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As students' achievement is correlated with self-regulation, finding interventions promoting self-regulated learning (SRL) in online courses is a current focus of research. However, few studies have explored the potential of contract learning in scaffolding and developing SRL in non-traditional learners who have work and family and are at risk of dropout. Here, we investigate the utility of contract learning as an individualized learning approach in developing self-regulation in non-traditional learners during the COVID-19 pandemic. Using a qualitative approach, we collected data from the experience of one teacher and seven non-traditional learners in an online English for Specific Purposes (ESP) course. The data were collected from teacher logs over eight months and semi-structured interviews with the students. The results of deductive thematic analysis of the data indicate that contract learning positively affected the forethought, performance, and self-reflection phases. Further, despite the cognitive, emotional, external, motivational, and behavioral challenges aggravated by the pandemic, the teacher's efforts to implement contract learning affected the persistence and effort, goal setting, strategic planning, and time management of most learners (N = 4). Possible reasons for the learners' success and failure and the implications for developing SRL skills in students at risk of dropout in online English courses are discussed.

Throughout history, coronaviruses have posed challenges to both public health and the global economy; nevertheless, methods to combat them remain rudimentary, primarily due to the absence of experiments to understand the function of various viral components. Among these, membrane (M) proteins are one of the most elusive because of their small size and challenges with expression. Here, we report the development of an expression system to produce tens to hundreds of milligrams of M protein per liter of Escherichia coli culture. These large yields render many previously inaccessible structural and biophysical experiments feasible. Using cryo-electron microscopy and atomic force microscopy, we image and characterize individual membrane-incorporated M protein dimers and discover membrane thinning in the vicinity, which we validated with molecular dynamics simulations. Our results suggest that the resulting line tension, along with predicted induction of local membrane curvature, could ultimately drive viral assembly and budding.

Understanding the mechanisms of the cellular aging processes is crucial for attempting to extend organismal lifespan and for studying age-related degenerative diseases. Yeast cells divide through budding, providing a classical biological model for studying cellular aging. With their powerful genetics, relatively short cell cycle, and well-established signaling pathways also found in animals, yeast cells offer valuable insights into the aging process. Recent experiments suggested the existence of two aging modes in yeast characterized by nucleolar and mitochondrial declines, respectively. By analyzing experimental data, this study shows that cells evolving into those two aging modes behave differently when they are young. While buds grow linearly in both modes, cells that consistently generate spherical buds throughout their lifespan demonstrate greater efficacy in controlling bud size and growth rate at young ages. A three-dimensional multiscale chemical-mechanical model was developed and used to suggest and test hypothesized impacts of aging on bud morphogenesis. Experimentally calibrated model simulations showed that during the early stage of budding, tubular bud shape in one aging mode could be generated by locally inserting new materials at the bud tip, a process guided by the polarized Cdc42 signal. Furthermore, the aspect ratio of the tubular bud could be stabilized during the late stage as observed in experiments in this work. The model simulation results suggest that the localization of new cell surface material insertion, regulated by chemical signal polarization, could be weakened due to cellular aging in yeast and other cell types, leading to the change and stabilization of the bud aspect ratio.

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric = 1.28E-09 and 4.10E-18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.

Introduction: The genus Rhodococcus, first described by Zopf in 1891, comprises 56 species with validly published names. This bacterium belongs to the phylum Actinobacteria, class Actinobacteria, order Mycobacteriales, and family Nocardiaceae. Notable characteristics of this bacterial genus include: Gram-positive, partially acid-fast positive, catalase-positive, non-spore-forming, and non-motile. The use of molecular methods such as 16S rRNA gene sequencing, along with phenotypic methods, has been suggested for the identification of this bacterium. To date, nine species of the genus Rhodococcus have been reported from clinical specimens. Currently, microbroth dilution is the standard method for assessing the antibiotic susceptibility testing of Rhodococcus species. The pattern of antibiotic resistance in Rhodococcus species varies based on geographical region, and it is recommended that antibiotic susceptibility testing be performed for clinical isolates.

Abstract Optimum design of truss structures can be a challenging and difficult field of study specially if an optimum design problem is comprised of continuous and discrete decision variables such as in truss size and layout optimization problems. Usually metaheuristic approaches are selected to solve these kind of problems and there are many different metaheuristic methods that have been used in dealing with truss optimization problems, which provide optimum solutions for those problems. However, among these various proposed metaheuristics, it is very seldom to face and find a method that is based on technologies such as computer networks. This paper aims to utilize a metaheuristic algorithm which is based on some principals of connected banking systems, which is called the Connected Banking System Optimizer(CBSO) algorithm. The performance of the CBSO is tested against three benchmark truss size/layout optimum design problems namely 15, 18 and 25 bar truss structures. Besides the truss size/layout problems, CEC-BC-2017 test functions with ten dimensions and also three other constrained optimum design problems are also investigated to examine the performance of the CBSO in dealing with these standard unconstrained and constrained test functions. The results of the CBSO optimum designs are presented and compared with some of the available studies in the literature. Statistical analyses are done to have a fair evaluation of the CBSO algorithm’s efficiency and its ability in dealing with the benchmark truss size/layout optimization problems. The CBSO outperforms its rivals and presents lighter weight truss structures. There is no need for configuration of extra parameters in the CBSO algorithm when solving the examined benchmark truss structures, which makes it a robust parameter-free optimization algorithm.

Abstract Background Theoretically, endocrine fluctuations occurring during infertility treatments, including ovulation induction (OI) and assisted reproductive techniques (ART), could be associated with an increased risk of benign breast diseases (BBDs). To date, no studies have been conducted on this association. Therefore, the present study investigated the association between different types of infertility treatments and BBDs. Methods This case‒control study was conducted in Arash Women’s Hospital, Tehran, Iran. The case group included infertile women diagnosed with BBDs without atypia, and the control group included infertile women without breast disease. Breast imaging studies (mammography/ultrasound) were performed for BBD screening, and the diagnosis was confirmed by histopathological examination. Study variables were collected retrospectively from medical records, hospital databases, and questionnaires. Results Finally, 154 infertile women, including 50 cases (BBDs) and 104 controls (no BBDs), were compared. Our data showed that 66% of cases and 61.4% of controls had undergone at least one course of infertility treatment. There was no association between BBD risk and previous infertility treatments (OR = 1.21; 95% CI = 0.59–2.46), ART (OR = 1.14; 95% CI = 0.90–1.44), or OI cycles (OR = 1.13; 95% CI = 0.98–1.32). Stratification by confounding variables did not change these results. Conclusions It seems that there is no association between BBDs in infertile women and the type, duration, or number of prior infertility treatments; however, considering the small sample size of the study, the clinical significance of this finding should not be neglected. Therefore, we consider it essential to carry out more extensive, detailed, and prospective studies to distinguish the association of BBDs with different infertility treatments and medications.

This study explores the verbal engagement strategies that EFL instructors adopt when teaching English over Instagram. These instructors create videos where they teach English and share them on Instagram. Twenty-five highly engaging English teaching videos on Instagram were used as the data of this study. To discover the engagement strategies that the instructors used, a combination of Hyland's Engagement Model and Martin and White's Appraisal Model, was applied to the teachers' talk. The results indicated a slight difference in the frequency of engagement resources between the two models. Moreover, the results related to Hyland's framework revealed that listener pronouns (second person pronouns) had considerably higher frequency than other engagement devices in the instructors' speech. Nevertheless, personal aside and appeals to shared knowledge were used to a limited extent. Regarding the model of Martin and White, the result indicated that the overall engagement resources of heterogloss were about twice those of the monogloss. Moreover, between two broad categories of heterogloss, the resources of the contract category were significantly more common than those of the expand category in the teachers' talk. The findings of this study have fruitful pedagogical implications. As the results suggest, teachers can bring the learners to discourse using listener pronouns, and in this way increase learner engagement in lessons. Moreover, the heterogloss resources, specifically the contract ones, can assist teachers in engaging learners.

AimsStudies have indicated inconsistent results regarding the association between plasma levels of Lipoprotein(a) [Lp(a)] and coronary artery calcification (CAC). We performed a systematic review and meta-analysis to investigate the association between elevated levels of Lp(a) and risk of CAC in populations free of cardiovascular disease (CVD) symptoms.Data synthesisPubMed, Web of Science, Embase, and Scopus were searched up to July 2022 and the methodological quality was assessed using Newcastle-Ottawa Scale (NOS) scale. Random-effects meta-analysis was used to estimate pooled odds ratio (OR) and 95% confidence interval. Out of 298 studies, data from 8 cross-sectional (n = 18,668) and 4 cohort (n = 15,355) studies were used in meta-analysis. Cohort studies demonstrated a positive significant association between Lp(a) and CAC, so that individuals with Lp(a)≥30-50 exposed to about 60% risk of CAC incidence compared to those with lower Lp(a) concentrations in asymptomatic CVD subjects (OR, 1.58; 95% CI, 1.38-1.80; l2, 0.0%; P, 0.483); Subgroup analysis showed that a cut-off level for Lp(a) measurement could not statistically affect the association, but race significantly affected the relationship between Lp(a) and CAC (OR,1.60; 95% CI, 1.41-1.81). Analyses also revealed that both men and women with higher Lp(a) concentrations are at the same risk for increased CAC.ConclusionsBlood Lp(a) level was significantly associated with CAC incidence in asymptomatic populations with CVD, indicating that measuring Lp(a) may be a useful biomarker for diagnosing subclinical atherosclerosis in individuals at higher risk of CAC score.Prospero registration numberCRD42022350297.

Background: Substantial blood loss is a major concern in primary total hip arthroplasty and thus various blood conservation techniques are used to reduce blood loss in this surgery. Clonidine is frequently used in anesthesia for its antihypertensive property, however, the efficacy of oral clonidine premedication in reducing bleeding in total hip arthroplasty is not clear.Objectives: This study aimed to evaluate the impact of a single oral dose of clonidine premedication on blood loss and transfusion needs in patients undergoing primary total hip arthroplasty with balanced general anesthesia.Methods: Sixty patients classified as American Society of Anesthesiologists physical status 1 and 2, aged between 18 and 75 years, were randomly assigned to either the clonidine group or the placebo group, with 30 patients in each group. In this study, 60 minutes prior to the surgery, patients in the clonidine group received oral clonidine 0.2 mg and the placebo group received the identical placebo tablet. Intraoperative blood loss, transfusion requirements, need for supplementary remifentanil and nitroglycerin infusion, hemodynamic parameters, postoperative transfusion needs and hemoglobin level were measured and compared between the two groups.Results: The results showed that intraoperative blood loss, remifentanil usage, and the mean systolic and mean arterial blood pressures were significantly lower in the clonidine group compared with the placebo group. The postoperative hemoglobin value was significantly higher, and the reduction in the postoperative Hb level as well as the number of transfused patients were significantly less in the clonidine group.Conclusion: It can be concluded that 0.2 mg oral clonidine premedication significantly reduced intraoperative bleeding, and decreased postoperative changes in hemoglobin level and the number of transfused patients in primary total hip arthroplasty.

Background: The reemergence of monkeypox virus (Mpox, formerly monkeypox) in 2022 in non-endemic countries has raised significant concerns for global health due to its high transmissibility and mortality rate. A major challenge in combating Mpox is its ability to evade the host’s innate immune system, the first line of defense against viral infections. Summary: Mpox encodes various proteins that interfere with key antiviral pathways and mechanisms, such as the nuclear factor kappa B signaling, cytokine production, complement and inflammasome activation, and chemokine binding. These proteins modulate the expression and function of innate immune mediators, such as interferons, interleukins, and Toll-like receptors, and impair the recruitment and activation of innate immune cells, such as natural killer cells. By suppressing or altering these innate immune responses, Mpox enhances its replication and infection in the host tissues and organs, leading to systemic inflammation, tissue damage, and organ failure. Key Messages: This study reveals new insights into the molecular and cellular interactions between Mpox and the host’s innate immune system. It identifies potential targets and strategies for antiviral interventions, highlighting the importance of understanding these interactions to develop effective treatments and improve global health responses to Mpox outbreaks.

Background: Driver drowsiness is a significant factor contributing to road accidents and the overall increase in road mortality rates. This study investigates the relationship between lane crossing (LC), the number of lateral position deviations and driver drowsiness. The proposed method, due to precision and convenience, has great potential for developing driver-assistant systems. Materials and Methods: In this experimental research, 34 sub-urban bus drivers participated in a 2-h driving session in a simulator designed based on visual reality. Sensors attached to the steering recorded right and left deviations and relevant information was matched with the receded videos and the amount of the standard deviation of lane position (SDLP). The number of LC was determined with the designed indicator on the road software. Then, the association between SDLP and the number of LC was compared with the results of the Karolinska sleepiness scale (KSS) and observer rating of drowsiness (ORD), which determined the level of drowsiness, by facial features. Results: The results of multivariate analysis of variance indicated that the time variable has a significant effect on both ORD and SDLP (P

As buildings account for approximately 40% of global energy consumption and associated greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The increased integration of variable energy sources, such as renewables, introduces uncertainties and unprecedented flexibilities, necessitating buildings to adapt their energy demand to enhance grid resiliency. Consequently, buildings must transition from passive energy consumers to active grid assets, providing demand flexibility and energy elasticity while maintaining occupant comfort and health. This fundamental shift demands advanced optimal control methods to manage escalating energy demand and avert power outages. Reinforcement learning (RL) emerges as a promising method to address these challenges. In this paper, we explore ten questions related to the application of RL in buildings, specifically targeting flexible energy management. We consider the growing availability of data, advancements in machine learning algorithms, open-source tools, and the practical deployment aspects associated with software and hardware requirements. Our objective is to deliver a comprehensive introduction to RL, present an overview of existing research and accomplishments, underscore the challenges and opportunities, and propose potential future research directions to expedite the adoption of RL for building energy management.

This study was conducted to examine the influence of access to and use of information and communication technology (ICT) on academic achievement and motivation. Data were collected from 300 students (12- to 16-year-old; 160 boys) from Sanandaj, Iran. The ICT Familiarity Questionnaire was used to assess the level of students' access to and use of ICT at school and outside of school. Students' Grade Point Average was considered as an indicator of academic achievement and their scores of Academic Motivation Scale as a measure of academic motivation. The findings revealed that students had more access to and use of ICT at home than school. The majority of participants reported that they used digital devices for the first time when they were between 7 and 10 years old. The students spent more time using the Internet outside of school during weekdays. They used digital devices more frequently for general than academic activities. The canonical analysis indicated that ICT-related factors are significantly correlated with academic performance. However, students' access to and use of ICT at school was the most influential factors affecting academic achievement. Furthermore, access to ICT at school and using digital devices at an earlier age were the most contributing factors correlated with academic motivation.

Background: Smartphone-based digital phenotyping can provide novel transdiagnostic markers of mental illness including circadian routines and anhedonia. In proposing transdiagnostic digital phenotypes for circadian routines and anhedonia in depression and bipolar disorder patients, this paper explores their derivation, comparison to naive models, and replicability across two different research sites/teams. Methods: 84 participants (bipolar disorder, depression, controls) used the mindLAMP app for 12 weeks to capture digital phenotypes on their personal smartphones. mindLAMP was used to deliver surveys about mood symptoms while collecting device acceleration, geolocation, and screen on/off state. Participant chronotype was derived from this sensor data. Within-participant and between-participant models were created to assess how time-varying features collected through digital phenotyping could predict weekly anhedonia survey responses. Results: Within-person models outperformed between-person models in predicting anhedonia. Chronotype was the strongest predictor of weekly anhedonia scores as indicated by Shapley scores. Shapley scores also revealed that many of the time-varying predictor variables are significant but differ in their direction of action. Discussion: This analysis reveals the meaningful but potentially misleading nature of digital phenotyping signals. Results suggest that each participant has a unique set of relationships between time-varying digital phenotype variables; therefore, it is challenging to predict trends between participants. Bayesian models, with appropriate population priors, may offer the next step for improving the potential of personalized digital phenotyping insights.

Bilateral acetabular fractures are a rare type of acetabular fracture and because of a lack of information about these types of fractures, the management of bilateral acetabular fractures is challenging. We show a case of a 25-year-old young man with a bilateral acetabular fracture (left transverse fracture of the acetabulum and right posterior column fracture of the acetabulum) secondary to severe trauma following a road accident with no other pelvic damage who was treated in our department.

Several methods have been used to solve structural optimum design problems since the creation of a need for light weight design of structures and there is still no single method for solving the optimum design problems in structural engineering field that is capable of providing efficient solutions to all of the structural optimum design problems. Therefore, there are several proposed and utilized methods to deal with optimum design issues and problems, that sometimes give promising results and sometimes the solutions are quite unacceptable. This issue with metaheuristic algorithms, which are suitable approaches to solve these set of problems, is quite usual and is supported by the “No Free Lunch theorem”. Researchers try harder than the past to propose methods capable of presenting robust and optimal solutions in a wider range of structural optimum design problems, so that to find an algorithm that can cover a wider range of structural optimization problems and obtain a better optimum design. Truss structures are one of these problems which have extremely complex search spaces to conduct search procedures by metaheuristic algorithms. This paper proposes a method for optimum design of truss sizing problems. The presented method is used against 6 well-known benchmark truss structures (10 bar, 17 bar, 18 bar, 25 bar, 72 bar and 120 bar) and its results are compared with some of the available studies in the literature. The performance of the presented algorithm can be considered as very acceptable.

The current research utilized visual characteristics obtained from RGB images and qualitative characteristics to investigate changes in surface defects, predict physical and chemical characteristics, and classify sweet cherries during storage. It was achieved with the help of ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System) models. The ANN used in this study was a Multilayer Perceptron (MLP) with SigmoidAxon and TanhAxon threshold functions, trained with the Momentum training function. Additionally, ANFIS with a Mamdani system and Triangle, Gauss, and Trapezoidal membership functions, was employed to predict sweet cherries' physical and chemical properties and their quality classification. Both models incorporate four algorithms. Additionally, the algorithms use color statistical features and color texture features combined with physical and chemical properties, including weight loss, firmness, titratable acidity, and total anthocyanin content. The image color and texture characteristics were used by ANN and ANFIS models to predict physical and chemical properties with high accuracy. ANN and ANFIS models accurately estimate sweet cherry quality grades in all four algorithms with over 90 % accuracy. According to the findings, the ANN and ANFIS models have demonstrated satisfactory performance in the qualitative classification and prediction of sweet cherries' physical and chemical properties.