Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of a transitionally occupied state and thermal unfolding of domain 1.1 of σA factor of RNA polymerase from Bacillus subtilis

σ factors are essential parts of bacterial RNA polymerase (RNAP) as they allow to recognize promotor sequences and initiate transcription. Domain 1.1 of vegetative σ factors occupies the primary channel of RNAP and also prevents binding of the σ factor to promoter DNA alone. Here, we show that domain 1.1 of Bacillus subtilis σA exists in more structurally distinct variants in dynamic equilibrium. The major conformation at room temperature is represented by a previously reported well‐folded structure solved by nuclear magnetic resonance (NMR), but 4% of the protein molecules are present in a less thermodynamically favorable state. We show that this population increases with temperature and we predict its significant elevation at higher but still biologically relevant temperatures. We characterized the minor state of the domain 1.1 using specialized methods of NMR. We found that, in contrast to the major state, the detected minor state is partially unfolded. Its propensity to form secondary structure elements is especially decreased for the first and third α helices, while the second α helix and β strand close to the C‐terminus are more stable. We also analyzed thermal unfolding of the domain 1.1 and performed functional experiments with full length σA and its shortened version lacking domain 1.1 ( σA_Δ1.1 ). The results revealed that while full length σA increases transcription activity of RNAP with increasing temperature, transcription with σA_Δ1.1 remains constant. In summary, this study reveals conformational dynamics of domain 1.1 and provides a basis for studies of its interaction with RNAP and effects on transcription regulation.
Associations between neuropsychiatric symptoms of affective and vegetative domains and brain morphology in aging people with mild cognitive impairment and Alzheimer's disease

Neuropsychiatric symptoms (NPS) are common in mild cognitive impairment (MCI) and even more in Alzheimer's disease (AD). The symptom‐based cluster including nighttime disturbances, depression, appetite changes, anxiety, and apathy (affective and vegetative symptoms) was associated with an increased risk of dementia in MCI and has common neuroanatomical associations. Our objective was to investigate the differences in brain morphology associations with affective and vegetative symptoms between three groups: cognitively normal older adults (CN), MCI and AD.
Performance of 2-D Turbulence Rans Models for Prediction of Flow Past a Staggered Tube Bank Array

Abstract Two dimensional computational analysis of nine RANS turbulence models is carried out and the results are compared with the benchmark test data for the cross flow past tube banks. The ERCOFTAC-IAHR experimental test data of Simonin and Barcouda (1988) formed the database for benchmarking. Apart from the dimensionless quantities like stream-wise velocity, transverse velocity and Reynolds stresses, the pressure coefficient, skin friction factor around the tubes, recirculation lengths behind the tube and flow separation angles are compared with literature. Contrary to the claims made in the published literature, the accuracy of predictions is not satisfactory with the two-dimensional computational analysis, irrespective of the turbulence model employed.
Association of School Health Relationship Difficulties with Lack of Regular Physical Activity in Younger Adolescents: A Population Based Study

Objective: Lack of regular physical activity (LPA) and the number of days/week with physical activity >60 minutes/day (Ndw) may be associated with school-health-relational difficulties (SHRDs) during the life-course. This study assessed their associations and the confounding role of socioeconomic features (nationality, family structure, parents’ education/occupation/income) among younger adolescents. Methods: This cross-sectional population-based study included 1,559 middle-school adolescents from north-eastern France (10–18 years, 98% under 16 years, 778 boys and 781 girls). They completed over one-hour teaching period a questionnaire collecting last-12-month LPA and last-7-day Ndw (dependent variables), and risk factors including socioeconomic features, SHRDs (previous grade repetitions, last-12-month poor general health status/physical health/psychological health/social-relationships, depressive symptoms (and age at onset and duration from onset); cumulated number noted SHRDcn). Data were analyzed using logistic/linear regression models. Results: LPA was common (32.5%) and associated with all SHRDs (sex-age-adjusted odds ratio saOR reaching 1.99, p < .001) and depressive-symptoms duration (saOR 1.63 (p < .05) and 2.11 (p < .001) for 1–2 and ≥3 years, respectively, vs. no depressive symptoms). A dose-effect association was found between SHRDcn and LPA (24.0, 33.7, 45.6, and 53.5%; saOR 1.59, 2.58, and 3.43; for SHRDcn 1–2, 3–4, and ≥5 respectively, vs. SHRDcn = 0, p < .001). Ndw was similarly associated with all SHRDs and SHRDcn (sex-age-adjusted regression coefficient reaching −1.10 day/week, p < .001). These results were robust when controlling for socioeconomic features (18%<contribution<51%). Conclusions: Physical activity is associated with SHRDs, and socioeconomic features have an elevated confounding role. Healthcare providers can detect/reduce SHRDs and consequently improve physical activity among adolescents and prevent physical inactivity in adulthood. Highlights • Lack of physical activity (LPA) is strongly associated with school-health-relational difficulties (SHRDs) affecting most adolescents. • There are dose-effect associations between cumulative number of SHRDs and both LPA and number of days/week with physical activity >60 min/day (Ndw). • These links are robust when controlling for socioeconomic features which have a strong confounding role. • The risk of LPA and reduced Ndw increase with the duration of depressive symptoms from their onset. • Prevention to detect/reduce SHRDs may improve physical activity, especially for most at-risk adolescents.
Underwater sound of three unoccupied aerial vehicles at varying altitudes and horizontal distances.

Unoccupied aerial vehicles (UAVs), or "drones," are increasingly used as a tool for cetacean research, but knowledge about how these tools contribute to underwater sound is lacking. In this study, underwater sound levels of three commonly used UAV models (Mavic Pro Platinum, Phantom 4 Pro v2.0, Inspire 1 Pro) were recorded. For each model, three replicate flights were conducted at 36 positions at standardized horizontal (0-30 m) and vertical (2-40 m) distances from a hydrophone (1 m depth). Median broadband received levels of the Inspire were highest at 96.5 dBrms 141-17 783 Hz re 1 μPa2, followed by the Phantom (92.4 dBrms 141-17 783 Hz re 1 μPa2) and Mavic, which was quietest (85.9 dBrms 141-17 783 Hz re 1 μPa2). Median ambient sound levels in the absence of an UAV were 82.7 dBrms 141-17 783 Hz re 1 μPa2. Significant increases in ambient sound levels associated with UAV flights occurred at higher altitudes than previously reported, and received levels decreased more with increasing horizontal distance of the UAV than with altitude. To minimize potential noise impacts on sensitive marine animal subjects, we recommend increasing horizontal distance to the animal, rather than altitude, and choosing the quietest UAV feasible.
MiR‐199a‐5p enhances neuronal differentiation of neural stem cells and promotes neurogenesis by targeting Cav‐1 after cerebral ischemia

MicroRNAs (miRs) are involved in endogenous neurogenesis, enhancing of which has been regarded as a potential therapeutic strategy for ischemic stroke treatment; however, whether miR‐199a‐5p mediates postischemic neurogenesis remains unclear. This study aims to investigate the proneurogenesis effects of miR‐199a‐5p and its possible mechanism after ischemic stroke.
Transparent Management of BFT Systems with TEE

Achieving distribution transparency is an important goal in distributed system development since it ensures a positive user experience for end-users. In our previous research, we utilized the Intel SGX Trusted Execution Environment (TEE) to facilitate trusted execution of client-side Byzantine Fault-Tolerance (BFT) library functionality on the server-side, enabling legacy clients to access replicated services in a transparent manner. Nonetheless, improving distribution transparency increases the middleware layer's complexity, posing a hindrance to BFT systems' ability to have more transparency in deploying and managing BFT protocols. To resolve this issue, we propose a configurable framework that can manage BFT systems' middleware components flexibly, utilizing a Trusted Execution Environment (TEE) of the emerging RISC-V architecture. The framework offers large-size enclaves that securely execute BFT protocols and other middleware functions, such as network processing, permitting the transparent management of server-side BFT systems without imposing a significant overhead.
A First Look at Dark Mode in Real-World Android Apps

Android apps often have a “dark mode” option used in low light situations, for those who find the conventional color palette problematic, or because of personal preferences. Typically developers add a dark mode option for their apps with different backgrounds, text, and sometimes iconic forms. We wanted to understand the actual provision of this dark mode in real-world Android apps through an empirical study of posts from Stack Overflow and real-world Android app analysis. Using these approaches, we identified the aspects of dark mode that developers implemented as well as the key difficulties they experienced in implementing it. We performed a quantitative analysis using open-coding of more than 300 discussions threads to create a taxonomy regarding the aspects discussed by developers with respect to dark mode in Android. Our quantitative analysis of over 6,000 Android apps highlights which dark mode features are typically provided in Android apps, and what aspects developers care about during dark mode design. We also examined four app development support tools to see how well they aid Android app development for dark mode. From our analysis, we distilled some key lessons to guide further research and actions in aiding developers with supporting users who require such assistive features. For example, developers should be aware of the potential risks in using unsuitable dark mode design schema and researchers should take dark mode features into consideration when developing app development support tools.
Deep Learning for Zero-day Malware Detection and Classification: A Survey

Zero-day malware is malware that has never been seen before or is so new that no anti-malware software can catch it. This novelty and the lack of existing mitigation strategies make zero-day malware challenging to detect and defend against. In recent years, deep learning has become the dominant and leading branch of machine learning in various research fields, including malware detection. Considering the significant threat of zero-day malware to cybersecurity and business continuity, it is necessary to identify deep learning techniques that can somehow be effective in detecting or classifying such malware. But so far, such a comprehensive review has not been conducted. In this article, we study deep learning techniques in terms of their ability to detect or classify zero-day malware. Based on our findings, we propose a taxonomy and divide different zero-day resistant, deep malware detection and classification techniques into four main categories: unsupervised, semi-supervised, few-shot, and adversarial resistant. We compare the techniques in each category in terms of various factors, including deep learning architecture, feature encoding, platform, detection or classification functionality, and whether the authors have performed a zero-day evaluation. We also provide a summary view of the reviewed papers and discuss their main characteristics and challenges.
A Survey of Next-generation Computing Technologies in Space-air-ground Integrated Networks

Space-air-ground integrated networks (SAGINs) are key elements for facilitating high-speed seamless connectivity to the devices/users in infrastructure-less environments, where the traditional terrestrial networks are critically infeasible or uneconomical to be fully deployed. This article comprehensively surveys the advanced computing technologies that support the utilization of SAGINs for infrastructure-less environments. The advanced computing technologies refer to the emerging computing techniques, tools, and the processes that can be utilized to support SAGINs in handling the increasing computing tasks. The main contents include: (1) background of SAGINs, (2) typical use cases of SAGINs in infrastructure-less environments, (3) advanced computing technologies to assist SAGINs to meet the requirements of various services in infrastructure-less environments, (4) the related practical initiatives, and (5) the open research challenges and the future research directions.
lgrgtest: Lagrange multiplier test after constrained maximum-likelihood estimation

Besides the Wald and likelihood-ratio tests, the Lagrange multiplier test (Rao, 1948, Mathematical Proceedings of the Cambridge Philosophical Society 44: 50–57; Aitchison and Silvey, 1958, Annals of Mathematical Statistics 29: 813–828; Silvey, 1959, Annals of Mathematical Statistics 30: 389–407) is the third canonical approach to testing hypotheses after maximum likelihood estimation. While the Stata commands test and lrtest implement the first two, Stata does not have an official command for implementing the third. The community-contributed boottest package (Roodman et al., 2019, Stata Journal 19: 4–60) focuses on methods of bootstrap inference and also implements the Lagrange multiplier test functionality. In this article, I introduce the new community-contributed postestimation command lgrgtest, which allows for straightforwardly using the Lagrange multiplier test after constrained maximum-likelihood estimation. lgrgtest is intended to be compatible with all Stata estimation commands that use maximum likelihood and allow for the options constraints(), iterate(), and from(). lgrgtest can also be used after cnsreg.
Rank Analysis of Incomplete Block Designs: A Method of Paired Comparisons Employing Unequal Repetitions on Pairs

Rank analysis of incomplete block designs using a method of paired comparisons is covered in the references. In these papers it is assumed that there is an experiment consisting of I treatments with n repetitions of each of the 1(1 1)/2 pairs (i, j). It is furthermore postulated that associated with each of the treatments, denoted by T1, I , T, ,there exist parameters, 7ir for Ti , such that 7ri > 0 and i=, 7ri1. The parameters are further defined with the probability statement that, if Xi generally denotes an observation on a sample of Ti,
ginteff: A generalized command for computing interaction effects

Interaction analyses are useful tools to examine complex socioeconomic outcomes in which the effect of one variable depends on the presence or values of another variable. Interaction effects capture simultaneous changes in two (or more) covariates, and their computation is especially challenging in nonlinear models. For such models, a statistically significant interaction-term coefficient does not necessarily indicate significant interactive effects. For analyses in which the interaction effect cannot be inferred from the model estimates, I introduce ginteff, a new command that automatically computes two- and three-way interaction effects. The command accommodates a large suite of estimation models and allows researchers to use either the partial derivative or the first difference to model the effect of the interacted variables.
Deming-based Lean Six Sigma stewardship

Abstract This paper describes the synergistic style of stewardship created by integrating the meta theory of management developed by W. Edwards Deming, the tools and methods of Lean Manufacturing and Six Sigma Management, and the administrative systems of the Japanese Way of Total Quality Control to improve the welfare of all shareholders of an organization: investors, employees, customers, vendors, regulatory agencies, buildings and grounds, the environment, etc. It is called Deming-based Lean Six Sigma Management (DBLSS). It also expands Dr. Deming’s meta theory of management by adding a fifth discipline which is the Theory of Communication. Its purpose is to spread DBLSS and any other new idea within and between organizations and people. Further, the paper presents a major change to the purpose of the Japanese Way of Total Quality Control by restating the purpose of JTQC from “improve customer satisfaction” to “improve customer satisfaction and employee satisfaction. ” Finally, the paper explains that Lean thinking and Six Sigma Management are sets of tools and methods with no philosophical guidance to ensure they are used to improve the satisfaction of all shareholders. It clarifies that traditional management’s purpose is to optimize stockholder welfare, while DBLSS management’s purpose is to optimize all shareholders’ welfare.
Enhancing the quality and reputation of Critical Reviews in Environmental Science and Technology journal: 2023 updates

ainstitute of soil and water resources and environmental science, College of environmental and resource sciences Zhejiang University, hangzhou, China; bdepartment of environmental and occupational health, and department of Civil and environmental engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, Usa; cKey laboratory of Pollution exposure and health intervention, interdisciplinary research academy, Zhejiang shuren University, hangzhou, China
An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Abstract In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.
GrDBSCAN: A Granular Density–Based Clustering Algorithm

Abstract Density-based spatial clustering of applications with noise (DBSCAN) is a commonly known and used algorithm for data clustering. It applies a density-based approach and can produce clusters of any shape. However, it has a drawback—its worst-case computational complexity is O(n2) with regard to the number of data items n. The paper presents GrDBSCAN: a granular modification of DBSCAN with reduced complexity. The proposed GrDBSCAN first granulates data into fuzzy granules and then runs density-based clustering on the resulting granules. The complexity of GrDBSCAN is linear with regard to the input data size and higher only for the number of granules. That number is, however, a parameter of the GrDBSCAN algorithm and is (significantly) lower than that of input data items. This results in shorter clustering time than in the case of DBSCAN. The paper is accompanied by numerical experiments. The implementation of GrDBSCAN is freely available from a public repository.
Single-neuron and population measures of neuronal activity in working memory tasks

Information represented in working memory is reflected in the firing rate of neurons in the prefrontal cortex and brain areas connected to it. In recent years, there has been an increased realization that population measures capture more accurately neural correlates of cognitive functions. We examined how single neuron firing in the prefrontal and posterior parietal cortex of two male monkeys compared with population measures in spatial working memory tasks. Persistent activity was observed in the dorsolateral prefrontal and posterior parietal cortex and firing rate predicted working memory behavior, particularly in the prefrontal cortex. These findings had equivalents in population measures, including trajectories in state space that became less separated in error trials. We additionally observed rotations of the stimulus space for different task conditions, which was not obvious in firing rate measures. These results suggest that population measures provide a richer view of how neuronal activity is associated with behavior, however, largely confirm that persistent activity is the core phenomenon that maintains visual-spatial information in working memory. NEW & NOTEWORTHY Recordings from large numbers of neurons led to a re-evaluation of neural correlates of cognitive functions, which traditionally were defined based on responses of single neurons, or averages of firing rates. Analysis of neuronal recordings from the dorsolateral prefrontal and posterior parietal cortex revealed that properties of neuronal firing captured in classical studies of persistent activity can account for population representations, though some population characteristics did not have clear correlates in single neuron activity.
(Micro)saccade-related potentials during face recognition: A study combining EEG, eye-tracking, and deconvolution modeling

Under natural viewing conditions, complex stimuli such as human faces are typically looked at several times in succession, implying that their recognition may unfold across multiple eye fixations. Although electrophysiological (EEG) experiments on face recognition typically prohibit eye movements, participants still execute frequent (micro)saccades on the face, each of which generates its own visuocortical response. This finding raises the question of whether the fixation-related potentials (FRPs) evoked by these tiny gaze shifts also contain psychologically valuable information about face processing. Here we investigated this question by co-recording EEG and eye movements in an experiment with emotional faces (happy, angry, neutral). Deconvolution modeling was used to separate the stimulus-ERPs to face onset from the FRPs generated by subsequent microsaccades-induced refixations on the face. As expected, stimulus-ERPs exhibited typical emotion effects, with a larger early posterior negativity (EPN) for happy/angry compared to neutral faces. Eye-tracking confirmed that participants made small saccades within the face in 98% of the trials. However, while each saccade produced a strong response over visual areas, this response was unaffected by the face’s emotional expression, both for the first and for subsequent (micro)saccades. This finding suggests that the face’s affective content is rapidly evaluated after stimulus onset, leading to only a short-lived sensory enhancement by arousing stimuli that does not repeat itself during immediate refixations. Methodologically, our work demonstrates how eye-tracking and deconvolution modeling can be used to extract several brain responses from each EEG trial, providing insights into neural processing at different latencies after stimulus onset.