Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of Alcohol Control Policies for Health Equity (SIMAH): Study Design and First Results.

Since about 2010, life expectancy at birth in the United States (US) has stagnated and begun to decline with concurrent increases in the socioeconomic divide in life expectancy. The Simulation of Alcohol Control Policies for Health Equity (SIMAH) project uses a novel microsimulation approach to investigate the extent to which alcohol use, socioeconomic status (SES), and race/ethnicity contribute to unequal developments in US life expectancy and how alcohol control interventions could reduce such inequalities. Representative, secondary data from several sources will be integrated into one coherent, dynamic microsimulation to model life course changes in SES and alcohol use and cause-specific mortality attributable to alcohol use by SES, race/ethnicity, age, and sex. Markov models will be used to inform transition intensities between levels of SES and drinking patterns. The model will be used to compare a baseline to multiple counterfactual intervention scenarios. The preliminary results indicate that the crucial microsimulation component provides a good fit to observed demographic changes in the population, providing a robust baseline model for further simulation work. By demonstrating the feasibility of this novel approach, the SIMAH project promises to offer superior integration of relevant empirical evidence to inform public health policy for a more equitable future.
Thermo-neutrophilic cellulases and chitinases characterized from a novel putative antifungal biocontrol agent: Bacillus subtilis TD11

Cellulose and chitin are the most abundant naturally occurring biopolymers synthesized in plants and animals and are used for synthesis of different organic compounds and acids in the industry. Therefore, cellulases and chitinases are important for their multiple uses in industry and biotechnology. Moreover, chitinases have a role in the biological control of phytopathogens. A bacterial strain Bacillus subtilis TD11 was previously isolated and characterized as a putative biocontrol agent owing to its significant antifungal potential. In this study, cellulase and chitinase produced by the strain B. subtilis TD11 were purified and characterized. The activity of the cellulases and chitinases were optimized at different pH (2 to 10) and temperatures (20 to 90°C). The substrate specificity of cellulases was evaluated using different substances including carboxymethyl cellulose (CMC), hydroxyethyl cellulose (HEC), and crystalline substrates. The cellulase produced by B. subtilis TD11 had a molecular mass of 45 kDa while that of chitinase was 55 kDa. The optimal activities of the enzymes were found at neutral pH (6.0 to 7.0). The optimum temperature for the purified cellulases was in the range of 50 to 70°C while, purified chitinases were optimally active at 50°C. The highest substrate specificity of the purified cellulase was found for CMC (100%) followed by HEC (>50% activity) while no hydrolysis was observed against the crystalline substrates. Moreover, it was observed that the purified chitinase was inhibitory against the fungi containing chitin in their hyphal walls i.e., Rhizoctonia, Colletotrichum, Aspergillus and Fusarium having a dose-effect relationship.
Cysteinyl leukotriene receptor 1 is a potent regulator of the endosomal‐lysosomal system in the ARPE‐19 retinal pigment epithelial cell line

The endosomal‐lysosomal system is central for cell homeostasis and comprises the functions and dynamics of particular organelles including endosomes, lysosomes and autophagosomes. In previous studies, we found that the cysteinyl leukotriene receptor 1 (CysLTR1) regulates autophagy in the retinal pigment epithelial cell line ARPE‐19 under basal cellular conditions. However, the underlying mechanism by which CysLTR1 regulates autophagy is unknown. Thus, in the present study, the effects of CysLTR1 inhibition on the endosomal‐lysosomal system are analyzed in detail to identify the role of CysLTR1 in cell homeostasis and autophagy regulation. CysLTR1 inhibition in ARPE‐19 cells by Zafirlukast, a CysLTR1 antagonist, depleted the lysosomal pool. Furthermore, CysLTR1 antagonization reduced endocytic capacity and internalization of epidermal growth factor and decreased levels of the transferrin receptor, CD71. Serum starvation abolished the effect of Zafirlukast on the autophagic flux, which identifies the endocytic regulation of serum components by CysLTR1 as an important autophagy‐modulating mechanism. The role of CysLTR1 in inflammation and cell stress has been exceedingly studied, but its involvement in the endosomal‐lysosomal pathway is largely unknown. This current study provides new insights into basal activity of CysLTR1 on cellular endocytosis and the subsequent impact on downstream processes like autophagy.
Perfect fluid space-times whose energy-momentum tensor is conformal Killing

We show that the energy-momentum tensor T of an expanding perfect fluid space-time (M,g) is a nontrivial conformal Killing tensor if and only if M is shear-free, vorticity-free, and satisfies certain energy and force equations. In particular, if T is conformal Killing, we show that the perfect fluid is stationary and its energy-density and pressure are constant.
On the Problem of Periodic Evolution Inclusions of the Subdifferential Type

We examine nonlinear periodic evolution inclusions of the subdifferential type and prove two existence theorems: one for the "non-convex, lower semicontinuous" problem and the other for the "convex, h-upper semicontinuous" problem. Our method of proof is based on the theory of nonlinear operators of monotone type and on multi-valued analysis. We also present three examples from partial and ordinary differential inclusions, illustrating the applicability of our work.
Humor experience facilitates ongoing cognitive tasks: Evidence from pun comprehension

Empirical findings on embodied cognition have shown that bodily states (e.g., bodily postures and affective states) can influence how people appreciate humor. A case in point is that participants were reported to read pleasant sentences faster than the unpleasant controls when their muscles responsible for smiling were activated. However, little research has examined whether the feeling of amusement derived from humor processing like pun comprehension can exert a backward influence on ongoing cognitive tasks. In the present study, the participants’ eye movements were tracked while they rated the comprehensibility of humorous sentences (homophone puns) and two types of unfunny control sentences (congruent and incongruent). Fixation measures showed an advantage in the critical homophone region for the congruent controls relative to the homophone puns; however, this pattern was reversed in terms of total sentence reading time. In addition, the humor rating scores acquired after the eye-tracking experiment were found negatively correlated to the overall sentence reading time, suggesting that the greater amusement the participant experienced the faster they would finish the rating task. Taken together, the current results indicate that the positive affect derived from humor can in turn provide immediate feedback to the cognitive system, which enhances text comprehension. As a result, the current finding provides more empirical evidence for the exploration of the interaction between the body and cognition.
The biological impacts of CEBPD on urothelial carcinoma development and progression

Urothelial carcinoma (UC), which includes urinary bladder urothelial carcinoma (UBUC) and upper tract urothelial carcinoma (UTUC), is one of the most common malignancies worldwide. Accordingly, a comprehensive understanding of the underlying mechanism governing UC development is compulsory. Aberrant CCAAT/enhancer-binding protein delta (CEBPD), a transcription factor, displays an oncogene or tumor suppressor depending on tumor type and microenvironments. However, CEBPD has been reported to possess a clear oncogenic function in UC through multiple regulation pathways. Genomic amplification of CEBPD triggered by MYC-driven genome instability is frequently examined in UC that drives CEBPD overexpression. Upregulated CEBPD transcriptionally suppresses FBXW7 to stabilize MYC protein and further induces hexokinase II (HK2)-related aerobic glycolysis that fuels cell growth. Apart from the MYC-dependent pathway, CEBPD also downregulates the level of hsa-miR-429 to enhance HK2-associated glycolysis and induce angiogenesis driven by vascular endothelial growth factor A (VEGFA). Additionally, aggressive UC is attributed to the tumor metastasis regulated by CEBPD-induced matrix metalloproteinase-2 (MMP2) overexpression. Furthermore, elevated CEBPD induced by cisplatin (CDDP) is identified to have dual functions, namely, CDDP-induced chemotherapy resistance or drive CDDP-induced antitumorigenesis. Given that the role of CEBPD in UC is getting clear but pending a more systemic reappraisal, this review aimed to comprehensively discuss the underlying mechanism of CEBPD in UC tumorigenesis.
Evidence for a Supermassive Black Hole in the Nucleus of M31

Stellar rotation velocities and velocity dispersions along three slit positions in M31 have been measured, and three-dimensional velocity and velocity dispersion fields are obtained in order to search for a central black hole. Synthetic apparent spectra are constructed to eliminate problems with the reaction of the Fourier quotient program to stellar population mixes. Models are constructed to explore parameter space. The large velocities and velocity gradients that are found imply that the total mass-to-light ratio rises sharply at r of 0.5 arcsec or less to values greater than 100. This is much larger than normal for old stellar populations. Velocity anisotropies are not a major uncertainty because of the rapid rotation. There is thus strong evidence for a dark central mass, probably a supermassive black hole. 48 references.
A Competency Management Model to Support Digital Transformation Initiatives in a Public Organization

Context: In recent years, public organizations are adopting new technologies and innovating processes to improve the quality of services provided to society. In the context of digital transformation, in addition to the use of new technologies, organizations need to transform their culture and acquire new competencies so that teams can change their mindset and adopt innovative work practices. Problem: Digital transformation requires changes in internal processes, as well as in the technical and behavioral skills of teams, involving new ways of evaluating and directing the professional development of employees. Method: We conducted a case study at a public organization where a competency management model was developed based on the analysis of internal documentation and discussions with the organization’s stakeholders. The evaluation of the proposed model was carried out through a focus group with 11 professionals. Results: The article describes a competency management model to support digital transformation and business process improvement projects. The competency management model includes 4 roles and 37 competencies for teams. The model includes a competency matrix to map and assess the competencies needed for each role and actions to develop new competencies. Lessons Learned: Our experience suggests that developing competencies to support digital transformation initiatives requires establishing a shared alignment and a long-term vision to ensure effective organizational learning.
T-GCN: A Sampling Based Streaming Graph Neural Network System with Hybrid Architecture

As many real-world applications are streaming and attached with time instances, a few works have been proposed to learn streaming graph neural networks (GNNs). Unfortunately, current streaming GNNs are observed to have a large training overhead and suffer from bad parallel scalability on multiple GPUs. These drawbacks pose severe challenges to online learning of streaming GNNs and their application to real-time scenarios. To improve training efficiency, one promising solution is to use sampling, a technique widely used in static GNNs. However, to the best of our knowledge, sampling has not been investigated in learning streaming GNNs. Based on these observations, in this paper, we propose T-GCN, the first sampling-based streaming GNN system, which targets temporal-aware streaming graphs and takes advantage of a hybrid CPU-GPU co-processing architecture to achieve high throughput and low latency. T-GCN proposes an efficient sampling method, namely Segment Its Search, to offer high sampling speed with respect to three typical types of general graph sampling methods (i.e., node-wise, layer-wise, and subgraph sampling). We propose a locality-aware data partitioning method to reduce CPU-GPU communication latency and data transfer overhead, and an NVLink-specific task schedule to fully exploit NVLink's fast speed and improve GPU-GPU communication efficiency. Besides, we further pipeline the computation and the communication by introducing an efficient memory management mechanism, to improve scalability while hiding data communication. Overall, with respect to end-to-end performance, for single-GPU training, T-GCN achieves up to 7.9× speedup than state-of-the-art works. In terms of scalability, T-GCN runs 5.2× faster on average with 8 GPUs than one GPU. Additionally, in terms of sampling, T-GCN also yields a maximum of 38.8× speedup with our Segment Its Search sampling method.
The GOES I–M Imagers: New Tools for Studying Microphysical Properties of Boundary Layer Stratiform Clouds

Abstract This study reviews the capability of the advanced imagers on Geostationary Operational Environmental Satellites (GOES) I–M to provide quantitative information about bulk microphysical properties of low–level stratiform clouds, namely, cloud liquid water path (LWP) and droplet effective radius (re). Previous studies show that accurate estimates of cloud LWP from GOES imagers are possible, as evaluated from both ground–based and spaceborne passive microwave measurements, provided care is taken in vicarious calibration of the visible channel. GOES estimates of re have yet to be validated. However, the re versus LWP relationship derived from GOES and Special Sensor Microwave/Imager data shows good agreement with theory. The unique high–temporal sampling of the imager allows for detailed study of daytime characteristics of cloud microphysical properties and, possibly, indirect aerosol effect. Microphysical information for drizzling marine stratocumuli was also obtained, which was confirmed by direct c.
Assessing DX through the correlation between affectivity and self-assessed productivity in a public institution

The human aspects of software development have received attention in recent years, especially with studies related to Developer Experience (DX). Activities related to development have a cognitive processing character. There is a link between affectivity (emotions, moods and feelings), cognitive processing and individual productivity. The objective of this study is to evaluate the DX in the development teams in the public service, in the environment of the Municipality. To achieve this objective, we used the correlation between the affective dimensions of valence, arousal and dominance with self-assessed productivity (sPR). This correlation measures the impact of affectivity effects on the productivity of development activities. The study was carried out with 11 municipal public service software development professionals in their daily activities through the use of interviews, questionnaires and the Self-Assessment Manikin (SAM) technique. The results demonstrate the importance of DX assessment in individual productivity, valuing human aspects and their influence on software development.
Research on the revision of the nonwork orientations relative to career questionnaire based on the reliability and validity test of big data

In order to investigate individuals' consideration and emphasis on different non-work roles (family, personal, community service) in the occupational field, this study intends to revise the Chinese version of the Non-work Orientation Relative to Career Questionnaire, verify the factor structure of the scale in the Chinese context, and test its reliability and validity. This research adopts the combination of qualitative and quantitative research methods, including semi-structured interviews, qualitative analysis, questionnaire survey and statistical analysis based on SPSS. This study first conducted semi-structured interviews with 11 employees, and the results showed that nonwork orientations relative to career included three dimensions: family orientation, private life orientation and community service orientation. Secondly, the questionnaire method was used to conduct exploratory factor analysis on the investigation results of 278 subjects. According to the results of principal component factor analysis and gravel map, the questionnaire with 3 factors and 14 items was obtained. Finally, a total of 664 subjects were selected as the research objects. Discriminant validity test showed that employees' non-job orientation and work-family centrality were two variables with different connotations. Confirmatory factor analysis showed that the fitting index of the three-factor model was significantly better than that of the single-factor model. The overall research results show that the three-dimension nonwork orientations relative to career questionnaire has good reliability and validity in Chinese employees and can be used as a measurement tool in related research.
Understanding and Reaching the Performance Limit of Schedule Tuning on Stable Synchronization Determinism

Deterministic MultiThreading (DMT) systems eliminate nondeterminism from the dynamic executions of multithreaded programs. They can greatly simplify multithreaded programming and ease the deployment of systems that rely on replication. We first categorize and compare existing DMT system designs along three axes, incorporating the most recent advances in DMT systems. From our study, we conclude that stable synchronization determinism is the most cost-effective design, and it is thus the focus of our work. To reduce the overhead of enforcing stable synchronization determinism, previous work has explored scheduling-based methods that tune the synchronization schedule. However, it is not clear how low the performance overhead can be through schedule tuning and how to reach the performance limit. To answer these questions, we then follow an iterative process of understanding the performance limit of schedule tuning on stable synchronization determinism and designing new scheduling policies to reach the performance limit. Through this process, we identify two types of scheduling-oblivious overheads that cannot be eliminated by schedule tuning alone. In addition, we also design a group of new policies and implement them in minSMT. Our evaluation shows that minSMT successfully reaches the performance limit of stable synchronization determinism on 107 out of 108 benchmarks after excluding the impact of scheduling-oblivious overheads, and this also results in significant performance improvements compared with state-of-the-art stable synchronization-determinism systems on 9 benchmarks. Our results also suggest that, to further improve the performance of stable synchronization determinism, future research should focus on addressing the two types of scheduling-oblivious overheads with approaches other than schedule tuning.
FlatPack: Flexible Compaction of Compressed Memory

The capacity and bandwidth of main memory is an increasingly important factor in computer system performance. Memory compression and compaction have been combined to increase effective capacity and reduce costly page faults. However, existing systems typically maintain compaction at the expense of bandwidth. One major cause of extra traffic in such systems is page overflows, which occur when data compressibility degrades and compressed pages must be reorganized. This paper introduces FlatPack, a novel approach to memory compaction which is able to mitigate this overhead by reorganizing compressed data dynamically with less data movement. Reorganization is carried out by an addition to the memory controller, without intervention from software. FlatPack is able to maintain memory capacity competitive with current state-of-the-art memory compression designs, while reducing mean memory traffic by up to 67%. This yields average improvements in performance and total system energy consumption over existing memory compression solutions of 31--46% and 11--25%, respectively. In total, FlatPack improves on baseline performance and energy consumption by 108% and 40%, respectively, in a single-core system, and 83% and 23%, respectively, in a multi-core system.
A neuro-genetic approach for inferring gene regulatory networks from gene expression data

Accurate prediction of gene regulation rules is important for understanding complex life processes. Existing computational algorithms designed for bulk transcriptome datasets typically require a large number of time points to infer gene regulatory networks (GRNs), are suitable for a small number of genes, and cannot efficiently detect potential regulatory relationships. We propose an approach based on a deep learning framework to reconstruct GRNs from bulk transcriptome datasets, assuming that the expression levels of transcription factors involved in gene regulation are strong predictors of the expression of their target genes. The algorithm uses multilayer perceptrons to infer the regulatory relationship between multiple transcription factors and a gene, and uses genetic algorithms to search for the best regulatory gene combination. The results show that our approach is more accurate than other methods for reconstructing gene regulatory networks on real-world and simulated bulk transcriptome gene expression datasets.
Batched Graph Community Detection on GPUs

Graph clustering or community detection is a novel method to identify closely related structures within a graph. Applications of graph clustering can be found in cybersecurity, computational biology, proteomics, power grids, et al. As such, developing high performance implementations of graph clustering can enhance analytics pipelines. Currently, GPUs are an integral part of HPC systems, especially dense-GPU systems are ubiquitous. Hence, there is a significant effort to port applications on GPUs to extract maximal performance from modern single-node HPC platforms. In general, designing graph applications efficiently on GPUs is challenging, due to relatively low arithmetic computational intensity and arbitrarily high irregular memory accesses. The graph clustering problem is inherently serial, and the current modularity-maximization based parallel graph clustering implementations usually incorporate additional heuristics such as distance-k coloring to improve the overall quality and convergence. In contrast, we introduce a batched method for clustering on GPUs that allow users to achieve the desirable amount of quality without compromising the performance and involving sophisticated algorithms/heuristics. Due to a partition-based design, we can process graphs larger than the combined GPU memory of a node. Despite of increasing the frequency of synchronization through batches (batches are processed in a bulk-synchronous manner among GPUs), the effect on the performance is mild for large graphs. Our implementation is based on the popular Louvain method for graph clustering, and we compare our performance and quality with existing parallel Louvain implementations on CPU and GPU systems. Overall, we observed speedups of about 2--14×/1.3--7.5× on up to 16/8 NVIDIA V100/A100 GPUs and a maximum speedup of 50×/30× as compared to state-of-the-art shared-memory and GPU-based Louvain implementations. We also demonstrate competitive if not better quality as compared to the existing parallel implementations of Louvain using tens to hundreds of batches.