Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation of Hepatocyte Nuclear Factor 4α Attenuated Lipotoxicity but Increased Bile Acid Toxicity in Non-Alcoholic Fatty Liver Disease

Simple Summary Hepatic HNF4α inhibition can attenuate bile acid toxicity in NAFLD. However, it can aggravate hepatic steatosis, so it is necessary to study the optimal timing of HNF4α inhibition and the development of surrogate markers that can predict it. It is known that bile acid–induced toxicity, along with lipotoxicity, plays a very important role in NAFLD patients. Except in very exceptional conditions, lipotoxicity caused by free fatty acids and bile acid toxicity caused by bile acid always coexist in the NAFLD human condition. In this respect, we believe that inhibiting HNF4α is more effective as a therapeutic strategy for NAFLD. Abstract Hepatocyte nuclear factor 4 alpha (HNF4α) is a key master transcriptional factor for hepatic fat and bile acid metabolic pathways. We aimed to investigate the role of HNF4α in non-alcoholic fatty liver disease (NAFLD). The role of HNF4α was evaluated in free fatty acid–induced lipotoxicity and chenodeoxycholic acid (CDCA)-induced bile acid toxicity. Furthermore, the role of HNF4α was evaluated in a methionine choline deficiency (MCD)-diet-induced NAFLD model. The overexpression of HNF4α reduced intracellular lipid contents and attenuated palmitic acid (PA)-induced lipotoxicity. However, the protective effects of HNF4α were reversed when CDCA was used in a co-treatment with PA. HNF4α knockdown recovered cell death from bile acid toxicity. The inhibition of HNF4α decreased intrahepatic inflammation and the NAFLD activity score in the MCD model. Hepatic HNF4α inhibition can attenuate bile acid toxicity and be more effective as a therapeutic strategy in NAFLD patients; however, it is necessary to study the optimal timing of HNF4α inhibition.
A High-Precision Method of Stiffness Axes Identification for Axisymmetric Resonator Gyroscopes

Axisymmetric resonators are key elements of Coriolis vibratory gyroscopes (CVGs). The performance of a CVG is closely related to the stiffness and damping symmetry of its resonator. The stiffness symmetry of a resonator can be effectively improved by electrostatic tuning or mechanical trimming, both of which need an accurate knowledge of the azimuth angles of the two stiffness axes of the resonator. Considering that the motion of a non-ideal axisymmetric resonator can be decomposed as two principal oscillations with two different natural frequencies along two orthogonal stiffness axes, this paper introduces a novel high-precision method of stiffness axes identification. The method is based on measurements of the phase difference between the signals detected at two orthogonal sensing electrodes when an axisymmetric resonator is released from all the control forces of the force-to-rebalance mode and from different initial pattern angles. Except for simplicity, our method works with the eight-electrodes configuration, in no need of additional electrodes or detectors. Furthermore, the method is insensitive to the variation of natural frequencies and operates properly in the cases of either large or small frequency splits. The introduced method is tested on a resonator gyroscope, and two stiffness axes azimuth angles are obtained with a resolution better than 0.1°. A comparison of the experimental results and theoretical model simulations confirmed the validity of our method.
Josephson-Threshold Calorimeter

We suggest a single-photon thermal detector based on the abrupt jump of the critical current of a temperature-biased tunnel Josephson junction formed by different superconductors, working in the dissipationless regime. The electrode with the lower critical temperature is used as radiation sensing element, so it is kept thermally floating and is connected to an antenna. The warming up resulting from the absorption of a photon can induce a drastic measurable enhancement of the critical current of the junction. We propose a detection scheme based on a threshold mechanism for single- or multi-photon detection. This Josephson threshold detector has indeed calorimetric capabilities being able to discriminate the energy of the incident photon. So, for the realistic setup that we discuss, our detector can efficiently work as a calorimeter for photons from the mid infrared, through the optical, into the ultraviolet, specifically, for photons with frequencies in the range $[30-9\times10^4]\;\text{THz}$. In the whole range of detectable frequencies a resolving power significantly larger than one results. In order to reveal the signal, we suggest the fast measurement of the Josephson kinetic inductance. Indeed, the photon-induced change in the critical current affects the Josephson kinetic inductance of the junction, which can be non-invasively read through a LC tank circuit, inductively coupled to the junction. Finally, this readout scheme shows remarkable multiplexing capabilities.
Investigation of Deep Spiking Neural Networks Utilizing Gated Schottky Diode as Synaptic Devices

Deep learning produces a remarkable performance in various applications such as image classification and speech recognition. However, state-of-the-art deep neural networks require a large number of weights and enormous computation power, which results in a bottleneck of efficiency for edge-device applications. To resolve these problems, deep spiking neural networks (DSNNs) have been proposed, given the specialized synapse and neuron hardware. In this work, the hardware neuromorphic system of DSNNs with gated Schottky diodes was investigated. Gated Schottky diodes have a near-linear conductance response, which can easily implement quantized weights in synaptic devices. Based on modeling of synaptic devices, two-layer fully connected neural networks are trained by off-chip learning. The adaptation of a neuron’s threshold is proposed to reduce the accuracy degradation caused by the conversion from analog neural networks (ANNs) to event-driven DSNNs. Using left-justified rate coding as an input encoding method enables low-latency classification. The effect of device variation and noisy images to the classification accuracy is investigated. The time-to-first-spike (TTFS) scheme can significantly reduce power consumption by reducing the number of firing spikes compared to a max-firing scheme.
Effective management of cardiothoracic surgical wounds: a scoping review

Cardiac surgery is included among the seven surgical procedures with the highest surgical site infection rate. Surgical wounds have been a major topic worldwide, having the healthcare team to face a big challenge on their management, giving a multidisciplinary approach to the patient. The following scoping review aims to capture the most effective management of the cardiothoracic surgical wound, comparing conventional and more advanced techniques. Methods. A scoping review completed of seven full text free of charge publications corresponding to the research topic. Selection criteria for publi cations were determined by researchers. A scoping review and analysis of the scientific literature was performed. Results. There is no consensus between which of the two therapies is the best for cardiac wound management. While some of the authors consider negative pressure wound therapy a better choice due to healing time, cost-effectivity and the number of complications, yet others claim that there are no differences. Despite of whether this therapy is considered a superior or equal method to wound dressings, each particular case must be individually evaluated and all methods are taken for consideration. Conclusions. Two principal methods were found for wound management - conventional wound dressings and negative pressure wound therapy. By reviewing the publications hardly any differences were found in the use of one or the other therapy.
A Routing Optimization Method for Software-Defined Optical Transport Networks Based on Ensembles and Reinforcement Learning

Optical transport networks (OTNs) are widely used in backbone- and metro-area transmission networks to increase network transmission capacity. In the OTN, it is particularly crucial to rationally allocate routes and maximize network capacities. By employing deep reinforcement learning (DRL)- and software-defined networking (SDN)-based solutions, the capacity of optical networks can be effectively increased. However, because most DRL-based routing optimization methods have low sample usage and difficulty in coping with sudden network connectivity changes, converging in software-defined OTN scenarios is challenging. Additionally, the generalization ability of these methods is weak. This paper proposes an ensembles- and message-passing neural-network-based Deep Q-Network (EMDQN) method for optical network routing optimization to address this problem. To effectively explore the environment and improve agent performance, the multiple EMDQN agents select actions based on the highest upper-confidence bounds. Furthermore, the EMDQN agent captures the network’s spatial feature information using a message passing neural network (MPNN)-based DRL policy network, which enables the DRL agent to have generalization capability. The experimental results show that the EMDQN algorithm proposed in this paper performs better in terms of convergence. EMDQN effectively improves the throughput rate and link utilization of optical networks and has better generalization capabilities.
Stock Prediction Analysis and Risk Conduction Path Research Based on EMD-LSTM Model

Stock market, government bond market and corporate bond market are important components of my country's financial market. It is of great significance to study stock market price fluctuations and their forecasting methods, and to reveal the risk transmission path between them. The first part of this paper mainly studies the construction of the index price prediction model, and makes a comprehensive comparison of the high-frequency fluctuation characteristics, long-term trend characteristics and average trend characteristics of the EMD decomposition results of the Shanghai Composite Index, the SSE Government Bond Index, and the SSE Corporate Bond Index. The LSTM prediction model and the EMD-LSTM prediction model were established for the three sequences respectively, and the prediction effects of the models were comprehensively compared by calculating the RMSE and MAPE of the predicted values of the test set. The results show that after the introduction of the EMD method, the prediction effects of the SSE Composite Index and the SSE Corporate Bond Index prediction model have been greatly improved, but the prediction effect of the SSE Government Bond Index prediction model is far inferior to that of the LSTM model. The second part of this paper mainly studies the financial market risk analysis and transmission path. First, the fluctuation risk analysis and stationarity analysis of the logarithmic return series of the three index price series are carried out. The yield series are stable. Then, the LSTM models based on different training data predict three index price series and compare the prediction results to study the similarity of the price fluctuation mechanism of the three financial markets. The results show that the price fluctuation mechanism of the stock market is similar to that of the corporate bond market, and the price fluctuation mechanism of the government bond market is more similar to the corporate bond market than the stock market. The paper conducts E-G cointegration test on three logarithmic return series that have been tested for stationarity to study the long-term equilibrium relationship between the three markets and finds that there is a cointegration relationship between the SSE Composite Index and the SSE Government Bond Index, as well as the SSE Government Bond Index and the SSE Corporate Bond Index. Finally, the Granger causality test is carried out between the sequences with cointegration relationship, and the result shows that the risk transmission mechanism between the national debt market and the corporate bond market is strong, while the risk transmission mechanism between the stock market and the bond market is weak.
Photoreceptor Enhanced Light Therapy (PELT): A Framework for Implementing BiologicallyDirected Integrative Lighting

ABSTRACT We introduce a lighting technology designed to produce photoreceptor-directed lights (PrD). This photoreceptor-enhanced light therapy (PELT) differs from conventional supplemental lighting by using multiple limited-bandwidth primaries to generate spectra that appear white, and that are tailored to produce circadian equivalent (CE) lights for selectively increasing or decreasing the relative activation levels of specific photoreceptor classes in the human eye. Rather than designing a device to match a spectrum’s shape, we optimize the available hardware, so it best matches the biological effects of that spectrum. It goes beyond three and four dimensions (three cones plus melanopsin) to consider the biological responses mediated via all five photoreceptor classes (including rhodopsin); the inclusion of a fifth photoreceptor class is non-trivial both in implementation and biological effect. Here, we describe the technical specifications of the PELT device and its calibration procedures. Photoreceptor-directed lights with variable melanopsin and rhodopsin excitations and equal photometric luminance are presented. Device application examples are provided that include personalized supplemental light spectra for patients with photoreceptor sensitivity loss, for healthy people exposed to extreme seasonal or work-related variation in their ambient lighting patterns, and as a stimulus generator to evaluate the effects of light on human health and behavior mediated via the melanopsin expressing intrinsically photosensitive retinal ganglion cells (ipRGCs). In integrative lighting practice, the PELT method extends to dynamic control of the biological potency of the melanopsin and rhodopsin excitations over a large range, independent of perceived changes in correlated color temperature (CCT).
Evidence for a strain-tuned topological phase transition in ZrTe5

In situ strain is used to induce a topological phase transition in single-crystal ZrTe5. A phase transition between topologically distinct insulating phases involves closing and reopening the bandgap. Near the topological phase transition, the bulk energy spectrum is characterized by a massive Dirac dispersion, where the bandgap plays the role of mass. We report measurements of strain dependence of electrical transport properties of ZrTe5, which is known to host massive Dirac fermions in the bulk due to its proximity to a topological phase transition. We observe that the resistivity exhibits a pronounced minimum at a critical strain. We further find that the positive longitudinal magnetoconductance becomes maximal at the critical strain. This nonmonotonic strain dependence is consistent with the switching of sign of the Dirac mass and, hence, a strain-tuned topological phase transition in ZrTe5.
Pairwise velocities in the "Running FLRW" cosmological model

We present an analysis of the pairwise velocity statistics from a suite of cosmological N-body simulations describing the "Running Friedmann-Lema\^itre-Robertson-Walker" (R-FLRW) cosmological model. This model is based on quantum field theory in a curved space-time and extends {\Lambda}CDM with a time-evolving vacuum energy density. To enforce local conservation of matter a time-evolving gravitational coupling is also included. Our results constitute the first study of velocities in the R-FLRW cosmology, and we also compare with other dark energy simulations suites, repeating the same analysis. We find a strong degeneracy between the pairwise velocity and {\sigma}_8 at z=0 for almost all scenarios considered, which remains even when we look back to epochs as early as z=2. We also investigate various Coupled Dark Energy models, some of which show minimal degeneracy, and reveal interesting deviations from {\Lambda}CDM which could be readily exploited by future cosmological observations to test and further constrain our understanding of dark energy.
Neoantigen Vaccines; Clinical Trials, Classes, Indications, Adjuvants and Combinatorial Treatments

Simple Summary Personalized neoantigen vaccines are a diverse group of personally tailored cancer vaccines that strengthen patients´ own immune reaction against cancer antigens. We analyzed 147 neoantigen vaccine clinical trials from ClinicalTrials.gov database and showed that peptide vaccines were the dominating vaccine type, while there were multiple new neoantigen vaccine types in the field. We also showed that neoantigen vaccines were mostly used in the treatment of small cell lung cancer, non-small cell lung cancer, melanoma, and glioma. According to our results neoantigen vaccines work at their best when combined with other treatments such as immune checkpoint inhibitors, chemotherapy, and radiation therapy. The effect of some neoantigen vaccine types were also often promoted with adjuvant therapy where poly-ICLC were the most recurrent adjuvant choice. Abstract Personalized neoantigen vaccines are a highly specific cancer treatment designed to induce a robust cytotoxic T-cell attack against a patient’s cancer antigens. In this study, we searched ClinicalTrials.gov for neoantigen vaccine clinical trials and systematically analyzed them, a total of 147 trials. Peptide vaccines are the largest neoantigen vaccine type, comprising up to 41% of the clinical trials. However, mRNA vaccines are a growing neoantigen vaccine group, especially in the most recent clinical trials. The most common cancer types in the clinical trials are glioma, lung cancer, and malignant melanoma, being seen in more than half of the clinical trials. Small-cell lung cancer and non-small-cell lung cancer are the largest individual cancer types. According to the results from the clinical trials, neoantigen vaccines work best when combined with other cancer treatments, and popular combination treatments include immune checkpoint inhibitors, chemotherapy, and radiation therapy. Additionally, half of the clinical trials combined neoantigen vaccines with an adjuvant to boost the immune effects, with poly-ICLC being the most recurrent adjuvant choice. This study clarifies the rapid clinical trial development of personalized neoantigen vaccines as an emerging class of cancer treatment with increasingly diversified opportunities in classes, indications, and combinatorial treatments.
Research of dynamics of the vibrating machine's downloading with the vertical movement

The purpose of research. The main purpose of the work is to develop mathematical software for studying the dynamics of vibration machines of volumetric processing, and calculate the influence of various factors on the efficiency of the process based on applied systems of automated mathematical calculations, including MathCad and MatLab. Method. The research was carried out on the basis of a class of vibrating machines with unbalanced drive type and spring suspension. A mathematical model of loading vibrating processing machines with vertical perturbation is constructed, which is represented by a layering of flat beams that perform vertical oscillations. Methods of nonlinear mechanics were used to build a mathematical model. Results. The dynamic processes at vibration compaction of loading and separation of its fractions for the purpose of increase of intensity of the given technological processes are investigated. The dependences for determining the influence of physical and mechanical properties of loading components on the process dynamics are obtained. Scientific novelty. New approaches for construction of mathematical models of research of processes in machines of vibration processing, in particular vibration consolidation and separation are considered. In the course of research, differential equations were used to determine the change in the amplitude and frequency of loading from the influence of external and internal parameters of the vibration processing process. Practical significance. The obtained mathematical model makes it possible to investigate the dynamics of loading of the vibrating machine, to determine the amplitude and frequency of oscillations of loading of the vibrating machine depending on its properties and the properties of the vibrating machine. The obtained solutions of nonlinear differential equations to describe the motion of loading in vibrating machines allow to automatically determine the amplitudes, frequencies and trajectories of different components of loading depending on the parameters of the system "vibrating machine - loading", by algorithmizing them in applied systems of automated mathematical calculations.
TVID: Three-loop Vacuum Integrals from Dispersion relations

TVID is a program for the numerical evaluation of general three-loop vacuum integrals with arbitrary masses. It consists of two parts. An algebraic module, implemented in Mathematica, performs the separation of the divergent pieces of the master integrals and identifies special cases. The numerical module, implemented in C, carries out the numerical integration of the finite pieces. In this note, the structure of the program is explained and a few usage examples are given.
EXPERIENCES OF FAMILIES CARING FOR THE ELDERLY WITH DEMENTIA: SCOPING REVIEW

Introduction. The increase in life expectancy of the elderly continues to increase, which has implications for the increase in the incidence of Alzheimer's dementia in the elderly. The elderly with Alzheimer's dementia will experience changes that cause them elderly to be different from their previous personality and behaviour. This will be a burden for the family.This study was to obtain an overview of families' experiences in caring for the elderly with Alzheimer's dementia. Methods.The studyused is a scoping with conducting a focusing review with the PEOS framework (Population, Exposure, Outcome and Study Design), conducting literature searching using relevant databases. Select relevant studies, perform data extraction, analyze and report results. PRISMA Flowchart (Preferred Reporting Items for systematic reviews and Meta-Analyses) was used to describe the literature search flow about experiences of families caring for the elderly with dementia. Results & Analysis. Eight (8) literatures were selected, and positive and negative themes emerged. The upbeat theme is that religious and cultural beliefs patiently cause families to care for the elderly with Alzheimer's dementia. While the negative themes that emerged were Caregiver experiences (emotional and psychological), Loss and stress. Discussion. The most common theme in this study is a negative theme, so it is necessary to be aware of the importance of mentoring and support assistance for families who care for the elderly with dementia.