Miracle-dz/UniFAR-FTUs huggingface dataset:
Acute glucose fluctuation induces inflammation and neurons apoptosis in hippocampal tissues of diabetic rats

It is well‐recognized that glycemic disorders are leading causes of diabetic complications and acute fluctuation of blood glucose and reported more likely being related to oxidative stress, vasculopathy, and other diabetic complications than continuous hyperglycemia in patients with diabetic and animal models. To explore the hypothesis that acute glucose fluctuation (GF) aggravates inflammatory lesions and neuron apoptosis in the hippocampus of diabetic rats. Twenty female GK rats were randomly allocated into a glucose fluctuating group (GK‐GF) and a continuous hyperglycemia group (GK‐CHG) and 10 age‐matched female Wistar rats served as controls. GF was induced in the GK‐GF group by injection with glucose and insulin at different periods of time per day for 6 weeks. Body weight was determined weekly. At the end of the study, blood hemoglobin A1c (HbA1c) and serum lipids were measured. Serum and hippocampus interleukin 1β (IL‐1β), IL‐6, IL‐8, and tumor necrosis factor‐α (TNF‐α) were measured by enzyme‐linked immunosorbent assay and real‐time quantitative reverse transcription‐polymerase chain reaction (qRT‐PCR). Hippocampus Bcl‐2, Bax, Pten, fas, and myc were quantified by qRT‐PCR and Western blot analysis and Mirror Water Maze (MWM) test was performed. We successfully established an animal model with daily GF and a control model with CHG using GK diabetic rats. The GF and CHG rats showed lower weight gain during the 6‐week experimental period with no significant difference in the levels of serum lipids such as total triglyceride, total cholesterol, high‐density lipoprotein cholesterol, and low‐density lipoprotein cholesterol compared with the control rats at the end of the study. Meanwhile, the GF and CHG rats showed higher blood HbA1c levels than that of control rats. MWM trainings tests detected that glucose disorders in GF and CHG rats tend to present longer latencies, more cross times and longer path length compared with those of the control rats, indicating impaired the hippocampus‐regulated behavioral function such as spatial orientating and memory. Importantly, it was found that GF promoted the expression of TNF‐α and IL‐1β in the hippocampus of the GF rats while continuous hyperglycemia in CHG rats had little effect on that. Furthermore, both GF and CHG diabetic rats had abnormal expression of apoptosis‐associated genes in the hippocampus compared with control Wistar rats and neurons apoptosis in GF rats appears to be more severe than CHG rats. Overall, this study confirmed that GF is a more critical factor that would promote the neuron apoptosis and induce inflammation in the hippocampus than continuous hyperglycemia in diabetic animals, which shed new light on the importance of monitoring and administration of blood glucose in the prevention and therapy for diabetes.
Cerebellar Transcranial Direct Current Stimulation (tDCS), Leaves Virtual Navigation Performance Unchanged

Spatial cognition is an umbrella term used to refer to the complex set of abilities necessary to encode, categorize, and use spatial information from the surrounding environment to move effectively and orient within it. Experimental studies indicate that the cerebellum belongs to the neural network involved in spatial cognition, although its exact role in this function remains unclear. Our aim was to investigate in a pilot study using a virtual reality navigation task in healthy subjects whether cerebellar transcranial direct current stimulation (tDCS), a non-invasive technique, influences spatial navigation. Forty healthy volunteers (24 women; age range = 20–42 years; years of education range 13–18) were recruited. The virtual reality spatial navigation task comprised two phases: encoding, in which participants actively navigated the environment and learned the spatial locations for one object, and retrieval, in which they retrieved the position of the object they had discovered and memorized in the previous encoding phase, starting from another starting point. Participants received tDCS stimulation (anodal or sham according to the experimental condition they were assigned to) for 20 min before beginning the retrieval phase. Our results showed that cerebellar tDCS left the accuracy of the three indexes used to measure effective navigational abilities unchanged. Hence, cerebellar tDCS had no influence on the retrieval phase for the spatial maps stored. Further studies, enrolling a larger sample and testing a different stimulation protocol, may give a greater insight into the role of the cerebellum in spatial navigation.
Bone Mesenchymal Stem Cell-Derived Extracellular Vesicles Promote Recovery Following Spinal Cord Injury via Improvement of the Integrity of the Blood-Spinal Cord Barrier

Mesenchymal stem cell (MSC) transplantation has been shown to represent a potential treatment for traumatic spinal cord injury (SCI). However, there are several obstacles that need to be overcome before MSCs can be considered for clinical application, such as failure of MSCs to reach the spinal cord lesion core and possible tumor formation. Recent studies have suggested that MSC treatment is beneficial owing to paracrine-secreted factors. Extracellular vesicles are considered to be some of the most valuable paracrine molecules. However, the therapeutic mechanism of extracellular vesicles on spinal cord injury has not been studied clearly. Therefore, our study investigated the effect of systemic administration of extracellular vesicles on the loss of motor function after SCI and examined the potential mechanisms underlying their effects. Disruption of the blood-spinal cord barrier (BSCB) is a crucial factor that can be detrimental to motor function recovery. Pericytes are an important component of the neurovascular unit, and play a pivotal role in maintaining the structural integrity of the BSCB. Our study demonstrated that administration of bone mesenchymal stem cell-derived extracellular vesicles (BMSC-EV) reduced brain cell death, enhanced neuronal survival and regeneration, and improved motor function compared with the administration of BMSC-EV free culture media (EV-free CM). Besides, the BSCB was attenuated and pericyte coverage was significantly decreased in vivo. Furthermore, we found that exosomes reduced pericyte migration via downregulation of NF-κB p65 signaling, with a consequent decrease in the permeability of the BSCB. In summary, we identified that extracellular vesicles treatment suppressed the migration of pericytes and further improved the integrity of the BSCB via NF-κB p65 signaling in pericytes. Our data suggest that extracellular vesicles may serve as a promising treatment strategy for SCI.
Ca2+/Calmodulin Binding to PSD-95 Downregulates Its Palmitoylation and AMPARs in Long-Term Depression

AMPA-type glutamate receptors (AMPARs) are clustered into functional nanodomains at postsynaptic sites through anchorage by the scaffolding protein, postsynaptic density protein-95 (PSD-95). The synaptic abundance of AMPARs is dynamically controlled in various forms of synaptic plasticity. Removal of AMPARs from the synapse in long-term depression (LTD) requires mobilization of PSD-95 away from the synapse. The molecular mechanisms underlying PSD-95 dispersal from the synapse during LTD are not completely understood. Here we show that, following Ca2+ influx, binding of Ca2+/calmodulin (CaM) to PSD-95 triggers loss of synaptic PSD-95 as well as surface AMPARs during chemically induced LTD in cultured rat neurons. Our data suggest that a reduction in PSD-95 palmitoylation mediates the effect of Ca2+/CaM on PSD-95 synaptic levels during LTD. These findings reveal a novel molecular mechanism for synaptic AMPAR regulation in LTD.
Mutation p.R356Q in the Collybistin Phosphoinositide Binding Site Is Associated With Mild Intellectual Disability

The recruitment of inhibitory GABAA receptors to neuronal synapses requires a complex interplay between receptors, neuroligins, the scaffolding protein gephyrin and the GDP-GTP exchange factor collybistin (CB). Collybistin is regulated by protein-protein interactions at the N-terminal SH3 domain, which can bind neuroligins 2/4 and the GABAAR α2 subunit. Collybistin also harbors a RhoGEF domain which mediates interactions with gephyrin and catalyzes GDP-GTP exchange on Cdc42. Lastly, collybistin has a pleckstrin homology (PH) domain, which binds phosphoinositides, such as phosphatidylinositol 3-phosphate (PI3P/PtdIns3P) and phosphatidylinositol 4-monophosphate (PI4P/PtdIns4P). PI3P located in early/sorting endosomes has recently been shown to regulate the postsynaptic clustering of gephyrin and GABAA receptors and consequently the strength of inhibitory synapses in cultured hippocampal neurons. This process is disrupted by mutations in the collybistin gene (ARHGEF9), which cause X-linked intellectual disability (XLID) by a variety of mechanisms converging on disrupted gephyrin and GABAA receptor clustering at central synapses. Here we report a novel missense mutation (chrX:62875607C>T, p.R356Q) in ARHGEF9 that affects one of the two paired arginine residues in the PH domain that were predicted to be vital for binding phosphoinositides. Functional assays revealed that recombinant collybistin CB3SH3-R356Q was deficient in PI3P binding and was not able to translocate EGFP-gephyrin to submembrane microaggregates in an in vitro clustering assay. Expression of the PI3P-binding mutants CB3SH3-R356Q and CB3SH3-R356N/R357N in cultured hippocampal neurones revealed that the mutant proteins did not accumulate at inhibitory synapses, but instead resulted in a clear decrease in the overall number of synaptic gephyrin clusters compared to controls. Molecular dynamics simulations suggest that the p.R356Q substitution influences PI3P binding by altering the range of structural conformations adopted by collybistin. Taken together, these results suggest that the p.R356Q mutation in ARHGEF9 is the underlying cause of XLID in the probands, disrupting gephyrin clustering at inhibitory GABAergic synapses via loss of collybistin PH domain phosphoinositide binding.
The Last Mile: Where Artificial Intelligence Meets Reality

Although much effort is focused on improving the technical performance of artificial intelligence, there are compelling reasons to focus more on the implementation of this technology class to solve real-world applications. In this “last mile” of implementation lie many complex challenges that may make technically high-performing systems perform poorly. Instead of viewing artificial intelligence development as a linear one of algorithm development through to eventual deployment, there are strong reasons to take a more agile approach, iteratively developing and testing artificial intelligence within the context in which it finally will be used.
Heart Rate Response During Treadmill Exercise Test in Children and Adolescents With Congenital Heart Disease

Background: Impaired exercise capacity is a common feature of congenital heart disease (CHD). In adults with CHD, it has been shown that impaired heart rate response during exercise may contribute to exercise limitation. Systematic data in children and adolescents on this topic is limited. We therefore purposed to assess heart rate response during treadmill exercise testing in children and adolescents with CHD compared to healthy controls. Methods: One hundred and sixty three children and adolescents (103 with CHD, median age 15 years and 60 age-matched controls) performed cardiopulmonary exercise testing and were included in this study. Beyond peak oxygen consumption, increase in heart rate from resting level to peak exercise (heart rate reserve) and decrease of heart rate after peak exercise (heart rate recovery) were measured. Chronotropic index was defined as percentage of age predicted maximal heart rate reserve. According to data from adults on bicycle exercise, chronotropic incompetence was assumed for chronotropic index below 0.8. Results: While resting heart rate was similar between both groups, peak heart rate, heart rate reserve as well as chronotropic index were lower in the CHD group than in controls. Chronotropic index was lowest in patients with single ventricle hemodynamics and correlated with peak oxygen consumption. Heart rate recovery was impaired in the CHD group 1 and 2 min after peak exercise compared to controls and correlated with peak oxygen consumption. Chronotropic index below 0.8 was a relatively frequent finding even in the control group suggesting that the threshold of 0.8 appears inadequate for the identification of chronotropic incompetence using treadmill exercise testing in children. After normalizing to the 2.5th chronotropic index percentile of the control group we obtained a chronotropic incompetence threshold of 0.69. Conclusion: As an adjunct to measurement of peak oxygen consumption, heart rate response to exercise appears to be a physiologically important diagnostic parameter in children and adolescents with CHD. However, interpretation of heart rate response needs to consider specific age characteristics and the mode of exercise test. Our data may help future studies on chronotropic incompetence using treadmill ergometer protocols in children and adolescents.
Quantitative Determination of Cellular-and Neurite Motility Speed in Dense Cell Cultures

Mobility quantification of single cells and cellular processes in dense cultures is a challenge, because single cell tracking is impossible. We developed a software for cell structure segmentation and implemented 2 algorithms to measure motility speed. Complex algorithms were tested to separate cells and cellular components, an important prerequisite for the acquisition of meaningful motility data. Plasma membrane segmentation was performed to measure membrane contraction dynamics and organelle trafficking. The discriminative performance and sensitivity of the algorithms were tested on different cell types and calibrated on computer-simulated cells to obtain absolute values for cellular velocity. Both motility algorithms had advantages in different experimental setups, depending on the complexity of the cellular movement. The correlation algorithm (COPRAMove) performed best under most tested conditions and appeared less sensitive to variable cell densities, brightness and focus changes than the differentiation algorithm (DiffMove). In summary, our software can be used successfully to analyze and quantify cellular and subcellular movements in dense cell cultures.
The Conjoint Analysis of Microstructural and Morphological Changes of Gray Matter During Aging

Macromorphological and microstructural changes of gray matter (GM) happen during brain normal aging. However, the mechanism of macro-microstructure association is still unclear, which is of guidance for understanding many neurodegenerative diseases. In this study, adopting structural magnetic resonance imaging (sMRI) and diffusion kurtosis imaging (DKI), GM aging pattern was characterized and its macro-microstructure associations were revealed. For 60 subjects among the ages of 47–79, the DKI and T1-weighted images were investigated with voxel-based analysis. The results showed age-related overlapped patterns between morphological and microstructural alterations during normal aging. It was worth noting that morphological changes and mean diffusivity (MD) indexes abnormalities mainly overlapped in the following regions, superior frontal gyrus, inferior frontal gyrus, cingulum gyrus, superior temporal gyrus, insula, and thalamus. Besides, overlapped with GM atrophies, mean kurtosis (MK) abnormalities were observed in superior frontal gyrus, inferior frontal gyrus, transverse temporal gyrus, insula, and thalamus. What important was that intrinsic aging independent associations between macrostructure and microstructure were found especially in media superior frontal gyrus, which revealed the potential mechanisms in the process of aging. The physiological mechanism may be associated with the elimination of neurons and synapses and the shrinkage of large neurons. Understanding the associations of GM volume changes and microstructural changes can account for the underlying mechanisms of aging and age-related neurodegenerative diseases.
Fair Logistic Regression: An Adversarial Perspective

Fair prediction methods have primarily been built around existing classification techniques using pre-processing methods, post-hoc adjustments, reduction-based constructions, or deep learning procedures. We investigate a new approach to fair data-driven decision making by designing predictors with fairness requirements integrated into their core formulations. We augment a game-theoretic construction of the logistic regression model with fairness constraints, producing a novel prediction model that robustly and fairly minimizes the logarithmic loss. We demonstrate the advantages of our approach on a range of benchmark datasets for fairness.
Multinomial Random Forests: Fill the Gap between Theoretical Consistency and Empirical Soundness

Random forests (RF) are one of the most widely used ensemble learning methods in classification and regression tasks. Despite its impressive performance, its theoretical consistency, which would ensure that its result converges to the optimum as the sample size increases, has been left far behind. Several consistent random forest variants have been proposed, yet all with relatively poor performance compared to the original random forests. In this paper, a novel RF framework named multinomial random forests (MRF) is proposed. In the MRF, an impurity-based multinomial distribution is constructed as the basis for the selection of a splitting point. This ensures that a certain degree of randomness is achieved while the overall quality of the trees is not much different from the original random forests. We prove the consistency of the MRF and demonstrate with multiple datasets that it performs similarly as the original random forests and better than existent consistent random forest variants for both classification and regression tasks.
Genomic Testing for Human Health and Disease Across the Life Cycle: Applications and Ethical, Legal, and Social Challenges

The expanding use of genomic technologies encompasses all phases of life, from the embryo to the elderly, and even the posthumous phase. In this paper, we present the spectrum of genomic healthcare applications, and describe their scope and challenges at different stages of the life cycle. The integration of genomic technology into healthcare presents unique ethical issues that challenge traditional aspects of healthcare delivery. These challenges include the different definitions of utility as applied to genomic information; the particular characteristics of genetic data that influence how it might be protected, used and shared; and the difficulties applying existing models of informed consent, and how new consent models might be needed.
Design Exploration of Multi-FPGAs for Accelerating Deep Learning

Due to the low power consumption and reconfigurability of FPGA, the use of FPGA for accelerating calculations is becoming more and more hot, including deep learning. However, due to limited hardware resource, single FPGA-based accelerator cannot configure optimal parameters for each layer, and its performance is also limited by the data memory bandwidth. To accelerate the calculation of neural network, this work designs a calculation module, and based on this module, further optimizes the data transmission path between multi-FPGA, thus achieving nearly linear performance growth between performance and the number of FPGAs.
Water stress combined with sulfur deficiency in pea affects yield components but mitigates the effect of deficiency on seed globulin composition

Abstract Water stress and sulfur (S) deficiency are two constraints increasingly faced by crops due to climate change and low-input agricultural practices. To investigate their interaction in the grain legume pea (Pisum sativum), sulfate was depleted at the mid-vegetative stage and a moderate 9-d water stress period was imposed during the early reproductive phase. The combination of the stresses impeded reproductive processes in a synergistic manner, reducing seed weight and seed number, and inducing seed abortion, which highlighted the paramount importance of sulfur for maintaining seed yield components under water stress. On the other hand, the moderate water stress mitigated the negative effect of sulfur deficiency on the accumulation of S-rich globulins (11S) in seeds, probably due to a lower seed sink strength for nitrogen, enabling a readjustment of the ratio of S-poor (7S) to 11S globulins. Transcriptome analysis of developing seeds at the end of the combined stress period indicated that similar biological processes were regulated in response to sulfur deficiency and to the combined stress, but that the extent of the transcriptional regulation was greater under sulfur deficiency. Seeds from plants subjected to the combined stresses showed a specific up-regulation of a set of transcription factor and SUMO ligase genes, indicating the establishment of unique regulatory processes when sulfur deficiency is combined with water stress.
Corrector for a diffusion process in a rarefied binary structure

We study the homogenization of a diffusion process which takes place in a binary structure formed by an ambiental connected phase surrounding a suspension of very small spheres distributed in an e-periodic network. The asymptotic distribution of the concentration is determined for both phases as e → 0, assuming that the suspension has mass of unity order and vanishing volume. Three cases are distinguished according to the values of a certain limit capacity. When it is positive and finite, the macroscopic system involves a two-concentration system, coupled through a term accounting for the non local effects. In the other two cases, where the capacity is either infinite or going to zero, although the form of the system is much simpler, some peculiar effects still account for the presence of the suspension. AMS 2000 Subject Classification: 35B27, 35K57, 76R50.
Imputation estimators for unnormalized models with missing data

Several statistical models are given in the form of unnormalized densities, and calculation of the normalization constant is intractable. We propose estimation methods for such unnormalized models with missing data. The key concept is to combine imputation techniques with estimators for unnormalized models including noise contrastive estimation and score matching. In addition, we derive asymptotic distributions of the proposed estimators and construct confidence intervals. Simulation results with truncated Gaussian graphical models and the application to real data of wind direction reveal that the proposed methods effectively enable statistical inference with unnormalized models from missing data.