Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Non-photochemical Quenching (NPQ) Measurements for Identifying Flood-Tolerant Soybean Genotypes in the Era of Climate Change

Climate change has negatively affected agriculture worldwide, including soybean production. Studies have shown that rising temperatures and extreme weather events like droughts and floods significantly reduce soybean yields. Developing flood-tolerant soybean genotypes is crucial for ensuring food security. Conventional breeding programs are limited by laborious and imprecise visual rating methods for flooding tolerance identification. High-throughput platforms for plant phenotyping using imaging techniques offer potential solutions, but they lack information on underlying physiological mechanisms. Non-photochemical quenching (NPQ) is a molecular adaptation in photosynthesis that dissipates excess light energy, protecting plants from damage. This study aimed to integrate NPQ measurements into high-throughput phenotyping procedures to identify flooding-tolerant soybean genotypes. The study evaluated 160 soybean genotypes for flooding tolerance, identifying those with higher grain yield potential. Subsequently, ten genotypes were selected for monitoring NPQ responses under flooded conditions. Results showed that genotypes with higher grain yields also exhibited superior NPQ performance, suggesting a positive correlation between flooding tolerance and energy dissipation capacity. Among these genotypes, 58I60 RSF IPRO, 64HO130 I2X and BRS 525 displayed superior potential and could be further exploited in breeding efforts, considering their grain yield capacity, plant leaf area, and photoprotective capacity under flooding conditions. These findings suggest that integrating NPQ measurements into high-throughput phenotyping platforms can aid in identifying flood-tolerant soybean genotypes for breeding programs, leading to more resilient crops in the face of climate change. Further field studies are warranted to validate these hypotheses and improve crop models for future climate scenarios.
Editorial: Value-based healthcare in oncology

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Realizing Degree Sequences with Graphs Having Nowhere-Zero Realizing Degree Sequences with Graphs Having Nowhere-Zero 3-Flows 3-Flows

The following open problem was proposed by Archdeacon: Characterize all graphical sequences π such that some realization of π admits a nowhere-zero 3-ﬂow. The purpose of this paper is to resolve this problem and present a complete characterization: A graphical sequence π = ( d 1 ,d 2 ,.,d n ) with minimum degree at least two has a realization that admits a nowhere-zero 3-ﬂow if and only if π (cid:2) = (3 4 , 2), ( k, 3 k ), ( k 2 , 3 k − 1 ), where k is an odd integer.
Fluvial Response to Climate Change: A Case Study of Northern Russian Rivers

The cold regions of North Eurasia include very sensitive fluvial systems. Rapid changes in climate are reported for these areas. The aim of this study is to propose a framework for fast climate-driven predictions of fluvial systems, and to apply it for rivers of the northern part of the East European Plain and West Siberian Plain. The general approach consists of integrating outputs from climate models into a hydrological model, and then driving a catchment and morphodynamic model using output from the hydrological model. Modelled by AOGCMs, future climate shifts are the drivers of significant changes in surface flow. Predictions of an up to 25 percent decrease in annual runoff by the middle of the 21st century enables us to forecast changes in sediment migration rates, stream energy and water-channel boundary interactions, changes in channel morphology and channel patterns shifts using corresponding physically-based equations. Whereas high dimensional models are still computationally too expensive for long-term morphological predictions, simple 1-D equations enable us to make assessments of channel system response. We tested a suit of 1-D models to estimate fluvial response to climate change for the middle of the 21st century of medium and large rivers draining the north of Russia. Comparison with regional predictions for other territories is the special task of the study.
Detecting Minor Symptoms of Parkinson’s Disease in the Wild Using Bi-LSTM with Attention Mechanism

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and nonmotor impairment with various implications on patients’ quality of life. Since currently available therapies are only symptomatic, identifying individuals with prodromal, preclinical, or early-stage PD is crucial, as they would be ideal candidates for future disease-modifying therapies. Our analysis aims to develop a robust model for accurate PD detection using accelerometer data collected from PD and non-PD individuals with mild or no tremor during phone conversations. An open-access dataset comprising accelerometer recordings from 22 PD patients and 11 healthy controls (HCs) was utilized. The data were preprocessed to extract relevant time-, frequency-, and energy-related features, and a bidirectional long short-term memory (Bi-LSTM) model with attention mechanism was employed for classification. The performance of the model was evaluated using fivefold cross-validation, and metrics of accuracy, precision, recall, specificity, and f1-score were computed. The proposed model demonstrated high accuracy (98%), precision (99%), recall (98%), specificity (96%), and f1-score (98%) in accurately distinguishing PD patients from HCs. Our findings indicate that the proposed model outperforms existing approaches and holds promise for detection of PD with subtle symptoms, like tremor, in the wild. Such symptoms can present in the early or even prodromal stage of the disease, and appropriate mobile-based applications may be a practical tool in real-life settings to alert individuals at risk to seek medical assistance or give patients feedback in monitoring their symptoms.
Using the Correlation Intensity Index to Build a Model of Cardiotoxicity of Piperidine Derivatives

The assessment of cardiotoxicity is a persistent problem in medicinal chemistry. Quantitative structure–activity relationships (QSAR) are one possible way to build up models for cardiotoxicity. Here, we describe the results obtained with the Monte Carlo technique to develop hybrid optimal descriptors correlated with cardiotoxicity. The predictive potential of the cardiotoxicity models (pIC50, Ki in nM) of piperidine derivatives obtained using this approach provided quite good determination coefficients for the external validation set, in the range of 0.90–0.94. The results were best when applying the so-called correlation intensity index, which improves the predictive potential of a model.
Influence de la nanostructuration sur le comportement à la corrosion de revêtements multicouches élaborés par PVD

Cette these se place dans le cadre d’un projet ANR qui vise a remplacer des depots electrolytiques sacrificiels par des depots PVD a base d’aluminium, moins nefastes pour l’environnement. Trois criteres ont ete definis :un comportement sacrificiel avec de faibles cinetiques de dissolution, une bonne resistance au grippage et la possibilite d’elaboration du depot avant emboutissage du substrat acier. Des revetements monocouches ont ete elabores pour etudier l’effet d’un element d’alliage dans l’aluminium. Certains elements (Mo, Mn) ameliorent les proprietes mecaniques et d’autres (Mg, Zn) les proprietes sacrificielles des revetements. Cette etude a permis de mettre en evidence qu’il est difficile de combiner de bonnes proprietes mecaniques et sacrificielles. Pour pallier a ce probleme, deux voies peuvent etre explorees. L’elaboration d’alliages ternaires et celle d’architectures multicouches.Ce travail s’est focalise de maniere plus approfondie sur la deuxieme voie. Des architectures multicouches ont ete elaborees avec deux types de couches, chaque type de couche possedant l’une des deux proprietes souhaitees pour le revetement. Les configurations etudiees sont les suivantes : Al-Mo/Al-Zn, Al-Mo/Al-Mg et Al-Mn/Al-Mg, avec differentes periodes. Cette etude a permis de trouver une architecture multicouche qui repond tout a fait aux deux premiers criteres :Al-Mn/Al-Mg 15 nm.Une etude preliminaire en corrosion apres deformation a ete realisee sur cette derniere. Elle perd son caractere sacrificiel en immersions salines de 48h a partir de 1% de deformation. L’optimisation des parametres de depot pour ameliorer la tenue mecanique de cette architecture est une prochaine etape.
A rare case of mosaic Klinefelter syndrome in a 45-year-old man leading to successful live birth through ejaculated spermatozoa: a case report and literature review

Background: Men diagnosed with Klinefelter syndrome (KS) commonly exhibit non-obstructive azoospermia or rarely having sperm in their ejaculate, rendering them traditionally considered sterile prior to the introduction of intracytoplasmic sperm injection (ICSI). The presence of mosaic KS may mask the classical phenotype, resulting in underdiagnosis throughout their lifetime. Surgical sperm retrieval through Microdissection Testicular Sperm Extraction (Micro-TESE) combined with ICSI has become the gold standard approach, maximizing reproductive outcomes in these individuals. However, it is noteworthy that approximately 7% of men with KS may exhibit sperm in their ejaculate, providing an opportunity for them to achieve biological parenthood through ICSI. Case Presentation: In this report, we present an exceptional case of a 45-year-old man with Mosaic KS and severe oligozoospermia who successfully achieved pregnancy utilizing ICSI with freshly ejaculated sperm. Remarkably, this case represents the oldest recorded instance of a man with Klinefelter syndrome fathering his own biological child using sperm derived from fresh ejaculate. Conclusion: Although this case is exceedingly rare, it underscores the critical importance of exhausting all possibilities to facilitate biological parenthood in men with KS before considering alternative options such as sperm donation or adoption. By recognizing the potential for successful conception using ejaculated sperm in this population, we can provide individuals with mosaic KS the opportunity to fulfill their desire for biological offspring.
Exercise Cardiac Load and Autonomic Nervous System Recovery during In-Season Training: The Impact on Speed Deterioration in American Football Athletes

Fully restoring autonomic nervous system (ANS) function is paramount for peak sports performance. Training programs failing to provide sufficient recovery, especially during the in-season, may negatively affect performance. This study aimed to evaluate the influence of the physiological workload of collegiate football training on ANS recovery and function during the in-season. Football athletes recruited from a D1 college in the southeastern US were prospectively followed during their 13-week “in-season”. Athletes wore armband monitors equipped with ECG and inertial movement capabilities that measured exercise cardiac load (ECL; total heartbeats) and maximum running speed during and baseline heart rate (HR), HR variability (HRV) 24 h post-training. These metrics represented physiological load (ECL = HR·Duration), ANS function, and recovery, respectively. Linear regression models evaluated the associations between ECL, baseline HR, HRV, and maximum running speed. Athletes (n = 30) were 20.2 ± 1.5 years, mostly non-Hispanic Black (80.0%). Negative associations were observed between acute and cumulative exposures of ECLs and running speed (β = −0.11 ± 0.00, p < 0.0000 and β = −0.15 ± 0.04, p < 0.0000, respectively). Similarly, negative associations were found between baseline HR and running speed (β = −0.45 ± 0.12, 95% CI: −0.70, −0.19; p = 0.001). HRV metrics were positively associated with running speed: (SDNN: β = 0.32 ± 0.09, p < 0.03 and rMSSD: β = 0.35 ± 0.11, p < 0.02). Our study demonstrated that exposure to high ECLs, both acutely and cumulatively, may negatively influence maximum running speed, which may manifest in a deteriorating ANS. Further research should continue identifying optimal training: recovery ratios during off-, pre-, and in-season phases.
GMM-Based Expanded Feature Space as a Way to Extract Useful Information for Rare Cell Subtypes Identification in Single-Cell Mass Cytometry

Cell subtype identification from mass cytometry data presents a persisting challenge, particularly when dealing with millions of cells. Current solutions are consistently under development, however, their accuracy and sensitivity remain limited, particularly in rare cell-type detection due to frequent downsampling. Additionally, they often lack the capability to analyze large data sets. To overcome these limitations, a new method was suggested to define an extended feature space. When combined with the robust clustering algorithm for big data, it results in more efficient cell clustering. Each marker’s intensity distribution is presented as a mixture of normal distributions (Gaussian Mixture Model, GMM), and the expanded space is created by spanning over all obtained GMM components. The projection of the initial flow cytometry marker domain into the expanded space employs GMM-based membership functions. An evaluation conducted on three established cellular identification algorithms (FlowSOM, ClusterX, and PARC) utilizing the most substantial publicly available annotated dataset by Samusik et al. demonstrated the superior performance of the suggested approach in comparison to the standard. Although our approach identified 20 cell clusters instead of the expected 24, their intra-cluster homogeneity and inter-cluster differences were superior to the 24-cluster FlowSOM-based solution.
Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs
Differential Game Study of Competitive Green Supply Chain Based on Green Public Technology Innovation and Product Greenness

Green public technological innovation is regarded as an indispensable tool for realizing sustainable development and coping with global challenges, and the major social challenges and global problems faced by mankind nowadays have put forward higher requirements for green technological innovation in the public sector. This paper considers the dynamic process of technological innovation, constructs a green public technology innovation supply chain consisting of the government and two competing manufacturers, uses a stochastic stopping model to portray the impact of the dynamic process of technological innovation on the green technology innovation supply chain, and studies the equilibrium strategies of green technology innovation under three models of decentralized decision-making, centralized decision-making, and government subsidy decision-making between the government and the manufacturers based on a differential game model. It is found that: 1) Higher probability of successful technological innovation and product greenness enhancement can effectively incentivize the government and manufacturers to increase R&D investment in green technological innovation before successful technological innovation; 2) Supply chain members will consider the marginal profit after technological innovation when deciding the R&D investment before technological innovation success; 3) Manufacturers’ competition is beneficial to increase manufacturers’ investment in technological innovation, but it will lead to a decrease in the rate of government subsidy. Government subsidies help to increase both parties’ technological innovation investment and product greenness and achieve higher supply chain profits; 4) The centralized decision-making model can achieve the optimal green technology innovation strategy, product greenness, and supply chain profits compared with the decentralized decision-making model.
Effects of Salt Stress on Grain Yield and Quality Parameters in Rice Cultivars with Differing Salt Tolerance

Rice yield and grain quality are highly sensitive to salinity stress. Salt-tolerant/susceptible rice cultivars respond to salinity differently. To explore the variation in grain yield and quality to moderate/severe salinity stress, five rice cultivars differing in degrees of salt tolerance, including three salt-tolerant rice cultivars (Lianjian 5, Lianjian 6, and Lianjian 7) and two salt-susceptible rice cultivars (Wuyunjing 30 and Lianjing 7) were examined. Grain yield was significantly decreased under salinity stress, while the extent of yield loss was lesser in salt-tolerant rice cultivars due to the relatively higher grain filling ratio and grain weight. The milling quality continued to increase with increasing levels. There were genotypic differences in the responses of appearance quality to mild salinity. The appearance quality was first increased and then decreased with increasing levels of salinity stress in salt-tolerant rice but continued to decrease in salt-susceptible rice. Under severe salinity stress, the protein accumulation was increased and the starch content was decreased; the content of short branched-chain of amylopectin was decreased; the crystallinity and stability of the starch were increased, and the gelatinization temperature was increased. These changes resulted in the deterioration of cooking and eating quality of rice under severe salinity-stressed environments. However, salt-tolerant and salt-susceptible rice cultivars responded differently to moderate salinity stress in cooking and eating quality and in the physicochemical properties of the starch. For salt-tolerant rice cultivars, the chain length of amylopectin was decreased, the degrees of order of the starch structure were decreased, and pasting properties and thermal properties were increased significantly, whereas for salt-susceptible rice cultivars, cooking and eating quality was deteriorated under moderate salinity stress. In conclusion, the selection of salt-tolerant rice cultivars can effectively maintain the rice production at a relatively high level while simultaneously enhancing grain quality in moderate salinity-stressed environments. Our results demonstrate specific salinity responses among the rice genotypes and the planting of salt-tolerant rice under moderate soil salinity is a solution to ensure rice production in China.
Planting Date and Biculture Affect Sunn Hemp Productivity in the Midwest

Sunn hemp (Crotalaria juncea L.) is recommended as a warm season cover crop in the Midwest due to its ability to produce high levels of biomass and fix atmospheric nitrogen. It can also be grown in biculture with other cover crops to enhance overall ecosystem services. Two field experiments were conducted over four growing seasons (2014 – 2017) in Wisconsin on a forest derived Fox silt loam (Fine-loamy, mixed, mesic Typic Hapludalfs) under organic certification to determine the effect of planting date on sunn hemp dry matter yield, N and C addition and to determine the effect of species ratio in a biculture with sorghum-sudan [Sorghum bicolor (L.) Moench] on the same output variables as well as seed cost and the related per unit cost of production. Planting dates significantly affected all biomass yield variables, which declined linearly from the initial date and appear related to growing degree accumulation. Regression analysis revealed a biomass yield decline of 1.3% per day (8.9% week-1) in relative yield, and 0.90 Mg day-1 (0.61 Mg week-1) in actual yield. In biculture, sunn hemp grown in a planting ratio of 50:50 with sorghum-sudan maximized N addition through nitrogen fixation and added N from dry matter, without a significant difference in the dry matter recorded. Analysis of seed cost data revealed that as the ratio of sunn hemp in the planting mixture decreased, the cost per hectare decreased. The cost of production per unit of DM, N, C and CO2 equivalent at this planting ratio were 7.08 $ Mg-1, 0.57 $ kg-1, 17.51 $ t-1 and 4.78 $ t-1 respectively. In pure culture, early planting dates (June 15th to July 15th) are recommended for sunn hemp, and in biculture, a planting ratio of 50:50 with sorghum-sudan could serve Midwestern producers well by reducing per unit cost of biomass production.
From the tethys ocean to the mediterranean seas: a plate tectonic model of the evolution of the western alpine system

We propose a series of paleogeographic maps of the western alpine system, from Triassic to Present, based on the geologic structure and evolution of the mediterranean,its adjacent folded belts and the associated basins, and on the evolution of the North Atlantic ocean according to the magnetic anomaly informations. The kinematic framework is relatively precise for the Cenozoic but the accuracy is quite poorer for the Mesozoic.Between Triassic and Upper Cretaceous,the reconstructed continental margins can be compared to present passive or active margins.The geologic evolution can be described in terms of plate tectonics. After Upper Cretaceous, the oceanic areas are quite limited and the geologic evolution is dominated by the continental collision processes. [NOT CONTROLLED OCR]