Miracle-dz/UniFAR-FTUs huggingface dataset:
Antithrombotic Therapy and Cardiovascular Outcomes After Transcatheter Aortic Valve Implantation in Patients Without Indications for Chronic Oral Anticoagulation: A systematic review and network meta-analysis of randomized controlled trials.

AIMS As the antithrombotic regimen which may best prevent ischemic complications along with the lowest bleeding risk offset following transcatheter aortic valve implantation (TAVI) remains unclear, we aimed to compare the safety and efficacy of antithrombotic regimens in patients without having an indication for chronic oral anticoagulation. METHODS AND RESULTS We conducted a Prospero-registered (CRD42021247924) systematic review and network meta-analysis of randomized controlled trials evaluating post-TAVI antithrombotic regimens up to April 2022. We estimated the relative risk (RR) and 95% confidence intervals (95%CI) using a random-effects model in a frequentist pairwise and network metanalytic approach. We included 7 studies comprising of 4 006 patients with a mean weighted follow-up of 12.9 months. Risk of all-cause death was significantly reduced with dual antiplatelet therapy (DAPT) compared to low-dose rivaroxaban + 3-month single antiplatelet therapy (SAPT) (RR 0.60, 95%CI 0.41-0.88) while no significant reduction was observed with SAPT versus DAPT (RR 1.02 95%CI 0.67-1.58) and SAPT and DAPT compared to apixaban or edoxaban (RR:0.60 95%CI:0.32-1.14 and RR:0.59 95%CI 0.34-1.02, respectively). SAPT was associated with a significant reduction of life-threatening, disabling, or major bleeding compared to DAPT (RR 0.45 95%CI 0.29-0.70), apixaban or edoxaban alone (RR 0.45, 95%CI 0.25-0.79) and low-dose rivaroxaban + 3-month SAPT (RR 0.30, 95%CI 0.16-0.57). There were no differences between the various regimens with respect to myocardial infarction, stroke, or systemic embolism. CONCLUSION Following TAVI in patients without an indication for chronic oral anticoagulant, SAPT more than halved the risk of bleeding compared to DAPT and direct oral anticoagulant-based regimens without significant ischemic offset.
Climate change framing and innovator attention: Evidence from an email field experiment

Significance Addressing climate change critically depends on innovation. However, there is little work on understanding how to shift the attention of innovators (who have particularly specialized skillsets) toward climate change. We perform a field experiment with successful innovators to understand how sensitive they are to climate change scenarios. Innovators increase their attention when stated climate change impact is more imminent or has the potential for greater human casualties, partly depending on their location characteristics and technological focus. Our results suggest that messaging experiments, emphasizing the cost of climate change, can shift attention (and potentially action) toward this problem.
Nicotinamide riboside improves muscle mitochondrial biogenesis, satellite cell differentiation, and gut microbiota in a twin study

Nicotinamide adenine dinucleotide (NAD+) precursor nicotinamide riboside (NR) has emerged as a promising compound to improve obesity-associated mitochondrial dysfunction and metabolic syndrome in mice. However, most short-term clinical trials conducted so far have not reported positive outcomes. Therefore, we aimed to determine whether long-term NR supplementation boosts mitochondrial biogenesis and metabolic health in humans. Twenty body mass index (BMI)–discordant monozygotic twin pairs were supplemented with an escalating dose of NR (250 to 1000 mg/day) for 5 months. NR improved systemic NAD+ metabolism, muscle mitochondrial number, myoblast differentiation, and gut microbiota composition in both cotwins. NR also showed a capacity to modulate epigenetic control of gene expression in muscle and adipose tissue in both cotwins. However, NR did not ameliorate adiposity or metabolic health. Overall, our results suggest that NR acts as a potent modifier of NAD+ metabolism, muscle mitochondrial biogenesis and stem cell function, gut microbiota, and DNA methylation in humans irrespective of BMI.
Postural and longitudinal variability in seismocardiographic signals

Objective. Low frequency cardiovascular vibrations detectable on the chest surface (termed seismocardiography or SCG) may be useful for non-invasive diagnosis and monitoring of various cardiovascular conditions. A potential limitation of using SCG for longitudinal patient monitoring is the existence of intra-subject variability, which can contribute to errors in calculating SCG features. Improved understanding of the contribution of intra-subject variability sources may lead to improved SCG utility. This study aims to quantify postural and longitudinal SCG variability in healthy resting subjects during normal breathing. Approach. SCG and ECG signals were longitudinally acquired in 19 healthy subjects at different postures (supine, 45° head up, and sitting) during five recording sessions over five months. SCG cycles were segmented using the ECG R wave. Unsupervised machine learning was used to reduce SCG variability due to respiration by grouping the SCG signals into two clusters with minimized intra-cluster waveform heterogeneity. Several SCG features were assessed at different postures and longitudinally. Main results. SCG waveform morphological variability was calculated within each cluster (intra-cluster) and between two clusters (inter-cluster) at each posture and data collection session. The variabilities were significantly different between the supine and sitting but not between supine and 45° postures. For the 45° and sitting postures, the intra-cluster variability was not significantly different, while the inter-cluster variability difference was significant. The energy ratio between different frequency bands to total spectral energy in 0.5–50 Hz were calculated and were comparable for all postures. The combined cardiac timing intervals from the two clusters showed significant variation with postural changes. There was significant heart rate difference between the clusters and between postural positions. The SCG features were compared between longitudinal sessions and all features were not significantly different, Significance. Several SCG features significantly varied with posture suggesting that posture needs to be specified when comparing SCG changes over time. Longitudinally comparable SCG feature values suggests that significant longitudinal differences, if observed, may reflect true alternations in the cardiac functioning over time.
Journal of Quantitative Analysis in Sports A Starting Point for Analyzing Basketball Statistics

The quantitative analysis of sports is a growing branch of science and, in many ways one that has developed through non-academic and non-traditionally peer-reviewed work. The aim of this paper is to bring to a peer-reviewed journal the generally accepted basics of the analysis of basketball, thereby providing a common starting point for future research in basketball. The possession concept, in particular the concept of equal possessions for opponents in a game, is central to basketball analysis. Estimates of possessions have existed for approximately two decades, but the various formulas have sometimes created confusion. We hope that by showing how most previous formulas are special cases of our more general formulation, we shed light on the relationship between possessions and various statistics. Also, we hope that our new estimates can provide a common basis for future possession estimation. In addition to listing data sources for statistical research on basketball, we also discuss other concepts and methods, including offensive and defensive ratings, plays, per-minute statistics, pace adjustments, true shooting percentage, effective field goal percentage, rebound rates, Four Factors, plus/minus statistics, counterpart statistics, linear weights metrics, individual possession usage, individual efficiency, Pythagorean method, and Bell Curve method. This list is not an exhaustive list of methodologies used in the field, but we believe that they provide a set of tools that fit within the possession framework and form the basis of common conversations on statistical research in basketball.
Predictors of sleep quality for autistic people across adulthood

Poor sleep can have a significant impact on physical health and well‐being. Sleep problems are common among autistic children, but less is known about sleep across the autistic adult lifespan. Autistic adults (n = 730, aged 18–78 years) were recruited via Simons Powering Autism Research for Knowledge Research Match. Participants completed online surveys asking about demographics, health problems, social support, symptoms of anxiety and depression, and overall and specific aspects of sleep quality. Regression analyses explored the variables associated with sleep quality. Physical health, assigned female sex at birth and self‐reported anxiety symptoms significantly contributed to models for all aspects of sleep. Perceived stress contributed to models of overall and subjective sleep quality, and daytime dysfunction. Depression symptoms did not contribute significantly to any of the models of sleep quality. However, utilizing government support mechanisms (such as social security) contributed to the model of sleep efficiency. Age contributed little to models of sleep quality, whereas perceived stress and psychotropic medication use contributed to some but not all aspects of sleep. Sleep quality is poor for autistic people across the adult lifespan. Given known impacts of poor sleep on health, cognition and quality of life, attention should be paid to sleep and its possible everyday effects for autistic people of all ages.
Luminosity Function of Solar‐Neighborhood OB Stars

The luminosity function of solar‐neighborhood OB stars is derived from a sample of nearly 2600 such objects brighter than mpg = 10 via a method that makes use of varying completeness‐to‐distance estimates depending on the absolute magnitudes of the sample members and their (assumed) exponential density distribution perpendicular to the Galactic plane. The derived luminosity function is in good agreement with that published previously by Humphreys and McElroy over the range −7<MV< -4. The integrated Galactic plane density of OB stars within this range of absolute magnitudes is estimated at 9.1 × 10-7 stars pc−3, and the total number of OB stars within the solar circle is estimated at ∼19,000. The predicted number of OB stars more massive than 20 solar masses within 2.5 kpc of the Sun, 636, is 50% greater than the value of 424 given earlier by Garmany et al., indicating that their sample was not as complete as they had assumed.
Aristotle's Treatment of Probability and Signs

Probability and Frequency. Aristotle frequently used the concept of probability, but apparently he did not make any persistent effort to clarify or analyze it. His description of afortiori argument in The Topics (115a, 6-14), e.g., depends upon "the more or less likely or probable," but he does not explore this notion. In The Rhetoric, where he applies himself to a puzzle about probability which the Sophists had advanced (1402a, 5-30), he comes closer to an analysis of probability. Aristotle quotes Agathon,
Event-driven muti-agent evacuation based on reinforcement learning

Various emergencies occur frequently, posing threats and challenges to people’s lives and social security. In consequence, the evacuation of multi-Agent has become a significant part of the emergency response process. However, a few existing works only focus on the evacuation of a small number of agents, which does not consider the problem of multi-Agent cooperation caused by the increase of the number of agents and the impact of emergencies. Therefore, a framework for event-driven multi-Agent evacuation is proposed in this paper, which includes three parts: event collection, event sending, and task execution. During task execution, agents are divided into groups and select the leader in the group, while other agents in the group move with the leader. Then, the reinforcement learning algorithm Space Multi-Agent Deep Deterministic Policy Gradient (SMADDPG), proposed in this paper, is used for path planning. In addition, the state, action and reward based on the Markov game are designed, and an environment with emergencies is presented as agents evacuation scenario. The experiment results show that the method proposed can shorten the length of path, and improve the interoperability between multi-Agent when emergencies occur, which can provide decision-making reference for emergency departments to formulate evacuation plans.
ETL based data integration scheduling

As the open source ETL tool with the largest number of users, Kettle's powerful and concise functions are welcomed by the majority of ETL practitioners. ETL is the process of extracting, converting and loading data from the data source to the target. Based on the enterprise data model, ETL builds a reasonable data storage mode, establishes an enterprise data exchange platform, meets the data exchange needs between various application systems, provides all-round data services, and meets the data support needs of enterprise decision-making [1]. In addition to formulating the corresponding extraction, transformation and loading tasks, ETL tools are also particularly important for the formulation of scheduling monitoring. For industrial economic data, benefit data and production data are generated periodically (such as daily, weekly and daily). Through the scheduler, users can more easily realize ETL task scheduling management, task execution performance and other data monitoring and analysis. However, Kettle's scheduling and monitoring function is very weak. This paper will use the scheduling system to monitor and schedule tasks to improve the efficiency of data integration.
Thin cloud image technology of LLNet-based enhanced optimal remote sensing satellite

This paper proposes improved low-light net (LLNet) parameters based on the imaging features of optical remote sensing satellites, which are built on the conventional LLNet low-light image enhancement network. The network can be better adapted to the characteristics of thin cloud optical remote sensing satellite images by using the improved network parameters for denoising auto-encoder (DA) layer design, data training, and improvement effect evaluation of LLNet. This will enable the network to extract data features from the images and carry out adaptive image enhancement without over amplifying the highlighted areas. A set of specially produced high-definition satellite images is used as the experimental data set to validate the network parameter design experimentally, and the results show that the proposed improved network parameters can effectively enhance the simulated and real thin cloud optical remote sensing satellite images and can eliminate/diminish the serious influence of thin clouds on the features to a certain extent. After the enhancement of the simulated thin cloud image, its structural similarity with the reference image is improved by about 300%. Its subjective enhancement effect is more obvious even for the real thin cloud image.
Researches advanced in medical detection based on deep learning

Medical image analysis based on computer vision technology has always been a research hotspot in the community, which aims to assist doctors in diagnosis by accurately analyzing pathological images to divide the patient’s condition and the patient’s lesions. Thanks to the rapid development of deep learning, the application of computer image recognition technology in medicine is becoming more and more widespread, while still facing a series of challenges such as low data set data, insufficient performance of algorithms and fine delineation of lesions. In order to solve these problems, based on extensive literature research, this paper first compares the algorithms in the application for Corona Virus Disease 2019, skin cancer and liver cancer. Then we introduce the improvement of these algorithms by expanding the number of data sets, optimizing the algorithms, and fitting the neural networks and models, whcih can improve the accuracy of image recognition technology to assist doctors in identifying lesions in clinical practice. The algorithms are further compared quantitatively on the basis of the training set in clinical diseases, and the difficulties to be overcome in image recognition and the future development trend are explained and predicted from the analysis of the comparison. Many new algorithms and excellent models are being gradually improved with the development of the times, and image recognition technology will also develop towards more research fields in the future.
The performance analysis of model parallelism in deep residual network

With the rapid information and communications technology growth and continuous invention, the concept of parallel computing has become the core of computer science, and its capabilities are continually documented in providing high processing solutions. The graphic processing unit (GPU) plays a crucial role in deep learning and parallel computing. The primary purpose of this paper is to discuss two concepts of deep computing: data parallelism and model parallelism. Additionally, the discussion will delve into their importance and associated challenges in parallel processing. Furthermore the paper summarises the Residual Network (ResNet) system with the basic principles and formulas. Establishing efficient parallel algorithms for GPU processing is necessary. This paper evaluates the performance and speed between single-core and multi-core working environments for two kinds of convolutional neural network structures with different types of GPUs. The first one is LeNet, and the other one is VGG16. There are two final results of this experiment. By analyzing the data of performance, VGG16 has better performance than LeNet. By analyzing the data of speed, the speed of LeNet is faster than that of VGG16.
Novel CAF-identifiers via transcriptomic analysis in oral cancer patients

Background Cancer-associated fibroblasts (CAFs), a prominent component of the tumor microenvironment, plays an important role in tumor development, invasion, and drug resistance. The expression of distinct “CAF markers,” which separates CAFs from normal fibroblasts and epithelial cells, have traditionally been used to identify them. These commonly used CAF markers have been reported to differ greatly across microenvironmental subpopulations even within a cancer site. Methods Using an unbiased data analysis approach utilizing publicly available and in-house gene expression data from patient derived novel CAF cells, we identified a collection of markers in oral cancer to distinguish CAF populations from tumor epithelia and normal oral fibroblasts. Results COL1A1, SPARC, COL1A2, COL3A1 and TIMP-1 were identified as potential markers which can be utilized to differentiate cancer associated fibroblast from all other cell types including normal fibroblasts in oral cancer.
Modeling and Control of an Exhaust Recompression HCCI Engine Using Split Injection

Homogeneous charge compression ignition (HCCI) is currently being pursued as a cleaner and more efficient alternative to conventional engine strategies. Control of the load and phasing of combustion is critical in the effort to ensure reliable operation of an HCCI engine over a wide operating range. This paper presents an approach for modeling the effect of a small pilot injection during the recompression process of an HCCI engine, and a controller that uses the timing of this pilot injection to control the phasing of combustion. The model is a nonlinear physical model that captures the effect of fuel quantity and intake and exhaust valve timings on work output and combustion phasing. It is seen that around the operating points considered, the effect of a pilot injection can be modeled as a change in the Arrhenius threshold, an analytical construct used to model the phasing of combustion as a function of the thermodynamic state of the reactant mixture. The relationship between injection timing and combustion phasing can be separated into a linear, analytical component and a nonlinear, empirical component. Two different control strategies based on this model are presented, both of which enabled steady operation at low load conditions and effectively track desired load-phasing trajectories. These strategies demonstrate the potential of split injection as a practical cycle-by-cycle control knob requiring only minimal valve motion that would be easily achievable on current production engines equipped with cam phasers.
Particle manipulation using an ultrasonic micro-gripper

We show that ultrasonic micro-grippers, 100 μm high segmented circular structures actuated with piezoelectric elements, can be used to establish a localised resonant pressure field within a fluid droplet, and hence allow effective manipulation of silica microspheres independently from the global boundaries of the fluid volume. We demonstrate through experiments and simulations that despite variations in the fluid shape and location, the method achieves particle clustering in consistent locations at fixed operating frequencies.
Three-dimensional hydrodynamic simulations of narrow-angle-tail radio sources. I: The Begelman, Rees, and Blandford model

Narrow-angle-tail radio sources have been explained by various authors as being the result of ram-pressure-induced bending of supersonic radio jets by a transonic crosswind. The details of the models differ, but their common element is the necessity of a supersonic jet and a crosswind. We have, therefore, made a computational study of the bending of light supersonic jets by a wind flowing normally to it using the highly accurate RIEMANN code run at the highest resolutions that are currently feasible. RIEMANN is a three-dimensional hydrocode that implements the piecewise parabolic method of Colella and Woodward
System identification for limit cycling systems: a case study for combustion instabilities

Presents a case study in system identification for limit cycling systems. The focus of the paper is on (a) the use of a model structure derived from physical considerations and (b) the use of algorithms for the identification of component subsystems of this model structure. The physical process used in this case study is that of a reduced order model for combustion instabilities for lean premixed systems. The identification techniques applied in this paper are the use of linear system identification tools, time delay estimation and qualitative validation of model properties using harmonic balance and describing function methods. The novelty of the paper, apart from its practical application, is that closed loop limit cycle data is used together with a priori process structural knowledge to identify both linear dynamic forward and nonlinear feedback paths.
A Dust Lane Leading the Galactic Bar at Negative Galactic Longitudes

Comparing the different wavelengths of the DIRBE integrated surface brightness maps at positive and negative Galactic longitudes on the plane near the Galactic center, it is shown that there exists a thick dust lane (between 1.0 and 2.6 mag of absorption in K) at negative longitudes (as compared to positive ones), which follows the “standard” interstellar extinction law. A likely explanation for this effect is the existence of a thick dust lane preceding the bar. This will have to be confirmed by future infrared surveys, such as DENIS or 2MASS, that will cover negative Galactic longitudes, where a heavy extinction dust lane about 2° wide, close to the Galactic plane, should be seen.