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Temporal multiomics gene expression data across human embryonic stem cell-derived polyhormonal cell differentiation
No full textAbstract Human embryonic stem cells (hESCs) provide a powerful in vitro model to study lineage specification and the regulatory programs underlying early human development. Here, we present a high-resolution, temporal multi-omics dataset tracking mRNA, translation, and protein expression dynamics during hESC differentiation into definitive endoderm and subsequent polyhormonal (PH) cells, a key pancreatic lineage. RNA-seq, ribosome profiling, and quantitative mass spectrometry-based proteomics were performed on matched samples collected at ten time points in biological duplicates, allowing detailed characterization of transcriptional, translational, and protein abundance changes over the differentiation timeline. The dataset exhibits high technical quality, with strong reproducibility between replicates and rigorous quality control metrics across all omics platforms. This extensive dataset provides critical insights into the complex regulatory mechanisms driving polyhormonal cell differentiation and serves as a valuable resource for the research community, enabling deeper exploration of mammalian development, endodermal lineage specification, and gene regulation
A Dataset of Vertical Carbon Fluxes from a Georgia Tidal Salt Marsh from 2014 to 2024
No full textAbstract We present our methodology and data for science ready vertical carbon fluxes from a Spartina alterniflora tidal salt marsh as part of the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site on Sapelo Island, Georgia, USA. Vertical carbon fluxes were measured using the eddy covariance (EC) method from 2014 to 2024. The EC flux tower was located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. The proportional influence of marsh habitats on the flux measurements were 4% tall, 38% short, and 58% medium height form Spartina alterniflora. We provide the net ecosystem exchange (NEE), ecosystem respiration (ER), and gross primary production (GPP) at 30-minute fluxes (μmol CO2 m−2s−1), daily averages (μmol CO2 m−2s−1) and totals (g C m−2 day−1), and annual (g C m−2 year−1) quantities. We estimated uncertainty for each flux at each integrated timescale as 95% confidence intervals. Providing open access to 10-year carbon flux datasets can facilitate collaboration for advancing regional and global blue carbon synthesis and scale-up studies
Manure-based composts influence soil quality after lettuce (Lactuca sativa L.) production
No full textAbstract Declining soil fertility is a major constraint for vegetable crops like lettuce in sub-Saharan Africa. With the purpose of contributing to enhancing crop yields and soil productivity, while safeguarding the environment, this study aims to assess the effect of composts made from chicken droppings (CCD) and horse dung (CHD) on soil quality after lettuce growing. The phytotoxicity of the composts was assessed by testing the germination of two varieties of lettuce (V1: Eden and V2: Blonde de Paris) with aqueous extracts of the composts at different concentrations (0, 25, 50, 75 and 100%). For the field evaluation, treatments (C0: control without fertilizer, C1: 15 t. ha− 1 of chicken droppings, C2: 15 t.ha− 1 of horse dung, C3: chemical fertilizers) and varieties were distributed in a completely randomized block design with three replications. Physico-chemical soil analysis was carried out before (Ct) and after lettuce production depending on the treatments. Soil’s parameters determined included: pH, organic matter, carbon, C/N ratio, granulometry, cation exchange capacity, exchangeable bases, nitrogen, phosphorus, calcium, magnesium, sodium and potassium contents. The results of the phytotoxicity test showed that the lowest germination percentages were obtained at V1C2100%Ster + (48.33 ± 6.01%) and V2C2100% Ster + (53.33 ± 6.01%). These values were around the standard (50%), showing that these composts were non- toxic. The results showed that overall, the composts improved soil properties, with significant increases in P and Ca content. So, soil Ca contents of C0, C1, C2 and C3 were respectively 2, 3, 2 and 2 times that of Ct. CCD and CHD can be therefore recommended for growing lettuce and maintaining soil quality
Association between MEF2A variants and ischemic stroke risk: a case-control study and two prospective cohort studies in a Chinese population
No full textAbstract Background Transcriptional regulators encoded by the myocyte enhancer factor 2 (MEF2) gene family play a crucial role in cardiac development, homeostasis, and pathology. The relationship between MEF2A and ischemic stroke (IS) remains unclear. Methods We performed MEF2A polymorphism genotyping in a case-control study (2497 patients with IS vs. 3135 controls) and a cohort study involving 4080 non-stroke participants, which included up to 11.54 years of follow-up. Additionally, the mortality outcomes of 2298 patients with IS were followed up for 6.49 years. Furthermore, 301 IS and 313 controls were selected from the case-control study for MEF2A mRNA expression quantification using RT-qPCR. The modified Rankin Scale (mRS) scores of IS at the time of discharge and, one, three, six month post-discharge was collected. Multiple Cox regression analyses were used to estimate the hazard ratio (HR) with 95% confidence interval (CI). Restricted cubic spline (RCS) regression analyses were used to evaluate the dose-response relationship between mRNA expression levels and IS. Linear mixed-effects models were applied to examine the associations of the two SNPs and mRNA expression with the mRS scores. Results Carriers of the 2292288-rs3743248 G-T haplotype had a higher IS risk compared with carriers of the G-C haplotype; OR (95% CI]) were as follows: 1.417(1.120, 1.792), 1.581 (1.172, 2.133), 1.314 (0.991, 1.741) for patients with IS, large-artery atherosclerosis subtype, small-artery occlusion, respectively. Sex-stratified analysis identified rs2292288-AA as a female-specific risk factor for IS prevalence (HR = 1.755, 95% CI: 1.179–2.613), while in patients > 65 years, A-allele carriers showed worse functional recovery (higher mRS, P = 0.012). There was a non-linear correlation between MEF2A mRNA expression level and IS risk (P nonlinear= 0.001), after adjustment for covariates. Conclusions Our findings indicate that the MEF2A G-T haplotype is associated with IS susceptibility. While the rs2292288 variant demonstrates sex-specific effects on disease incidence and age-specific effects on recovery. Lower MEF2A mRNA expression was associated with an increased IS risk
Development and validation of a neural network survival prediction model for ischemic heart disease
No full textAbstract Background Current risk prediction models for ischemic heart disease in clinical use are relatively simple and use a limited collection of well-known risk factors. Using machine learning to integrate a broader panel of features from electronic health records (EHRs) may improve post-angiography prognostication. Methods This retrospective model development and validation study was based on Danish EHR data. Icelandic EHR data were used for external test. Patients with a coronary angiography-confirmed diagnosis of coronary atherosclerosis between 2006 and 2016 were included for model development (n = 39,746). Time to all-cause mortality, the prediction target, was tracked until 2019, or up to 5 years, whichever came first. To model time-to-event data and deal with censoring, neural network-based discrete-time survival models were used. The model, PMHnet, uses 584 different features including clinical characteristics, laboratory tests, and diagnosis and procedure codes. Model performance was evaluated using time-dependent AUC (tdAUC) and the Brier score. PMHnet was benchmarked against the updated GRACE2.0 risk score and less feature-rich neural network models. Models were evaluated using hold-out data (n = 5000) and external validation data from Iceland. Feature importance and model explainability were assessed using SHAP analysis. Results On the test set (n = 5000), the tdAUC of PMHnet was 0.88 [ 0.86–0.90] (case count = 196) at six months, 0.88 [0.86–0.90] (cc = 261) at one year, 0.84 [0.82–0.86] (cc = 395) at three years, and 0.82 [0.80–0.84] (cc = 763) at five years. PMHnet showed similar performance in the Icelandic data. Compared to the GRACE2.0 score and intermediate models limited to GRACE2.0 features or single data modalities, PMHnet had significantly better model discrimination across all evaluated prediction timepoints. Conclusions More complex and feature-rich machine learning models can better predict all-cause mortality in ischemic heart disease and may be used by clinicians and patients to inform and guide treatment and management
Joint association of estimated glucose disposal rate and aggregate index of systemic inflammation with mortality in general population: a nationwide prospective cohort study
No full textAbstract Background The COLCOT trial showed that patients with diabetes may benefit from low-dose colchicine, suggesting a potential interplay between insulin resistance (IR) and inflammation. Whether their combined assessment improves mortality risk stratification in the general population remains unclear. Methods We analyzed 50,654 adults from NHANES 1999–2018 linked to the National Death Index. IR and inflammation were assessed using estimated glucose disposal rate (eGDR) and the log₂-transformed aggregate index of systemic inflammation (AISI), respectively. Survey-weighted Cox proportional hazards models were used for all-cause mortality. For cardiovascular (CVD) mortality, cumulative incidence functions (CIFs) were estimated with Gray’s test for between-group comparisons, and Fine–Gray subdistribution hazard models were fitted treating non-CVD death as a competing event. Discrimination was assessed using time-dependent ROC curves at 5 and 10 years. Robustness was evaluated through sensitivity analyses excluding immune-modifying conditions/treatments, applying a 24-month lag, and excluding extreme absolute lymphocyte counts. Results Over a median follow-up of 120 months, 6,936 all-cause deaths and 2,170 CVD deaths occurred. Higher eGDR was inversely associated with mortality (all-cause HR per 1-unit increase 0.90, 95% CI 0.88–0.92; CVD sHR 0.88, 95% CI 0.85–0.91), whereas higher log₂(AISI) was positively associated (all-cause HR per doubling 1.10, 95% CI 1.06–1.15; CVD sHR 1.13, 95% CI 1.06–1.20). In joint analyses, participants with low eGDR (≤ 8.40) and high log₂(AISI) (> 7.98) had the highest risks of all-cause mortality (HR 1.58, 95% CI 1.38–1.81) and CVD mortality (cause-specific HR 2.09, 95% CI 1.58–2.77; Fine–Gray sHR 2.13, 95% CI 1.66–2.74), with graded separation of CIFs (Gray’s test P < 0.001). The combined model showed improved discrimination (AUCs at 5/10 years: all-cause 0.705/0.723; CVD 0.754/0.769). Results were consistent across sensitivity analyses. Conclusion In a nationally representative U.S. cohort, eGDR and log₂(AISI) were independently and jointly associated with all-cause and CVD mortality. Their combined assessment improves risk stratification and may help identify individuals most likely to benefit from targeted preventive and anti-inflammatory strategies. Graphical abstrac
Field-tailoring quantum materials via magneto-synthesis: metastable metallic and magnetically suppressed phases in a trimer iridate
No full textAbstract We demonstrate that applying modest magnetic fields (<0.1 T) during high-temperature crystal growth can profoundly alter the structure and ground state of a spin-orbit-coupled, antiferromagnetic trimer lattice. Using BaIrO₃ as a model system, whose ground state is intricately dictated by the trimer lattice, we show that magneto-synthesis, a field-assisted synthesis approach, stabilizes a structurally compressed, metastable metallic and magnetically suppressed phases inaccessible via conventional methods. These effects include a 0.85% reduction in unit cell, 4-order-of-magnitude decrease in resistivity, a 10-fold enhancement of the Sommerfeld coefficient, and the collapse of long-range magnetic order -- all intrinsic and bulk in origin. First-principles calculations confirm that the field-stabilized structure lies substantially above the ground state in energy, highlighting its metastable character. These large, coherent and correlated changes across multiple bulk properties, unlike those caused by dilute impurities, defects or off-stoichiometry, point to an intrinsic field-induced mechanism. The findings establish magneto-synthesis as a powerful new pathway for accessing non-equilibrium quantum phases in strongly correlated materials
Kitaev interaction and proximate higher-order skyrmion crystal in the triangular lattice van der Waals antiferromagnet NiI 2
No full textAbstract Topological spin textures are a spectacular manifestation of the chirality of the magnetic nanostructures protected by topology. Most known skyrmion systems are restricted to a topological charge of one, require an external magnetic field for stabilization, and are only reported in a few materials. Here, we investigate the possibility that the Kitaev anisotropic-exchange interaction stabilizes a higher-order skyrmion crystal in the insulating van der Waals magnet NiI2. We unveil and explain the incommensurate static and dynamic magnetic correlations across three temperature-driven magnetic phases of this compound using neutron scattering measurements, simulations, and modeling. Our parameter optimisation yields a minimal Kitaev-Heisenberg Hamiltonian for NiI2 which reproduces the experimentally observed magnetic excitations. Monte Carlo simulations for this model predict the emergence of the higher-order skyrmion crystal but neutron diffraction and optical experiments in the candidate intermediate temperature regime are inconclusive. We discuss possible deviations from the Kitaev-Heisenberg model that explains our results and conclude that NiI2, in addition to multiferroic properties in the bulk and few-layer limits, is a Kitaev bulk material proximate to the finite temperature higher-order skyrmion crystal phase
Method for quantification of microplastic release from plastic-based materials during weathering
No full textAbstract Most microplastics (MPs) are generated as a result of photodegradation during the life of plastic products and after the end-of-life as mismanaged waste. At present, it is impossible to avoid plastic-based materials altogether because of their unique properties, versatility, and price. An option is to set limits on how much MPs these materials can release over their lifetime, which would not only reduce MPs pollution, but also improve product quality. However, there is a lack of reliable methodologies for assessing the generation of MPs from these products during use or when they are left as unmanaged waste. The objective of this study was to develop a novel method to assess (collect and quantify) MPs formation from plastic-based materials during weathering. The developed process design is based on a well-established accelerated weathering tester that has been modified by incorporation of a sieve system and water recirculation. The case study is carried out on a recycled polypropylene (rPP) and wood plastic composite (WPC) made from wood particles and the same rPP. Despite the lower plastic content, WPC released significantly more MPs (up to 9.4 g/m2) than the rPP (up to 0.3 g/m2) in the same weathering conditions and duration. Examination of the degraded surfaces revealed that the wood particles facilitated the release of MPs most likely due to moisture fluctuations causing wood swelling induced internal stresses. The collected MPs were mainly below 500 μm and their properties were different comparing to MPs made by cryogenic milling. PY-GC-MS did not detect MPs smaller than 20 μm that could pass through the smallest sieve and end up in the effluent. The reproducibility of the measured MPs release using the process design was very good during the tested weathering period, with variations of less than 7%
Polyhalite nutrients driving balanced crop nutrition and sustainable agricultural productivity
No full textAbstract Polyhalite, a naturally occurring evaporite mineral, has emerged as an alternative multi-nutrient fertilizer due to its composition of potassium (K), calcium (Ca), magnesium (Mg) and sulfur (S). Unlike conventional potassium fertilizers, polyhalite offers a slow-release nutrient supply, improving nutrient use efficiency and reducing environmental impact. This review explores the role of polyhalite in crop nutrition, comparing its effectiveness with traditional fertilizers like muriate of potash (MOP) and sulfate of potash (SOP). Studies indicate that polyhalite enhances crop growth, yield and quality across various agricultural systems, including cereals, legumes, tuber crops and oilseeds. Its application also improves soil fertility by positively influencing cation exchange capacity, microbial activity and soil structure. Additionally, polyhalite’s chloride-free nature makes it suitable for chloride-sensitive crops, minimizing soil salinity risks. Moreover, polyhalite presents a promising solution for enhancing agricultural productivity while maintaining environmental sustainability. Despite its benefits, a few challenges such as limited availability and higher initial costs hinder its widespread adoption. Future research should focus on optimizing application methods, assessing its long-term residual effects and integrating it into precision agriculture for sustainable nutrient management
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