and structure of organs like the brain or heart, providing insights into their impact on behavior and disease phenotypes. The use of organ-wide imaging endophenotypes has increasingly been used to identify potential genes associated with complex disorders. However, analyzing organ-wide imaging data alongside genetic data presents two significant challenges: high dimensionality and complex relationships.

Source-free unsupervised domain adaptation: A survey.

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden. To tackle this issue, many source-free unsupervised domain adaptation (SFUDA) methods have been proposed recently, which perform knowledge transfer from a pre-trained source model to the unlabeled target domain with source data inaccessible.

Merging or ensembling: integrative analysis in multiple neuroimaging studies.

The aim of this paper is to systematically investigate merging and ensembling methods for spatially varying coefficient mixed effects models (SVCMEM) in order to carry out integrative learning of neuroimaging data obtained from multiple biomedical studies. The "merged" approach involves training a single learning model using a comprehensive dataset that encompasses information from all the studies. Conversely, the "ensemble" approach involves creating a weighted average of distinct learning models, each developed from an individual study.

Structural MRI Harmonization via Disentangled Latent Energy-Based Style Translation.

Multi-site brain magnetic resonance imaging (MRI) has been widely used in clinical and research domains, but usually is sensitive to non-biological variations caused by site effects (, field strengths and scanning protocols). Several retrospective data harmonization methods have shown promising results in removing these non-biological variations at feature or whole-image level. Most existing image-level harmonization methods are implemented through generative adversarial networks, which are generally computationally expensive and generalize poorly on independent data.

A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression.

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication.

Identification and validation of supervariants reveal novel loci associated with human white matter microstructure.

As an essential part of the central nervous system, white matter coordinates communications between different brain regions and is related to a wide range of neurodegenerative and neuropsychiatric disorders. Previous genome-wide association studies (GWASs) have uncovered loci associated with white matter microstructure. However, GWASs suffer from limited reproducibility and difficulties in detecting multi-single-nucleotide polymorphism (multi-SNP) and epistatic effects.

The interaction effects of age, APOE and common environmental risk factors on human brain structure.

Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81.