Miracle-dz/UniFAR-FTUs huggingface dataset:
Smartphone-assisted real-time estimation of chlorophyll and carotenoid contents in spinach following the inversion of red and green color features

Purpose Chlorophyll (Chl) content is a reliable indicator of leaf nitrogen content and plant health status. Currently available methods for image-based Chl estimation require complex mathematical derivations and high-throughput imaging set-up along with multiplex image-preprocessing steps. Further, the influence of carotenoid (CAR) content has been largely ignored in the process. The present study describes a smartphone-based leaf image analysis method for real-time estimation of Chl content and Chl/CAR ratio. Methods Color features were obtained from RGB (red, green, blue) images of spinach leaves using a smartphone, and inverse R and G values were calculated. Correlation analysis of color indices and photosynthetic pigment (PP) contents was performed, followed by principal component analysis (PCA). Linear mathematical modeling was performed for describing regression equations for predicting PP contents. Results 1/R and 1/G showed strong positive linear correlation (0.93 < r2 < 0.96) with Chl and CAR contents, respectively. Furthermore, 1/R+1/G and [1/R]/[1/G] presented strong positive linear correlation with Chl + CAR (r2 = 0.95) and Chl/CAR (r2 = 0.88), respectively. PCA confirmed the association of color indices with the respective PP features, which were subsequently estimated using the correlation models. A smartphone-based companion application was developed using the linear models for non-invasive, real-time estimation of Chl content and Chl/CAR ratio. Conclusion The ratios 1/R and 1/G indicate the contents of Chl and CAR via linear models. The smartphone application developed using the linear models enables real-time estimation of Chl content and Chl/CAR ratio without complicated image preprocessing steps or mathematical derivations.
In situ maturated early-stage human induced pluripotent stem cell-derived cardiomyocytes improve cardiac function by enhancing segmentary contraction in infarcted rats

The scant ability of cardiomyocytes to proliferate makes heart regeneration one of the biggest challenges of science. Current therapies do not contemplate heart re-muscularization. In this scenario, stem cell-based approaches have been proposed to overcome the lack of regeneration. We hypothesize PluriCell hiPSC-derived cardiomyocytes (hiPSC-CMs) could enhance rat’s cardiac function after myocardial infarction (MI). Animals were subjected to permanent occlusion of the Left-Ventricle (LV) anterior descending coronary artery (LAD). Seven days after MI, Early-stage hiPSC-CMs were injected intramyocardially. Rats were subjected to Echocardiography pre- and post-treatment. Thirty days after injections, treated rats displayed 6.2% human cardiac grafts, which were characterized molecularly. Left ventricle ejection fraction (LVEF) was improved by 7.8% in cell-injected rats, while placebo controls showed an 18.2% deterioration. Also, cell-treated rats displayed a 92% and 56% increase in radial and circumferential strains, respectively. Human cardiac grafts maturate in situ, preserving proliferation with 10% Ki67 and 3% PHH3 positive nuclei. Grafts were perfused by host vasculature with no evidence for immune rejection nor ectopic tissue formations. Our findings support PluriCell hiPSC-CMs as an alternative therapy to treat MI. The next steps of preclinical development include efficacy studies in large animals on the path to clinical-grade regenerative therapy targeting human patients.
Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies

Genome-wide association studies (GWAS) are primarily conducted in single-ancestry settings. The low transferability of results has limited our understanding of human genetic architecture across a range of complex traits. In contrast to homogeneous populations, admixed populations provide an opportunity to capture genetic architecture contributed from multiple source populations and thus improve statistical power. Here, we provide a mechanistic simulation framework to investigate the statistical power and transferability of GWAS under directional polygenic selection or varying divergence. We focus on a two-way admixed population and show that GWAS in admixed populations can be enriched for power in discovery by up to 2-fold compared to the ancestral populations under similar sample size. Moreover, higher accuracy of cross-population polygenic score estimates is also observed if variants and weights are trained in the admixed group rather than in the ancestral groups. Common variant associations are also more likely to replicate if first discovered in the admixed group and then transferred to an ancestral population, than the other way around (across 50 iterations with 1,000 causal SNPs, training on 10,000 individuals, testing on 1,000 in each population, p=3.78e-6, 6.19e-101, ~0 for FST = 0.2, 0.5, 0.8, respectively). While some of these FST values may appear extreme, we demonstrate that they are found across the entire phenome in the GWAS catalog. This framework demonstrates that investigation of admixed populations harbors significant advantages over GWAS in single-ancestry cohorts for uncovering the genetic architecture of traits and will improve downstream applications such as personalized medicine across diverse populations.
An objective-based prioritization approach to improve trophic complexity through ecological restoration

Reassembling ecological communities and rebuilding habitats through active restoration treatments requires curating the selection of plant species to use in seeding and planting mixes. Ideally, these mixes should be assembled based on attributes that support ecosystem function and services, promote plant and animal species interactions and ecological networks in restoration while balancing project constraints. Despite these critical considerations, it is common for species mixes to be selected opportunistically. Reframing the selection of seed mixes for restoration around ecological objectives is essential for success but accessible methods and tools are needed to support this effort. We developed a framework to optimize species seed mixes based on prioritizing plant species attributes to best support different objectives for ecosystem functions, services, and trophic relationships such as pollination, seed dispersal, and herbivory. We compared results to approaches where plant species are selected to represent plant taxonomic richness, dominant species, and at random. We tested our framework for 176 plant species found in European alpine grasslands and identified 163 associated attributes affiliated to trophic relationships, ecosystem functions, and services. In all cases, trophic relationships, ecosystem functions, and services can be captured more efficiently through objective-based prioritization using the functional identity of plant species. Solutions (plant species lists) can be compared quantitatively, in terms of costs, species, or objectives. We confirm that a random draw of plant species from the regional plant species pool cannot be assumed to support other trophic groups and ecosystem functions and services. Synthesis and Applications. Our framework is presented as a proof of concept to help restoration practitioners better apply quantitative decision–support to plant species selection in order to meet ecological restoration outcomes. Our approach may be tailored to any restoration initiative and habitat where seeding or planting mixes will be applied in active treatments. As global priority and resources are increasingly placed into restoration, this approach could be advanced to help make efficient decisions for many stages of the restoration process.
Diversify your workflow! - An inconvenient suggestion to analyze spike data from intracranial recordings

An important challenge of neuroscience research and future brain machine interfacing is the reliable assignment of spikes to putative neurons. By means of extracellular recordings, researchers try to match different types action potentials with their putative neuronal source and timing. Unfortunately, this procedure is by far not standardized and reliable, leading to many different suggestions and as many differing results. It appears that sharing of data is thus hampered by different processing pipelines in different labs, thus playing along the reproducibility crisis in neurosciences. To systematically shed light on this issue, we present preliminary results of several easy event detection schemes on one data set, meant to illustrate the inconsistencies arising from different processing pipelines already in its initial step. The results indicate that thresholding choices alter findings due to a lack of a ground truth for spike sorting. We suggest to increase reliability in findings by only accepting and further processing events accepted by more than one processing pipeline.
A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins

Traditional food allergen identification mainly relies on in vivo and in vitro experiments, which often needs a long period and high cost. The artificial intelligence (AI)-driven rapid food allergen identification method has solved the above mentioned two drawbacks and is becoming an efficient auxiliary tool. Aiming to overcome the limitations of lower accuracy of traditional machine learning models in predicting the allergenicity of food proteins, this work proposed to introduce deep learning model - transformer with self-attention mechanism, ensemble learning models (representative as Light Gradient Boosting Machine (LightGBM) eXtreme Gradient Boosting (XGBoost)) to solve the problem. In order to highlight the superiority of the proposed novel method, the study also selected various commonly used machine learning models as the baseline classifiers. The results of 5-fold cross-validation showed that the AUC of the deep model was the highest (0.9578), which was better than the ensemble learning and baseline algorithms. But the deep model need to be pre-trained, and the training cost is the highest. By comparing the characteristics of the transformer model and boosting models, it can be analyzed that, each model has its own advantage, which provides novel clues and inspiration for the rapid prediction of food allergens in the future.
Neutralization of Acid by Steel-Making Slags

Steel-making slag residues are potentially useful in the neutralization of acidic liquors such as acid-mine drainage (AMD) because of their high content of basic minerals. This work examined the neutralization capacity of slags by batch titration with sulfuric acid. Measurements of pH and elemental solution composition over time provided input to a dissolution model based on saturation driven surface kinetics, a process dependent on the relative degree of saturation of the dissolving solid, and solution equilibrium. The heterogeneous composition of slag grains was simplified to three compositional groups based on their solubility. Group 1 included free lime, magnesium and silicon oxides, and dicalcium ferrite. Group 2 included weakly bound lime and magnesium oxides. Group 3 contained tightly bound lime and magnesium oxides, and wüstite. Neutralization profiles consistently revealed a two-phased kinetics. Group 1 oxides dissolved almost entirely in the first days according to saturation driven kinetics. Group 2 dissolved over the next 10 to 80 days, driven by a pH gradient in a quasi-first order reaction rate with respect to H+. Group 3 did not dissolve. Indirect evidence suggests that the transfer from saturation to pH driven dissolution would be caused by armoring of the grain surface by precipitating iron oxides. Although only 60% in volume of slag dissolves, it still possess the same neutralizing capacity as calcite and can neutralize acidic liquors like AMD. However, due to the slag having slow dissolution kinetics and the necessity to use fine powders, rapid neutralization is hindered because of the high cost of crushing.
The FDA-approved drug cobicistat synergizes with remdesivir to inhibit SARS-CoV-2 replication

Combinations of direct-acting antivirals are needed to minimize drug-resistance mutations and stably suppress replication of RNA viruses. Currently, there are limited therapeutic options against the Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) and testing of a number of drug regimens has led to conflicting results. Here we show that cobicistat, which is an-FDA approved drug-booster that blocks the activity of the drug metabolizing proteins Cytochrome P450-3As (CYP3As) and P-glycoprotein (P-gp), inhibits SARS-CoV-2 replication. Cell-to-cell membrane fusion assays indicated that the antiviral effect of cobicistat is exerted through inhibition of spike protein-mediated membrane fusion. In line with this, incubation with low micromolar concentrations of cobicistat decreased viral replication in three different cell lines including cells of lung and gut origin. When cobicistat was used in combination with the putative CYP3A target and nucleoside analog remdesivir, a synergistic effect on the inhibition of viral replication was observed in cell lines and in a primary human colon organoid. The cobicistat/remdesivir combination was able to potently abate viral replication to levels comparable to mock-infected cells leading to an almost complete rescue of infected cell viability. These data highlight cobicistat as a therapeutic candidate for treating SARS-CoV-2 infection and as a potential building block of combination therapies for COVID-19.
Bidirectional modulation of pain-related behaviors in the zona incerta

Central amygdala neurons expressing protein kinase C-delta (CeA-PKCδ) are sensitized following nerve injury and promote pain-related responses in mice. The neural circuits underlying modulation of pain-related behaviors by CeA-PKCδ neurons, however, remain unknown. In this study, we identified a functional monosynaptic inhibitory neural circuit that originates in CeA-PKCδ neurons and terminates in the ventral region of the zona incerta (ZI), a subthalamic structure previously linked to pain processing. Behavioral experiments further show that chemogenetic inhibition of GABAergic ZI neurons is sufficient to induce bilateral hypersensitivity in uninjured mice as well as contralateral hypersensitivity after nerve injury. In contrast, chemogenetic activation of GABAergic ZI neurons reverses nerve injury-induced hypersensitivity, demonstrating that silencing of the ZI is required for injury-induced behavioral hypersensitivity. Our results identify a previously unrecognized inhibitory efferent pathway from CeA-PKCδ neurons to the ZI and demonstrate that ZI-GABAergic neurons can bidirectionally modulate pain-related behaviors in mice.
Label free sensing with Terahertz multiple ring resonators

Apart from their relevance for spectroscopy and imaging, terahertz signals have attracted a lot of interest for sensing. In this paper, a label free terahertz sensor is proposed, which can be employed to detect the presence of molecules in the environment. The proposed sensor consists of an array of ring resonators, resonating at the frequency of f=1.2 THz. By providing full-wave numerical simulations, it is shown that the proposed sensor is able to sense the variation of the refractive index in the environment. The proposed structure is found to show larger sensitivity compared to previous reports. Our findings provide a novel platform to realize label free terahertz sensors with extremely large sensitivity.
The effect of thermal annealing on the properties of PECVD hydrogenated silicon nitride

Silicon nitride (SiNx) thin films were grown on silicon by plasma-enhanced chemical vapor deposition (PECVD) method at low temperature by varying the silane (SiH4) to nitrogen (N2) ratio in the plasma. As-deposited samples were investigated by varying annealing temperature from 400 °C to 950 °C in infrared (IR) heated belt furnace, in order to study their properties and correlate them to the chemical composition of the layers to find the optimized condition for application in silicon solar cells (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Seasonal dynamics and repeatability of gastrointestinal parasite burdens in wild Soay sheep

Seasonality is a ubiquitous feature of wildlife disease ecology, but is determined by a complex interplay of environmental, parasitological and host factors. Gastrointestinal parasites often exhibit strong seasonal dynamics in wild vertebrate populations due to, for example, environmental influences on free-living or vectored life stages, and variation in the physiological and immune status of hosts across their annual cycle. At the same time, wild populations are typically infected with multiple parasites. The seasonal dynamics of co-infecting parasites may differ depending on age and reproductive status, and associations among parasites may be driven by short-term within-individual changes or longer-term interactions that are consistent among hosts. Here, we used faecal samples and egg counts collected repeatedly from individually marked and monitored wild Soay sheep that were part of a long-term study to investigate seasonal dynamics of six gastrointestinal parasite groups (strongyle nematodes, coccidian protozoa, Capillaria, Strongyloides, Nematodirus, and Moniezia). Prevalence and abundance generally tended to be higher spring and summer, and burdens were higher in lambs than adults. Within the highly prevalent strongyle nematode group, we found differences in seasonality of egg counts depending on adult reproductive status. Reproductive ewes had increased counts in spring around the time of birth followed by a drop in abundance in summer, while barren ewes showed little evidence of seasonality. Males showed a sustained rise in egg counts through spring and summer, and sex differences were only strongly apparent in summer. In contrast, in similarly prevalent coccidia we found a peak in faecal oocyst counts in spring but no differences in seasonality among males, barren and pregnant ewes. Using multivariate mixed-effects models, we went on to show that both strongyle and coccidia counts are moderately repeatable across seasons among individuals. We further show that apparent positive correlation between strongyle and coccidia counts was driven by short-term within-individual changes in both parasite burdens rather than long-term among-individual covariation. Overall, our results demonstrate that seasonality varies across demographic and parasite groups and highlight the value of investigating fluctuating susceptibility and exposure over time for understanding epidemiology of a population.
Winter rye cover cropping changes squash (Cucurbita pepo) phyllosphere microbiota and reduces Pseudomonas syringae symptoms

Cover cropping is a soil conservation practice that may reduce the impacts of the economically important pathogen Pseudomonas syringae on crops including squash (Cucurbita pepo). To date, no studies have directly quantified the effect of rye cover crops on P. syringae populations, nor on the bacterial community of squash leaves. In this work, we tested the hypothesis that the protective effects of cover cropping on squash may be mediated by cover cropping effects on the plant’s microbiota that in turn protects against P. syringae. Using combined 16S sequencing and culture-based approaches, we showed that rye cover cropping protects squash against P. syringae, by decreasing pathogen population size on squash leaves and increasing fruit health and marketability at harvest. We also found evidence of a strong effect of rye cover crops on bacterial communities of the squash phyllosphere. Those findings were more striking early in the growing season. Finally, we identified numerous phyllosphere bacteria belonging to the genera Sphingomonas, Methylobacterium and Pseudomonas that were promoted by rye cover crops. Overall, our findings suggest cover cropping is effective for the sustainable management of P. syringae on squash and may provide a reservoir of potential microbial biocontrol agents colonizing the phyllosphere.
Eremophilenolide-Type Sesquiterpenes from Hertia intermedia

Hertidins A–E (1–5, resp.), new eremophilenolide-type sesquiterpenes, have been isolated from the AcOEt-soluble fraction of Hertia intermedia. Structures of 1–5 were elucidated on the basis of extensive spectroscopic studies.
Mapping of Whole-Brain Resting-State Networks with Half-Millimetre Resolution

Resting-state fMRI has been used in numerous studies to map networks in the brain that employ spatially disparate regions. However, attempts to map networks with high spatial resolution have been hampered by conflicting technical demands and associated problems. Results from recent fMRI studies have shown that spatial resolution remains around 0.7 × 0.7 × 0.7 mm3, with only partial brain coverage. This work presents a novel fMRI method, TR-external EPI with keyhole (TR-external EPIK), which can provide a nominal spatial resolution of 0.51 × 0.51 × 1.00 mm3 (0.26 mm3 voxel) with whole-brain coverage. TR-external EPIK enabled the identification of various resting-state networks distributed throughout the brain from a single fMRI session, with mapping fidelity onto the grey matter at 7T. The high-resolution functional image further revealed mesoscale anatomical structures, such as small cerebral vessels and the internal granular layer of the cortex within the postcentral gyrus.
Cadmium Uptake for Swiss Chard Grown on Composted Sewage Sludge Treated Field Plots: Plateau or Time Bomb?

Two hypotheses have been proposed to describe the phytoavailability of potentially toxic metals in sewage sludge treated soils. The plateau theory argues that the metal adsorption capacity added with sludge will persist as long as the metals of concern persist in the soil and the metals would remain in chemical forms not readily available for plant uptake. Therefore, the metal concentrations of plant tissue will reach a plateau as sewage sludge mass loading increases and remain at this plateau after termination of sludge application. The sludge time bomb hypothesis postulates that a soil's metal adsorption capacity is augmented by soil organic matter added as sewage sludge. This capacity, however, will revert back to its original background level with time following termination of sewage sludge application as mineralization of organic matter releases metals into more soluble forms, thus a time bomb. We employed a set of experimental data obtained from a 10-yr field-based sewage sludge land application experiment to evaluate the hypotheses of the plateau and the time bomb. The evaluation involved analysis of Cd concentration of Swiss chard grown on soils that either received sewage sludge application continuously or no longer received sewage sludge application. The results indicate that necessary conditions for plateau and time bomb to take place may be found. But an actual plateau or time bomb was not evident from 10 yr of experimental data in which the sewage sludge application reached 2880 Mg ha -1 .
Bayesian Multivariate Growth Mixture Modeling of Longitudinal Data: An Application to Alzheimer’s Disease Study

Alzheimer’s disease (AD) studies often collect longitudinal biomarker measures of multiple cohorts at different stages of disease and follow these biomarkers with a relatively short period of time. The heterogeneity of the longitudinal patterns of biomarkers can be ubiquitous across both individual trajectories and cognitive domains. We propose a flexible Bayesian multivariate growth mixture model to identify distinct longitudinal patterns of data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. A Gibbs sampling is implemented for achieving the Bayesian inference. We perform a simulation study to demonstrate the adequate performance of our proposed approach and apply the model to identify three latent cognitive decline patterns among patients from the ADNI study.