A Comparative Study on the Roll-to-Roll Processing of a Silicate-Polyvinyl Alcohol Composite Barrier Lacquer Using Slot-Die and Reverse Gravure Coating Techniques.

The integration of platelet-shaped montmorillonite particles to improve the oxygen barrier of polyvinyl-alcohol-based barrier layers is state-of-the-art, but research on roll-to-roll coatings of such composite barrier lacquers has not been widely published. In this study, two different coating techniques, slot-die and reverse gravure, were used on a roll-to-roll scale to apply barrier lacquers comprising polyvinyl alcohol and montmorillonite. The lacquers were analyzed regarding viscosity at certain shear rates and surface energy and the dried coating layers regarding oxygen barrier, surface morphology, and particle orientation.

Climatic water availability modifies tree functional diversity effects on soil organic carbon storage in European forests.

Forest stand and environmental factors influence soil organic carbon (SOC) storage, but little is known about their relative impacts in different soil layers. Moreover, how environmental factors modulate the impact of stand factors, particularly species mixing, on SOC storage, is largely unexplored. In this study, conducted in 21 forest triplets (two monocultures of different species and their mixture on the same site) distributed in Europe, we tested the hypothesis that stand factors (functional identity and diversity) have stronger effects on topsoil (FF + 0-10 cm) C storage than environmental factors (climatic water availability, clay + silt content, oxalate-extractable Al-Al) but that the opposite occurs in the subsoil (10-40 cm).

Unlocking complex soil systems as carbon sinks: multi-pool management as the key.

Much research focuses on increasing carbon storage in mineral-associated organic matter (MAOM), in which carbon may persist for centuries to millennia. However, MAOM-targeted management is insufficient because the formation pathways of persistent soil organic matter are diverse and vary with environmental conditions. Effective management must also consider particulate organic matter (POM).

Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps.

Highly multiplexed imaging holds enormous promise for understanding how spatial context shapes the activity of the genome and its products at multiple length scales. Here, we introduce a deep learning framework called CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis), which uses a conditional variational autoencoder to learn representations of molecular pixel profiles that are consistent across heterogeneous cell populations and experimental perturbations. Clustering these pixel-level representations identifies consistent subcellular landmarks, which can be quantitatively compared in terms of their size, shape, molecular composition and relative spatial organization.

Multimodal characterization of murine gastruloid development.

Gastruloids are 3D structures generated from pluripotent stem cells recapitulating fundamental principles of embryonic pattern formation. Using single-cell genomic analysis, we provide a resource mapping cell states and types during gastruloid development and compare them with the in vivo embryo. We developed a high-throughput handling and imaging pipeline to spatially monitor symmetry breaking during gastruloid development and report an early spatial variability in pluripotency determining a binary response to Wnt activation.

Skin substitutes based on gellan gum with mechanical and penetration compatibility to native human skin.

The study reports on a simple system to fabricate skin substitutes consisting of a naturally occurring bacterial polysaccharide gellan gum. Gelation was driven by the addition of a culture medium whose cations induced gellan gum crosslinking at physiological temperature, resulting in hydrogels. Human dermal fibroblasts were incorporated in these hydrogels and their mechanical, morphological, and penetration characteristics were studied.

In Vitro Investigation of the Interaction of Avian Metapneumovirus and Newcastle Disease Virus with Turkey Respiratory and Reproductive Tissue.

In poultry, several respiratory viral infections lead to a drop in egg production associated with high economic losses. While the virus-host interactions at the respiratory epithelium are well studied, less is known about these interactions in the oviduct. To investigate possible differences between virus infections at these epithelial structures, we compared the interactions of two important poultry viruses on turkey organ cultures.

Multi-scale systems genomics analysis predicts pathways, cell types, and drug targets involved in normative variation in peri-adolescent human cognition.

An open challenge in human genetics is to better understand the systems-level impact of genotype variation on developmental cognition. To characterize the genetic underpinnings of peri-adolescent cognition, we performed genotype-phenotype and systems analysis for binarized accuracy in nine cognitive tasks from the Philadelphia Neurodevelopmental Cohort (~2,200 individuals of European continental ancestry aged 8-21 years). We report a region of genome-wide significance within the 3' end of the Fibulin-1 gene (P = 4.

mlf-core: a framework for deterministic machine learning.

Motivation: Machine learning has shown extensive growth in recent years and is now routinely applied to sensitive areas. To allow appropriate verification of predictive models before deployment, models must be deterministic. Solely fixing all random seeds is not sufficient for deterministic machine learning, as major machine learning libraries default to the usage of nondeterministic algorithms based on atomic operations.

Best practices for single-cell analysis across modalities.

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights.

Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks.

The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this regard, epistasis models have shown to be capable of increasing the prediction accuracy while their high computational load is challenging. In this study, we investigated the predictive ability obtained in additive and epistasis models when utilizing haplotype blocks versus pruned sets of SNPs by including phenotypic information from the last growing season.

The potential role of serum extracellular vesicle derived small RNAs in AML research as non-invasive biomarker.

Background: Extracellular vesicles (EV) are cell-derived vesicles released by all cells in health and disease. Accordingly, EVs are also released by cells in acute myeloid leukemia (AML), a hematologic malignancy characterized by uncontrolled growth of immature myeloid cells, and these EVs likely carry markers and molecular cargo reflecting the malignant transformation occurring in diseased cells. Monitoring antileukemic or proleukemic processes during disease development and treatment is essential.

Synthetic enforcement of STING signaling in cancer cells appropriates the immune microenvironment for checkpoint inhibitor therapy.

Immune checkpoint inhibitors (ICIs) enhance anticancer immunity by releasing repressive signals into tumor microenvironments (TMEs). To be effective, ICIs require preexisting immunologically "hot" niches for tumor antigen presentation and lymphocyte recruitment. How the mutational landscape of cancer cells shapes these immunological niches remains poorly defined.

Amino acid digestibility and metabolizable energy of soybean meal of different origins in cecectomized laying hens.

This study investigated the variation in amino acid (AA) digestibility and MEn of 18 samples of solvent-extracted soybean meal (SBM; 6 European, 7 Brazilian, 2 Argentinian, 2 North American, 1 Indian) in cecectomized laying hens. The experimental diets contained either 300 g/kg of cornstarch or one of the SBM samples. Pelleted diets were fed to 10 hens in two 5 × 10 row-column designs so that 5 replicates were obtained from each diet during 5 periods.

reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters.

Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. While computational tools for scRNA-seq data analysis to decipher differential gene expression profiles and differential pathway expression exist, we still lack methods to learn differential regulatory disease mechanisms directly from the single-cell data. Here, we provide a new methodology, named DiNiro, to unravel such mechanisms and report them as small, easily interpretable transcriptional regulatory network modules.

modulates systemic immune signaling in .

Pectin- and hemicellulose-associated structures of plant cell walls participate in defense responses against pathogens of different parasitic lifestyles. The resulting immune responses incorporate phytohormone signaling components associated with salicylic acid (SA) and jasmonic acid (JA). SA plays a pivotal role in systemic acquired resistance (SAR), a form of induced resistance that - after a local immune stimulus - confers long-lasting, systemic protection against a broad range of biotrophic invaders.

Zinc, Copper, and Manganese Homeostasis and Potential Trace Metal Accumulation in Dairy Cows: Longitudinal Study from Late Lactation to Subsequent Mid-Lactation.

Background: Trace metals are supplemented in cattle to prevent nutrient deficiencies. Levels supplemented to mitigate worst-case basal supply and availability scenarios can, however, result in trace metal intakes far above the nutritional requirements of dairy cows with high feed intakes.

Objectives: We evaluated Zn, Mn, and Cu balance in dairy cows from late lactation through the subsequent mid-lactation, a period of 24 wk characterized by large changes in dry matter intake.

AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms.

Deep-learning (DL) methods like DeepMind's AlphaFold2 (AF2) have led to substantial improvements in protein structure prediction. We analyse confident AF2 models from 21 model organisms using a new classification protocol (CATH-Assign) which exploits novel DL methods for structural comparison and classification. Of ~370,000 confident models, 92% can be assigned to 3253 superfamilies in our CATH domain superfamily classification.

Biologically informed deep learning to query gene programs in single-cell atlases.

The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways.

Assessing the Economic Resilience of Different Management Systems to Severe Forest Disturbance.

Given the drastic changes in the environment, resilience is a key focus of ecosystem management. Yet, the quantification of the different dimensions of resilience remains challenging, particularly for long-lived systems such as forests. Here we present an analytical framework to study the economic resilience of different forest management systems, focusing on the rate of economic recovery after severe disturbance.

Network-based approaches for modeling disease regulation and progression.

Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses.

CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models.

Motivation: CATH is a protein domain classification resource that exploits an automated workflow of structure and sequence comparison alongside expert manual curation to construct a hierarchical classification of evolutionary and structural relationships. The aim of this study was to develop algorithms for detecting remote homologues missed by state-of-the-art hidden Markov model (HMM)-based approaches. The method developed (CATHe) combines a neural network with sequence representations obtained from protein language models.