Miracle-dz/UniFAR-FTUs huggingface dataset:
MAP4K3/GLK inhibits Treg differentiation by direct phosphorylating IKKβ and inducing IKKβ-mediated FoxO1 nuclear export and Foxp3 downregulation

Rationale: GLK (MAP4K3) activates PKCθ-IKKβ axis in T-cell activation and induces IL-17A-mediated autoimmune diseases. Attenuation of Treg differentiation and function by GLK could also contribute to autoimmune diseases. Methods: We analyzed the roles of GLK and IKKβ in Treg differentiation and function using T-cell-specific GLK transgenic mice and IKKβ conditional knockout mice. The mechanism of GLK/IKKβ-mediated attenuation of Treg differentiation/function was studied by chromatin-immunoprecipitation, reporter assays, in vitro kinase assays, protein-protein interaction assays, mass spectrometry, confocal microscopy, flow cytometry, and single-cell RNA sequencing (scRNA-seq) analysis. Results: We found that GLK signaling inhibited Foxp3 transcription by blocking the function of the transcription factor FoxO1. Mechanistically, GLK directly phosphorylated and activated IKKβ at Ser733 in a PKCθ-independent manner. The phospho-IKKβ Ser733 induced FoxO1 Ser319 phosphorylation and nuclear export, leading to Foxp3 downregulation. Consistently, scRNA-seq analyses showed that Foxp3 mRNA levels were inversely correlated with FoxO1 mRNA levels in GLK transgenic CD4+ T cells. Conclusions: GLK-IKKβ-FoxO1 signaling axis inhibits Foxp3 transcription, leading to reduction of Treg differentiation and suppressive activity, as well as induction of autoimmune disease.
Common Structure Mining of 3D Model Assembly Model Based on Frequent Subgraphs

As an important data source of complex product design and manufacturing, 3D assembly model has accumulated a large number of 3D assembly models in various manufacturing industries under the background of widely used digital design technology. In order to make better use of reusable common structure information about 3D assembly model, reduce repetitive labor, and shorten product development cycles, a common structure mining method of 3D assembly models was proposed. Firstly, based on the attribute neighbourhood diagram of individual parts of the 3D assembly model, the information of the assembly feature attributes is maintained and the nonassembly feature attributes are simplified to form an attributed assembly feature neighbourhood diagram; then, based on the assembly relationship between the parts of the 3D assembly model, the attributed assembly feature neighbourhood diagrams of the parts are combined to form a 3D assembly model attribute neighbourhood diagram. Secondly, the common structure of the 3D assembly model is extracted by the frequent subgraph mining algorithm. Finally, a set of fixture models is used to verify the results, which show that this 3D assembly model common structure mining method can accurately and effectively explore the common structure information of the product and has good results.
MEMS Sensors Based on Very Thin LTCC

The application of thin LTCC is a very interesting and promising approach to the fabrication of ceramic MEMS gas sensors. The attempts to use this material were restricted till now by the thickness of commercial material (>50 μm). In this work, we found a possibility to fabricate thin LTCC membranes (20–30 μm) stretched on a frame made of 100 μm thick LTCC. Aerosol jet printed Pt microheater and laser cutting of the membrane gave a cantilever shaped microhotplate with hot spot of about 300 × 300 μm. Power consumption of the heater is ~150 mW at 450 °C.
Implementation of the regularized Rudin-Osher-Fatemi denoising method in the coherent wave burst pipeline for gravitational-wave data analysis

The data collected by the current network of gravitational-wave detectors are largely dominated by instrumental noise. Total variation methods based on L 1 -norm minimization have recently been proposed as a powerful technique for noise removal in gravitational-wave data. In particular, the regularized Rudin-Osher-Fatemi (rROF) model has proven eﬀective to denoise signals embedded in either simulated Gaussian noise or actual detector noise. Importing the rROF model to existing search pipelines seems therefore worth considering. In this paper, we discuss the implementation of two variants of the rROF algorithm as two separate plug-ins of the coherent Wave Burst (cWB) pipeline designed to conduct searches of unmodelled gravitational-wave burst sources. The ﬁrst approach is based on a single-step rROF method and the second one employs an iterative rROF procedure. Both approaches are calibrated using actual gravitational-wave events from the ﬁrst three observing runs of the LIGO-Virgo-KAGRA collaboration, namely GW1501914, GW151226, GW170817, and GW190521, encompassing diﬀerent types of compact binary coalescences. Our analysis shows that the iterative version of the rROF denoising algorithm implemented in the cWB pipeline eﬀectively eliminates noise while preserving the waveform signals intact. Therefore, the combined approach yields higher signal-to-noise values than those computed by the cWB pipeline without the rROF denoising step. The incorporation of the iterative rROF algorithm in the cWB pipeline might hence impact the detectability capabilities of the pipeline along with the inference of source properties.
Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS) has attracted much attention because of its high sensitivity, high speed, and simple sample processing, and has great potential for application in the field of pesticide residue detection. However, SERS is susceptible to the influence of a complex detection environment in the detection of pesticide residues on the surface of fruits, facing problems such as interference from the spectral peaks of detected impurities, unclear dimension of effective correlation data, and poor linearity of sensing signals. In this work, the enhanced raw data of the pesticide thiram residues on the fruit surface using gold nanoparticle (Au-NPs) solution are formed into the raw data set of Raman signal in the IoT environment of Raman spectroscopy principal component detection. Considering the non-linear characteristics of sensing data, this work adopts kernel principal component analysis (KPCA) including radial basis function (RBF) to extract the main features for the spectra in the ranges of 653∼683 cm−1, 705∼728 cm−1, and 847∼872 cm−1, and discusses the effects of different kernel function widths (σ) to construct a qualitative analysis of pesticide residues based on SERS spectral data model, so that the SERS spectral data produce more useful dimensionality reduction with minimal loss, higher mean squared error for cross-validation in non-linear scenarios, and effectively weaken the interference features of detecting impurity spectral peaks, unclear dimensionality of effective correlation data, and poor linearity of sensing signals, reflecting better extraction effects than conventional principal component analysis (PCA) models.
Cratering Experiments on Granular Targets With a Variety of Particle Sizes: Implications for Craters on Rubble‐Pile Asteroids

We performed cratering experiments on targets composed of glass beads with a power‐law size distribution that simulated the surface of rubble‐pile asteroids, and we improved the previously studied reduction factor on the crater size scaling relationship including the armoring effect using the momentum transfer efficiency. Cratering experiments were conducted using light gas guns at Kobe University and ISAS/JAXA to control the impact velocity, vi ${v}_{\mathrm{i}}$ , from 50 to 4,400 m s−1. Two kinds of mixed targets were prepared by mixing glass beads with diameters of 0.1, 1, 3, and 10 mm—one with the smallest diameter beads, and one without. The size ratio of the target bead to the projectile (diameter of 1–3 mm), ϕ $\phi $ , changed from 0.03 to 10. The crater size scaling relationships for the mixed targets were found to depend on the first contacted bead size. Notably, first contact with a 10 mm‐sized bead reduced the crater radius by 35% in maximum. The reduction factor due to this armoring effect on the crater size scaling relationship is written as follows: f(ϕ)=1+8.99×10−3vi0.630.34ϕ−0.19 $f(\phi )={\left[1+\left(8.99\times {10}^{-3}\right){v}_{\mathrm{i}}^{0.63}\right]}^{0.34}{\phi }^{-0.19}$ ; it decreased with the increase of the size ratio of the target bead to the projectile, while it increased with the increase of the impact velocity and approached unity. Our improved crater size scaling relationship that includes the reduction factor could be used to reconstruct the crater size frequency distribution on rubble‐pile asteroids such as Ryugu, and it may lead to a revision of the crater chronology of asteroids.
Dihydrocapsiate does not increase energy expenditure nor fat oxidation during aerobic exercise in men with overweight/obesity: a randomized, triple-blinded, placebo-controlled, crossover trial

ABSTRACT Background Prior evidence suggests that capsinoids ingestion may increase resting energy expenditure (EE) and fat oxidation (FATox), yet whether they can modulate those parameters during exercise conditions remains poorly understood. We hypothesized that dihydrocapsiate (DHC) ingestion would increase EE and specifically FATox during an acute bout of aerobic exercise at FATmax intensity (the intensity that elicits maximal fat oxidation during exercise [MFO]) in men with overweight/obesity. Since FATmax and MFO during aerobic exercise appear to be indicators of metabolic flexibility, whether DHC has an impact on FATox in this type of population is of clinical interest. Methods A total of 24 sedentary men (age = 40.2 ± 9.2 years-old; body mass index = 31.6 ± 4.5 kg/m2 [n = 11 overweight, n = 13 obese]) participated in this randomized, triple-blinded, placebo-controlled, crossover trial (registered under ClinicalTrials.gov Identifier no. NCT05156697). On the first day, participants underwent a submaximal exercise test on a cycle ergometer to determine their MFO and FATmax intensity during exercise. After 72 hours had elapsed, the participants returned on 2 further days (≥ 72 hours apart) and performed a 60 min steady-state exercise bout (i.e. cycling at their FATmax, constant intensity) after ingesting either 12 mg of DHC or placebo; these conditions were randomized. Respiratory gas exchange was monitored by indirect calorimetry. Serum marker concentrations (i.e. glucose, triglycerides, non-esterified fatty acids (NEFAs), skin temperature, thermal perception, heart rate, and perceived fatigue) were assessed. Results There were no significant differences (P > 0.05) between DHC and placebo conditions in the EE and FATox during exercise. Similarly, no significant changes were observed in glucose, triglycerides, or NEFAs serum levels, neither in the skin temperature nor thermal perception across conditions. Heart rate and perceived fatigue did not differ between conditions. Conclusions DHC supplementation does not affect energy metabolism during exercise in men with overweight/obesity.
Prediction of the wheel profile wear using railroad vehicle dynamics simulation: Comparison of simulation and test results

Wear is a fundamental problem in the railroad vehicle design and performance evaluation. Slight changes in the profile shape result in significant impact on the contact geometry characteristics of wheel and rail, thereby altering the dynamic performance as well as derailment safety of railroad vehicles. In this study, a wear simulation capability integrated with multibody railroad vehicle dynamics simulation algorithms is developed considering multipoint contact exhibited between wheel and rail profiles in the wear evolution and validated against experimentally measured profiles obtained by the two-roller and scaled wheel wear tests. It was shown that worn profile shape as well as the magnitude of the weight loss agreed well between the simulation and test results in the two-roller test. Using wear test of a scaled wheel, it was found that the flange contact falls into severe wear regime, while the tread contact is in mild wear regime in the two-roller wear test data. However, a severe flange contact led to much higher wear rate than that obtained using the two-roller test for the same material and the same wear index, while the wear rate for the tread contact adhered to that of the two-roller test in the mild wear regime. This is attributed to the fact that contact condition of the severe flange contact scenario is greatly different from the two-roller contact condition due to the high contact pressure and slippage occurring in the small thin contact patch, leading to a vastly different wear rate per contact area. It is demonstrated that utilizing the flange wear rate leads to good agreement with the measured profile shape, while the two-roller wear rate leads to a significant underestimate of the flange wear due to significantly lower wear rate in severe wear regime.
Impacts of Circadian Gene Period2 Knockout on Intestinal Metabolism and Hepatic Antioxidant and Inflammation State in Mice

The period circadian regulator 2 (Per2) gene is important for the modulations of rhythmic homeostasis in the gut and liver; disruption will cause metabolic diseases, such as obesity, diabetes, and fatty liver. Herein, we investigated the alterations in intestinal metabolic and hepatic functions in Per2 knockout (Per2−/−, KO) and wild-type (Per2+/+, WT) mice. Growth indices, intestinal metabolomics, hepatic circadian rhythms, lipid metabolism, inflammation-related genes, antioxidant capacity, and transcriptome sequencing were performed after euthanasia. Data indicated that KO decreased the intestinal concentrations of amino acids such as γ-aminobutyric acid, aspartic acid, glycine, L-allothreonine, methionine, proline, serine, and valine while it increased the concentrations of carbohydrates such as cellobiose, D-talose, fucose, lyxose, and xylose compared with WT. Moreover, the imbalance of intestinal metabolism further seemed to induce liver dysfunction. Data indicated that Per2 knockout altered the expression of hepatic circadian rhythm genes, such as Clock, Bmal1, Per1, Per3, Cry1, and Cry2. KO also induced hepatic lipid metabolism, because of the increase of liver index and serum concentrations of low-density lipoprotein, and the upregulated expression of Pparα, Cyp7a1, and Cpt1. In addition, KO improved hepatic antioxidant capacity due to the increase activities of SOD and GSH-Px and the decrease in concentrations of MDA. Lastly, KO increased the relative expression levels of hepatic inflammation-related genes, such as Il-1β, Il-6, Tnf-α, Myd88, and Nf-κB p65, which may potentially lead to hepatic inflammation. Overall, Per2 knockout induces gut metabolic dysregulation and may potentially trigger alterations in hepatic antioxidant and inflammation responses.
Peak extraction in daylight simulations using BSDF data

Bidirectional scattering distribution function (BSDF) data are used in design practice to represent optically complex daylighting and solar control systems in lighting and energy simulation software. Visual comfort assessments (e.g., daylight glare) require accurate determination of luminance and corresponding solid angle of glare sources. For the sun, the necessary resolution of the BSDF causes problems both in terms of data volume and computational effort. With “peak extraction” (PE), we present a new method that simulates the direct solar contribution at its real size and spread, while efficiently using the underlying BSDF data set for the scattered light. PE enables practitioners to evaluate daylight performance metrics for their designs at improved accuracy.
A cross-cultural investigation on perseverance, self-regulated learning, motivation, and achievement

ABSTRACT Research on grit indicates that perseverance positively predicts academic achievement. Yet, the mechanisms through which perseverance might lead to academic success remain less explored, particularly in cross-cultural research. The current study investigated such mechanisms by examining possible mediating effects of students’ use of self-regulated learning strategies (control, memorisation, and elaboration) on the predictive relation of students’ perseverance on their academic achievement, in students from East Asian and Anglo-Saxon English speaking Western countries. The sample came from the OECD PISA study and included 24,352 population-representative 15-year-old students from Hong Kong, the Republic of Korea, Australia, New Zealand, Scotland, and the US. Results revealed that perseverance had a more positive association with achievement in East Asian cultures than Western cultures. Control strategy was stronger positive mediators of achievement in Western countries, whereas memorisation and elaboration strategy use and instrumental motivation more negatively mediated the effect of perseverance on achievement in Western countries.
Promoting ion and heat transfer of solid polymer electrolytes by tuning polymer chain length and salt concentration

ABSTRACT The heat and mass performance of solid polymer electrolytes (SPEs) directly affects their application in solid-state batteries (SSBs). However, high crystallinity and charged clusters hinder the transport of Li+ in SPEs, resulting in low ion diffusivities and thermal conductivities. Therefore, understanding the effects of SPE structure on ion transport and heat conduction is critical for SPE designing. In this work, the ion diffusivities and thermal conductivities of SPEs containing polyethylene oxide (PEO) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt with different PEO chain lengths and salt concentrations are studied and the structural properties are analyzed to determine the mechanisms of ion transport and heat conduction. The results show that the crystallinity and charged clusters decrease in SPEs with short-chain PEO and low salt concentration, thereby improving ion transfer and heat conduction. Moreover, phonon spectra shift to a higher-frequency domain when salt concentration increases, which induces a decrease in thermal conductivity. Decreased salt concentration and chain length increased conductivity by 350% and 176%, respectively, and thermal conductivity by 122% and 140%, respectively. These findings, which indicate that SPEs with short-chain PEO and low salt concentration have better ion and heat transfer, provide valuable insight into the design of PEO-LiTFSI electrolytes. GRAPHICAL ABSTRACT Highlights Ion and heat transfer in PEO-LiTFSI electrolytes are studied. High crystallinity hinders ion and heat transfer in long-chain PEO electrolytes. Poor ion and heat transfer are due to ion clusters at high salt concentrations. Enhancement of high-frequency phonon scattering decreases thermal conductivity. Useful insights into ion and heat transfer are obtained for designing SPEs.
Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping

Due to failures or even the absence of an electricity grid, microgrid systems are becoming popular solutions for electrifying African rural communities. However, they are heavily stressed and complex to control due to their intermittency and demand growth. Demand side management (DSM) serves as an option to increase the level of flexibility on the demand side by scheduling users’ consumption patterns profiles in response to supply. This paper proposes a demand-side management strategy based on load shifting and peak clipping. The proposed approach was modelled in a MATLAB/Simulink R2021a environment and was optimized using the artificial neural network (ANN) algorithm. Simulations were carried out to test the model’s efficacy in a stand-alone PV-battery microgrid in East Africa. The proposed algorithm reduces the peak demand, smoothing the load profile to the desired level, and improves the system’s peak to average ratio (PAR). The presence of deferrable loads has been considered to bring more flexible demand-side management. Results promise decreases in peak demand and peak to average ratio of about 31.2% and 7.5% through peak clipping. In addition, load shifting promises more flexibility to customers.
Torsionally broken symmetry assists infrared excitation of biomimetic charge-coupled nuclear motions in the electronic ground state

The concerted interplay between reactive nuclear and electronic motions in molecules actuates chemistry. Here, we demonstrate that out-of-plane torsional deformation and vibrational excitation of stretching motions in the electronic ground state modulate the charge-density distribution in a donor-bridge-acceptor molecule in solution. The vibrationally-induced change, visualised by transient absorption spectroscopy with a mid-infrared pump and a visible probe, is mechanistically resolved by ab initio molecular dynamics simulations. Mapping the potential energy landscape attributes the observed charge-coupled coherent nuclear motions to the population of the initial segment of a double-bond isomerization channel, also seen in biological molecules. Our results illustrate the pivotal role of pre-twisted molecular geometries in enhancing the transfer of vibrational energy to specific molecular modes, prior to thermal redistribution. This motivates the search for synthetic strategies towards achieving potentially new infrared-mediated chemistry.
Scene Spatio-Temporal Graph Convolutional Network for Pedestrian Intention Estimation

For safe and comfortable navigation of autonomous vehicles, it is crucial to know the pedestrian’s intention of crossing the street. Generally, human drivers are aware of the traffic objects (e.g., crosswalks and traffic lights) in the environment while driving; likewise, these objects would play a crucial role for autonomous vehicles. In this research, we propose a novel pedestrian intention estimation method that not only takes into account the influence of traffic objects but also learns their contribution levels on the intention of the pedestrian. Our proposed method, referred to as Scene SpatioTemporal Graph Convolutional Network (Scene-STGCN), takes benefits from the strength of Graph Convolutional Networks and efficiently encodes the relationships between the pedestrian and the scene objects both spatially and temporally. We conduct several experiments on the Pedestrian Intention Estimation (PIE) dataset and illustrate the importance of scene objects and their contribution levels in the task of pedestrian intention estimation. Furthermore, we perform statistical analysis on the relevance of different traffic objects in the PIE dataset and carry out an ablation study on the effect of various information sources in the scene. Finally, we demonstrate the significance of the proposed Scene-STGCN through experimental comparisons with several baselines. The results indicate that our proposed Scene-STGCN outperforms the current state-of-the-art method by 0.03 in terms of ROC-AUC metric.
Vision Transformer for Learning Driving Policies in Complex and Dynamic Environments

Driving in a complex and dynamic urban environment is a difficult task that requires a complex decision policy. In order to make informed decisions, one needs to gain an understanding of the long-range context and the importance of other vehicles. In this work, we propose to use Vision Transformer (ViT) to learn a driving policy in urban settings with birds-eye-view (BEV) input images. The ViT network learns the global context of the scene more effectively than with earlier proposed Convolutional Neural Networks (ConvNets). Furthermore, ViT’s attention mechanism helps to learn an attention map for the scene which allows the ego car to determine which surrounding cars are important to its next decision. We demonstrate that a DQN agent with a ViT backbone outperforms baseline algorithms with ConvNet backbones pre-trained in various ways. In particular, the proposed method helps reinforcement learning algorithms to learn faster, with increased performance and less data than baselines.