Mode of delivery predicts postpartum maternal leukocyte telomere length.

Background: Recent studies have suggested that pregnancy accelerates biologic aging, yet little is known about how biomarkers of aging are affected by events during the peripartum period. Given that immune shifts are known to occur following surgery, we explored the relation between mode of delivery and postpartum maternal leukocyte telomere length (LTL), a marker of biologic aging.

Study Design: Postpartum maternal blood samples were obtained from a prospective cohort of term, singleton livebirths without hypertensive disorders or peripartum infections between 2012 and 2018.

Computational Approaches for Connecting Maternal Stress to Preterm Birth.

Multiple studies have hinted at a complex connection between maternal stress and preterm birth (PTB). This article describes the potential of computational methods to provide new insights into this relationship. For this, we outline existing approaches for stress assessments and various data modalities available for profiling stress responses, and review studies that sought either to establish a connection between stress and PTB or to predict PTB based on stress-related factors.

Multimodal machine learning for modeling infant head circumference, mothers' milk composition, and their shared environment.

Links between human milk (HM) and infant development are poorly understood and often focus on individual HM components. Here we apply multi-modal predictive machine learning to study HM and head circumference (a proxy for brain development) among 1022 mother-infant dyads of the CHILD Cohort. We integrated HM data (19 oligosaccharides, 28 fatty acids, 3 hormones, 28 chemokines) with maternal and infant demographic, health, dietary and home environment data.

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs).

Cross-species comparative analysis of single presynapses.

Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.

Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature.

Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome.

Combined proteomics and CRISPR‒Cas9 screens in PDX identify ADAM10 as essential for leukemia in vivo.

Background: Acute leukemias represent deadly malignancies that require better treatment. As a challenge, treatment is counteracted by a microenvironment protecting dormant leukemia stem cells.

Methods: To identify responsible surface proteins, we performed deep proteome profiling on minute numbers of dormant patient-derived xenograft (PDX) leukemia stem cells isolated from mice.

Endothermic physiology of extinct megatooth sharks.

The evolution of the extinct megatooth shark, , and its close phylogenetic relatives remains enigmatic. A central question persists regarding the thermophysiological origins of these large predatory sharks through geologic time, including whether was ectothermic or endothermic (including regional endothermy), and whether its thermophysiology could help to explain the iconic shark's gigantism and eventual demise during the Pliocene. To address these uncertainties, we present unique geochemical evidence for thermoregulation in from both clumped isotope paleothermometry and phosphate oxygen isotopes.

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries.

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics.

Evidence for human milk as a biological system and recommendations for study design-a report from "Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)" Working Group 4.

Human milk contains all of the essential nutrients required by the infant within a complex matrix that enhances the bioavailability of many of those nutrients. In addition, human milk is a source of bioactive components, living cells and microbes that facilitate the transition to life outside the womb. Our ability to fully appreciate the importance of this matrix relies on the recognition of short- and long-term health benefits and, as highlighted in previous sections of this supplement, its ecology (i.

Ribonucleoprotein (RNP)-Mediated Allele Replacement in Barley (Hordeum vulgare L.) Leaves.

Varietal differences within a species with agronomic importance are often based on minor changes in the genomic sequence. For example, fungus-resistant and fungus-susceptible wheat varieties may vary in only one amino acid. The situation is similar with the reporter genes Gfp and Yfp where two base pairs cause a shift in the emission spectrum from green to yellow.

Ribonucleoprotein (RNP)-Mediated Targeted Mutagenesis in Barley (Hordeum vulgare L.).

The crop species barley is a genetic model for the small grain temperate cereals. Thanks to the availability of whole genome sequence and the development of customizable endonucleases, site-directed genome modification has recently revolutionized genetic engineering. Several platforms have been established in plants, with the most flexible one offered by the clustered regularly interspaced short palindromic repeats (CRISPR) technology.

Data-driven longitudinal characterization of neonatal health and morbidity.

Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018.

Prediction of neuropathologic lesions from clinical data.

Introduction: Post-mortem analysis provides definitive diagnoses of neurodegenerative diseases; however, only a few can be diagnosed during life.

Methods: This study employed statistical tools and machine learning to predict 17 neuropathologic lesions from a cohort of 6518 individuals using 381 clinical features (Table S1). The multisite data allowed validation of the model's robustness by splitting train/test sets by clinical sites.

Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning.

Unlabelled: Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand.

Early prediction and longitudinal modeling of preeclampsia from multiomics.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.

In-silico generation of high-dimensional immune response data in patients using a deep neural network.

Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts.

Comparative Life Cycle Assessment of Chemical and Biocatalytic 2'3'-Cyclic GMP-AMP Synthesis.

Life cycle assessments (LCAs) can provide insights into the environmental impact of production processes. In this study, a comparative LCA was performed for the synthesis of 2'3'-cyclic GMP-AMP (2'3'-cGAMP) in an early development stage. The cyclic dinucleotide (CDN) is of interest for pharmaceutical applications such as cancer immunotherapy.

Cenozoic megatooth sharks occupied extremely high trophic positions.

Trophic position is a fundamental characteristic of animals, yet it is unknown in many extinct species. In this study, we ground-truth the N/N ratio of enameloid-bound organic matter (δN) as a trophic level proxy by comparison to dentin collagen δN and apply this method to the fossil record to reconstruct the trophic level of the megatooth sharks (genus ). These sharks evolved in the Cenozoic, culminating in , a shark with a maximum body size of more than 15 m, which went extinct 3.