Persistently Trained, Diffusion-assisted Energy-based Models

Maximum likelihood (ML) learning for energy-based models (EBMs) is challenging, partly due to non-convergence of Markov chain Monte Carlo.Several variations of ML learning have been proposed, but existing methods all fail to achieve both post-training image generation and proper density estimation. We propose to introduce diffusion data and learn a joint EBM, called diffusion assisted-EBMs, through persistent training (i.e., using persistent contrastive divergence) with an enhanced sampling algorithm to properly sample from complex, multimodal distributions. We present results from a 2D illustrative experiment and image experiments and demonstrate that, for the first time for image data, persistently trained EBMs can {\it simultaneously} achieve long-run stability, post-training image generation, and superior out-of-distribution detection.Comment: main text 8 page

An Improved Transplantation Strategy for Mouse Mesenchymal Stem Cells in an Acute Myocardial Infarction Model

To develop an effective therapeutic strategy for cardiac regeneration using bone marrow mesenchymal stem cells (BM-MSCs), the primary mouse BM-MSCs (1st BM-MSCs) and 5th passage BM-MSCs from β-galactosidase transgenic mice were respectively intramyocardially transplanted into the acute myocardial infarction (AMI) model of wild type mice. At the 6th week, animals/tissues from the 1st BM-MSCs group, the 5th passage BM-MSCs group, control group were examined. Our results revealed that, compared to the 5th passage BM-MSCs, the 1st BM-MSCs had better therapeutic effects in the mouse MI model. The 1st BM-MSCs maintained greater differentiation potentials towards cardiomocytes or vascular endothelial cells in vitro. This is indicated by higher expressions of cardiomyocyte and vascular endothelial cell mature markers in vitro. Furthermore, we identified that 24 proteins were down-regulated and 3 proteins were up-regulated in the 5th BM-MSCs in comparison to the 1st BM-MSCs, using mass spectrometry following two-dimensional electrophoresis. Our data suggest that transplantation of the 1st BM-MSCs may be an effective therapeutic strategy for cardiac tissue regeneration following AMI, and altered protein expression profiles between the 1st BM-MSCs and 5th passage BM-MSCs may account for the difference in their maintenance of stemness and their therapeutic effects following AMI

Strong Room-Temperature Bulk Nonlinear Hall Effect in a Spin-Valley Locked Dirac Material

Nonlinear Hall effect (NLHE) is a new type of Hall effect with wide application prospects. Practical device applications require strong NLHE at room temperature (RT). However, previously reported NLHEs are all low-temperature phenomena except for the surface NLHE of TaIrTe4. Bulk RT NLHE is highly desired due to its ability to generate large photocurrent. Here, we show the spin-valley locked Dirac state in BaMnSb2 can generate a strong bulk NLHE at RT. In the microscale devices, we observe the typical signature of an intrinsic NLHE, i.e. the transverse Hall voltage quadratically scales with the longitudinal current as the current is applied to the Berry curvature dipole direction. Furthermore, we also demonstrate our nonlinear Hall device's functionality in wireless microwave detection and frequency doubling. These findings broaden the coupled spin and valley physics from 2D systems into a 3D system and lay a foundation for exploring bulk NLHE's applications

Altered dynamic functional network connectivity in drug-naïve Parkinson’s disease patients with excessive daytime sleepiness

BackgroundExcessive daytime sleepiness (EDS) is a frequent nonmotor symptoms of Parkinson’s disease (PD), which seriously affects the quality of life of PD patients and exacerbates other nonmotor symptoms. Previous studies have used static analyses of these resting-state functional magnetic resonance imaging (rs-fMRI) data were measured under the assumption that the intrinsic fluctuations during MRI scans are stationary. However, dynamic functional network connectivity (dFNC) analysis captures time-varying connectivity over short time scales and may reveal complex functional tissues in the brain.PurposeTo identify dynamic functional connectivity characteristics in PD-EDS patients in order to explain the underlying neuropathological mechanisms.MethodsBased on rs-fMRI data from 16 PD patients with EDS and 41 PD patients without EDS, we applied the sliding window approach, k-means clustering and independent component analysis to estimate the inherent dynamic connectivity states associated with EDS in PD patients and investigated the differences between groups. Furthermore, to assess the correlations between the altered temporal properties and the Epworth sleepiness scale (ESS) scores.ResultsWe found four distinct functional connectivity states in PD patients. The patients in the PD-EDS group showed increased fractional time and mean dwell time in state IV, which was characterized by strong connectivity in the sensorimotor (SMN) and visual (VIS) networks, and reduced fractional time in state I, which was characterized by strong positive connectivity intranetwork of the default mode network (DMN) and VIS, while negative connectivity internetwork between the DMN and VIS. Moreover, the ESS scores were positively correlated with fraction time in state IV.ConclusionOur results indicated that the strong connectivity within and between the SMN and VIS was characteristic of EDS in PD patients, which may be a potential marker of pathophysiological features related to EDS in PD patients

Phase transitions associated with magnetic-field induced topological orbital momenta in a non-collinear antiferromagnet

Resistivity measurements are widely exploited to uncover electronic excitations and phase transitions in metallic solids. While single crystals are preferably studied to explore crystalline anisotropies, these usually cancel out in polycrystalline materials. Here we show that in polycrystalline Mn3Zn0.5Ge0.5N with non-collinear antiferromagnetic order, changes in the diagonal and, rather unexpected, off-diagonal components of the resistivity tensor occur at low temperatures indicating subtle transitions between magnetic phases of different symmetry. This is supported by neutron scattering and explained within a phenomenological model which suggests that the phase transitions in magnetic field are associated with field induced topological orbital momenta. The fact that we observe transitions between spin phases in a polycrystal, where effects of crystalline anisotropy are cancelled suggests that they are only controlled by exchange interactions. The observation of an off-diagonal resistivity extends the possibilities for realising antiferromagnetic spintronics with polycrystalline materials.Comment: 4 figures, 1 tabl

Operation and evaluation of digitalized retail electricity markets under low-carbon transition: recent advances and challenges

With the growth of electricity consumers purchasing green energy and the development of digital energy trading platforms, the role of digitalized retail electricity markets in the low-carbon transition of electric energy systems is becoming increasingly crucial. In this circumstance, the research work on retail electricity markets needs to be further analyzed and expanded, which would facilitate the efficient decision-making of both market players and policymakers. First, this paper introduces the latest developments in the retail electricity market under low-carbon energy transition and analyzes the limitations of the existing research works. Second, from three aspects of power trading strategy, retail pricing methodology, and market risk management, it provides an overview of the existing operation and mechanism design strategies of the retail electricity market; then, it provides a systematic introduction to the evaluation system and monitoring methodology of electricity markets, which is not sufficient for the current digitalized retail electricity markets. Finally, the issues regarding operation evaluation and platform optimization of the current digitalized retail electricity market are summarized, and the research topics worth further investigations are recommended

Exploring the perspectives of pharmaceutical experts and healthcare practitioners on senolytic drugs for vascular aging-related disorder: a qualitative study

Objective: The field of targeting cellular senescence with drug candidates to address age-related comorbidities has witnessed a notable surge of interest and research and development. This study aimed to gather valuable insights from pharmaceutical experts and healthcare practitioners regarding the potential and challenges of translating senolytic drugs for treatment of vascular aging-related disorders.Methods: This study employed a qualitative approach by conducting in-depth interviews with healthcare practitioners and pharmaceutical experts. Participants were selected through purposeful sampling. Thematic analysis was used to identify themes from the interview transcripts.Results: A total of six individuals were interviewed, with three being pharmaceutical experts and the remaining three healthcare practitioners. The significant global burden of cardiovascular diseases presents a potentially large market size that offer an opportunity for the development and marketability of novel senolytic drugs. The pharmaceutical sector demonstrates a positive inclination towards the commercialization of new senolytic drugs targeting vascular aging-related disorders. However potential important concerns have been raised, and these include increasing specificity toward senescent cells to prevent off-site targeting, thus ensuring the safety and efficacy of these drugs. In addition, novel senolytic therapy for vascular aging-related disorders may encounter competition from existing drugs that treat or manage risk factors of cardiovascular diseases. Healthcare practitioners are also in favor of recommending the novel senolytic drugs for vascular aging-related disorders but cautioned that its high cost may hinder its acceptance among patients. Besides sharing the same outcome-related concerns as with the pharmaceutical experts, healthcare practitioners anticipated a lack of awareness among the general public regarding the concept of targeting cellular senescence to delay vascular aging-related disorders, and this knowledge gap extends to healthcare practitioner themselves as well.Conclusion: Senolytic therapy for vascular aging-related disorders holds great promise, provided that crucial concerns surrounding its outcomes and commercial hurdles are effectively addressed

Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms

Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of chronic kidney disease (CKD) and one of the causes of long-term renal loss in transplanted kidneys. Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.Methods: We screened all 162 “kidney transplant”–related cohorts in the GEO database and obtained five data sets (training sets: GSE98320 and GSE76882, validation sets: GSE22459 and GSE53605, and survival set: GSE21374). The training set was constructed after removing batch effects of GSE98320 and GSE76882 by using the SVA package. The differentially expressed gene (DEG) analysis was used to identify necroptosis-related DEGs. A total of 13 machine learning algorithms—LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs—were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values. Post-transplantation patients were then grouped using consensus clustering, and the different subgroups were further explored using PCA, Kaplan–Meier (KM) survival analysis, functional enrichment analysis, CIBERSOFT, and single-sample Gene Set Enrichment Analysis.Results: A total of 55 necroptosis-related DEGs were identified by taking the intersection of the DEGs and necroptosis-related gene sets. Stepglm[both]+RF is the optimal model with an average AUC of 0.822. A total of four molecular subgroups of renal transplantation patients were obtained by clustering, and significant upregulation of fibrosis-related pathways and upregulation of immune response–related pathways were found in the C4 group, which had poor prognosis.Conclusion: Based on the combination of the 13 machine learning algorithms, we developed 114 IFTA classification models. Furthermore, we tested the top model using two independent data sets from GEO