Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Feed Supplementation with Bacillus coagulans on Nrf Gene Family Expression in Common Carp (Cyprinus carpio) under Long-Term Exposure to Cd2+

Nuclear factor-E2-related factor (Nrf) belongs to the Cap ‘n’ collar basic leucine zipper (CNC-bZIP) family, which plays an important role in the resistance to oxidative stress in the body. In this study, 12 Nrf genes were identified in the common carp genome database. Comparative genomic analysis showed that the Nrf genes of common carp had significant amplification, confirming that the common carp had experienced four genome-wide replication events. Phylogenetic analysis showed that all common carp Nrf clustered with scleractinian fish Nrf, indicating that they were highly conserved during evolution. In addition, tissue distribution results showed that most Nrf genes had a broad tissue distribution but exhibited tissue-specific expression patterns, demonstrating functional differences after WGD events. At 30 and 60 days of Cd2+ stress, most of the Nrf genes showed an increase in expression compared with the control group, indicating that they played a key role in the organism’s response to oxidative stress. To find a suitable concentration of Bacillus coagulans to activate the Nrf genes, we added three different concentrations (2.0 × 107 CFU/g, 2.0 × 108 CFU/g, and 2.0 × 109 CFU/g) of B. coagulans into the feed and defined them as L1, L2, and L3 groups, respectively. We investigated the effect of different concentrations of B. coagulans in the feed on the expression level of Nrf genes in the intestine of common carp under Cd2+ stress at 30 and 60 days. The results showed that, compared with the control/stress group, the expression of different Nrf genes was improved to varying degrees at three concentrations, and the effect of the L2 group (2.0 × 108 CFU/g) was the best. This suggests that the L2 group is the optimum concentration for activating Nrf gene expression when subjected to heavy metal Cd2+ stress and may act as an activation switch with a prominent role in the body’s resistance to oxidative stress and immune response.
Chemotherapy-Free Targeted Anti-BCR-ABL+ Acute Lymphoblastic Leukemia Therapy May Benefit the Heart

Simple Summary Risk-adapted multiagent chemotherapy has led to a remarkable improvement in the life expectancy of patients with acute lymphoblastic leukemia (ALL). Nevertheless, in high-risk subgroups such as BCR-ABL+ ALL, relapse rates remain high without allogeneic hematopoietic stem cell transplantation, and the adverse effects of chemotherapy may cause acute and chronic cardiac complications or dysfunction. Here, we demonstrated that chemotherapy-free targeted therapies designed to optimize apoptosis induction in BCR-ABL+ ALL may circumvent cardiac on-target side effects and may even activate cardiac recovery. Abstract Targeted therapies are currently considered the best cost–benefit anti-cancer treatment. In hematological malignancies, however, relapse rates and non-hematopoietic side effects including cardiotoxicity remain high. Here, we describe significant heart damage due to advanced acute lymphoblastic leukemia (ALL) with t(9;22) encoding the bcr-abl oncogene (BCR-ABL+ ALL) in murine xenotransplantation models. Echocardiography reveals severe cardiac dysfunction with impaired left ventricular function and reduced heart and cardiomyocyte dimensions associated with increased apoptosis. This cardiac damage is fully reversible, but cardiac recovery depends on the therapy used to induce ALL remission. Chemotherapy-free combination therapy with dasatinib (DAS), venetoclax (VEN) (targeting the BCR-ABL oncoprotein and mitochondrial B-cell CLL/Lymphoma 2 (BCL2), respectively), and dexamethasone (DEX) can fully revert cardiac defects, whereas the depletion of otherwise identical ALL in a genetic model using herpes simplex virus type 1 thymidine kinase (HSV-TK) cannot. Mechanistically, dexamethasone induces a pro-apoptotic BCL2-interacting mediator of cell death (BIM) expression and apoptosis in ALL cells but enhances pro-survival B-cell lymphoma extra-large (BCLXL) expression in cardiomyocytes and clinical recovery with the reversion of cardiac atrophy. These data demonstrate that therapies designed to optimize apoptosis induction in ALL may circumvent cardiac on-target side effects and may even activate cardiac recovery. In the future, combining the careful clinical monitoring of cardiotoxicity in leukemic patients with the further characterization of organ-specific side effects and signaling pathways activated by malignancy and/or anti-tumor therapies seems reasonable.
Régulation des pratiques amateurs et accompagnement de la professionnalisation : la stratégie de YouTube dans la course aux contenus exclusifs

Vincent Bullich Maître de conférences en sciences de l’information et de la communication – UFR des sciences de la communication Paris-Nord 13 et membre du LabSic (laboratoire des sciences de l’information et de la communication) et de l’OMIC de la Maison des Sciences de l’Homme Paris Nord. Ses travaux concernent principalement l’analyse socio-économique des industries culturelles et communicationnelles ainsi que l’économie politique de la propriété intellectuelle. vincent.bullich@laposte.net.
Small Molecules as Modulators of Voltage-Gated Calcium Channels in Neurological Disorders: State of the Art and Perspectives

Voltage-gated calcium channels (VGCCs) are widely expressed in the brain, heart and vessels, smooth and skeletal muscle, as well as in endocrine cells. VGCCs mediate gene transcription, synaptic and neuronal structural plasticity, muscle contraction, the release of hormones and neurotransmitters, and membrane excitability. Therefore, it is not surprising that VGCC dysfunction results in severe pathologies, such as cardiovascular conditions, neurological and psychiatric disorders, altered glycemic levels, and abnormal smooth muscle tone. The latest research findings and clinical evidence increasingly show the critical role played by VGCCs in autism spectrum disorders, Parkinson’s disease, drug addiction, pain, and epilepsy. These findings outline the importance of developing selective calcium channel inhibitors and modulators to treat such prevailing conditions of the central nervous system. Several small molecules inhibiting calcium channels are currently used in clinical practice to successfully treat pain and cardiovascular conditions. However, the limited palette of molecules available and the emerging extent of VGCC pathophysiology require the development of additional drugs targeting these channels. Here, we provide an overview of the role of calcium channels in neurological disorders and discuss possible strategies to generate novel therapeutics.
Low-Rank Tensor Based Proximity Learning for Multi-View Clustering

Graph-oriented multi-view clustering methods have achieved impressive performances by employing relationships and complex structures hidden in multi-view data. However, most of them still suffer from the following two common problems. (1) They target at studying a common representation or pairwise correlations between views, neglecting the comprehensiveness and deeper higher-order correlations among multiple views. (2) The prior knowledge of view-specific representation can not be taken into account to obtain the consensus indicator graph in a unified graph construction and clustering framework. To deal with these problems, we propose a novel Low-rank Tensor Based Proximity Learning (LTBPL) approach for multi-view clustering, where multiple low-rank probability affinity matrices and consensus indicator graph reflecting the final performances are jointly studied in a unified framework. Specifically, multiple affinity representations are stacked in a low-rank constrained tensor to recover their comprehensiveness and higher-order correlations. Meanwhile, view-specific representation carrying different adaptive confidences is jointly linked with the consensus indicator graph. Extensive experiments on nine real-world datasets indicate the superiority of LTBPL compared with the state-of-the-art methods.
Speak *Now* or Forever Hold Your Peace: Power Law Chronemics of Turn-Taking and Response in Asynchronous CMC

Turn-taking is a key characteristic of conversation, and is well studied in diverse contexts. How do insights about chronemics in face-to-face communication transfer to the online context? The research reported here looks into turn-taking in asynchronous computer mediated communication by analyzing three diverse datasets comprising a total of more than 150,000 responses: email responses created by corporate employees in work contexts, responses created by university students participating in course discussion groups, and responses to questions posted in a public, commercial information seeking virtual forum. The response times revealed in each of the datasets yielded a power-law distribution. Moreover, most of the responses (at least 70%) were created within the average response time of the responders, and very few (at most 4%) of the responses were created after a period longer than ten times the average response time. These generalized findings persist across various users, contexts, and even across traditional as well as online communication media. Asynchronous CMC seems more synchronous than is implied by its reputation. email implies a widespread expectation of fairly rapid response. The implications of this uniformity are discussed, as well as some possible applications of the findings to the improvement of computer mediated communication and to the research of online silence.
First Report of Isolation and Molecular Characterization of Felid Herpesvirus-1 from Symptomatic Domestic Cats in Egypt

Feline herpesvirus 1 (FHV-1) is one of the main causes of upper respiratory tract infection in cats. Despite its veterinary importance, no previous studies investigated the occurrence of this virus in Egypt. In the present work, a total number of one hundred forty (N = 140) conjunctival and/or oropharyngeal swabs were collected from symptomatic cats during veterinary clinic visits located in two Egyptian provinces. Virus isolation was performed in the Chorioallantoic membranes (CAMs) of 12-days-old SPF eggs. Interestingly, the embryos showed stunting growth and abnormal feathering and infected CAMs showed edematous thickening and cloudiness with characteristic white opaque pock lesions. Polymerase chain reaction (PCR) amplification of the thymidine kinase gene (TK) was successful in 16/140 (11.4%) of the suspected cases. Two of the amplified genes were sequenced and the TK gene sequences of the FHV-1 isolates were highly similar to other reference strains in the GenBank database. Given the above information, the present study represents the first report of feline herpesvirus type 1 (FHV-1) in domestic cats in Egypt. Further studies on the causes of upper respiratory tract infections in cats as well as vaccine efficacy are needed.
The Method for the Optimal Generation of Test Sequence for CTCS-3 On-Board Equipment

According to the functional testing of the on-board equipment of CTCS3 train control system,the optimal generation of test sequence for the on-board equipment by solving Chinese postman problem is studied.Firstly,on the basis of analyzing the structure method of test sequence,it is proposed that the optimal generation of test sequence can be converted into the solution of a Chinese postman problem for multi-edge directed graph.Secondly,the Edmonds-Johnson algorithm is suitably modified and applied to solve the Chinese postman problem with the assistance of LINGO modeling tool,namely,through a series of steps of establishing the capacity network,solving the minimum cost maximum flow with LINGO,and finding the optimal path in the Euler graph.Finally,considering that some of the test sequences resulted from the aforementioned process may be too long to be suitable for practical use,an effective method is proposed to cut these sequences into shorter ones.The entire process is illustrated with the schematic diagram of the transitions between operating modes of CTCS-3 on-board equipment as an example.
Reinforcement Learning Guided by Provable Normative Compliance

: Reinforcement learning (RL) has shown promise as a tool for engineering safe, ethical, or legal behaviour in autonomous agents. Its use typically relies on assigning punishments to state-action pairs that constitute unsafe or unethical choices. Despite this assignment being a crucial step in this approach, however, there has been limited discussion on generalizing the process of selecting punishments and deciding where to apply them. In this paper, we adopt an approach that leverages an existing framework – the normative supervisor of (Neufeld et al., 2021) – during training. This normative supervisor is used to dynamically translate states and the applicable normative system into defeasible deontic logic theories, feed these theories to a theorem prover, and use the conclusions derived to decide whether or not to assign a punishment to the agent. We use multiobjective RL (MORL) to balance the ethical objective of avoiding violations with a non-ethical objective; we will demonstrate that our approach works for a multiplicity of MORL techniques, and show that it is effective regardless of the magnitude of the punishment we assign.
A Framework for Generating Playstyles of Game AI with Clustering of Play Logs

Many attempts have been made to implement agents for playing games with particular playstyles. Most of these were aimed at generating agents with predetermined playstyles. To this end, they set the reward function to increase the reward as the agent acquires their intended playstyles. However, it is not easy to generate unexpected playstyles through this approach. In this study, we propose a framework to generate multiple playstyles without predefining them. The proposed framework first arranges a set of reward functions regarding the target game and repeats to select a function and make an agent learn with it. Each learned agent is made to play the game, and those whose scores are higher than a predetermined threshold are selected. Finally, each cluster obtained from clustering the play logs (i.e., metrics on the behavior in the game) of the selected agents is considered a playstyle. As a result, it is possible to generate playstyles that play the game well using this procedure. We also applied the proposed framework to a roguelike game, MiniDungeons, and observed that multiple playstyles were generated.
Classification of Table Tennis Strokes in Wearable Device using Deep Learning

: Analysis of sports performance using mobile and wearable devices is becoming increasingly popular, helping users improve their sports practice. In this context, the goal of this work has been the development of an Apple Watch application, capable of detecting important strokes in the table tennis sport, using a deep learning (DL) model. A dataset of table tennis strokes has been created based on the watch’s accelerometer and gyroscope sensors. The dataset collection was done in the Portuguese table tennis federation training sites, from several athletes, supervised by their coaches. To obtain the best DL model, three different architecture models where trained, compared and evaluated, using the complete dataset: a LSTM based on Create ML/Core ML frame-works (62.70% F1 score) and two Tensorﬂow based architectures, a CNN-LSTM (96.02% F1 score) and a ConvLSTM (97.33% F1 score).
A statistical framework of data-driven bottleneck identification in manufacturing systems

Data-driven bottleneck identification has received an increasing interest during the recent years. This approach locates the throughput bottleneck of manufacturing systems based on indicators derived from measured machine performance metrics. However, the variability in manufacturing systems may affect the quality of bottleneck indicators, leading to possible inaccurate detection results. This paper presents a statistical framework (SF) to decrease the data-driven detection inaccuracy caused by system variability. Using several statistical tools as building blocks, the proposed SF is able to analyse the logical conditions under which a machine is detected as the bottleneck, and rejects the proposal of bottleneck when no sufficient statistical evidence is collected. A full factorial design experiment is used to study the parameter effects of the SF, and to calibrate the SF. The proposed SF was numerically verified to be effective in decreasing the wrong bottleneck detection rate in serial production lines.
Bearing performance degradation assessment based on Bayesian inference and self-organizing map

A bearing performance degradation assessment method based on Bayesian inference and Self-Organizing Map(SOM) was proposed.Independent Component Analysis(ICA) was used to extract feature set of normal operation bearing from original feature set,and a SOM model was established to describe the healthy bearing state space.Furthermore,Negative Log Likelihood Probability(NLLP)-based performance quantification index and Bayesian Inference-based failure probability calculation method were developed to evaluate and identify dynamical running states of bearings online.In comparison with traditional feature indexes and Support Vector Data Description(SVDD) based performance degradation assessment,the experimental results of a bearing test bed showed that the proposed evaluation index was more effective to quantify the performance degradation propagation on the whole life of bearings.It could provide important healthy information for making maintenance plan.
How to Simplify Law Automatically? A Study on South Korean Legislation and Its Simplified Version

People with a low literacy level have problems understanding complex texts. Especially legal texts can be challenging. Automatic Text Simplification (TS) can help to make the legal text more accessible. However, most TS research is based on Wikipedia articles and newspaper articles. To be able to use automatic TS on the legal text we have to understand what constitutes simple legal text. Therefore, we examine the English translation of South Korean legislation and its official simplification. Subsequently, we use state of the art TS models on the legislation text. The models simplify the text only quantitatively lacking in retaining the context of the original text.
Development of an Automated Methodology for Calibration of Simplified Air-Side HVAC System Models and Estimation of Potential Savings from Retrofit/Commissioning Measures

Development of an Automated Methodology for Calibration of Simplified Air-Side HVAC System Models and Estimation of Potential Savings from Retrofit/Commissioning Measures. (December 2006) Juan Carlos Baltazar Cervantes, B.S., University of Guanajuato, México; M.S., University of Guanajuato, México; M.S., Texas A&M University Chair of Advisory Committee: Dr. David E. Claridge This dissertation provides one methodology to determine potential energy savings of buildings with limited information. This methodology is based upon the simplified energy analysis procedure of HVAC systems and the control of the comfort conditions. Numerically, the algorithm is a tailored exhaustive search over all the independent variables that are commonly controlled for a specific type of HVAC system. The potential energy savings methodology has been applied in several buildings that have been retrofitted and/or commissioned previously. Results from the determined savings for the Zachry building at Texas A&M after being commissioned show a close agreement to the calculated potential energy savings (about 85%). Differences are mainly attributed to the use of simplified models. Due to the restriction of limited information about the building characteristics and operational control, the potential energy savings method requires the determination of parameters that characterize its thermal performance. Thus, a calibrated building is needed. A general procedure has been developed to carry out automated calibration of building energy use simulations. The methodology has been tested successfully on building simulations based on the simplified energy analysis procedure. The automated calibration is the minimization of the RMSE of the energy use over daily conditions. The minimization procedure is fulfilled with a non-canonical optimization algorithm,
Data-Driven Modeling and Control Considering Time Delays for WPT System

A controller for output voltage and power regulation is important for the wireless charging system. For the controller design, it is crucial to establish the accurate system model with considering the time delay, as high time delay exists due to wireless communication, data sampling, and processor calculations for a practical wireless power transfer system. Presently, the common modeling methods, such as the coupled-mode method are based on circuit theories and the component parameters. However, the exact parameters are hard to get due to the diversity of the equipment and application scenarios. Especially, the circuit theories modeling cannot deal with the time delay from hardware limitation and wireless communication. Alternatively, a data-driven modeling method based on the simplified refined instrumental variable method is proposed in this article to get the control-oriented model with time delay. Moreover, based on the estimated model, a controller based on the internal model control is designed to regulate the output voltage. It is shown that the models obtained based on different sampling times are able to accurately describe the system dynamics and the controllers designed based on them are able to achieve the desired control performance.
Local Group timing argument and virial theorem mass estimators from cosmological simulations

We identify Local Group (LG) analogs in the IllustrisTNG cosmological simulation, and use these to study two mass estimators for the LG: one based on the timing argument (TA) and one based on the virial theorem (VT). Including updated measurements of the Milky Way-M31 tangential velocity and the cosmological constant, we show that the TA mass estimator slightly overestimates the true median LG-mass, though the ratio of the TA to the true mass is consistent at the approximate 90% c.l. These are in broad agreement with previous results using dark matter-only simulations. We show that the VT estimator better estimates the true LG-mass, though there is a larger scatter in the virial mass to true mass ratio relative to the corresponding ratio for the TA. We attribute the broader scatter in the VT estimator to several factors, including the predominantly radial orbits for LG satellite galaxies, which differs from the VT assumption of isotropic orbits. With the systematic uncertainties we derive, the updated measurements of the LG mass at 90% c.l. are $4. 75_{-2.41}^{+2.22} \times 10^{12}$ M⊙ from the TA and $2. 0_{-1.5}^{+2.1} \times 10^{12}$ M⊙ from the VT. We consider the LMC’s effect on the TA and VT LG mass estimates, and do not find exact LMC-MW-M31 analogues in the Illustris simulations. However, in LG simulations with satellite companions as massive as the LMC we find that the effect on the TA and VT estimators is small, though we need further studies on a larger sample of LMC-MW-M31 systems to confirm these results.