Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of 1600 replication origins in S. cerevisiae

There are approximately 500 known origins of replication in the yeast genome, and the process by which DNA replication initiates at these locations is well understood. In particular, these sites are made competent to initiate replication by loading of the Mcm replicative helicase prior to the start of S phase; thus, “a site to which MCM is bound in G1” might be considered to provide an operational definition of a replication origin. By fusing a subunit of Mcm to micrococcal nuclease, a technique referred to as “Chromatin Endogenous Cleavage”, we previously showed that known origins are typically bound by a single Mcm double hexamer, loaded adjacent to the ARS consensus sequence (ACS). Here we extend this analysis from known origins to the entire genome, identifying candidate Mcm binding sites whose signal intensity varies over at least 3 orders of magnitude. Published data quantifying the production of ssDNA during S phase showed clear evidence of replication initiation among the most abundant 1600 of these sites, with replication activity decreasing in concert with Mcm abundance and disappearing at the limit of detection of ssDNA. Three other hallmarks of replication origins were apparent among the most abundant 5,500 sites. Specifically, these sites (1) appeared in intergenic nucleosome-free regions that were flanked on one or both sides by well-positioned nucleosomes; (2) were flanked by ACSs; and (3) exhibited a pattern of GC skew characteristic of replication initiation. Furthermore, the high resolution of this technique allowed us to demonstrate a strong bias for detecting Mcm double-hexamers downstream rather than upstream of the ACS, which is consistent with the directionality of Mcm loading by Orc that has been observed in vitro. We conclude that, if sites at which Mcm double-hexamers are loaded can function as replication origins, then DNA replication origins are at least 3-fold more abundant than previously assumed, and we suggest that replication may occasionally initiate in essentially every intergenic region. These results shed light on recent reports that as many as 15% of replication events initiate outside of known origins, and this broader distribution of replication origins suggest that S phase in yeast may be less distinct from that in humans than is widely assumed.
ARDitox: platform for the prediction of TCRs potential off-target binding

Cellular immunotherapies, such as those utilizing T lymphocytes expressing native or engineered T cell receptors (TCRs), have already demonstrated therapeutic efficacy. However, some high-affinity TCRs have also proved to be fatal due to off-target immunotoxicity. This process occurs when the immune system acts against epitopes found on both tumor cells and healthy tissues. Moreover, some TCRs can be cross-reactive to epitopes with highly dissimilar sequences. To address this issue, we developed ARDitox, a novel in silico method based on computational immunology and artificial intelligence (AI) for predicting and analyzing potential off-target binding. We tested the performance of ARDitox in silico on different cases found in the literature where TCRs were used to target cancer-related antigens, as well as on a set of TCRs targeting a viral epitope. ARDitox was able to identify previously reported cross-reactive epitopes in line with the data available in the literature. In addition, we investigated a TCR targeting an HLA-A*02:01-restricted immunodominant epitope from the glioblastoma-associated antigen NLGN4X, identifying a cross-reactive ADH1A epitope that would not be detected in murine models. In conclusion, our in silico approach is a powerful tool that identifies potential off-target epitopes, complementing preclinical studies in developing safer cell therapies targeting tumor(- associated) antigens.
A molecular arm: the molecular bending–unbending mechanism of integrin

The balance of integrin activation and deactivation regulates its function and mediates cell behaviors. Mechanical force triggers the unbending and activation of integrin. However, how an activated and extended integrin spontaneously bends back is unclear. I performed all-atom molecular dynamics simulations on an integrin or its subunits to reveal the bending-unbending mechanism of integrin. According to the simulations, the integrin structure works like a human arm. The integrin α subunit serves as the bones, while the β leg serves as the bicep. The integrin extension results in the stretching of the β leg, and the extended integrin spontaneously bends as a consequence of the contraction of the β leg. This study provides new insights into the mechanism of how the integrin secures in the bent inactivated state and sheds light on how the integrin could achieve a stable extended state.
Modelling human KCNT1-epilepsy in Drosophila: a seizure phenotype and drug responses

Mutations in the KCNT1 potassium channel cause severe forms of epilepsy which are resistant to current treatments. In vitro studies have shown that KCNT1-epilepsy mutations are gain of function, significantly increasing K+ current amplitudes. To investigate if Drosophila can be used to model human KCNT1 epilepsy, we generated Drosophila melanogaster lines carrying human KCNT1 with the patient mutation G288S, R398Q or R928C. Expression of each mutant channel in GABAergic neurons gave a seizure phenotype which was sensitive to drugs currently used to treat patients with KCNT1-epilepsy. Cannabidiol showed the greatest reduction of the seizure phenotype while some drugs increased the seizure phenotype. Our study shows that Drosophila can be used to model human KCNT1-epilepsy and potentially used as a tool to assess new treatments for KCNT1 epilepsy.
Evaluation of Physics-Informed Neural Network Solution Accuracy and Efficiency for Modeling Aortic Transvalvular Blood Flow

Physics-Informed Neural Networks (PINNs) are a new class of machine learning algorithms that are capable of accurately solving complex partial differential equations (PDEs) without training data. By introducing a new methodology for fluid simulation, PINNs provide the opportunity to address challenges that were previously intractable, such as PDE problems that are ill-posed. PINNs can also solve parameterized problems in a parallel manner, which results in favorable scaling of the associated computational cost. The full potential of the application of PINNs to solving fluid dynamics problems is still unknown, as the method is still in early development: many issues remain to be addressed, such as the numerical stiffness of the training dynamics, the shortage of methods for simulating turbulent flows and the uncertainty surrounding what model hyperparameters perform best. In this paper, we investigated the accuracy and efficiency of PINNs for modeling aortic transvalvular blood flow in the laminar and turbulent regimes, using various techniques from the literature to improve the simulation accuracy of PINNs. Almost no work has been published, to date, on solving turbulent flows using PINNs without training data, as this regime has proved difficult. This paper aims to address this gap in the literature, by providing an illustrative example of such an application. The simulation results are discussed, and compared to results from the Finite Volume Method (FVM). It is shown that PINNs can closely match the FVM solution for laminar flow, with normalized maximum velocity and normalized maximum pressure errors as low as 5.74% and 9.29%, respectively. The simulation of turbulent flow is shown to be a greater challenge, with normalized maximum velocity and normalized maximum pressure errors only as low as 41.8% and 113%, respectively.
Orbit-based analysis of nonlinear energetic ion dynamics in tokamaks. I. Effective mode number profile and resonant frequency tracking

The nonlinear interactions between shear Alfven modes and tangentially injected beam ions in the 150–400 keV range are studied numerically in a JT-60U tokamak scenario with realistic geometry, large magnetic drifts, and strong beam drive. For this purpose, the recently developed orbit-based resonance analysis (ORA) method for circulating particles is extended, so that it can be applied to the nonlinear regime, where the spectrum of orbit-based poloidal mode numbers morb varies in time as the fast ions undergo wave-particle trapping and radial transport. In particular, the extended ORA method captures the effect of nonlinear overlaps between resonances associated with neighboring harmonics (morb,n) and (morb+1,n) that cause long-distance ballistic transport. Two cases with low toroidal mode numbers n≳1 are studied: an n = 1 mode without resonance overlap and a strongly driven n = 3 mode with resonance overlap. For both cases, an effective radial profile of the resonant poloidal mode number mres=Meff(r) is .
INFORMATION DIGITALIZATION, CORPORATE SOCIAL RESPONSIBILITY AND ITS EFFECT ON FIRM VALUE

The main objective of this research is to analyze the effect of the level of information on digitalization and corporate social responsibility to firm value. Studies on property and real estate sector companies listed on the IDX for the 2019-2021 period. This type of research is quantitative research. The sampling technique used purposive sampling method and obtained a sample of 39 companies. This study use secondary data such are annual report and sustainability report. The data analysis method in this study used multiple regression analysis using SPSS version 26. The results showed that the level of information about digitalization and corporate social responsibility had influences to firm value with significance level of 0,000. In partially, the level of information about digitalization had influences to firm value with significance level of 0,001. While corporate social responsibility had effect to firm value with significance level of 0,000.
The Governance Effect of China's Green Finance Policy from an International Perspective

Most of the previous studies on the utility of green financial policies have focused on provincial data, exploring the differences between different provinces at the same time; few have focused on China as the main body, focusing on the positive effects of national-level green financial policies on the rapid development of the economy and society effect. Based on the macro data of 43 countries, this paper uses the SBM model of undesired output to measure the main characteristics of social and economic development. It estimates the net effect of China's green financial policy through the non-parametric synthetic control method. The study found that China's green financial policy has a significant positive effect on the improvement of factor productivity. Further exploration of the predictor variables shows that China's industrial structure and technical level are relatively low, and the use efficiency of green financial funds and government green finance need to be optimized.
Self-Reported Chronic Back Pain and Current Depression in Brazil: A National Level Study

There is limited literature investigating the association between chronic back pain (CBP) and depression in Brazil. This study evaluates the association between CBP, CBP-related physical limitations (CBP-RPL), and self-reported current depression (SRCD), in a nationally representative sample of Brazilian adults. The data for this cross-sectional study came from the 2019 Brazilian National Health Survey (n = 71,535). The Personal Health Questionnaire depression scale (PHQ-8) was used to measure the SRCD outcome. The exposures of interest were self-reported CBP and CBP-RPL (none, slight, moderate, and high limitation). Multivariable weighted and adjusted logistic regression models were used to investigate these associations. The weighted prevalence of SRCD among CBP was 39.5%. There was a significant weighted and adjusted association between CBP and SRCD (weighted and adjusted odds ratio (WAOR) 2.69 (95% CI: 2.45–2.94). The WAOR of SRCD among individuals with high, moderate, and slight levels of physical limitation was significantly greater than for those without physical limitation due to CBP. Among Brazilian adults with high levels of CBP-RPL, there was over a five-fold increased risk of SRCD compared to those without CBP-RPL. These results are important for increasing awareness of the link between CBP and SRCD and for informing health services policies.
On frustration of the majority by fulfilment of the majority's will

Here we have eleven voters, A-K, voting on eleven questions. Seven of them, A-G, vote in the minority in a majority of the decisions: A-F in seven out of the eleven cases, G in six. The majority is always 6-5. These figures can of course be varied. If we imagine an ideal democracy with a whole population voting directly on all questions, there will obviously be room for much variation in results over a long period, all of which however conform to the description: the majority votes in the minority in a majority of cases. This fact, I thought when I stumbled on it, must be familiar to voting experts. But I have not found it remarked upon. It sometimes startles people, eliciting the reaction: 'But doesn't this make nonsense of democracy? ' In the West, and perhaps in the whole world where Western forms of education prevail, men are brought up in a conviction of the unique fairness of democracy. It is even conceived to be as it were the sole legitimate form of government. 'It's not democratic' is a condemnation. Pope Pius XII once spoke in a Christmas allocution of the right of democracies to defend themselves by whatever means they might think necessary. One could hardly have better proof of the pervasiveness of
How Hard Is It Really? Assessing Game-Task Difficulty Through Real-Time Measures of Performance and Cognitive Load

Background Serious and entertainment game designers strive to create engaging, immersive, and often, challenging games. This task involves modifying game mechanics or environments to create experiences with differing levels of challenge to meet player skill. The balance between different game mechanics or environments, and the differing levels of challenge they pose, is typically understood through iterative testing. Balance and challenge becomes increasingly important in serious games and simulation training as these games commonly need to be engaging and impart learning content. Overburdening players’ cognitive capacity with either too much gameplay challenge or learning content may reduce the educational effectiveness of the game. Aim. In this research, we develop a game-based driving simulation with different gameplay tasks to explore the impact of different types of challenges and game aesthetics on real-time cognitive load and task performance, which may inform serious game design. We also test the validity of a game-embedded real-time cognitive load measuring method. Method A total of 31 participants undertook the driving simulation experiment under three different aesthetic conditions using a within-subject experimental design. Cognitive load was measured using three different methods, and performance was measured via in-game metrics. Additionally, demographic and engagement surveys were also completed. Results Player performance and cognitive load respond differently to different types of challenge, and an appropriate level of game challenge can lower cognitive load. The embedded cognitive load measure was validated as an effective method for evaluating real-time cognitive load during gameplay. Conclusion The results demonstrate the validity of a dual measure approach for future adaptive serious games and simulation training environments combining performance and cognitive load. An easy to implement, and robust, in-game measure for cognitive load has been validated in real-world conditions. From these results, a system for dynamic difficulty adjustment is proposed tailored towards serious games and simulation.
A New Pachycephalosaurid from the Hell Creek Formation, Garfield County, Montana, U.S.A.

ABSTRACT A partial skull of a pachycephalosaurid from the Upper Cretaceous Hell Creek Formation, Montana, is interpreted as a new taxon, Platytholus clemensi gen. et sp. nov. MOR 2915 does not fit into an ontogenetic continuum of known pachycephalosaurids from the Hell Creek Formation, Montana, and contemporaneous sediments from the Western Interior. Comparisons to known ontogimorphs of Sphaerotholus and Pachycephalosaurus preclude including this specimen into an ontogenetic series of either taxon. We hypothesize that MOR 2915 is a new species based on a relatively low, broad dome at this advanced ontogenetic age that is neither round nor oval in dorsal view, distinct but fused lateral cranial elements fully incorporated into the dome without any dorsal lobe differentiation, and individual tab-like tubercle ornamentation dorsolaterally. Phylogenetic analysis posits that Platytholus clemensi is a Prenocephale-grade taxon deeply nested within Pachycephalosaurinae, but it is not a member of Pachycephalosaurini. Platytholus clemensi is intermediate in size between the other contemporaneous pachycephalosaurids in the Hell Creek Formation and suggests a diverse set of taxa-partitioned ecological niches by body size. We confirm a well organized, major internal vascular network using high resolution computed tomography. Foramina present on the orbital roofs indicate these canals penetrated the entire ceiling of the orbits within the frontal and supraorbital bones. Abundant neurovascular canals passing through the dome to the ectocranial surface indicate a keratinous structure of some kind, possibly with a vertical structural framework, was present on the dome. We review the history of the head-butting hypothesis and associated behavioral implications.
Forecast-Assisted Service Function Chain Dynamic Deployment for SDN/NFV-Enabled Cloud Management Systems

Software-defined network (SDN) and network function virtualization (NFV) are acknowledged as the most promising technologies to effectively allocate resource for network service. A service function chain (SFC), which can deploy virtualized network functions (VNFs) and chain them with associated flows allocation, can be used to represent each network service owing to the introduction of the SDN/NFV technology. Co-hosted applications on multiple Internet of Things terminals have dynamic and time-varying service requirements, in order to allocate network resources optimally and meet the end-to-end delay requirements of services, sufficient strategies are required to satisfy the continuously changing service demands. In this article, a deep learning model that combines the multitask regression layer above the graph neural networks is first presented to predict the future resource requirements of each VNF instance. The SFC deployment problem is then solved using the integer nonlinear programming (INLP) approach, and a novel prediction-assisted Viterbi algorithm is presented to overcome the scalability problem of the INLP approach. According to the simulation findings, the proposed deep model provides a minimum of a 6.2% improvement in prediction accuracy over baseline prediction models, and the proposed SFC deployment strategy has been demonstrated to deliver better performance in terms of acceptance ratio and revenue, compared to the current passive deployment algorithms.
An Immune Memory Clone Algorithm for Constrained Multi-Objective Optimization

A new algorithm for constrained multi-objective optimization is presented.The algorithm treats the constraints as an objective and the immune clone and immune memory mechanism are introduced.Therefore,the new algorithm could find the Pareto-optimal solutions from the feasible region and the edge of the infeasible region,which assures both the convergence and diversity of the obtained solutions.Simulation results show that the new algorithm has much better performance in finding a much better spread of solutions,in maintaining a better uniformity of the solutions and in obtaining a better convergence.
Flexibility Analysis and Experimental Design of Dual-Chamber Bellows Isolator for Low-Frequency Damping Performance

To achieve an effective and versatile solution for reducing vibrations and reducing maintenance costs, the dual-chamber flexible bellows isolator (D-CFBI) of rubber material is designed. The outer chamber of D-CFBI is used as an actuator to change its own variable stiffness and control the feedback force by filling negative pressure gas. The inner chamber is used as a rubber air spring to work as the main vibration isolation element. Under different loads and sinusoidal excitation forces, the damping characteristics and damping effects of the D-CFBI with different stiffness are investigated. By exciting the isolator, the frequency sweep experiment is carried out, the dynamic flexibility in the initial state is studied, and a preload of the D-CFBI is given to change the stiffness of the D-CFBI, and the changing trend between restoring force and amplitude and frequency is found out. Conclusions can be summarized that increasing the preload of the D-CFBI leads to increased stiffness and better damping effect. For compression amounts of 20% and frequencies higher than 7Hz or amplitudes lower than 2mm, the damping effect is also better. Consequently, this D-CFBI offers satisfactory damping and increased safety at low amplitudes and frequencies. To achieve optimal damping, it is recommended to adjust the stiffness of the driver.
Asphalt Pavement Residual Life Assessment Based on SmartRock Sensors

Real-time structural health monitoring (SHM) is important for the performance evaluation and maintenance management of asphalt pavement. This study applies SmartRock, a wireless embedded sensor widely used in track health monitoring, to the long-term SHM of asphalt pavement to achieve its residual life assessment. According to the structural characteristics of asphalt pavement, a novel processing method of SmartRock monitoring data is proposed. First, an improved genetic algorithm (GA) for back-calculation of pavement dynamic modulus using SmartRock monitoring data is proposed. Then, the evaluated dynamic moduli are used to predict the residual rutting life andresidual fatigue life of the pavement structure. Finally, the two types of residual life are combined to achieve the pavement residual life. This research uses the real-time monitoring data of a test road in Shandong Province, China, to carry out a case study to verify the proposed approach.
Recess time: Help or hindrance to the social-emotional development of young children?

ABSTRACT Recess is an important and unique component of the school day, and yet there is a relative paucity of research on its impact on young children. Moreover, recesses are often perceived negatively by educators because they can detract from academic instructional time and, depending on the kind of play and interactions between students, may also be times of social conflict and, in some cases, exclusion and bullying. This article examines several facets of recess and its importance: it outlines what students do at recess and the social processes that take place, as well as challenges specific to recess time, and its impact on social-emotional learning. It then reviews what constitutes a high-quality recess and synthesizes scholarship on the effectiveness of recess interventions around the world. The article evaluates whether recess is ultimately a help or a hindrance to young children’s social-emotional development, and concludes with both policy and practice recommendations for educators.
Simulink Model Based Fish Disease Analysis

To identify diseased fishes are at early stage to prevent and spreading diseases. This study deals with histogram of the images of diseased fish. Taken four Epizootic Ulcerative Syndrome (EUS) diseased fish images as a case study to evaluated the proposed approach. The technique are implemented using Matlab and Simulink. This design is analysed and tested in Matlab Simulink R2011a. The model is generated using Matlab software.