Miracle-dz/UniFAR-FTUs huggingface dataset:
Butyrate Improves Neuroinflammation and Mitochondrial Impairment in Cerebral Cortex and Synaptic Fraction in an Animal Model of Diet-Induced Obesity

Neurodegenerative diseases (NDDs) are characterized by cognitive impairment and behavioural abnormalities. The incidence of NDDs in recent years has increased globally and the pathological mechanism is not fully understood. To date, plentiful evidence has showed that metabolic alterations associated with obesity and related issues such as neuroinflammation, oxidative stress and mitochondrial dysfunction may represent an important risk factor, linking obesity and NDDs. Numerous studies have indicated a correlation between diet and brain activities. In this context, a key role is played by mitochondria located in the synaptic fraction; indeed, it has been shown that high-fat diets cause their dysfunction, affecting synaptic plasticity. In this scenario, the use of natural molecules that improve brain mitochondrial function represents an important therapeutic approach to treat NDDs. Recently, it was demonstrated that butyrate, a short-chain fatty acid is capable of counteracting obesity in an animal model, modulating mitochondrial function. The aim of this study has been to evaluate the effects of butyrate on neuroinflammatory state, oxidative stress and mitochondrial dysfunction in the brain cortex and in the synaptic fraction of a mouse model of diet-induced obesity. Our data have shown that butyrate partially reverts neuroinflammation and oxidative stress in the brain cortex and synaptic area, improving mitochondrial function and efficiency.
A Variable Weight Function Model in Geological Variables Function Research

In mineral resources prediction and other research of geological variables, stability exactness of quantitative models concern modeling conditions, geological variables from model and the status of the variable. In traditional geological modeling process, variable support is measured under some contrains weight and this kind of weight is characterized by constant coefficients. Constant weight[1] has some limitations due to structuredness and dependency of variable. For overcoming the inflexibility of constant weight, this paper proposes geological variable mathematics model basedd state variable vector. We revise existing form of state variable weight and provide logarithm state variable vector as measurement level of geological variable weight coefficients. According to 1:200000 scale geochemistry measured data from Baishan area, we calculate the samples unit connection degree based on exponent and logarithm state variable vector and compare the connection degree based on constant weight. The connection degree sorting has the similarity as a whole among them, but there is the obvious difference locally. We can conclude that geological variable weight function based on state variable vector is more flexible and fine.
The rationale behind the success of multi-model ensembles in seasonal forecasting-II

This invention relates to a selection mechanism for a postage meter in which a single Thomas-type mutilated drum actuator is rigidly mounted on a hollow cylindrical drive shaft on which is also mounted the postage meter printing head, and comprises a series of selection bars lying longitudinally of the drive shaft and angularly therearound, each of which bars is set by a suitable manual positioning means, such as a selection wheel, each such bar carrying a yoke to position a driven gear of a first register to be driven by the actuator, a like series of auxiliary setting bars, each of which is rigidly yoked to the respective first setting bar and which carries a yoke for setting a register drive gear of another register of a postage meter. The main setting bar, at its forward end, is provided with gear teeth which drive a gear train that meshes with a rack carried by the actuator drive shaft to set the mechanism in the print wheel.
Social Skill Transfer from a Sport-Based Positive Youth Development Program to the School Setting

The present study explored the degree to which participants of one sport-based positive youth development (PYD) camp learned self-control skills and transferred self-control skills to school during the year after participation. The influence of contextual and personal factors on transfer also were examined. The sample included 176 who participated in the 2017 LiFEsports camp and then returned in 2018. Significant (p<.05) increases in self-control from the beginning (M=3.56) to the end of the 2017 camp (M=3.77) were found. Survey responses in 2018 found that 91% reported using self-control at school during the academic year post-camp participation. Open-ended responses revealed youths’ application of self-control at school to avoid problematic confrontations (e.g., walking away from a fight) and approach academics (e.g., working quietly by oneself). The frequency of self-control transfer to school was significantly predicted by a set of contextual and personal factors, (R2 = .48) including reflection on learning, personal importance, perceived autonomy, confidence in transferring self-control, and peers value of their use of self-control at school. Results suggest youth participating in sport-based PYD programs report transfer of self-control to school and point to important contextual and personal processes affecting life skills transfer.
Transcriptome and Metabolite Profiling of Tomato SGR-Knockout Null Lines Using the CRISPR/Cas9 System

Stay-green 1 (SGR1) protein is a critical regulator of chlorophyll degradation and senescence in plant leaves; however, the functions of tomato SGR1 remain ambiguous. Here, we generated an SGR1-knockout (KO) null line via clustered regularly interspaced palindromic repeat (CRISPR)/CRISPR-associated protein 9-mediated gene editing and conducted RNA sequencing and gas chromatography–tandem mass spectrometry analysis to identify the differentially expressed genes (DEGs). Solanum lycopersicum SGR1 (SlSGR1) knockout null line clearly showed a turbid brown color with significantly higher chlorophyll and carotenoid levels than those in the wild-type (WT) fruit. Differential gene expression analysis revealed 728 DEGs between WT and sgr#1-6 line, including 263 and 465 downregulated and upregulated genes, respectively, with fold-change >2 and adjusted p-value < 0.05. Most of the DEGs have functions related to photosynthesis, chloroplasts, and carotenoid biosynthesis. The strong changes in pigment and carotenoid content resulted in the accumulation of key primary metabolites, such as sucrose and its derivatives (fructose, galactinol, and raffinose), glycolytic intermediates (glucose, glucose-6-phosphate, and fructose-6-phosphate), and tricarboxylic acid cycle intermediates (malate and fumarate) in the leaves and fruit of the SGR-KO null lines. Overall, the SGR1-KO null lines developed here provide new evidence for the mechanisms underlying the roles of SGR1 as well as the molecular pathways involved in photosynthesis, chloroplasts, and carotenoid biosynthesis.
Antiparkinsonian Agents in Investigational Polymeric Micro- and Nano-Systems

Parkinson’s disease (PD) is a devastating neurodegenerative disease characterized by progressive destruction of dopaminergic tissue in the central nervous system (CNS). To date, there is no cure for the disease, with current pharmacological treatments aimed at controlling the symptoms. Therefore, there is an unmet need for new treatments for PD. In addition to new therapeutic options, there exists the need for improved efficiency of the existing ones, as many agents have difficulties in crossing the blood–brain barrier (BBB) to achieve therapeutic levels in the CNS or exhibit inappropriate pharmacokinetic profiles, thereby limiting their clinical benefits. To overcome these limitations, an interesting approach is the use of drug delivery systems, such as polymeric microparticles (MPs) and nanoparticles (NPs) that allow for the controlled release of the active ingredients targeting to the desired site of action, increasing the bioavailability and efficacy of treatments, as well as reducing the number of administrations and adverse effects. Here we review the polymeric micro- and nano-systems under investigation as potential new therapies for PD.
Neural Manifold Modulated Continual Reinforcement Learning for Musculoskeletal Robots

The continual learning and development are significant for robots to learn multiple tasks sequentially. The difficulty lies in balancing the efficient learning of new tasks and overcoming catastrophic forgetting of old tasks. Although many continual learning methods have been proposed for pattern recognition, continual reinforcement learning methods for redundant musculoskeletal and robotic systems are few and have limitations. Therefore, inspired by the developmental mechanisms in motor cortex, this article proposes a neural manifold modulated continual reinforcement learning method for musculoskeletal and robotic systems. First, a recurrent neural network (RNN) with an expected neural manifold is designed and conditions of weights are derived. Second, the ability of projectors for characterizing the neural manifold within RNN is analyzed. Furthermore, a continual reinforcement learning method of RNN is proposed with the modulation of a neural manifold. The method is validated by redundant musculoskeletal and robotic systems in simulation. The results suggest that it can realize continual reinforcement learning of multiple tasks in different movements and environments. Furthermore, compared with related works, the proposed method achieves better performance.
Particle analysis on concentrated particle suspensions by transmission fluctuation spectrometry with band-pass filters: part 1. Simulation

Transmission fluctuation spectrometry (TFS), as a new method of online and real-time particle analysis developed in recent years, can measure the particle size distribution and particle concentration simultaneously. In this work, TFS with band-pass filters is studied by numerical simulation in which the particle concentration varies in a large dynamic range. The results of the simulations show that the transmission fluctuation spectrum is related to the particle concentration of the suspension, the pathlength of the light beam within the particle system and the beam-to-particle diameter ratio. Empirical expressions to correct the effects of particle overlapping on the transmission fluctuation spectrum are obtained, which are found to be experimentally validated.
A Spectral Stochastic Kinetic Energy Backscatter Scheme and Its Impact on Flow-Dependent Predictability in the ECMWF Ensemble Prediction System

Understanding model error in state-of-the-art numerical weather prediction models and representing its impact on flow-dependent predictability remains a complex and mostly unsolved problem. Here, a spectral stochastic kinetic energy backscatter scheme is used to simulate upscale-propagating errors caused by unresolved subgrid-scale processes. For this purpose, stochastic streamfunction perturbations are generated by autoregressive processes in spectral space and injected into regions where numerical integration schemes and parameterizations in the model lead to excessive systematic kinetic energy loss. It is demonstrated how output from coarse-grained high-resolution models can be used to inform the parameters of such a scheme. The performance of the spectral backscatter scheme is evaluated in the ensemble prediction system of the European Centre for Medium-Range Weather Forecasts. Its implementation in conjunction with reduced initial perturbations results in a better spread‐error relationship, more realistic kinetic-energy spectra, a better representation of forecast-error growth, improved flow-dependent predictability, improved rainfall forecasts, and better probabilistic skill. The improvement is most pronounced in the tropics and for largeanomaly events. It is found that whereas a simplified scheme assuming a constant dissipation rate already has some positive impact, the best results are obtained for flow-dependent formulations of the unresolved processes.
The Next Generation of eHealth: A Multidisciplinary Survey

Over the past two years, the spread of COVID-19 has spurred the use of information and communication technologies (ICT) in aid of healthcare. The need to guarantee continuity to care has promoted research and industry activities aimed at developing solutions for the digitalization of the procedures to be performed to provide health services, even in emergency scenarios. Digital collection, transmission, and processing of health data represent the starting point for fulfilling this innovation process but also bring heterogeneous challenges. These motivations led to the elaboration of this work, which analyzes innovative and technological tools for the development of digital health (eHealth) through the collection of multisectoral literature, produced thanks to the cooperation of varied research groups, thus providing a multidisciplinary survey. Since digital health is expected to be one of the leading applications of the sixth-generation (6G) wireless cellular networks, this paper covers the related telecommunications aspects. Furthermore, the exploitation of artificial intelligence paradigms to elaborate massive amounts of biological data is examined. Given the extreme sensitivity of health data, this paper also investigates security and privacy issues. In particular, the main techniques and approaches to guarantee security properties (i.e., anonymity, responsibility, authentication, confidentiality, integrity, non-repudiation, and revocability) are studied. Applications involving innovative electromagnetic systems for healthcare and assisted living services are described to provide an example of an eHealth scenario leveraging ICT. Finally, the telemedicine-related regulations of the European Commission are analyzed, with particular reference to the General Data Protection Regulation (GDPR).
Discovery of super–insecticide-resistant dengue mosquitoes in Asia: Threats of concomitant knockdown resistance mutations

Aedes aegypti (Linnaeus, 1762) is the main mosquito vector for dengue and other arboviral infectious diseases. Control of this important vector highly relies on the use of insecticides, especially pyrethroids. The high frequency (>78%) of the L982W substitution was detected at the target site of the pyrethroid insecticide, the voltage-gated sodium channel (Vgsc) of A. aegypti collected from Vietnam and Cambodia. Alleles having concomitant mutations L982W + F1534C and V1016G + F1534C were also confirmed in both countries, and their frequency was high (>90%) in Phnom Penh, Cambodia. Strains having these alleles exhibited substantially higher levels of pyrethroid resistance than any other field population ever reported. The L982W substitution has never been detected in any country of the Indochina Peninsula except Vietnam and Cambodia, but it may be spreading to other areas of Asia, which can cause an unprecedentedly serious threat to the control of dengue fever as well as other Aedes-borne infectious diseases.
Recursive Combination Tests

We present a method that extends the flexibility of adaptive designs to the number of interim analyses and to the choice of decision boundaries. At each stage of the trial, the design of the next stage can be determined using all of the information gathered so far. Additionally, one can specify the next stage as the final stage or can plan a further interim analysis. The method is based on a recursive application of the two-stage combination tests for p values. The crucial point is the appropriate definition of a p value function that combines p values from two separate stages to a single p value. Formally, we start with a two-stage combination test. However, the p value of the second stage can be replaced by the p value of a further combination test. This applies if the experimenter decides in the first interim analysis to perform another interim analysis, thereby extending the design to at least three stages. Obviously, this can be also done in a recursive way in the following interim analyses. The test decision is based on an overall p value, which can be easily calculated by concatenation of the p value functions for the combination tests used. Under very general conditions, this procedure controls the level α. Recursive combination tests cover the classical group sequential and adaptive two-stage tests as special cases. By construction, they also allow for simple computation of confidence intervals and median unbiased point estimates.
Gilbert Damping for Various Ni80Fe20 Thin Films Investigated Using All-Optical Pump–Probe Detection and Ferromagnetic Resonance

All-optical pump–probe detection of magnetization precession has been performed to investigate Gilbert damping in several types of Ni80Fe20 films. The frequency and decay time of magnetization precession depended on pump fluence. At low pump fluence, Gilbert damping constants, derived from the magnetic field dependence on frequency and decay time of magnetization precession, were identical to values obtained from X-band ferromagnetic resonance within the experimental errors.
Animal Pose Estimation Algorithm Based on the Lightweight Stacked Hourglass Network

Pose estimation has been a hot topic in the field of machine vision in recent years. Animals exist widely in nature, and the analysis of their shape and movement is important in many fields and industries. In the pose estimation task, to improve the detection accuracy, the existing models often need to consume a lot of computing and memory resources. Therefore, it is a key problem for the pose estimation methods to carry out a lightweight model and reduce the computational overhead on the premise of ensuring model accuracy. In this paper, we focus on the structure of the convolutional neural network in animal pose estimation, construct a lightweight and efficient stacked hourglass network model oriented to optimize the balance of model computation and accuracy, and implement the application algorithm design based on it. Aiming at the problem of large parameters in depthwise convolutional neural networks, a lightweight residual module is proposed, that is, based on the lightweight efficient channel attention improved conditional channel-weighted method (ICCW-Bottle), thereby reducing the weight of the network and obtaining the feature information of different scales. Given the problem that a large amount of feature information is easily lost after the network pooling operation, a lightweight dual-branch fusion module is proposed that fully integrates high-level semantic information and low-level detailed features under the condition of a small number of parameters. Finally, the same as the CC-SSL method: the model is trained jointly using synthetic and real animal datasets, but the CC-SSL method does not take into account the computational power of the model, which consumes a lot of time and memory to run. Through experiments, it is known that compared with the CC-SSL method, the PCK@0.05 of this method is increased by 5.5% on the TigDog dataset. The model in this paper reduces the number of parameters and calculations of the network while ensuring less information loss and model accuracy. The ablation experiment verifies the advancement and effectiveness of the overall network.
Bariatric surgery for non-alcoholic fatty liver disease: Indications and post-operative management

The prevalence of obesity and metabolic consequences such as nonalcoholic fatty liver diseases (NAFLD) has become a crucial health problem. Lifestyle modifications, especially weight loss, effectively reduces liver injury in NAFLD patients. However, adherence to lifestyle changes is very low in the clinical setting. Bariatric surgery can improve metabolic components and cause long-term weight loss. Therefore, bariatric surgery could serve as an attractive treatment option for NAFLD patients. This review integrates data about the benefits of bariatric surgery on NAFLD but also describes the potential pitfalls.
Interpretation of the newly discovered submillimeter water maser

The 10(29)-9(36)321 GHz water maser discovered recently by Menten et al. (1990) may be pumped collisionally under a wide range of physical conditions. This range of conditions is similar but not identical to that required to excite the well-studied 22 GHz water maser. The ratio of the observed 22 GHz and 321 GHz intersteller maser luminosities can be explained by collisional excitation within the same warm, dense gas. Collisional pumping in a circumstellar envelope can account for the observed 321 GHz maser emission in the supergiant star VY CMa. 11 refs.
Use of R-matrix methods for light element evaluations

Some general aspects of parameterizing nuclear reaction data with a unitary, multichannel theory are discussed. The special case of R-matrix theory are discussed. The special case of R-matrix theory is considered, where the explicit separation of long- and short-range forces and the natural occurrence of energy pole terms afford a number of advantages in describing data for light-element reactions. Examples are given for both neutron- and charged-particle-induced reactions which illustrate multichannel R-matrix techniques, including the use of charge symmetry to relate data for mirror systems. The limitations of conventional R-matrix methods are discussed briefly.
Parallelization of Concise Convolutional Neural Networks for Plant Classification

Monitoring the agricultural field is the key to preventing the spread of disease and handling it quickly. The com - puter-based automatic monitoring system can meet the needs of large-scale and real-time monitoring. Plant classifiers that can work quickly in computer with limited resources are needed to realize this monitoring system. This study proposes convolutional neural network (CNN) architecture as a plant classifier based on leaf imagery. This architecture was built by parallelizing two concise CNN channels with different filter sizes using the addition operation. GoogleNet, SqueezeNet and MobileNetV2 were used to compare the performance of the proposed architecture. The classification performance of all these architectures was tested using the PlantVillage dataset which consists of 38 classes and 14 plant types. The experimental results indicated that the proposed architecture with a smaller number of parameters achieved nearly the same accuracy as the comparison architectures. In addition, the proposed architecture classified images 5.12 times faster than SqueezeNet, 8.23 times faster than GoogleNet, and 9.4 times faster than MobileNetV2. These findings suggest that when implemented in the agricultural field, the proposed architecture can be a reliable and faster plant classifier with fewer resources.