Miracle-dz/UniFAR-FTUs huggingface dataset:
Reproductive Biology and Breeding Systems of Two Opisthopappus Endemic and Endangered Species on the Taihang Mountains

Opisthopappus is a perennial, endemic herb of the Taihang Mountains in China. Two species of this genus (O. longilobus and O. taihangensis) are important wild genetic resources for Asteraceae; however, their reproductive biology has been lacking until now. This study is the first detailed report on the reproductive biology and breeding systems of two Opisthopappus species. Through field observations, the floral syndromes of O. longilobus and O. taihangensis were found to possess a similar pattern, although O. taihangensis has a relatively larger capitulum, more ray ligules, and disc florets. The flowers of both O. longilobus and O. taihangensis are protandrous, a character that can prevent autogamy at the single-flower level, and insects are required for pollination. Further, brightly ligules, brightly bisexual florets, unique fragrance, and amount of nectar suggest that these species propagate via an entomophilous pollination system. Hymenopteran and Diptera species were observed as the effective pollinators for these two species. The outcrossing index, pollen/ovule ratio and the results of hand pollination indicated that these Opisthopappus species might have a mixed mating system that combines cross-fertilization and partial self-fertilization for O. longilobus and O. taihangensis, outcrossing predominated in the breeding system, while self-pollination played an important role in seed production when insect pollination was unavailable, particularly in a harsh environment, such as the Taihang Mountains cliffs. Meanwhile, O. taihangensis might better adapt to severe surroundings with relatively complex floral syndromes, specifically through the attraction of visiting insects and a high seed set rate. The above results not only provide reference information toward a better understanding of the survival strategies of O. longilobus and O. taihangensis in the Taihang Mountains but also lay a solid foundation for further exploring the molecular mechanisms that underly their adaptation under cliff environments.
Comprehensive Analysis of Microbiome, Metabolome, and Transcriptome Revealed the Mechanisms of Intestinal Injury in Rainbow Trout under Heat Stress

Global warming is one of the most common environmental challenges faced by cold-water fish farming. Intestinal barrier function, gut microbiota, and gut microbial metabolites are significantly altered under heat stress, posing serious obstacles to the healthy artificial culture of rainbow trout. However, the molecular mechanisms underlying intestinal injury in rainbow trout under heat stress remain unclear. In the present study, the optimal growth temperature for rainbow trout (16 °C) was used for the control group, and the maximum temperature tolerated by rainbow trout (24 °C) was used for the heat stress group, which was subjected to heat stress for 21 days. The mechanism of intestinal injury in rainbow trout under heat stress was explored by combining animal histology, 16S rRNA gene amplicon sequencing, ultra-high performance liquid chromatography-mass spectrometry, and transcriptome sequencing. The results showed that the antioxidant capacity of rainbow trout was enhanced under heat stress, the levels of stress-related hormones were significantly increased, and the relative expression of genes related to heat stress proteins was significantly increased, indicating that the heat stress model of rainbow trout was successfully established. Secondly, the intestinal tract of rainbow trout showed inflammatory pathological characteristics under heat stress, with increased permeability, activation of the inflammatory factor signaling pathway, and increased relative expression of inflammatory factor genes, suggesting that the intestinal barrier function was impaired. Thirdly, heat stress caused an imbalance of intestinal commensal microbiota and changes in intestinal metabolites in rainbow trout, which participated in the stress response mainly by affecting lipid metabolism and amino acid metabolism. Finally, heat stress promoted intestinal injury in rainbow trout by activating the peroxisome proliferator-activated receptor-α signaling pathway. These results not only expand the understanding of fish stress physiology and regulation mechanisms, but also provide a scientific basis for healthy artificial culture and the reduction of rainbow trout production costs.
Pathophysiology and Clinical Impacts of Chronic Kidney Disease on Coronary Artery Calcification

The global prevalence of chronic kidney disease (CKD) has increased in recent years. Adverse cardiovascular events have become the main cause of life-threatening events in patients with CKD, and vascular calcification is a risk factor for cardiovascular disease. Vascular calcification, especially coronary artery calcification, is more prevalent, severe, rapidly progressive, and harmful in patients with CKD. Some features and risk factors are unique to vascular calcification in patients with CKD; the formation of vascular calcification is not only influenced by the phenotypic transformation of vascular smooth muscle cells, but also by electrolyte and endocrine dysfunction, uremic toxin accumulation, and other novel factors. The study on the mechanism of vascular calcification in patients with renal insufficiency can provide a basis and new target for the prevention and treatment of this disease. This review aims to illustrate the impact of CKD on vascular calcification and to discuss the recent research data on the pathogenesis and factors involved in vascular calcification, mainly focusing on coronary artery calcification, in patients with CKD.
An Efficient Compressive Sensing Event-Detection Scheme for Internet of Things System Based on Sparse-Graph Codes

This work studied the event-detection problem in an Internet of Things (IoT) system, where a group of sensor nodes are placed in the region of interest to capture sparse active event sources. Using compressive sensing (CS), the event-detection problem is modeled as recovering the high-dimensional integer-valued sparse signal from incomplete linear measurements. We show that the sensing process in IoT system produces an equivalent integer CS using sparse graph codes at the sink node, for which one can devise a simple deterministic construction of a sparse measurement matrix and an efficient integer-valued signal recovery algorithm. We validated the determined measurement matrix, uniquely determined the signal coefficients, and performed an asymptotic analysis to examine the performance of the proposed approach, namely event detection with integer sum peeling (ISP), with the density evolution method. Simulation results show that the proposed ISP approach achieves a significantly higher performance compared to existing literature at various simulation scenario and match that of the theoretical results.
The Influence of Photoperiod, Intake of Polyunsaturated Fatty Acids, and Food Availability on Seasonal Acclimatization in Red Deer (Cervus elaphus)

Simple Summary Northern mammals and birds withstand harsh winter conditions by hypometabolism and hypothermia. We investigated in an experimental study with a non-hibernating, but strongly seasonal species, the red deer, whether such seasonal acclimatization is controlled by photoperiod and modulated by the intake of essential polyunsaturated fatty acids (PUFA), like in hibernators. We used advanced data logging techniques to continuously measure heart rate (fH), a proxy of energy expenditure, core and peripheral body temperature (Tb), and activity in adult females fed with pellets ad libitum or with restricted daily rations and either enriched with linoleic acid (LA, C18:2 n-6) or α-linolenic acid (ALA, C18:3 n-3). Results show strong seasonal variation of measured parameters, exacerbated by restricted feeding, but with only few, mostly inconsistent, effects of supplementation with LA or ALA. By experimentally increasing the amount of circulating melatonin already in early summer to levels about three times higher than the winter peak, we induced a winter phenotype weeks ahead of time, demonstrating photoperiodic control of physiological and behavioral seasonal acclimatization. Abstract Hypometabolism and hypothermia are common reactions of birds and mammals to cope with harsh winter conditions. In small mammals, the occurrence of hibernation and daily torpor is entrained by photoperiod, and the magnitude of hypometabolism and decrease of body temperature (Tb) is influenced by the dietary supply of essential polyunsaturated fatty acids. We investigated whether similar effects exist in a non-hibernating large mammal, the red deer (Cervus elaphus). We fed adult females with pellets enriched with either linoleic acid (LA) or α-linolenic acid (ALA) during alternating periods of ad libitum and restricted feeding in a cross-over experimental design. Further, we scrutinized the role of photoperiod for physiological and behavioral seasonal changes by manipulating the amount of circulating melatonin. The deer were equipped with data loggers recording heart rate, core and peripheral Tb, and locomotor activity. Further, we regularly weighed the animals and measured their daily intake of food pellets. All physiological and behavioral parameters measured varied seasonally, with amplitudes exacerbated by restricted feeding, but with only few and inconsistent effects of supplementation with LA or ALA. Administering melatonin around the summer solstice caused a change into the winter phenotype weeks ahead of time in all traits measured. We conclude that red deer reduce energy expenditure for thermoregulation upon short daylength, a reaction amplified by food restriction.
An efficient computational method based on the hat functions for solving fractional optimal control problems

Abstract In this paper, an efficient and accurate computational method based on the hat functions (HFs) is proposed for solving a class of fractional optimal control problems (FOCPs). In the proposed method, the fractional optimal control problem under consideration is reduced to a system of nonlinear algebraic equations which can be simply solved. To this end, the fractional state and control variables are expanded by the HFs with unknown coefficients. Then, the operational matrix of fractional integration of the HFs with some properties of these basis functions are employed to achieve a nonlinear algebraic equation, replacing the performance index and a nonlinear system of algebraic equations, replacing the dynamical system in terms of the unknown coefficients. Finally, the method of constrained extremum is applied, which consists of adjoining the constraint equations derived from the given dynamical system to the performance index by a set of undetermined Lagrange multipliers. As a result, the necessary conditions of optimality are derived as a system of algebraic equations in the unknown coefficients of the state variable, control variable and Lagrange multipliers. Furthermore, the efficiency of the proposed method is shown for some concrete examples. The results reveal that the proposed method is very accurate and efficient.
SOIL CARBON CHECK: A TOOL FOR MONITORING AND GUIDING SOIL CARBON SEQUESTRATION IN FARMER FIELDS

ABSTRACT In 2015, 17 Sustainable Development Goals (SDGs) were approved, including SDG13, which addresses actions to increase carbon capture (CO 2 -C storage) for climate change mitigation. However, no analytical procedures have been defined for quantifying soil organic carbon (SOC) sequestration. This paper presents a rapid tool for guiding farmers and for monitoring SOC sequestration in farmer fields. The tool consists of multiconstituent soil analyses through near-infrared spectroscopy (NIRS) and an SOC mineralization model. The tool provides forecasts of SOC sequestration over time. Soil analyses by NIRS have been calibrated and validated for farmer fields in European countries, China, New Zealand
Predicting Multidimensional Poverty with Machine Learning Algorithms: An Open Data Source Approach Using Spatial Data

This paper presents a methodology to estimate the multidimensional poverty index using spatial data at the street block level. The data used in this study were obtained from Open Street Maps and ESA’s land use cover, which are freely available sources of spatial information. The study employs five machine-learning algorithms, including Catboost, Lightboost, and Random Forest, to estimate the multidimensional poverty index with spatial granularity. The results indicate that these models achieve promising performance in predicting poverty levels in Medellín, Colombia. The results showed that the Random Forest algorithm achieved the highest performance, with an MAE of 0.07504. Furthermore, the spatial distribution of the multidimensional poverty estimate was highly correlated with the true values of the distribution. This work contributes to predicting multidimensional poverty by demonstrating the potential of machine learning algorithms to utilize accessible spatial data. By providing evidence of the feasibility of estimating poverty levels at a granular spatial level, this methodology offers a powerful tool for policymakers to make poverty social interventions with low-cost evidence. Furthermore, this study has important implications for poverty eradication efforts in developing countries, where access to reliable data remains challenging.
Characterization of Systemic and Culprit-Coronary Artery miR-483-5p Expression in Chronic CAD and Acute Myocardial Infarction Male Patients

Coronary artery disease (CAD) is the leading cause of mortality worldwide. In chronic and myocardial infarction (MI) states, aberrant levels of circulating microRNAs compromise gene expression and pathophysiology. We aimed to compare microRNA expression in chronic-CAD and acute-MI male patients in peripheral blood vasculature versus coronary arteries proximal to a culprit area. Blood from chronic-CAD, acute-MI with/out ST segment elevation (STEMI/NSTEMI, respectively), and control patients lacking previous CAD or having patent coronary arteries was collected during coronary catheterization from peripheral arteries and from proximal culprit coronary arteries aimed for the interventions. Random coronary arterial blood was collected from controls; RNA extraction, miRNA library preparation and Next Generation Sequencing followed. High concentrations of microRNA-483-5p (miR-483-5p) were noted as ‘coronary arterial gradient’ in culprit acute-MI versus chronic-CAD (p = 0.035) which were similar to controls versus chronic-CAD (p < 0.001). Meanwhile, peripheral miR-483-5p was downregulated in acute-MI and chronic-CAD, compared with controls (1.1 ± 2.2 vs. 2.6 ± 3.3, respectively, p < 0.005). A receiver operating characteristic curve analysis for miR483-5p association with chronic CAD demonstrated an area under the curve of 0.722 (p < 0.001) with 79% sensitivity and 70% specificity. Using in silico gene analysis, we detected miR-483-5p cardiac gene targets, responsible for inflammation (PLA2G5), oxidative stress (NUDT8, GRK2), apoptosis (DNAAF10), fibrosis (IQSEC2, ZMYM6, MYOM2), angiogenesis (HGSNAT, TIMP2) and wound healing (ADAMTS2). High miR-483-5p ‘coronary arterial gradient’ in acute-MI, unnoticed in chronic-CAD, suggests important local mechanisms for miR483-5p in CAD in response to local myocardial ischemia. MiR-483-5p may have an important role as a gene modulator for pathologic and tissue repair states, is a suggestive biomarker, and is a potential therapeutic target for acute and chronic cardiovascular disease.
Prediction of Diabetes Mellitus Progression Using Supervised Machine Learning

Diabetic peripheral neuropathy (DN) is a serious complication of diabetes mellitus (DM) that can lead to foot ulceration and eventual amputation if not treated properly. Therefore, detecting DN early is important. This study presents an approach for diagnosing various stages of the progression of DM in lower extremities using machine learning to classify individuals with prediabetes (PD; n = 19), diabetes without (D; n = 62), and diabetes with peripheral neuropathy (DN; n = 29) based on dynamic pressure distribution collected using pressure-measuring insoles. Dynamic plantar pressure measurements were recorded bilaterally (60 Hz) for several steps during the support phase of walking while participants walked at self-selected speeds over a straight path. Pressure data were grouped and divided into three plantar regions: rearfoot, midfoot, and forefoot. For each region, peak plantar pressure, peak pressure gradient, and pressure–time integral were calculated. A variety of supervised machine learning algorithms were used to assess the performance of models trained using different combinations of pressure and non-pressure features to predict diagnoses. The effects of choosing various subsets of these features on the model’s accuracy were also considered. The best performing models produced accuracies between 94–100%, showing the proposed approach can be used to augment current diagnostic methods.
Automatic Calculation of Graviton Contributions and Hadronic Light-by-Light Scattering Contributions by Spin-2 Mesons to the Muon Anomalous Magnetic Moment

Extra-dimensional models provide rich and interesting phenomenology both in high-energy collider experiments and in low-energy observables (e.g., the anomalous magnetic moment of the muon g 2). In recent years, the g 2 was established as an essential observable for verifying or excluding models in particle physics, complementary to high-energy collider experiments. In this work, we present a calculation pipeline for the automatic calculation of graviton contributions to the g 2 by utilizing the computer algebra systems Mathematica and FORM and compare the obtained results to existing literature in the light of the recent Large Hadron Collider (LHC) outcomes. Furthermore, we present a novel ansatz, based on the AdS/CFT duality, to calculate resonance contributions from Spin-2 particles to the hadronic light-by-light scattering part of the anomalous magnetic moment of the muon.
Interannual variation of midsummer precipitation in the middle reaches of Yarlung Zangbo River and its possible mechanisms

Hydroclimatic process in Yarlung Zangbo River (YZR) plays a critical role in the water cycle of the whole Tibetan Plateau (TP), but there is a lack of understanding about it due to restrictive conditions. In this study, we further investigate the interannual variation characteristics and possible factors of midsummer (July–August) precipitation in the middle reaches of YZR (MYZR) based on the monthly precipitation data from four stations (Lhasa, Xigaze, Tsedang and Gyangze) in the Inner TP and ERA5 reanalysis dataset for the years 1961–2017. The results suggest that precipitation shows no obvious linear trend during the research period but is dominated by interannual oscillation with evident cycles of 3 years. Meanwhile, the interannual variation of precipitation is related to local changes of water vapour budget. The anomalous anticyclones over the Indian Peninsula‐Southeast Asia continuously transport water vapour from the western Pacific, South China Sea and Bay of Bengal (BOB) to the TP. Simultaneously, both the monsoon low and South Asia high in southern TP provide dynamic conditions for the convergence and uplift of water vapour.
Morphological, Nutritional, Chemical and Antioxidant Potential of Mahonia Jaunsarensis Ahrendt Fruit: A Narrow Endemic Wild Edible Species of Western Himalaya

ABSTRACT Mahonia jaunsarensis Ahrendt is a narrow endemic wild edible fruit-bearing species found in Uttarakhand Himalaya and has the potential of breeding compatibility with economically important Mahonia and Berberis species. Berry fruits of the species were explored for nutritional, phytochemical and antioxidant potential among three sampling locations. Fresh berries appeared a good source of diverse nutrients (carbohydrate 1.07–1.25 g/100 g, protein 0.97–1.13 g/100 g, non-reducing sugar 3.38–3.92 mg/g, and total sugar 9.86–12.87 mg/g); minerals (e.g. sodium 0.56–0.65 mg/100 g and potassium 0.99–1.14 mg/g); and vitamins (ascorbic acid 3.87–4.49 mg/g, thiamine 9.97–11.57 µg/g and carotenoids 1.67–1.94 mg/g). Similarly, phytochemicals (total anthocyanin 18.93–22.12 mg/g, phenolics 1.56–1.80 mg GAE/g, flavonoids 1.49–1.73 mg QE/g, flavonols 4.88–5.66 mg CE/g, and tannins 6.53–7.58 mg TAE/g) and in vitro antioxidant capacity (measured by ABTS assay, DPPH assay, OH· radical scavenging activity and FRAP assays) varied significantly (p < .05) among localities. Various phenolic compounds (particularly, chlorogenic acid 9.93–13.77 mg/g; caffeic acid 0.32–0.65 mg/g; syringic acid 0.30–0.47 mg/g fw) present in the methanolic extract also varied significantly among the localities. Thus, this phytonutrient- and antioxidant-rich genetic resource can be utilized for health-promoting functional foods. Also, the results of the present study indicated that the variation in nutritional, phytochemicals, and antioxidant activity among the locations can be utilized for elite selection, quality control, and breeding programs in the species.
Wasserstein GAN-Based Digital Twin-Inspired Model for Early Drift Fault Detection in Wireless Sensor Networks

In this Internet of Things (IoT) era, the number of devices capable of sensing their surroundings is increasing day by day. Based on the data from these devices, numerous services and systems are now offered where critical decisions depend on the data collected by sensors. Therefore, error-free data are most desirable, but due to extreme operating environments, the possibility of faults occurring in sensors is high. So, detecting faults in data obtained by sensors is important. In this article, a digital twin (DT)-inspired detection approach is proposed, and its ability to detect a single type of fault in several sensors is analyzed. The digital equivalent of the sensor is developed using a generative adversarial network (GAN). As GANs inherently perform well with images, Gramian angular field (GAF) encoding is used to convert time series data to images. The GAF encoding preserves the temporal relations of the time series data. The GAN is trained with the GAF images. The trained GAN model acts as the virtual representation of the sensor, and the discriminator network of the GAN model, once it has learned the pattern of normal data, is used as the fault detector. The performance of the virtual sensor is promising because it successfully generates data for normal conditions. The best fault detection accuracy achieved by the proposed model is 98.7%, which makes this GAN-based DT-inspired approach a promising candidate for sensor fault detection.