Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of univariate versus multivariate models in large scale sugar beet field trials

Sugar beet breeding programs aim at creating stable and dependable varieties of sugar beets that give an optimized yield of white sugar per unit area as a function of the cost of production and meet the requirements of the environment, growers and sugar factories. This study aimed at comparing the predicted genetic breeding values of traits obtained from various univariate and multivariate methods. Data used in the analysis was obtained from a series of sugar beet field trials carried out in several countries around Europe. Exploratory showed that there is a high correlation amongst the derived traits. The principal analysis was done using the mixed models approach taking care of within-field trial variability as well as the genotype × environment interaction. Estimated breeding values were obtained from the models as Best Linear Unbiased Predictions. The analysis showed that similar genetic rankings were obtained from different univariate methods; hence, predictions of the breeding values are robust to model selections. A multivariate model was aimed at jointly modeling the traits and estimation of the breeding values and assess any potential gain in prediction. The results showed small differences in rankings and estimated values from the multivariate and the univariate procedures. The study established that one potential gain that the breeders gain from a multivariate model is the possibility of estimating the genetic and phenotypic correlations from the variance covariance matrix of random effects.
Myosoft: An automated muscle histology analysis tool using machine learning algorithm utilizing FIJI/ImageJ software

Background Skeletal muscle is comprised of a heterogeneous population of muscle fibers which can be classified by their metabolic and contractile properties (fiber “types”). Fiber type is a primary determinant of muscle function along with fiber size (cross-sectional area). The fiber type composition of a muscle responds to physiological changes like exercise and aging and is often altered in disease states. Thus, analysis of fiber size and type in histological muscle preparations is a useful method for quantifying key indicators of muscle function and for measuring responses to a variety of stimuli or stressors. These analyses are near-ubiquitous in the fields of muscle physiology and myopathy, but are most commonly performed manually, which is highly labor- and time-intensive. To offset this obstacle, we developed Myosoft, a novel method to automate morphometric and fiber type analysis in muscle sections stained with fluorescent antibodies. Methods Muscle sections were stained for cell membrane (laminin) and myofiber type (myosin heavy chain isoforms). Myosoft, running in the open access software platform FIJI (ImageJ), was used to analyze myofiber size and type in transverse sections of entire gastrocnemius/soleus muscles. Results Myosoft provides accurate analysis of muscle histology >50-times faster than manual analysis. We demonstrate that Myosoft is capable of handling high-content images even when image or staining quality is suboptimal, which is a marked improvement over currently available, comparable programs. Conclusions Myosoft is a reliable, accurate, high-throughput, and convenient tool to analyze high-content muscle histology. Myosoft is freely available to download from Github at https://github.com/Hyojung-Choo/Myosoft/tree/Myosoft-hub.
Analyse transcriptomique complète du rôle du silicium chez Arabidopsis

Silicon the second most abundant élément in the earth's crust; it would hâve bénéficiai effects on the growth of several plants, among which the increase in résistance to various biotic and abiotic stresses is the best documented. Most of the silicon absorbed by a plant is polymerized under the form of phytoliths (amorphous silica, SiO2.nH2O). The initial theory for explaining its rôle, in particular in the context of plant-pathogen interactions, is that of the « mechanical barrier » which stipulâtes that a subcuticular layer of silica would prevent the pénétration of fungal pathogens. However, a certain quantity of silicon remains in solution as monosilicic acid (tLtSiO,*), and it is now believed that this fraction is responsible for the prophylactic properties of silicon by modulating the plant's défense response. The bénéficiai effects of silicon on the incidence and the intensity of fungal diseases were described in a variety of pathosystems. However, the exact nature of the interaction between silicon and plant défense or résistance mechanisms is unknown. We propose that silicon influences the biochemical activity of components responsible for post-elicitation intracellular signaling, and that it thus has a bioactive rôle in the défense or the résistance of plants against pathogenic fungi. In order to study the biological and prophylactic effects of silicon treatment, and using the oligonucleotide microarray technology, we carried out a complète transcriptome analysis of Arabidopsis plants inoculated or not with powdery mildew (Erysiphe cichoracearum) and treated or not with silicon. The application of silicon to non-inoculated plants signifïcantly altered the relative abundance of only two out of ca. 40,000 transcripts (28,500 gènes and over 10,000 unannotated transcripts). On the other hand, the inoculation with Erysiphe cichoracearum resulted in the significant induction of 2,387 gènes and to the repression of 1,583 gènes.
An interactive problem-posing guiding approach to bridging and facilitating pre- and in-class learning for flipped classrooms

ABSTRACT Educators have indicated that a good flipped learning design has the potential to enable teachers to use the class time effectively for conducting higher-order thinking activities and enhancing the interactions among peers and teachers. Several previous studies have also pointed out the importance of employing proper learning strategies, such that students’ learning performance would meet the expectations of both the pre- and in-class activities of flipped learning. In this study, an interactive problem-posing guiding strategy was proposed to guide students to understand the concepts of problem-posing by the use of multiple choice options before class, and to engage them in effective problem-posing activities during class. To examine the effectiveness of the proposed learning mode, a flipped learning system was developed and an experiment was conducted in a natural science learning activity in an elementary school. The participants were two classes of fifth graders of an elementary school, a total of 56 students, with one class as the experimental group and the other as the control group. The students in the experimental group adopted the interactive problem-posing guiding strategy in the flipped learning mode, while the control group used the conventional flipped learning mode. The experimental results confirmed that the flipped learning activity with the interactive problem-posing guiding strategy was more effective in terms of learning achievement, self-efficacy, and deep approaches to learning science in the natural science course than the conventional flipped learning method. It also confirmed the importance of integrating the stepwise learning strategy (e.g. guided problem-posing and collaborative problem-posing) into flipped learning to support students’ pre- and in-class learning.
Hardware in the loop as a tool for the development of wash machine suspension

During a traditional product or component development, the time and resources allocated for all project execution is generally scarce. In addition, the complexity and the non adherent results of some of the available modeling tools, render them inefficient in face of non-linearities and uncertainties. Focusing on accuracy and efficiency during a component development cycle, aiming at the right decisions at the right time, a technique known as hardware in the loop (HIL) has been widely applied. In general, this technique makes use the real physical subsystems (hardware) and the model (software) that represents the other parts of the electro-mechanical assembly. The present work presents an example of component development of a washing machine suspension using HIL method. This study aims at accessing the feasibility of using a closed-loop control system based on force and acceleration signals and an electrodynamic shaker as actuator, in a HIL approach where the hardware is a structural subsystem. The other study on going and presented here is the process of obtaining the state space matrices that are able to run in a Matlab/Simulink environment and how to derive the matrices from a multibody dynamic model built in ADAMS, both under a real-time framework. The process to translate the multibody model in a state space matrix, necessary for the real-time HIL simulation, will also be addressed.
An Artificial Intelligence (AI) Defect Detection Technology Based on Software Behavior Decision Tree

With the increase of software system complexity, a high requirement of the reliability, stability and security of software quality is put forward. At present, artificial intelligence (AI) defect detection adoptes machine learning technology to realize code scanning and semantic analysis on software defects. The traditional machine learning technology for software defect detection is generally based on algorithms such as BP neural network model, Naive-Bayes model, and fingerprint identification model, etc. Regarding the features of software artificial intelligence (AI) defect detection, this paper proposes a layered detection technology based on software behavior decision tree model. Furthermore, a corresponding test environment is established to make contrast test of previously tested software. The results of the experiment shows that, with the comprehensive consideration of building time cost and false alarm rate and other factors, the artificial intelligence (AI) defect detection technology based on software behavior decision tree model is superior to other technologies.
The impact of elevated temperature and drought on the ecology and evolution of plant–soil microbe interactions

Climate change is shifting the distribution of species, and may have a profound impact on the ecology and evolution of species interactions. However, we know little about the impact of increasing temperature and changing rainfall patterns on the interactions between plants and their beneficial and antagonistic root symbionts. Here, we used a reciprocal multifactorial growth chamber experiment with seeds and soil microbial communities from three origins to investigate the impact of temperature and soil moisture on the growth, arbuscular mycorrhizal (AM) fungal colonization and root‐associated fungal community of a perennial herb. Moreover, we tested whether plants and AM fungi performed better or worse when plants were grown with their local soil biota, for example, due to plant adaptation or changes in the genetic or species composition of the soil microbial community. Temperature and soil moisture generally increased plant growth, whereas temperature but not soil moisture increased AM fungal colonization. The strength and direction of the plants' response to temperature were dependent on soil moisture and differed among plant populations, and AM fungal colonization was further affected by the origin of the soil microbial community. The root‐associated fungal community structure was impacted by temperature, soil moisture and the soil microbial origin, with interactive effects between the microbial origin and the abiotic environment. Plant biomass was lower when plants were grown with their local soil microbes, potentially due to intraspecific negative plant–soil feedbacks. Synthesis. Our findings indicate that, beyond a relatively uniform increase of plant growth and arbuscular mycorrhizal (AM) fungal colonization with increasing temperature, plants and root‐associated fungi of different origins will vary in their response to climate change (i.e. elevated temperature and shifts in rainfall). This may create pronounced, but difficult to predict, spatial and temporal variation in the ecology and evolution of plant–microbe interactions with a changing climate.
Re-evaluation of a Tn5::gacA mutant of Pseudomonas syringae pv. tomato DC3000 uncovers roles for uvrC and anmK in promoting virulence

Pseudomonas syringae is a taxon of plant pathogenic bacteria that can colonize and proliferate within the interior space of leaf tissue. This process requires P. syringae to rapidly upregulate the production of virulence factors including a type III secretion system (T3SS) that suppress host defenses. GacS/A is a two-component system that regulates virulence of many plant and animal pathogenic bacteria including P. syringae. We recently investigated the virulence defect of strain AC811, a Tn5::gacA mutant of P. syringae pv. tomato DC3000 that is less virulent on Arabidopsis. We discovered that decreased virulence of AC811 is not caused by loss of GacA function. Here, we report the molecular basis of the virulence defect of AC811. We show that AC811 possesses a nonsense mutation in anmK, a gene predicted to encode a 1,6-anhydromuramic acid kinase involved in cell wall recycling. Expression of a wild-type allele of anmK partially increased growth of AC811 in Arabidopsis leaves. In addition to the defective anmK allele, we also show that the Tn5 insertion in gacA exerts a polar effect on uvrC, a downstream gene encoding a regulator of DNA damage repair. Expression of the wild-type anmK allele together with increased expression of uvrC fully restored the virulence of AC811 during infection of Arabidopsis. These results demonstrate that defects in anmK and uvrC are together sufficient to account for the decreased virulence of AC811, and suggest caution is warranted in assigning phenotypes to GacA function based on insertional mutagenesis of the gacA-uvrC locus.
A distinct population of L6 neurons in mouse V1 mediate cross-callosal communication

Through the corpus callosum, interhemispheric communication is mediated by callosal projection (CP) neurons. Using retrograde labeling, we identified a population of layer 6 (L6) excitatory neurons as the main conveyer of transcallosal information in the monocular zone of the mouse primary visual cortex (V1). Distinct from L6 corticothalamic (CT) population, V1 L6 CP neurons contribute to an extensive reciprocal network across multiple sensory cortices over two hemispheres. Receiving both local and long-range cortical inputs, they encode orientation, direction, and receptive field information, while are also highly spontaneous active. The spontaneous activity of L6 CP neurons exhibits complex relationships with brain states and stimulus presentation, distinct from the spontaneous activity patterns of the CT population. The anatomical and functional properties of these L6 CP neurons enable them to broadcast visual and nonvisual information across two hemispheres, and thus play a major role in regulating and coordinating brain-wide activity events.
Description of Ruminococcus catenae SW178 sp. nov., a new anaerobic species isolated from feral chicken

A Gram-positive, obligately anaerobic coccobacillus, with the white raised circular colony was isolated from the cecum of feral chickens in Brookings, South Dakota, USA. The 16S rRNA gene sequence analysis suggested that the closest species to strain SW178 was Ruminococcus torques ATCC 27756T (96.94% similarity) that belongs to the family Lachnospiraceae. The genome of strain SW178 is 3.18 Mbp with G+C content of 46.9 mol%. Based on the phylogenetic and phenotypic comparison, we propose that strain SW178 be assigned to the genus Ruminococcus as a novel species, for which the name Ruminococcus catenae is proposed. The type strain is SW178 (= CCOS 1886 T, =DSM 109242T).
Foliar Application of Gebbrillic and Salicylic Acids Improves Fruit Quality and Yield and Reduces Aphid Population in Pomegranate (Punica granatum L.)

Pomegranate crop suffers many problems during tree growth and fruit production stages. The pomegranate ramp, Aphis punicae (Passerini) is serious pests that attack pomegranate in Egypt. This study was carried out to examine the influence of ascorbic acid (ASA), gibbrellic acid (GA3) and salicylic acid (SA) on yield and fruit quality and aphid population in pomegranate. The application rates were (250, 500 and 1000 ppm) for ASA, 250, 500 and 1000 ppm for GA and 200, 400 and 600 ppm for SA thrice during the growing season. The foliar application of GA3 and SA improved yield (kg/tree), fruit weight (g) , fruit number/tree, fruit cracking, TSS, acidity %, TSS/acid % ratio , and reduced aphid population.
Deciphering anomalous heterogeneous intracellular transport with neural networks

Intracellular transport is predominantly heterogeneous in both time and space, exhibiting varying non-Brownian behavior. Characterization of this movement through averaging methods over an ensemble of trajectories or over the course of a single trajectory often fails to capture this heterogeneity. Here, we developed a deep learning feedforward neural network trained on fractional Brownian motion, providing a novel, accurate and efficient method for resolving heterogeneous behavior of intracellular transport in space and time. The neural network requires significantly fewer data points compared to established methods. This enables robust estimation of Hurst exponents for very short time series data, making possible direct, dynamic segmentation and analysis of experimental tracks of rapidly moving cellular structures such as endosomes and lysosomes. By using this analysis, fractional Brownian motion with a stochastic Hurst exponent was used to interpret, for the first time, anomalous intracellular dynamics, revealing unexpected differences in behavior between closely related endocytic organelles.
A unifying framework for synaptic organization on cortical dendrites

Dendritic synaptic inputs are organized into functional clusters with remarkable subcellular precision at the micron level. This organization emerges during early postnatal development through patterned spontaneous activity and manifests both locally where nearby synapses are significantly correlated, and globally with distance to the soma. We propose a biophysically motivated synaptic plasticity model to dissect the mechanistic origins of this organization during development, and elucidate synaptic clustering of different stimulus features in the adult. Our model captures local clustering of orientation in ferret vs. receptive field overlap in mouse visual cortex based on the cortical magnification of visual space. Including a back-propagating action potential explains branch clustering heterogeneity in the ferret, and produces a global retinotopy gradient from soma to dendrite in the mouse. Therefore, our framework suggests that sub-cellular precision in connectivity can already be established in development, and unifies different aspects of synaptic organization across species and scales.
Δ9-Tetrahydrocannabinol Suppresses Monocyte-Mediated Astrocyte Production of Monocyte Chemoattractant Protein 1 and Interleukin-6 in a Toll-Like Receptor 7–Stimulated Human Coculture

Cannabis is widely used in the United States, with an estimated prevalence of 9.5%. Certain cannabinoids in Cannabis sativa, Δ9-tetrahydrocannabinol (THC) in particular, possess immune-modulating and anti-inflammatory activity. Depending on the context, the anti-inflammatory activity of cannabinoids may be beneficial (e.g., in treating inflammatory diseases) or detrimental to normal immune defense against pathogens. The potential beneficial effect of cannabinoids on chronic neuroinflammation has gained recent attention. Monocyte migration to the brain has been implicated as a key event in chronic neuroinflammation and in the etiology of central nervous system diseases including viral infection (e.g., human immunodeficiency virus–associated neurocognitive disorder). In the brain, monocytes can contribute to neuroinflammation through interactions with astrocytes, including inducing astrocyte secretion of cytokines and chemokines. In a human coculture system, monocyte-derived interleukin (IL)-1β due to Toll-like receptor 7 (TLR7) activation has been identified to promote astrocyte production of monocyte chemoattractant protein (MCP)-1 and IL-6. THC treatment of the TLR7-stimulated coculture suppressed monocyte secretion of IL-1β, resulting in decreased astrocyte production of MCP-1 and IL-6. Furthermore, THC displayed direct inhibition of monocytes, as TLR7-stimulated monocyte monocultures treated with THC also showed suppressed IL-1β production. The cannabinoid receptor 2 (CB2) agonist, JWH-015, impaired monocyte IL-1β production similar to that of THC, suggesting that THC acts, in part, through CB2. THC also suppressed key elements of the IL-1β production pathway, including IL1B mRNA levels and caspase-1 activity. Collectively, this study demonstrates that the anti-inflammatory properties of THC suppress TLR7-induced monocyte secretion of IL-1β through CB2, which results in decreased astrocyte secretion of MCP-1 and IL-6. SIGNIFICANCE STATEMENT Because cannabis use is highly prevalent in the United States and has putative anti-inflammatory properties, it is important to investigate the effect of cannabinoids on immune cell function. Furthermore, cannabinoids have garnered particular interest due to their potential beneficial effects on attenuating viral-induced chronic neuroinflammation. This study utilized a primary human coculture system to demonstrate that the major psychotropic cannabinoid in cannabis, Δ9-tetrahydrocannabinol, and a cannabinoid receptor-2 selective agonist suppress specific monocyte-mediated astrocyte inflammatory responses.
Impact of Pesticides on Microbial Diversity and Enzymes in Soil

Introduction: The presence of pesticides in soils could result in alterations in microbial activities (enzyme activities, microbial populations), soil physical and chemical properties. Research gap/Challenges: Insufficient literatures on extensive monitoring of soil quality through enzyme activity, during pesticides application. Existing literatures concerning analysis of effect of pesticide application on soil enzyme activity are not comprehensive with respect to number of soil enzymes analysed. Aim/Objective: The study was conducted to investigate the effect of carbofuran and paraquat on soil biochemical characteristics on certain soils in the Niger Delta region of Nigeria. Methodology: These pesticides were applied at recommended doses, their effects on soil organic carbon, enzymes activity and microbial populations were assessed using standard methods. The enzymes monitored were amylase, invertase, protease, urease, phosphatase and dehydrogenase. Microbial counts were carried out for total heterotrophic bacteria, fungi, actinomycetes, nitrifying bacteria and phosphate solubilizers using the spread plate method. Results: There were variations in the different enzyme activities in carbofuran – and paraquat - treated soil during this research. Dehydrogenase activity increased in treated soils. Also, urease activity was lower compared to other enzyme activities. As the study progressed, variations in values of soil organic carbon were observed. There was a gradual increase in microbial counts and can be traceable to their ability to temporarily mineralize and use the pesticides as carbon and energy source. The soil organic carbon, enzymes and microbial counts values were significantly different at P=0.05. Conclusion: This research revealed that the pesticides cause temporal impact on microbial populations and enzyme activities, associated with the pesticide type at recommended field application rates. A change in numbers, activity and diversity of soil microorganisms may act as indicators of soil fertility and reflect the soil quality.