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Lipid nanoparticles (LNPs) are being intensively researched and developed to leverage their ability to safely and effectively deliver therapeutics. To achieve optimal therapeutic delivery, a comprehensive understanding of the relationship between formulation, structure, and efficacy is critical. However, the vast chemical space involved in the production of LNPs and the resulting structural complexity make the structure to function relationship challenging to assess and predict. New components and formulation procedures, which provide new opportunities for the use of LNPs, would be best identified and optimized using high-throughput characterization methods. Recently, a high-throughput workflow, consisting of automated mixing, small-angle X-ray scattering (SAXS), and cellular assays, demonstrated a link between formulation, internal structure, and efficacy for a library of LNPs. As SAXS data can be rapidly collected, the stage is set for the collection of thousands of SAXS profiles from a myriad of LNP formulations. In addition, correlated LNP small-angle neutron scattering (SANS) datasets, where components are systematically deuterated for additional contrast inside, provide complementary structural information. The centralization of SAXS and SANS datasets from LNPs, with appropriate, standardized metadata describing formulation parameters, into a data repository will provide valuable guidance for the formulation of LNPs with desired properties. To this end, we introduce Simple Scattering, an easy-to-use, open data repository for storing and sharing groups of correlated scattering profiles obtained from LNP screening experiments. Here, we discuss the current state of the repository, including limitations and upcoming changes, and our vision towards future usage in developing our collective knowledge base of LNPs.

Aim: The aim of this study was to make the Turkish cultural adaptation of the the Stroke Activity Scale (SAS). Method: This study was designed as a methodological research. The psychometric properties of the SAS scale were evaluated by validity and reliability analyses. Results: The average age of 84 stroke patients (mean ± SD) was 59.52 ± 14.03 years. Kaiser-Meyer-Olkin (0.816) and Bartlett's Test of Sphericity (χ2 = 288.968, df = 10, p < 0.001) showed that the data set can be factored. A one-factor structure was obtained with Principal Component Analysis (PCA). The factor loads of the items obtained with PCA and Confirmatory Factor Analysis (CFA) were found to be statistically significant. The obtained chi-square statistics (χ2 = 2.227, p = 0.694), normalized chi-square/freedom (χ2/sd) value (0 ≤ 0.557 ≤ 2) and other goodness of fit indices showed good compliance. The positive highly significant correlation (r = 0.846, p < 0.001) between the SAS and Rivermead Mobility Index (RMI) scales confirmed the Concurrent validity. Cronbach's alpha (0.864) value showed high reliability and Intraclass Correlation Coefficient (ICC) value (0.983, p < 0.001) showed excellent agreement. Conclusions: In this study, which was conducted for the Turkish adaptation of the SAS scale, the 5-item and 1-factor structure was evaluated as a valid and reliable scale for evaluating the balance abilities of stroke patients living in Turkey. [ABSTRACT FROM AUTHOR]

ABSTRACT Measurement burst designs typically administer brief cognitive tests four times per day for 1 week, resulting in a maximum of 28 data points per week per test for every 6 months. In Alzheimer's disease clinical trials, utilizing measurement burst designs holds great promise for boosting statistical power by collecting huge amount of data. However, appropriate methods for analyzing these complex datasets are not well investigated. Furthermore, the large amount of burst design data also poses tremendous challenges for traditional computational procedures such as SAS mixed or Nlmixed. We propose to analyze burst design data using novel hierarchical linear mixed effects models or hierarchical mixed models for repeated measures. Through simulations and real‐world data applications using the novel SAS procedure Hpmixed, we demonstrate these hierarchical models' efficiency over traditional models. Our sample simulation and analysis code can serve as a catalyst to facilitate the methodology development for burst design data. [ABSTRACT FROM AUTHOR]

Mixed lead–tin halide perovskites, as highly sensitive materials in the near-infrared region, hold significant potential for optoelectronic device applications. Here, mixed lead–tin halide perovskite saturable absorbers (SAs) have been developed by coupling with the side-polished surfaces of the single-mode fibers and excellent saturable absorption effects of the mixed lead–tin halide perovskite SAs have been demonstrated in the near-infrared region. By constructing the in-gap site assisted carrier transfer mode, the saturation absorption process of the mixed lead–tin halide perovskite SAs can be well explained, in which defects as in-gap sites can help the photon-generated carriers transfer into the conduction band and promote the Pauli-blocking-induced absorption bleaching in the SA. Moreover, ytterbium-doped fiber lasers based on perovskite SAs have been fabricated, and mode-locked operations at 1040 nm are achieved using the mixed lead–tin halide perovskite SA, generating ultra-short pulses with a pulse width of 683 fs, 3 dB bandwidth of 4.88 nm, signal-to-noise ratio exceeding 49.74 dB, and a repetition rate of 3.74 MHz. Our findings demonstrate that the mixed lead–tin halide perovskite SAs have excellent optical modulation capability and promising applications in the field of ultrafast photonics. [ABSTRACT FROM AUTHOR]

A 2-D Lattice Boltzmann Method, designed to ensure stability at high Reynolds numbers, is combined with an Actuator Line Model to compute the loads on a two-bladed vertical axis wind turbine. Tests on the kernel size at a high mesh resolution reveal that a size equal to half of the full chord length yields the most accurate results. The aerodynamic load solution is validated against a fully resolved Scale-Adaptive Simulation (SAS) output, demonstrating high correlation, and enabling an assessment of near wake and downstream effects. The model's adaptability to various rotor operating conditions is confirmed through tests at high and low tip-speed ratios. Additionally, a Biot–Savart-based Vortex Model (VM) is employed for further comparison, showing good agreement with the Lattice Boltzmann output. The results indicate that the Highly Stable Lattice Boltzmann Method integrated with the Actuator Line Model enhances the accuracy of flow field resolution and effectively captures complex aerodynamic phenomena, making it a valuable tool for simulating vertical axis wind turbines. [ABSTRACT FROM AUTHOR]

Special AT-rich sequence-binding protein 2 (SATB2) is a master regulator of gene expression. Mutations of the SATB2 gene results in the SATB2-associated syndrome (SAS), a genetic disorder characterized by neurodevelopmental disabilities and autism-related phenotype. The importance of plasma as an indicator of SAS phenotypes is unknown. We aim to investigate if pathogenic variants in SATB2 are associated with alteration to relevant pathways in the plasma of SAS patients and identify key differentially regulated proteins which may serve as biomarkers to improve diagnostic and future pharmacological approaches. We used well-validated proteomic technologies to determine the proteomic profile of plasma from SAS patients compared to healthy control subjects. Bioinformatical analysis was performed to identify significant proteins and functionally enriched pathways. We identified differentially expressed proteins in the plasma of SAS patients that are significantly involved in metabolism-related pathways. Energy metabolism, glucose metabolism and vitamin metabolism pathways are significantly enriched in SAS patients as compared to healthy controls. Our study linked SATB2 mutations to the impairment of plasma proteins involved in different metabolic pathways in SAS patients. [ABSTRACT FROM AUTHOR]

Objective: The mental health issues of individuals with coronavirus disease 2019 (COVID-19) are currently widespread. Traditional Chinese exercise (Daoyin) plays an important role in relieving patients’ psychological problems. This study aims to assess the efficacy of Daoyin in mitigating mental health issues among individuals diagnosed with COVID-19. Methods: PubMed, the Cochrane library, Embase, CNKI, Wanfang, VIP database, and SinoMed were searched from their inception to October 2023. Two researchers independently selected the eligible studies. The analysis and presentation of the findings were conducted using Review Manager 5.2 software. The data were analyzed using mean difference (MD), inverse variance, and 95% confidence intervals (CIs). Results: A total of 12 studies (N = 1291) were included in this study. The results showed that Daoyin can significantly reduce the scores of the Self-Rating Anxiety Scale (SAS: MD = −13.03, 95% CI −19.56 to −6.49, P<.49,yca Self-Rating Depression Scale (SDS: MD = −11.13, 95% CI −14.56 to −7.71, P<.71,sion Pittsburgh Sleep Quality Index (PSQI: MD = −2.00, 95% CI −5.43 to 1.43, P = 0.25), Hamilton Anxiety Scale (HAMA: MD = −2.42, 95% CI −5.25 to 0.41, P = 0.09), and Hamilton Depression Scale (HAMD: MD = −11.17, 95% CI −25.5 to 3.15, P = 0.13). Conclusion: In COVID-19 patients, Daoyin can alleviate feelings of anxiety and depression, as well as improve sleep quality. The use of Daoyin has no adverse effects and side effects and can reduce the cost of medication. Therefore, Daoyin can be widely promoted. Further research is warranted to analyze the effect of Daoyin on mental health. [ABSTRACT FROM AUTHOR]

High concentrations of low-density particles may cause effects in acute inhalation toxicity studies which can be easily underestimated or misinterpreted following strictly the OECD TG 436 , i.e., limited parameters as mortality and gross lesions will be evaluated only. Seven particle types (synthetic amorphous silica (SAS) HMDZ-SAS, silica gel, pyrogenic SAS, and precipitated SAS, calcium carbonate, aluminum oxide pyrogenic alumina, organic red pigment) were chosen at the highest technically feasible concentration of approximately 500 mg/m3 for acute inhalation studies with an expanded endpoint setup. Therefore additional parameters and a thorough histopathological evaluation of an extensive set of organs, including the respiratory tract emphasizing the nasal cavities were added. Six Crl:WI rats per study were exposed for four hours from which three animals were sacrificed after 24 hours and three animals after 14 days. HMDZ-SAS caused early death in all animals due to blockage of the nasal passages caused by its hydrophobicity. For all other Si-containing compounds, histology revealed minor inflammatory and reactive lesions in lungs after 24 hours that were still present after 14 days, except in silica gel-treated animals. After 14 days, for pyrogenic SAS, precipitated SAS, and pyrogenic alumina, granulomas formed in the BALT and lung-associated lymph nodes. In contrast, the calcium carbonate induced almost no findings, and the red pigment (also tested for the additional dose of 1000 mg/m3) stuck partially to the nasal mucosa without causing pathological damage and partly entered the lungs without showing any adverse effects. The results of the present study highlight the advantage of improving the rather simple study design of acute inhalation studies by implementing an extended study design. • High concentrations of low-density particles can have misinterpreted effects in acute inhalation toxicity studies. • Adding parameters and a histopathological evaluation revealed new insights of different particle types during acute inhalation studies. • Early death during acute inhalation studies due to nasal blockage highlights the importance of considering hydrophobicity in particle toxicity. • Implementing an extended study design improves the understanding of acute inhalation toxicity. [ABSTRACT FROM AUTHOR]

Longitudinal data from clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) when the time variable is categorical or linear mixed‐effects models (ie, random effects model) when the time variable is continuous. In these models, statistical inference is typically based on the absolute difference in the adjusted mean change (for categorical time) or the rate of change (for continuous time). Previously, we proposed a novel approach: modeling the percentage reduction in disease progression associated with the treatment relative to the placebo decline using proportional models. This concept of proportionality provides an innovative and flexible method for simultaneously modeling different cohorts, multivariate endpoints, and jointly modeling continuous and survival endpoints. Through simulated data, we demonstrate the implementation of these models using SAS procedures in both frequentist and Bayesian approaches. Additionally, we introduce a novel method for implementing MMRM models (ie, analysis of response profile) using the nlmixed procedure. [ABSTRACT FROM AUTHOR]

This research aims to create valid and effective Student Activity Sheets (SAS) and Scientific Creativity Tests (SCT) in order to enhance the creativity of open university students. The validity of SAS and SCT is determined by achieving a minimum score of 2.50 in both content validity and construct validity categories. Similarly, their effectiveness is measured by: (1) observing a statistically significant increase in students' scientific creativity at a 5% significance level; and (2) achieving a minimum average n-gain in the medium category. This research follows an Educational Design Research approach and utilizes a pre-experimental design with a one-group pre-test and post-test design. The research subjects consist of undergraduate students at Open University Surabaya during the 2023/2024 academic year. The data collection instruments used in this research include: (1) a Validity Assessment Sheet for SAS and SCT; and (2) the SCT test. The research data, which includes pre-test and post-test scores, are collected after conducting prerequisite tests such as normality and homogeneity tests. These scores are then analyzed using a paired t-test to identify any differences in students' scientific creativity between the pre-test and post-test stages. The average level of improvement in scores is calculated using the normalized gain (N-gain) method. The research data, in the form of scores obtained from the assessments of the validity of learning tools by three Learning Experts, are analyzed using the average method to determine validity, as well as by using Cronbach's alpha (α) to assess reliability. The same analysis is conducted for the Research Instrument scores. The research findings demonstrate that both SAS and SCT tools are valid and effective in enhancing student creativity.

In pharmaceutical manufacturing, especially biologics and vaccines manufacturing, emphasis on speedy process development can lead to inadequate process development, which often results in less robust commercial manufacturing process after launch. Process performance index (Ppk) is a statistical measurement of the ability of a process to produce output within specification limits over a period of time. In biopharmaceutical manufacturing, progression in process development is based on Critical Quality Attributes meeting their specification limits, lacking insight into the process robustness. Ppk is typically estimated after 15–30 commercial batches at which point it may be too late/too complex to make process adjustments to enhance robustness. The use of Bayesian statistics, prior knowledge, and input from Subject matter experts (SMEs) offers an opportunity to make predictions on process capability during the development cycle. Developing a standard methodology to assess long term process capability at various stages of development provides several benefits: provides opportunity for early insight into process vulnerabilities thereby enabling resolution pre‐licensure; identifies area of the process to prioritize and focus on during process development/process characterization (PC) using a data‐driven approach; and ultimately results in higher process robustness/process knowledge at launch. We propose a Bayesian‐based method to predict the performance of a manufacturing process at full manufacturing scale during the development and commercialization phase, before commercial data exists. Under Bayesian framework, limited development data for the process of interest at hand, data from similar products, general SME knowledge, and literature can be carefully formulated into informative priors. The implementation of the proposed approach is presented through two examples. To allow for continuous improvement during process development, we recommend to embed this approach of using predictive Ppk at pre‐defined commercialization stage‐gates, for example, at completion of process development, prior to and completion of PC, prior to technology transfer runs (Engineering/Process Performance Qualification, PPQ), and prior to commercial specification setting. [ABSTRACT FROM AUTHOR]

Cold rolling emulsion contains a variety of functional additives, which often exhibit complex interactions with each other. Sodium alkane sulfonate (SAS) is a common corrosion inhibitor used in cold rolling emulsions for temporary rust prevention. In this study, it was found that SAS would deteriorate the tribological properties of the emulsion. Emulsions containing SAS and different friction modifiers were prepared. Tribology tests were carried out on a four-ball friction and wear tester. White light interferometer was used to investigate the 3D morphology of the friction surface and wear volume. Microscopic morphology of friction surfaces was observed using a scanning electron microscope (SEM). The chemical activity and electrostatic potential of the molecules were calculated based on density functional theory (DFT). The adsorption energies of additives on metal surfaces were calculated via molecular dynamics (MD) simulation. The results indicate that the strong electrostatic force gives SAS an advantage in competitive adsorption with ester friction modifiers due to the positive charge on the metal surface. This results in the friction modifier not functioning properly and the tribological properties of the emulsion being significantly reduced. [ABSTRACT FROM AUTHOR]

In this study, the unsteady Reynolds-averaged Navier–Stokes (URANS) equations are employed in conjunction with the Menter Shear Stress Transport (SST)-Scale-Adaptive Simulation (SAS) turbulence model in compressible flow, with an unstructured mesh and complex geometry. While other scale-resolving approaches in space and time, such as direct numerical simulation (DNS) and large-eddy simulation (LES), supply more comprehensive information about the turbulent energy spectrum of the fluctuating component of the flow, they imply computationally intensive situations, usually performed over structured meshes and relatively simple geometries. In contrast, the SAS approach is designed according to "physically" prescribed length scales of the flow. More precisely, it operates by locally comparing the length scale of the modeled turbulence to the von Karman length scale (which depends on the local first- and second fluid velocity derivatives). This length-scale ratio allows the flow to dynamically adjust the local eddy viscosity in order to better capture the large-scale motions (LSMs) in unsteady regions of URANS simulations. While SAS may be constrained to model only low flow frequencies or wavenumbers (i.e., LSM), its versatility and low computational cost make it attractive for obtaining a quick first insight of the flow physics, particularly in those situations dominated by strong flow unsteadiness. The selected numerical application is the well-known M219 three-dimensional rectangular acoustic cavity from the literature at two different free-stream Mach numbers,  M ∞  (0.85 and 1.35) and a length-to-depth ratio of 5:1. Thus, we consider the "deep configuration" in experiments by Henshaw. The SST-SAS model demonstrates a satisfactory compromise between simplicity, accuracy, and flow physics description. [ABSTRACT FROM AUTHOR]

The object of this study is a programmable logic controller (safety PLC), which is part of an information and control system designed for safe management of important technological processes. The subject of this study is the substantiation of the legality of reusing the results obtained during the development of the safety PLC in accordance with the requirements of functional safety to assess the level of its cyber security. The purpose of this work is to investigate the possibility of "cross" evaluation of the safety characteristics of the safety PLC, namely, the possibility of evaluating the level of cyber security of the programmable logic controller based on known data regarding its level of functional safety, in order to optimize the use of available resources in the project. The study tasks are following: to provide a theoretical basis for the relationship between safety PLC characteristics such as functional safety and cybersecurity. Determine the metrics by which it will be possible to assess the degree of reuse of existing results. Perform an analysis of potential cyberattacks depending on the architecture of the information and control system, which performs security functions, as well as on the possible modes of its use. Determine and evaluate the degree of "cross" influence of critical characteristics of the research object. To perform a calculation analysis of the potential financial and time gain from the reuse of already known results for the minimum configuration of the safety PLC. Conclusions. The study demonstrated the relevance of the question of assessing the cybersecurity of a programmable logic controller based on the use of existing data, regarding its level of functional security (SIL). The proposed approach provides opportunities to significantly optimize the use of resources in safety PLC certification projects. However, the main methodological conclusion is that the well-known principle of Security Informed Safety can be developed and used in practice in the opposite direction, as Security supported/assessed by Safety. That is, to the principle of "assessment of functional safety taking into account/on information (cyber) security" the principle of "assessment of information (cyber) security with the support of/taking into account the results of the assessment of functional security" is added.

With the growing demand for delay-sensitive voice, video, and data-based applications, the challenge of efficient spectrum utilization is becoming more critical. To address the challenges of spectrum scarcity, the Federal Communications Commission (FCC) has authorized the sharing of the federally held Citizen Broadband Radio Service (CBRS) band, also known as the 3.5 GHz spectrum band, with commercial use. The 3.5 GHz spectrum band controlled by the spectrum access system (SAS) is shared with three types of users i.e., Incumbent users, Primary access licensee (PAL) users, and general authorized access (GAA) users as opportunistic users. In this paper we proposed the SAS-based channel allocation (SAS-CA) algorithm to optimize spectrum utilization and system performance, considering the potential risks of adding opportunistic GAA users. The proposed algorithm ensures low outage probability for PAL users and provides an optimized channel allocation strategy for GAA users when PAL users are inactive in PAL reserved channels. Additionally, a security module integrated with SAS enhances spectrum utilization and reduces interference levels. The algorithm’s efficiency is validated through multiple test sets, showing rapid convergence within 100 iterations. The integration of the security module results in a median spectrum utilization increase of 16 points and a notable reduction in interference levels, enhancing execution efficiency by approximately 37% compared to traditional methods. These findings demonstrate the substantial benefits of advanced allocation algorithms and robust security mechanisms in ensuring equitable access, stabilizing system performance, and optimizing spectrum utilization within the CBRS band.

A power system is not only capable to meet the present load but also has the flexibility to meet the future demands. A power system is designed to generate electric power in sufficient quantity, to meet the present and estimated future demands of the users in a particular area, to transmit it to the areas where it will be used and then distribute it within that area, on a continuous basis. Using the optimum protection and control systems with the high voltage substations are considered as the main objectives to save the system is reliable and curate operation. The protection system in high voltage substation had started by the electro-mechanical relays which improved to electrostatic relays and updated to the numerical protection relays. The switching and control of the substations had started depending on the manpower for operation and supervising the system which improved to depend on the SCADA system. This paper introduces the steps and the operation function of using the electro-mechanical, electrostatic designs and numerical relays to protect the electrical equipment in high voltage substations. Also, this paper study the improvement of the control and supervising the high voltage substations that reached now to depend on the IEC 61850 communication protocol for the optimum automation system. [ABSTRACT FROM AUTHOR]

Background: The Self-Absorption Scale (SAS) is one of the few instruments that measure dysfunctional selffocused attention or self-absorption, a transdiagnostic factor of vulnerability to various emotional disorders. The internal structure of the Spanish version of the SAS and its relationship with other variables have not been examined, nor has whether its subscales provide relevant information. These were the two goals of the present study. Method: The factor structure of the SAS, its internal consistency, and its relationship with depression and post-traumatic stress were analyzed in a Spanish community sample of 519 adults. Results: The SAS presented a symmetrical bifactor structure with a general factor of self-absorption that explained most of the variance in the items and two specific factors of private and public self-absorption. The total scale and the two subscales of the SAS exhibited excellent, good or adequate reliability coefficients (alphas/omegas = .70 - .88) and correlated with depression and post-traumatic stress (r = .34 - .46). Conclusions: The SAS provides reliable, valid measures of dysfunctional selffocused attention in Spanish adults, but its Private and Public Self-absorption subscales are not much more useful than the information provided by its total scale. [ABSTRACT FROM AUTHOR]

This document provides a practical guide to using Bayesian statistical methods in clinical and translational research. The authors explain the concepts and methods of Bayesian analysis, including Gibbs sampling and Metropolis-Hastings algorithms. They demonstrate how to apply Bayesian analyses using different software programs and discuss the importance of specifying prior beliefs and using domain knowledge. The document also covers topics such as model selection, informative priors, and model diagnostics. It aims to help researchers understand and evaluate Bayesian analyses in medical research. [Extracted from the article]

Abstract Background Using four case studies, we aim to provide practical guidance and recommendations for the analysis of cluster randomised controlled trials. Methods Four modelling approaches (Generalized Linear Mixed Models with parameters estimated by maximum likelihood/restricted maximum likelihood; Generalized Linear Models with parameters estimated by Generalized Estimating Equations (1st order or second order) and Quadratic Inference Function, for analysing correlated individual participant level outcomes in cluster randomised controlled trials were identified after we reviewed the literature. We systematically searched the online bibliography databases of MEDLINE, EMBASE, PsycINFO (via OVID), CINAHL (via EBSCO), and SCOPUS. We identified the above-mentioned four statistical analytical approaches and applied them to four case studies of cluster randomised controlled trials with the number of clusters ranging from 10 to 100, and individual participants ranging from 748 to 9,207. Results were obtained for both continuous and binary outcomes using R and SAS statistical packages. Results The intracluster correlation coefficient (ICC) estimates for the case studies were less than 0.05 and are consistent with the observed ICC values commonly reported in primary care and community-based cluster randomised controlled trials. In most cases, the four methods produced similar results. However, in a few analyses, quadratic inference function produced different results compared to the generalized linear mixed model, first-order generalized estimating equations, and second-order generalized estimating equations, especially in trials with small to moderate numbers of clusters. Conclusion This paper demonstrates the analysis of cluster randomised controlled trials with four modelling approaches. The results obtained were similar in most cases, however, for trials with few clusters we do recommend that the quadratic inference function should be used with caution, and where possible a small sample correction should be used. The generalisability of our results is limited to studies with similar features to our case studies, for example, studies with a similar-sized ICC. It is important to conduct simulation studies to comprehensively evaluate the performance of the four modelling approaches.

A 2-D Lattice Boltzmann Method, designed to ensure stability at high Reynolds numbers, is combined with an Actuator Line Model to compute the loads on a two-bladed vertical axis wind turbine. Tests on the kernel size at a high mesh resolution reveal that a size equal to half of the full chord length yields the most accurate results. The aerodynamic load solution is validated against a fully resolved Scale-Adaptive Simulation (SAS) output, demonstrating high correlation, and enabling an assessment of near wake and downstream effects. The model’s adaptability to various rotor operating conditions is confirmed through tests at high and low tip-speed ratios. Additionally, a Biot–Savart-based Vortex Model (VM) is employed for further comparison, showing good agreement with the Lattice Boltzmann output. The results indicate that the Highly Stable Lattice Boltzmann Method integrated with the Actuator Line Model enhances the accuracy of flow field resolution and effectively captures complex aerodynamic phenomena, making it a valuable tool for simulating vertical axis wind turbines.

Sleep apnea syndrome (SAS) is a common sleeprelated breathing disorder characterized by recurring cessations of airflow during sleep. The gold standard for diagnosing SAS is polysomnography (PSG) which requires the patient to spend one or several nights in a sleep clinic. A PSG involves a significant amount of contact-based sensors, which leads to discomfort and deviations in sleep behavior. In this work, a contactless, multispectral camera-based approach for the autonomous detection of events of nocturnal airflow reduction is presented. The detected events are further employed in estimators of sleep diagnostic metrics, such as the apnea-hypopnea index (AHI) and the SAS stage. The AHI estimation resulted in a Pearson correlation coefficient of r = 0.9993. The SAS stage estimator correctly predicted the SAS stage for all three recruited patients.

A crucial challenge in Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation is to diagnose the convergence of the chains so that the draws can be expected to closely approximate the posterior distribution on which inference is based. A close approximation guarantees that the MCMC error exhibits only a negligible impact on model estimates and inferences. However, determining whether convergence has been achieved can often be challenging and cumbersome when relying solely on inspecting the trace plots of the chain(s) or manually checking the stopping criteria. In this article, we present a SAS macro called %automcmc that is based on PROC MCMC and that automatically continues to add draws until a user-specified stopping criterion (i.e., a certain potential scale reduction and/or a certain effective sample size) is reached for the chain(s).

Lipid nanoparticles (LNPs) are being intensively researched and developed to leverage their ability to safely and effectively deliver therapeutics. To achieve optimal therapeutic delivery, a comprehensive understanding of the relationship between formulation, structure, and efficacy is critical. However, the vast chemical space involved in the production of LNPs and the resulting structural complexity make the structure to function relationship challenging to assess and predict. New components and formulation procedures, which provide new opportunities for the use of LNPs, would be best identified and optimized using high-throughput characterization methods. Recently, a high-throughput workflow, consisting of automated mixing, small-angle X-ray scattering (SAXS), and cellular assays, demonstrated a link between formulation, internal structure, and efficacy for a library of LNPs. As SAXS data can be rapidly collected, the stage is set for the collection of thousands of SAXS profiles from a myriad of LNP formulations. In addition, correlated LNP small-angle neutron scattering (SANS) datasets, where components are systematically deuterated for additional contrast inside, provide complementary structural information. The centralization of SAXS and SANS datasets from LNPs, with appropriate, standardized metadata describing formulation parameters, into a data repository will provide valuable guidance for the formulation of LNPs with desired properties. To this end, we introduce Simple Scattering, an easy-to-use, open data repository for storing and sharing groups of correlated scattering profiles obtained from LNP screening experiments. Here, we discuss the current state of the repository, including limitations and upcoming changes, and our vision towards future usage in developing our collective knowledge base of LNPs.

Abstract Background: Bayesian statistical approaches are extensively used in new statistical methods but have not been adopted at the same rate in clinical and translational (C&T) research. The goal of this paper is to accelerate the transition of new methods into practice by improving the C&T researcher’s ability to gain confidence in interpreting and implementing Bayesian analyses. Methods: We developed a Bayesian data analysis plan and implemented that plan for a two-arm clinical trial comparing the effectiveness of a new opioid in reducing time to discharge from the post-operative anesthesia unit and nerve block usage in surgery. Through this application, we offer a brief tutorial on Bayesian methods and exhibit how to apply four Bayesian statistical packages from STATA, SAS, and RStan to conduct linear and logistic regression analyses in clinical research. Results: The analysis results in our application were robust to statistical package and consistent across a wide range of prior distributions. STATA was the most approachable package for linear regression but was more limited in the models that could be fitted and easily summarized. SAS and R offered more straightforward documentation and data management for the posteriors. They also offered direct programming of the likelihood making them more easily extendable to complex problems. Conclusion: Bayesian analysis is now accessible to a broad range of data analysts and should be considered in more C&T research analyses. This will allow C&T research teams the ability to adopt and interpret Bayesian methodology in more complex problems where Bayesian approaches are often needed.

Background: Using four case studies, we aim to provide practical guidance and recommendations for the analysis of cluster randomised controlled trials. Methods: Four modelling approaches (Generalized Linear Mixed Models with parameters estimated by maximum likelihood/restricted maximum likelihood; Generalized Linear Models with parameters estimated by Generalized Estimating Equations (1st order or second order) and Quadratic Inference Function, for analysing correlated individual participant level outcomes in cluster randomised controlled trials were identified after we reviewed the literature. We systematically searched the online bibliography databases of MEDLINE, EMBASE, PsycINFO (via OVID), CINAHL (via EBSCO), and SCOPUS. We identified the above-mentioned four statistical analytical approaches and applied them to four case studies of cluster randomised controlled trials with the number of clusters ranging from 10 to 100, and individual participants ranging from 748 to 9,207. Results were obtained for both continuous and binary outcomes using R and SAS statistical packages. Results: The intracluster correlation coefficient (ICC) estimates for the case studies were less than 0.05 and are consistent with the observed ICC values commonly reported in primary care and community-based cluster randomised controlled trials. In most cases, the four methods produced similar results. However, in a few analyses, quadratic inference function produced different results compared to the generalized linear mixed model, first-order generalized estimating equations, and second-order generalized estimating equations, especially in trials with small to moderate numbers of clusters. Conclusion: This paper demonstrates the analysis of cluster randomised controlled trials with four modelling approaches. The results obtained were similar in most cases, however, for trials with few clusters we do recommend that the quadratic inference function should be used with caution, and where possible a small sample correction should be used. The generalisability of our results is limited to studies with similar features to our case studies, for example, studies with a similar-sized ICC. It is important to conduct simulation studies to comprehensively evaluate the performance of the four modelling approaches. [ABSTRACT FROM AUTHOR]

This study investigates the performance of estimation methods for multidimensional IRT models with dichotomous and polytomous data in two well-known IRT programs: SAS PROC IRT and the mirt package in R. A simulation study was used to compare performance on a simple structure Rasch model, complex structure 2PL model, and bifactor graded response model. Under RMSE and bias criteria regarding item parameter recovery, PROC IRT and mirt showed nearly identical performance in the simple structure condition. When a complex structure was used, mirt performed better in terms of the recovery of intercept parameters, while the recovery of slope parameters depended on the program and the sample sizes: PROC IRT tended to be better with small samples (N=500) according to RMSE, and mirt was better for larger samples (N=1000 and 2500) according to RMSE and bias for the slope parameter recovery. When a bifactor structure was used, mirt was preferred in all cases; differences lessened as sample size increased.

In this study, the unsteady Reynolds-averaged Navier–Stokes (URANS) equations are employed in conjunction with the Menter Shear Stress Transport (SST)-Scale-Adaptive Simulation (SAS) turbulence model in compressible flow, with an unstructured mesh and complex geometry. While other scale-resolving approaches in space and time, such as direct numerical simulation (DNS) and large-eddy simulation (LES), supply more comprehensive information about the turbulent energy spectrum of the fluctuating component of the flow, they imply computationally intensive situations, usually performed over structured meshes and relatively simple geometries. In contrast, the SAS approach is designed according to “physically” prescribed length scales of the flow. More precisely, it operates by locally comparing the length scale of the modeled turbulence to the von Karman length scale (which depends on the local first- and second fluid velocity derivatives). This length-scale ratio allows the flow to dynamically adjust the local eddy viscosity in order to better capture the large-scale motions (LSMs) in unsteady regions of URANS simulations. While SAS may be constrained to model only low flow frequencies or wavenumbers (i.e., LSM), its versatility and low computational cost make it attractive for obtaining a quick first insight of the flow physics, particularly in those situations dominated by strong flow unsteadiness. The selected numerical application is the well-known M219 three-dimensional rectangular acoustic cavity from the literature at two different free-stream Mach numbers, M∞ (0.85 and 1.35) and a length-to-depth ratio of 5:1. Thus, we consider the “deep configuration” in experiments by Henshaw. The SST-SAS model demonstrates a satisfactory compromise between simplicity, accuracy, and flow physics description.

Cold rolling emulsion contains a variety of functional additives, which often exhibit complex interactions with each other. Sodium alkane sulfonate (SAS) is a common corrosion inhibitor used in cold rolling emulsions for temporary rust prevention. In this study, it was found that SAS would deteriorate the tribological properties of the emulsion. Emulsions containing SAS and different friction modifiers were prepared. Tribology tests were carried out on a four-ball friction and wear tester. White light interferometer was used to investigate the 3D morphology of the friction surface and wear volume. Microscopic morphology of friction surfaces was observed using a scanning electron microscope (SEM). The chemical activity and electrostatic potential of the molecules were calculated based on density functional theory (DFT). The adsorption energies of additives on metal surfaces were calculated via molecular dynamics (MD) simulation. The results indicate that the strong electrostatic force gives SAS an advantage in competitive adsorption with ester friction modifiers due to the positive charge on the metal surface. This results in the friction modifier not functioning properly and the tribological properties of the emulsion being significantly reduced.

A crucial challenge in Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation is to diagnose the convergence of the chains so that the draws can be expected to closely approximate the posterior distribution on which inference is based. A close approximation guarantees that the MCMC error exhibits only a negligible impact on model estimates and inferences. However, determining whether convergence has been achieved can often be challenging and cumbersome when relying solely on inspecting the trace plots of the chain(s) or manually checking the stopping criteria. In this article, we present a SAS macro called %automcmc that is based on PROC MCMC and that automatically continues to add draws until a user-specified stopping criterion (i.e., a certain potential scale reduction and/or a certain effective sample size) is reached for the chain(s). [ABSTRACT FROM AUTHOR]

Lysosomal storage diseases (LSDs) consist of about 60 different monogenic disorders. Most of them occur due to protein misfolding. Only a few of those have been treated with molecular chaperones; the remaining either have limited treatment options or only management therapies. About 1860 single amino-acid substitutions (SAS) have been identified under LSDs. Merely, a handful of mutations have been studied experimentally. Availability of computational tools has made researchers turn toward genetic disorders to focus light on unexplored disorders and their mutations. Since all the LSDs are rare genetic disorders, not much research is carried out in this area. However, a mutational effect on protein function could be predicted, through bioinformatics tools. On that note, out of 1860 SAS, 58 predictions were neutral and 778 were predicted to be disease associated by all programs included in this study. The result of the prediction analysis of all mutations in each of the LSDs is curated into a database. This would make researchers know the deleterious nature of a mutation causing LSD. The database is available at http://lsddb.vit.ac.in:3000/. Lysosomal storage diseases (LSDs) are rare yet cytotoxic inherent metabolic aberrations, elucidated by excess accumulation of biomolecular substrates in cells of various organs, due to lysosomal defective functioning. To formulate effective analeptics against this detrimental and potentially life-threatening affliction, and to better comprehend the pathological mechanism behind LSDs, it is essential to know the integral detrimental mutations that actuate LSDs. To make researchers and the general populace aware of this pertinent information, we have designed a database that includes all the disorders categorized under LSDs. [ABSTRACT FROM AUTHOR]

Poloxamers or Pluronics®-based nanogels are one of the most used matrices for developing delivery systems. Due to their thermoresponsive and flexible mechanical properties, they allowed the incorporation of several molecules including drugs, biomacromolecules, lipid-derivatives, polymers, and metallic, polymeric, or lipid nanocarriers. The thermogelling mechanism is driven by micelles formation and their self-assembly as phase organizations (lamellar, hexagonal, cubic) in response to microenvironmental conditions such as temperature, osmolarity, and additives incorporated. Then, different biophysical techniques have been used for investigating those structural transitions from the mechanisms to the preferential component's orientation and organization. Since the design of PL-based pharmaceutical formulations is driven by the choice of the polymer type, considering its physico-chemical properties, it is also relevant to highlight that factors inherent to the polymeric matrix can be strongly influenced by the presence of additives and how they are able to determine the nanogels biopharmaceuticals properties such as bioadhesion, drug loading, surface interaction behavior, dissolution, and release rate control. In this review, we discuss the general applicability of three of the main biophysical techniques used to characterize those systems, scattering techniques (small-angle X-ray and neutron scattering), rheology and Fourier transform infrared absorption spectroscopy (FTIR), connecting their supramolecular structure and insights for formulating effective therapeutic delivery systems. [ABSTRACT FROM AUTHOR]

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In this work, a secondary alkane sulfonate (SAS) modified Li-Al LDH film (SLDH) with high corrosion resistance by a simple in-situ growth on aluminium alloy 6N01. Water contact angle (WCA) and scanning electron microscopy (SEM) revealed that the film was fabricated with a flower-shaped microstructure and highly superhydrophobic performance (WCA=153°). XRD patterns and FTIR spectrum revealed that SAS species were intercalated into SLDH. X-ray photoelectron spectroscopy (XPS) analysis indicated that SLDH was mainly composed of C, O, S, Al and Li. Potentiodynamic polarization (PDP) curves proved that SLDH (icorr = 9.169 × 10−8 A/cm2) provided pronounced protection than that of CLDH (icorr = 9.011 × 10−7 A/cm2), and electrochemical impedance spectra (EIS) demonstrated that corrosion resistance was improved. Neutral salt spray (NSS) tests exhibited that compared to CLDH with severe pitting corrosion, SLDH was virtually undamaged after 360 h exposure. Based on the above results, a sound inhibition mechanism of SLDH was inferred to be a dual effect, including the barrier action of superhydrophobic surface and the ion exchange of intercalated SAS with aggressive chlorides. In conclusion, the special divergent long-chain structure of SAS with low surface energy results in the superhydrophobicity of SLDH.

Abstract Background Few studies have investigated factors associated with anxiety and depression among patients with erectile dysfunction (ED). This study aimed to investigate associated factors and the prevalence of anxiety and depression in this special group in China. Methods Data from 511 patients with ED aged 18–60 years were collected between July 2021 and April 2022. The 5-item International Index of Erectile Function (IIEF-5) questionnaire, self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were used to evaluate erectile function, anxiety and depression, respectively. Univariate analysis and multivariate linear regression analyses were used to explore the associated factors of depression and anxiety. Results The prevalence of anxiety and depression among ED patients was 38.16% and 64.97%, respectively. The mean anxiety index score was 47.37 ± 6.69 points, and the mean depression index was 54.72 ± 9.10 points. Multiple linear regression analysis showed that worse ED, low education level, and smoking were positively associated with increased risk of anxiety and depression. In addition, younger age, longer onset time, and irregular sleep were positively associated with high risk of anxiety, and irregular exercise was associated with severe depression. Conclusions The prevalence of depression and anxiety in ED patients is high, and the severity of ED, age, education level, smoking, onset time, regular sleep, and exercise were associated with anxiety or depression. Reversible risk factors should be avoided and individualized psychological support services are necessary for ED patients.

As the services and requirements of next-generation wireless networks become increasingly diversified, it is estimated that the current frequency bands of mobile network operators (MNOs) will be unable to cope with the immensity of anticipated demands. Due to spectrum scarcity, there has been a growing trend among stakeholders toward identifying practical solutions to make the most productive use of the exclusively allocated bands on a shared basis through spectrum sharing mechanisms. However, due to the technical complexities of these mechanisms, their design presents challenges, as it requires coordination among multiple entities. To address this challenge, in this paper, we begin with a detailed review of the recent literature on spectrum sharing methods, classifying them on the basis of their operational frequency regime—that is, whether they are implemented to operate in licensed bands (e.g., licensed shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing (DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to narrow the gap between the standardization and vendor-specific implementations, we provide a detailed review of the potential implementation scenarios and necessary amendments to legacy cellular networks from the perspective of telecommunication vendors and regulatory bodies. Next, we analyze applications of artificial intelligence (AI) and machine learning (ML) techniques for facilitating spectrum sharing mechanisms and leveraging the full potential of autonomous sharing scenarios. Finally, we conclude the paper by presenting open research challenges, which aim to provide insights into prospective research endeavors.

In recent years, the Steer-by-Wire (SBW) technology has been gaining popularity and replacing classical steering systems. It plays the most crucial role in autonomous cars where the vehicle must perform maneuvers on its own without driver’s intervention. One of the key components of this system is the steering wheel angle sensor (SAS). Its reliability and performance may affect driver’s life and health. The purpose of this paper is to show a test system to comprehensively evaluate the performance of the steering wheel angle sensor in the SBW system during real-world maneuvers and show how SAS parameters such as accuracy of angle, angular speed etc. affect car trajectory resulting in hit cones. For this purpose, a test system was built, with the use of virtual test drives based on CarMaker software, CANoe and VTSystem hardware. In order to evaluate its performance, the errors introduced by the system were determined. Additionally, using the realised test system, three commercial steering wheel angle sensors were tested and compared during a virtual test drive. Their errors were determined, as well as their performance in the SBW technology and the consistency of the obtained results with the parameters declared by the manufacturer were verified as well.

Abstract Background We aimed to assess differences in health literacy between those who improved CPAP non‐adherent and those who remained non‐adherent. Methods We included patients newly diagnosed with sleep apnea syndrome who had started CPAP therapy between February 2019 and October 2020 with ≥6 follow‐up months or who self‐interrupted CPAP therapy

The transformation of research institutes and centres of the Slovak Academy of Sciences (SAS) into public research institutions (PRI) in 2022 represents a completion of a long-term transformation process that began legislatively 8 years ago, in 2014, and ideologically even earlier, approximately 14 years ago, in 2008. The new legal framework on the one hand, removed several previous legislative obstacles that fully prevented a commercialization of intellectual property as a result of research and innovation in the environment of the SAS. On the other hand, even with regard to the mentioned historical debt, the legislation could help the effective technology transfer much more significantly. Some specific suggestions are presented in this post.

This study investigates the performance of estimation methods for multidimensional IRT models with dichotomous and polytomous data in two well-known IRT programs: SAS PROC IRT and the mirt package in R. A simulation study was used to compare performance on a simple structure Rasch model, complex structure 2PL model, and bifactor graded response model. Under RMSE and bias criteria regarding item parameter recovery, PROC IRT and mirt showed nearly identical performance in the simple structure condition. When a complex structure was used, mirt performed better in terms of the recovery of intercept parameters, while the recovery of slope parameters depended on the program and the sample sizes: PROC IRT tended to be better with small samples ( N = 500 ) according to RMSE, and mirt was better for larger samples ( N = 1000 and 2500 ) according to RMSE and bias for the slope parameter recovery. When a bifactor structure was used, mirt was preferred in all cases; differences lessened as sample size increased. [ABSTRACT FROM AUTHOR]

Extracellular signal-regulated kinase 5 (ERK5), also known as BMK1 or MAPK7, represents a recent addition to the classical mitogen-activated protein kinase (MAPK) family. This family includes well-known members such as ERK1/2, c-Jun N-terminal kinase (JNK), and p38 mitogen-activated protein kinase (p38 MAPK), as well as atypical MAPKs such as ERK3, ERK4, ERK7 (ERK8), and Nemo-like kinase (NLK). Comprehensive reviews available elsewhere provide detailed insights into ERK5, which interested readers can refer to for in-depth knowledge (Nithianandarajah-Jones et al., 2012; Monti et al., Cancers (Basel), 2022, 14). The primary aim of this review is to emphasize the essential characteristics of ERK5 and shed light on the intricate nature of its activation, with particular attention to the catalytic-independent functions in disease pathways.