The GrassSyn dataset: Soil organic carbon stocks in Brazilian grassy ecosystems.

Although ecosystem management and restoration are known to enhance carbon storage, limited knowledge of ecosystem-specific soil organic carbon (SOC) stocks and processes hinders the development of climate-ready, biodiversity-focused policies. Baseline SOC stocks data for specific ecosystems is essential. This paper aims to: (i) examine SOC stock variability across major grassy ecosystems in Brazil and (ii) discuss data limitations and applications.

Contribution of mozambioside roasting products to coffee's bitter taste.

Roasting degrades the coffee compound mozambioside (1) into several products, including 17-O-β-D-glucosyl-11-hydroxycafestol-2-one (2), 11-O-β-D-glucosyl-16-desoxycafestol-2-one (3), 11-O-β-D-glucosyl-(S)-16-desoxy-17-oxocafestol-2-one (4), 11-O-β-D-glucosyl-15,16-dehydrocafestol-2-one (5), 11-O-β-D-glucosyl-(R)-16-desoxy-17-oxocafestol-2-one (6), bengalensol (7), and 11-hydroxycafestol-2-one (8). A UHPLC-MS/MS method was established to quantify 1-8 and monitor their formation during authentic coffee roasting. Concentrations of 1 and the dominant roasting products 4, 5, and 7 ranged from 21.

Plasticity of Human Microglia and Brain Perivascular Macrophages in Aging and Alzheimer's Disease.

The complex roles of myeloid cells, including microglia and perivascular macrophages, are central to the neurobiology of Alzheimer's disease (AD), yet they remain incompletely understood. Here, we profiled 832,505 human myeloid cells from the prefrontal cortex of 1,607 unique donors covering the human lifespan and varying degrees of AD neuropathology. We delineated 13 transcriptionally distinct myeloid subtypes organized into 6 subclasses and identified AD-associated adaptive changes in myeloid cells over aging and disease progression.

Considerations for building and using integrated single-cell atlases.

The rapid adoption of single-cell technologies has created an opportunity to build single-cell 'atlases' integrating diverse datasets across many laboratories. Such atlases can serve as a reference for analyzing and interpreting current and future data. However, it has become apparent that atlasing approaches differ, and the impact of these differences are often unclear.

How to build the virtual cell with artificial intelligence: Priorities and opportunities.

Cells are essential to understanding health and disease, yet traditional models fall short of modeling and simulating their function and behavior. Advances in AI and omics offer groundbreaking opportunities to create an AI virtual cell (AIVC), a multi-scale, multi-modal large-neural-network-based model that can represent and simulate the behavior of molecules, cells, and tissues across diverse states. This Perspective provides a vision on their design and how collaborative efforts to build AIVCs will transform biological research by allowing high-fidelity simulations, accelerating discoveries, and guiding experimental studies, offering new opportunities for understanding cellular functions and fostering interdisciplinary collaborations in open science.

Bilingual language model for protein sequence and structure.

Adapting language models to protein sequences spawned the development of powerful protein language models (pLMs). Concurrently, AlphaFold2 broke through in protein structure prediction. Now we can systematically and comprehensively explore the dual nature of proteins that act and exist as three-dimensional (3D) machines and evolve as linear strings of one-dimensional (1D) sequences.

Understanding the role of in atopic dermatitis: strain diversity, microevolution, and prophage influences.

Atopic dermatitis (AD) is a prevalent inflammatory skin disorder characterized by chronic inflammation, skin barrier dysfunction, and microbial dysbiosis, with playing a significant role in its pathogenesis. This paper explores the strain diversity and microevolution of within AD patients, emphasizing how specific strains adapt to the altered skin environment, exacerbating the condition. The review emphasizes the significance of variation in specific functional genes among strains, which enhances their ability to adapt to different microenvironments and shapes their pathogenic potential.

An integrated transcriptomic cell atlas of human neural organoids.

Human neural organoids, generated from pluripotent stem cells in vitro, are useful tools to study human brain development, evolution and disease. However, it is unclear which parts of the human brain are covered by existing protocols, and it has been difficult to quantitatively assess organoid variation and fidelity. Here we integrate 36 single-cell transcriptomic datasets spanning 26 protocols into one integrated human neural organoid cell atlas totalling more than 1.

Fava Bean Protein Nanofibrils Modulate Cell Membrane Interfaces for Biomolecular Interactions as Unveiled by Atomic Force Microscopy.

Functional amyloids (protein nanofibrils, PNF) synthesized from plant sources exhibit unique physicochemical and nanomechanical properties that could improve food texture. While environmental factors affecting PNFs are well-known, scientific evidence on how cells (focus on the oral cavity) respond to them under physiological conditions is lacking. Self-assembled PNFs synthesized from fava bean whole protein isolate show a strong pH- and solvent-dependent morphology and elasticity modification measured by atomic force microscopy (AFM).

Mapping lineage-traced cells across time points with moslin.

Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making. Recent computational approaches combine both modalities into cellular trajectories; however, they cannot make use of all available lineage information in destructive time-series experiments. Here, we present moslin, a Gromov-Wasserstein-based model to couple cellular profiles across time points based on lineage and gene expression information.

How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities.

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells.

Assessing the role of evolutionary information for enhancing protein language model embeddings.

Embeddings from protein Language Models (pLMs) are replacing evolutionary information from multiple sequence alignments (MSAs) as the most successful input for protein prediction. Is this because embeddings capture evolutionary information? We tested various approaches to explicitly incorporate evolutionary information into embeddings on various protein prediction tasks. While older pLMs (SeqVec, ProtBert) significantly improved through MSAs, the more recent pLM ProtT5 did not benefit.

Activation of the TRPML1 Ion Channel Induces Proton Secretion in the Human Gastric Parietal Cell Line HGT-1.

The lysosomal Ca channel TRPML1 was found to be responsible for gastric acid secretion in murine gastric parietal cells by inducing the trafficking of H/K-ATPase containing tubulovesicles to the apical membrane. Therefore, we hypothesized a similar role of TRPML1 in regulating proton secretion in the immortalized human parietal cell line HGT-1. The primary focus was to investigate the involvement of TRPML1 in proton secretion using the known synthetic agonists ML-SA1 and ML-SA5 and the antagonist ML-SI3 and, furthermore, to identify food-derived compounds that target the channel.

Fine-tuning protein language models boosts predictions across diverse tasks.

Prediction methods inputting embeddings from protein language models have reached or even surpassed state-of-the-art performance on many protein prediction tasks. In natural language processing fine-tuning large language models has become the de facto standard. In contrast, most protein language model-based protein predictions do not back-propagate to the language model.

Genomic and functional divergence of strains from atopic dermatitis patients and healthy individuals: insights from global and local scales.

Atopic dermatitis (AD) is the most common chronic inflammatory skin disease worldwide and is characterized by a complex interplay with skin microbiota, with often abnormally more abundant in AD patients than in healthy individuals (HE). harbors diverse strains with varied genetic compositions and functionalities, which exhibit differential connections with the severity of AD. However, the differences in strains between AD and HE remain unclear, with most variations seen at a specific geographic level, implying spontaneous adaptations rather than systematic distinctions.

Chromatin Immunoprecipitation in Adipose Tissue and Adipocytes: How to Proceed and Optimize the Protocol for Transcription Factor DNA Binding.

Chromatin immunoprecipitation (ChIP) coupled to qPCR or sequencing is a crucial experiment to determine direct transcriptional regulation under the control of specific transcriptional factors or co-regulators at loci-specific or pan-genomic levels.Here we provide a reliable method for processing ChIP from adipocytes or frozen adipose tissue collection, isolation of nuclei, cross-linking of protein-DNA complexes, chromatin shearing, immunoprecipitation, and DNA purification. We also discuss critical steps for optimizing the experiment to perform a successful ChIP in lipid-rich cells/tissues.

Transformers in single-cell omics: a review and new perspectives.

Recent efforts to construct reference maps of cellular phenotypes have expanded the volume and diversity of single-cell omics data, providing an unprecedented resource for studying cell properties. Despite the availability of rich datasets and their continued growth, current single-cell models are unable to fully capitalize on the information they contain. Transformers have become the architecture of choice for foundation models in other domains owing to their ability to generalize to heterogeneous, large-scale datasets.

scTab: Scaling cross-tissue single-cell annotation models.

Identifying cellular identities is a key use case in single-cell transcriptomics. While machine learning has been leveraged to automate cell annotation predictions for some time, there has been little progress in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues. Here, we propose scTab, an automated cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.

Injection of antioxidant trace minerals/vitamins into peripartum crossbred cows improves the nutritional and immunological properties of colostrum/milk and the health of their calves under heat stress conditions.

Multimineral and vitamin injections can provide better nutrient availability at the cellular level, which is essential for mitigating transition period stress and improving the wellbeing and productivity of dairy cows. The present study was conducted to assess the colostrum quality and calf health after intramuscular injection of multi-minerals (MM) and multi-vitamins (MV) to peripartum cows during winter (THI = 58 to 66) and summer (THI = 78 to 82) months. In each season, twenty-four pregnant crossbred Karan Fries cows were grouped into four, each consisting of six cows.

Distinct prophage gene profiles of strains from atopic dermatitis patients and healthy individuals.

Unlabelled: strains exhibit varying associations with atopic dermatitis (AD), but the genetic determinants underpinning the pathogenicity are yet to be fully characterized. To reveal the genetic differences between strains from AD patients and healthy individuals (HE), we developed and employed a random forest classifier to identify potential marker genes responsible for their phenotypic variations. The classifier was able to effectively distinguish strains from AD and HE.