Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis

Simple Summary Breast cancer is the most fatal female cancer, which the existing clinical and pathological information sometimes fails to diagnose accurately. Recent artificial intelligence-based studies have shown the capability of identifying molecular biomarkers using high-throughput genomics data. Our aim was to apply machine learning methods to a large cohort of transcriptomics data for gene reduction and the construction of a diagnostic model for cancer classification. Advanced statistical methods and cross-validation with another set of machine learning methods increased the accuracy of the diagnostic model and predicted a novel diagnostic nine-gene signature. Further, survival analysis revealed a novel prognostic model of eight-gene signatures. Experimental validation confirmed the expression of the identified gene signatures in breast cancer patients and increased the reliability of the study. The identified gene signature biomarkers have the potential to improve healthcare management with precise diagnosis and prognosis at a reduced cost. Abstract Background: Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. Methods: A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan–Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. Results: We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. Conclusion: The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.
Virtual Cold Chain Method to Evaluate the Effect of Rising Temperature on the Quality Evolution of Peach Fruit

Poor temperature management along a cold chain leads to fruit quality deterioration and loss. In order to determine the threshold value of temperature fluctuation in a cold chain, peach fruits were stored in four different virtual cold chains applying different temperature–time scenarios. Core temperature profiling, the physicochemical qualities, and the activities of the peaches’ antioxidant enzymes were monitored during cold storage and shelf life. Abusive temperature management (temperature increased to 20 and 15 °C three times) resulted in a significant increase in a peach’s core temperature to the highest temperature measured: 17.6 °C. The ethylene production rate at the end of the shelf life of peaches under these temperatures was 21.03–28.16% higher than the constant-temperature group and accompanied by significantly lower levels of flesh firmness, titratable acid content, total phenol and flavonoid content, and peroxidase (POD) and catalase (CAT) activities (p < 0.05). The results of a principal component analysis (PCA) and heatmap confirmed the results. Limited temperature increases (10 °C) in a cold chain had little impact on the quality of the peaches, while temperature increases higher than 15 °C three times would negatively affect the quality of the peaches significantly. The temperature of a cold chain needs to be controlled precisely to reduce the loss of peaches.
About the Assessment of Cover Crop Albedo Potential Cooling Effect: Risk of the Darkening Feedback Loop Effects

Today societies face an unprecedented challenge to limit global warming and restore agricultural soils. Recent studies show that the introduction of cover crops over Europe could result in a cooling impact due to an increase in soil organic carbon stocks, a decrease in the use of fertilizers, and an increase in surface albedo of the croplands. Based on the use of remote sensing data, land cover database, meteorological data, national agricultural statistics, and ground measurements, a generic model was developed to simulate the radiative forcing following the change in surface albedo. This article analyzes the impact of the introduction of cover crops in Europe during the fallow periods. Compared to previous studies, this work discusses: (i) The maximum greening potential in Europe and the associated indirect surface properties changes (ii) for snowfall episodes, and (iii) due to an increase in organic matter. This study shows that the mitigation potential of cover crops through albedo effects could reach 6.74 MtCO2-eq.yr−1 by extending the periods of the introduction of the cover crops to all possible fallow periods. This mitigation could be limited to 5.68 MtCO2-eq.yr−1 if the impact of snowfalls is considered. This would be equivalent to 9.12 gCO2.m−2.yr−1. Finally, this study investigates the feedback loop due to soil darkening with soil organic carbon content increase when cover crops are introduced, considering two scenarios. The first considers the soil organic carbon content increase following repeated incorporation of cover crop biomass into the soil, simulated with the DayCent model. The second, more conceptual and extreme scenario aims at alerting on the possible impact of a combination of carbon farming practices, such as biochar or organic amendments. Our results show that this effect could lead to a loss of 20% of the climate benefit (i.e., 5.39 MtCO2-eq.yr−1). In conclusion, this study shows that cover crops have a strong potential for climate mitigation through direct albedo effects (soil coverage). However, once introduced, cropland should be permanently covered by vegetation or straws in order to avoid this darkening feedback loop effect.
Psychometric Properties of Interpersonal Regulation Questionnaire for Chinese College Students: Gender Differences and Implications for Well-Being

Intrapersonal emotion dysregulation has been found to be a transdiagnostic predictor in the development of almost all affective disorders. Interpersonal resources are also involved in achieving people’s emotion regulation goals. The Interpersonal Regulation Questionnaire (IRQ) has been developed to assess the tendency and efficacy of people using external resources to help manage their emotions. Under the restrictions of the COVID-19 pandemic, the role of interpersonal emotion regulation in individuals’ adjustment and well-being remains unclear. This study aimed to investigate the optimal factor structure of the IRQ in Chinese culture using an exploratory structural equation modeling approach and to examine the associations between interpersonal emotion regulation, tested by the IRQ, and young people’s intrapersonal emotion dysregulation and social and emotional well-being. The sample consisted of 556 college students aged from 17 to 31 from Mainland China. Factor analyses suggested that the four-factor structure was the optimal model for the current data. Females reported a higher tendency to use external resources to regulate their negative emotions and higher efficacy in regulating negative emotions. The Chinese version of the IRQ (C-IRQ) presented adequate psychometric properties and would be a useful tool for measuring interpersonal emotion regulation behaviors.
Eight-Day Typhoon Quantitative Precipitation Forecasts in Taiwan by the 2.5 km CReSS Model, Part II: Reduced Control of Track Errors on Rainfall Prediction Quality for Typhoons Associated with Southwesterly Flow

Due to the enhancement by its steep mesoscale topography, the overall rainfall amount and distribution in Taiwan from typhoons, to a first degree, are determined by the storm track relative to the island. Therefore, the quality of typhoon quantitative precipitation forecasts (QPFs) from numerical models is often controlled by track errors, with better quality from those with smaller track errors. However, the present work demonstrates that in daily QPFs over Taiwan made by a cloud-resolving model during five seasons of 2012–2016, targeted for 84 days during 27 typhoons and at ranges of day one (0–24 h) to day eight (168–192 h), the control of track errors on QPF quality is reduced for typhoons associated with southwesterly flow, compared to those without, and decent QPFs could still be obtained with large track errors in some cases. Subsequently, the circumstances and reasons for good (or bad) QPFs in selected examples are further investigated to deepen our understanding of typhoon QPFs in Taiwan. Some common ingredients are found in three cases where good QPFs were produced at a longer range (day 7 or 8) without a good track: these typhoons passed near northern Taiwan and the southwesterly flow prevailed over much of the island during the accumulation period. Responsible for much of the rainfall in Taiwan, the southwesterly flow was reasonably captured, resulting in good QPFs. In another example where the typhoon moved across southern Taiwan, on the contrary, the rainfall was produced by the storm’s circulation, and the QPF was degraded without a good enough track prediction.
Comparison of Soil Taxonomy (2022) and WRB (2022) Systems for Classifying Paddy Soils with Different Drainage Grades in South Korea

Soil classification is the systematic classification of soils based on distinguishing the characteristics of soil, aiding in understanding the properties of soils through soil survey and establishing appropriate strategies for effective soil utilization and management. Globally, the Soil Taxonomy (ST) and the World Reference Base for soil resources (WRB) are widely used for soil classification. However, the two classification systems have differences in criteria, thus exhibiting difficulties in exchanging classification results. In South Korea, soil classification has been steadily implemented to provide useful soil information to farmers for efficient soil management, contributing to the sustainability of paddy lands, but it has not been easy to establish an accurate classification system due to intensive soil management and variation in soil redox conditions. In this study, two paddy soils with different drainage grades, pedon 1 and pedon 2, were classified using the ST and WRB, and based on the comparative results, a classification criterion for paddy soil in Korea was recommended. According to ST, pedon 1 was classified as a coarse loamy, mesic family (the mean annual soil temperature, 11–14 °C) of Anthroaquic Eutrudepts (artificially irrigated, base saturation > 60%), whereas pedon 2 was a coarse loamy, mesic family of Fluvaquentic Endoaquepts (organic carbon content > 0.2%, water-saturated across the soil profile). Based on the WRB, the two soils were categorized as follows: Stagnic Hydragric Anthrosols (Eutric, Loamic, Oxyaquic) (saturated with surface water, subsurface horizon that is wet-field and human-affected) for pedon 1 and Stagnic Gleyic Hydragric Anthrosols (Eutric, Loamic, Oxyaquic) (saturated with surface and ground water, subsurface horizon that is wet-field and human-affected) for pedon 2. Overall, the two classification systems categorized these pedons consistently by judging the soil properties according to depth, but there was a difference in layer classification upon saturation by water across the soil horizons. Poor soil drainage hinders rice growth in paddies due to lowering soil and water temperature and the occurrence of harmful reduction products. In this regard, we proposed a draft of the classification criteria specialized for paddy soils in Korea based on drainage grades. This will contribute to sustainable paddy soil management by accurately classifying paddy soils and providing better soil information to farmers.
Comparing topological and reliability-based vulnerability analysis of Iran power transmission network

Power grids are one of the most important and critical infrastructures which societies rely upon for well-being. Their topological (structural) characteristics related to vulnerability can be analyzed from the viewpoint of complex network theory. In this article, we incorporate into this viewpoint some relevant reliability properties and apply the analysis framework to study the high-voltage power grid in Iran. To identify weak points in the structure, we look at four different centrality measures, namely, degree, betweenness, information and closeness, and analyze their correlation properties. This allows providing a more comprehensive picture of the vulnerability characteristics of the power grid structure. By our analysis, we show that reliability-based characteristics are different from purely topological ones as they are mostly uncorrelated. We also use a voting aggregation method, the Borda Count method, to perform an overall ranking of the most vulnerable nodes, considering both intentional attacks and random failures.
HAVING EXPONENTIAL STATIONARY DISTRIBUTIONS

explicit formula is given for -y(IL) in terms of v and ,i. Motivated by applications in queueing theory, we next consider the case where G is a convex polyhedral domain and v is constant on each face of the boundary. Postponing for now the treatment of certain foundational questions, we work directly with a basic adjoint relation (BAR) that appears- to characterize stationary distributions for a wide class of RBM's in polyhedral domains. This analytic relation is motivated by formal analogy with the smooth case and will be rigorously justified in later work. As in the smooth case, it is found that (BAR) has a solution of exponential form for each eL E Rd if and only if v satisfies a certain skew symmetry condition. Moreover, under a mild nondegeneracy condition, it is shown that an exponential
A Customizable Suite of Methods to Sequence and Annotate Cattle Antibodies

Studying the antibody response to infection or vaccination is essential for developing more effective vaccines and therapeutics. Advances in high-throughput antibody sequencing technologies and immunoinformatic tools now allow the fast and comprehensive analysis of antibody repertoires at high resolution in any species. Here, we detail a flexible and customizable suite of methods from flow cytometry, single cell sorting, heavy and light chain amplification to antibody sequencing in cattle. These methods were used successfully, including adaptation to the 10x Genomics platform, to isolate native heavy–light chain pairs. When combined with the Ig-Sequence Multi-Species Annotation Tool, this suite represents a powerful toolkit for studying the cattle antibody response with high resolution and precision. Using three workflows, we processed 84, 96, and 8313 cattle B cells from which we sequenced 24, 31, and 4756 antibody heavy–light chain pairs, respectively. Each method has strengths and limitations in terms of the throughput, timeline, specialist equipment, and cost that are each discussed. Moreover, the principles outlined here can be applied to study antibody responses in other mammalian species.
Effect of Phosphorus, Iron, Zinc, and Their Combined Deficiencies on Photosynthetic Characteristics of Rice (Oryza sativa L.) Seedlings

Combined elemental deficiencies are more complex and insidious physiological metabolic responses than single elemental stresses. To determine the effects of phosphorus (P), iron (Fe), zinc (Zn), and their deficient combinations on photosynthetic characteristics of rice seedlings, we investigated their effects on dry weight, chlorophyll (Chl) content, rapid photosynthetic carbon assimilation CO2 responses, and Chl fluorescence in four-week-old rice (CB9 and BJ1 cultivars) seedlings. The results showed that the dry matter, maximum carboxylation efficiency (Vc,max), and maximum electron transfer efficiency (Jmax) of seedlings were all reduced to different degrees under the element deficiency treatments. JIP-test analysis showed that the decrease in the concentration of active PSII reaction centers (RC/ABS) under -Zn treatment was the main reason for the inhibition of performance index PIABS. The -P treatment reduced RC/ABS and inhibited electron transfer (ψEo). Primary photochemical reactions (φPo) of -P-Zn treated seedlings were also inhibited compared to the -P treatment. The -Fe and -Fe-Zn treatments inhibited photosynthesis most severely, which not only reduced RC/ABS but also severely inhibited φPo and ψEo. Notably, the -P-Fe and -P-Fe-Zn treatments of the CB9 improved the RC/ABS, alleviating the limitation of Fe deficiency. These results help enhance the understanding of the complex relationship between nutrient balance and photosynthesis, especially for P, Fe, Zn, and their combined deficiency.
Starvation Protects Hepatocytes from Inflammatory Damage through Paradoxical mTORC1 Signaling

Background and aims: Sepsis-related liver failure is associated with a particularly unfavorable clinical outcome. Calorie restriction is a well-established factor that can increase tissue resilience, protect against liver failure and improve outcome in preclinical models of bacterial sepsis. However, the underlying molecular basis is difficult to investigate in animal studies and remains largely unknown. Methods: We have used an immortalized hepatocyte line as a model of the liver parenchyma to uncover the role of caloric restriction in the resilience of hepatocytes to inflammatory cell damage. In addition, we applied genetic and pharmacological approaches to investigate the contribution of the three major intracellular nutrient/energy sensor systems, AMPK, mTORC1 and mTORC2, in this context. Results: We demonstrate that starvation reliably protects hepatocytes from cellular damage caused by pro-inflammatory cytokines. While the major nutrient- and energy-related signaling pathways AMPK, mTORC2/Akt and mTORC1 responded to caloric restriction as expected, mTORC1 was paradoxically activated by inflammatory stress in starved, energy-deprived hepatocytes. Pharmacological inhibition of mTORC1 or genetic silencing of the mTORC1 scaffold Raptor, but not its mTORC2 counterpart Rictor, abrogated the protective effect of starvation and exacerbated inflammation-induced cell death. Remarkably, mTORC1 activation in starved hepatocytes was uncoupled from the regulation of autophagy, but crucial for sustained protein synthesis in starved resistant cells. Conclusions: AMPK engagement and paradoxical mTORC1 activation and signaling mediate protection against pro-inflammatory stress exerted by caloric restriction in hepatocytes.
Interface Coordination Engineering of P-Fe3O4/Fe@C Derived from an Iron-Based Metal Organic Framework for pH-Universal Water Splitting

Developing electrocatalysts with high energy conversion efficiency is urgently needed. In this work, P-Fe3O4/Fe@C electrodes with rich under-coordinated Fe atom interfaces are constructed for efficient pH-universal water splitting. The introduction of under-coordinated Fe atoms into the P-Fe3O4/Fe@C interface can increase the local charge density and polarize the 3d orbital lone electrons, which promotes water adsorption and activation to release more H*, thus elevating electrocatalytic activity. As a donor-like catalyst, P-Fe3O4/Fe@C displays excellent electrocatalytic performance with overpotentials of 160 mV and 214 mV in acidic and alkaline electrolytes at 10 mA cm−2, in addition to pH-universal long-term stability.
Bio-Drying of Municipal Wastewater Sludge: Effects of High Temperature, Low Moisture Content and Volatile Compounds on the Microbial Community

This study examined microbiological processes during the bio-drying of municipal wastewater sludge (WS) from the waste treatment facilities of the Moscow region (Russia). In just 21 days of bio-drying, the moisture content of the mixture of WS and wood chips decreased by 19.7%. It was found that members of the genus Bacillus were the main organic matter destructors. In the period from 7 to 14 days, the rates of organic matter mineralization and moisture loss were the highest, and bacteria of the genus Bacillus dominated, accounting for 43.5 to 84.6% of the bacterial community with a total number of 1.20 (±0.09) × 106 to 6.70 (±0.44) × 105 gene copies µg−1. The maximum number of Amaricoccus was (15.7% of the total bacterial community) in the middle of bio-drying. There was an active accumulation of nitrate nitrogen due to the oxidation of nitrogen-containing substances during the same period of time. Bacteria of the genera Sphingobacterium, Brevundimonas, Brucella, Achromobacter and fungi of the genus Fusarium dominated in the biofilter, which removed volatile compounds from the waste air by 90%. The obtained results allow to model the further intensification of bio-drying, as well as its efficiency and safety.
SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber–Physical Systems

One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS). These networks are often developed using cyclic topologies and heterogeneous mixtures of spiking and non-spiking neurons, which is a difficult proposition for existing neural simulation software. Most solutions apply to either one of two extremes, the detailed multi-compartment neural models in small networks, and the large-scale networks of greatly simplified neural models. In this work, we present our open-source Python package SNS-Toolbox, which is capable of simulating hundreds to thousands of spiking and non-spiking neurons in real-time or faster on consumer-grade computer hardware. We describe the neural and synaptic models supported by SNS-Toolbox, and provide performance on multiple software and hardware backends, including GPUs and embedded computing platforms. We also showcase two examples using the software, one for controlling a simulated limb with muscles in the physics simulator Mujoco, and another for a mobile robot using ROS. We hope that the availability of this software will reduce the barrier to entry when designing SNS networks, and will increase the prevalence of SNS networks in the field of robotic control.
Effect of different sowing dates and varieties on yield and yield attributes of wheat

Two sowing dates viz., Normal (11th Nov.) and Late (27th Nov.) with six varieties viz., GW 344, HD 2189, Raj 4037, MACS 2846, NIDW 295 and GW 1189 were tested in split plot design on medium black soil to study the response of different varieties for normal and late sown conditions. The results revealed that normal sowing of wheat crop recorded maximum grain yield (27.77 q/ha) than late sowing.