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Research and policy often focus on reducing attrition from educational trajectories leading to careers in science, technology, engineering, and mathematics (STEM), but many students change career plans "within" STEM. This study examined how changing career plans within STEM fields was associated with psychological indicators of career readiness. We conducted a large online survey of undergraduate students (N = 1,727) across 42 courses covering every major STEM discipline at a large U.S. research-intensive public university. Students reported about their career plans, whether plans had changed, motivation for those career plans, and satisfaction with and certainty of persisting with those plans. A trained team of coders classified whether students reported having STEM career plans at the time of the survey and at the beginning of college. Students who said they had changed career plans within STEM fields during college also reported lower motivation for their new career plans, satisfaction with those plans, and certainty of persisting in them, compared to students who retained consistent STEM career plans. With few exceptions, these associations held across students' gender, race, year in school, and STEM field of study. Within-STEM career plan changes were very common, reported by 55% of fourth-year STEM students. Women reported changing career plans within STEM fields more often than men. Results suggest that changing career plans within STEM is an important phenomenon to consider in preparing a qualified and diverse STEM workforce. Students who change career plans within STEM fields may need additional supports for their career motivation and satisfaction compared to students who do not change plans.

Physical simulation serves as a crucial method for understanding the mechanisms of underground oil and gas migration and accumulation. To gain a deeper understanding of gas migration mechanisms in tight reservoirs under deep geological conditions, experimental models and boundary conditions were designed using the tight sandstone gas reservoirs of the Upper Paleozoic Shanxi Formation in the Yan'an Gas Field as a case study. Based on ultra-low permeability rock multiphase flow nuclear magnetic resonance online simulation experiments, the study investigated the critical pressure and dynamic conditions governing gas migration in tight sandstone, while also analyzing the factors influencing gas migration and accumulation. Different types of sandstones from the Shanxi Formation were selected, including quartz clean sandstone, quartz-rich low-plasticity particle detrital quartz sandstone, plastic particle-rich detrital sandstone, and tuffaceous matrix-rich quartz sandstone samples, representing reservoir rock facies with different porosity and permeability distributions. Experiments with constant low injection flow rates, different flow velocities (flow rates), and different pressure differences were conducted. The findings indicate that the critical charging pressure of tight sandstone reservoirs is primarily influenced by rock facies and permeability. Dominant rock facies with higher permeability exhibit lower critical charging pressures. For instance, the critical injection pressure of pure quartz sandstone gas typically falls below 1.2 MPa, while it generally remains below 1.5 MPa even for plastic-rich granular lithic sandstones and tuff-rich hybridquartz sandstones with inferior physical properties. Furthermore, there exists no absolute lower limit for the gas charging physical properties of tight sandstone. However, the charging efficiency and gas saturation of tight sandstone are positively correlated with reservoir physical properties, particularly permeability. The more developed the dominant rock facies and the higher the permeability, the higher the charging efficiency and gas saturation.

The resistance to intergranular corrosion (IGC) is crucial for 7xxx series high-strength aluminum alloy. The grain boundary characteristics and nanoscale intergranular precipitates are the most significant factors affecting the IGC performance of alloys. Exploring the influences of the two factors on the mechanism and kinetics of corrosion is a fundamental issue for IGC processes. In this paper, the multi-phase field model for electrochemical corrosion is employed to study the IGC processes. The effects of grain size, GB overpotential, and the status of nano-precipitates on the mechanism and kinetics of IGC are systematically and quantitatively investigated. Polycrystalline alloys with the averaged grain sizes in micrometer and nanoscale show typical IGC (GB network propagation) regime and massive corrosion regime respectively. The IGC tendency of micrometer-sized grains increases with the average grain size, while that of nanoscale grains exhibits the opposite trend due to different corrosion mechanisms. For the IGC in the presence of nano-precipitates, our simulations quantify that the orientation, size, and distribution of precipitates could substantially affect the corrosion rate. Nanoscale precipitates exhibit completely different corrosion regimes in the process of IGC, with trenching and penetrating corrosion patterns depending on the overpotential. The multi-phase field model lays a foundation for revealing the corrosion mechanism as well as designing new corrosion resistant materials.

The reconstruction of neural function and recovery of chronic damage following traumatic brain injury (TBI) remain significant clinical challenges. Exosomes derived from neural stem cells (NSCs) offer various benefits in TBI treatment. Numerous studies confirmed that appropriate preconditioning methods enhanced the targeted efficacy of exosome therapy. Interferon-gamma (IFN-γ) possesses immunomodulatory capabilities and is widely involved in neurological disorders. In this study, IFN-γ was employed for preconditioning NSCs to enhance the efficacy of exosome (IFN-Exo, IE) for TBI. miRNA sequencing revealed the potential of IFN-Exo in promoting neural differentiation and modulating inflammatory responses. Through low-temperature 3D printing, IFN-Exo was combined with collagen/chitosan (3D-CC-IE) to preserve the biological activity of the exosome. The delivery of exosomes via biomaterial scaffolds benefited the retention and therapeutic potential of exosomes, ensuring that they could exert long-term effects at the injury site. The 3D-CC-IE scaffold exhibited excellent biocompatibility and mechanical properties. Subsequently, 3D-CC-IE scaffold significantly improved impaired motor and cognitive functions after TBI in rat. Histological results showed that 3D-CC-IE scaffold markedly facilitated the reconstruction of damaged neural tissue and promoted endogenous neurogenesis. Further mechanistic validation suggested that IFN-Exo alleviated neuroinflammation by modulating the MAPK/mTOR signaling pathway. In summary, the results of this study indicated that 3D-CC-IE scaffold engaged in long-term pathophysiological processes, fostering neural function recovery after TBI, offering a promising regenerative therapy avenue.

Abstract The success of deep learning in various applications depends on task-specific architecture design choices, including the types, hyperparameters, and number of layers. In computational biology, there is no consensus on the optimal architecture design, and decisions are often made using insights from more well-established fields such as computer vision. These may not consider the domain-specific characteristics of genome sequences, potentially limiting performance. Here, we present GenomeNet-Architect, a neural architecture design framework that automatically optimizes deep learning models for genome sequence data. It optimizes the overall layout of the architecture, with a search space specifically designed for genomics. Additionally, it optimizes hyperparameters of individual layers and the model training procedure. On a viral classification task, GenomeNet-Architect reduced the read-level misclassification rate by 19%, with 67% faster inference and 83% fewer parameters, and achieved similar contig-level accuracy with ~100 times fewer parameters compared to the best-performing deep learning baselines.

Abstract Interstitial lung disease (ILD) can lead to lung cancer, which brings great challenges to differential diagnosis and comprehensive treatment. However, the clinical features of lung-dominant connective tissue disease (LD-CTD) related ILD combined with lung cancer has not been validated. We report the case of an 80-year-old woman with LD-CTD treated regularly with nintedanib who presented progressive dyspnoea and hypoxemia after recurrent viral infections. Her chest computed tomography (CT) showed aggravated interstitial fibrosis in both lower lungs with moderate right pleural effusion. Clinicians should be alert to lung cancer in patients who are experiencing poor responsiveness to treatment or acute progression of ILD. The available literatures about the differential diagnosis of clinical manifestations, imaging, treatment and prognosis of LD-CTD are reviewed and discussed in this study.

Abstract Background Research and policy often focus on reducing attrition from educational trajectories leading to careers in science, technology, engineering, and mathematics (STEM), but many students change career plans within STEM. This study examined how changing career plans within STEM fields was associated with psychological indicators of career readiness. We conducted a large online survey of undergraduate students (N = 1,727) across 42 courses covering every major STEM discipline at a large U.S. research-intensive public university. Students reported about their career plans, whether plans had changed, motivation for those career plans, and satisfaction with and certainty of persisting with those plans. A trained team of coders classified whether students reported having STEM career plans at the time of the survey and at the beginning of college. Results Students who said they had changed career plans within STEM fields during college also reported lower motivation for their new career plans, satisfaction with those plans, and certainty of persisting in them, compared to students who retained consistent STEM career plans. With few exceptions, these associations held across students’ gender, race, year in school, and STEM field of study. Within-STEM career plan changes were very common, reported by 55% of fourth-year STEM students. Women reported changing career plans within STEM fields more often than men. Implications Results suggest that changing career plans within STEM is an important phenomenon to consider in preparing a qualified and diverse STEM workforce. Students who change career plans within STEM fields may need additional supports for their career motivation and satisfaction compared to students who do not change plans.

Accumulating evidence has highlighted the influence of the gut microbiota on lung immunity. We examined the effects of changes in intestinal microecology on the development of Chronic Obstructive Pulmonary Disease (COPD) and identified microbial biomarkers for acute exacerbations of COPD (AECOPD). Fecal samples were collected from 30 patients with stable COPD, 30 patients with AECOPD, and 10 healthy individuals. Fecal microbiological profiles were analyzed using 16S rRNA gene sequencing. The results showed a distinct difference in the bacterial community composition between the AECOPD, COPD, and healthy control groups. The COPD and AECOPD groups had higher levels of Firmicutes but lower levels of Bacteroidetes compared to the healthy control group at the phylum level. At the genus level, there was an increased abundance of Lachnoclostridium, Alistipes, Streptococcus, and Prevotella in COPD and AECOPD patients. Increasing levels of Lachnoclostridium and Prevotella may indicate an acute exacerbation of COPD. This study identified specific microbial biomarkers associated with AECOPD and characterized the composition of gut microbiota in patients with AECOPD.

Background: Self-efficacy, or the beliefs learners hold about what they can do, develops largely from how learners perceive and interpret four main sources of information: mastery experiences, vicarious experiences, social persuasions and physiological and affective states. Although the relationship between these sources and self-efficacy is well-established, less is known about the factors that may influence how early adolescent learners perceive and interpret information from these sources. Aims: The purpose of this study was to investigate how the predisposition of perfectionism might predict how learners perceive efficacy-relevant information in the domain of math. Methods: Using a correlational design, this study considered whether perfectionism was associated with how middle school students (N = 1683) perceive information from the four hypothesized sources of self-efficacy. Participants completed a paper-based survey at two time points. Perfectionism was measured at Time 1. Self-efficacy and its sources were measured at Time 2. Structural equation modelling techniques were used to examine the relationship between factors. Results and Conclusions: Students who held themselves to high standards (i.e., greater self-oriented perfectionism) reported higher levels of mastery experiences, vicarious experiences, social messages and self-efficacy. Conversely, students who felt external pressure to be perfect (i.e., socially prescribed perfectionism) reported lower levels of mastery experiences, vicarious experiences and self-efficacy, as well as higher levels of negative physiological and affective states. The relationship between perfectionism and self-efficacy was partially mediated by students' perceptions of mastery. This study extends the current literature on the sources of math self-efficacy in early adolescence by showing how a predisposition like perfectionism is associated with how adolescent learners perceive and interpret efficacy-relevant information.

Over the past 2 decades, scholars in various educational contexts have examined Bandura's theorizing about how self-efficacy develops. Bandura proposed 4 primary informational sources of self-efficacy--enactive experiences, vicarious experiences, social persuasions, and physiological and affective states--each of which can be supported in different ways. This article first defines and situates self-efficacy and these sources within a broader social cognitive theoretical frame. Subsequent sections highlight specific ways that educators can apply insights from Bandura's theorizing and from the empirical literature that has examined self-efficacy development at different stages of learning and in diverse contexts. We address how educators can create instructional tasks that show progress, establish supportive social structures, and work with students' emotions in ways that foster self-efficacy. Special attention is given to the sociocultural factors that affect how learners evaluate efficacy-relevant information. Several directions for further applying Bandura's theory are offered.

Background: Decades of research have examined what motivates students to pursue careers in science, technology, engineering, and mathematics (STEM) fields, but STEM careers are a broad category encompassing hundreds of distinct vocations. The present study examined high school students' relative preferences for pursuing some types of STEM careers over others and explored what motivational beliefs (defined in accordance with situated expectancy value theory) most influenced students' relative career preferences. A secondary goal was to examine whether there were differences in any patterns as a function of students' intersecting gender and racial/ethnic identities. A large sample of high school students (N = 526) completed an online survey during class time about their beliefs regarding fifteen different STEM career categories. Results: Students' career preferences could be classified into four groups: appealing, unappealing, polarizing, or overlooked. The last category was the most common. Students primarily selected reasons related to utility and attainment value in influencing their choices of most- and least-preferred careers. However, within this category, beliefs about helping others were stronger influences on choosing most-preferred careers, whereas concerns about fitting in were more influential for choosing least-preferred careers. Gender and racial/ethnic comparisons suggest differentiation in how students think about the appeal of various career paths as early as high school. Implications: Findings shed light on how students come to perceive some STEM career paths as relatively more appealing than others, with attention to gender and racial/ethnic differences in these processes. Findings also point to specific and actionable ideas for how teachers, counselors, and administrators can target career education to cultivate students' interest in STEM career paths, where there are particular needs.

Background: Engineering self-efficacy, or the belief in one's own capabilities to complete engineering tasks, has been shown to predict greater motivation, academic performance, and retention of engineering students. Investigating the types of experiences that influence engineering students' self-efficacy can reveal ways to support students in their undergraduate engineering programs. Purpose/Hypothesis(es): The purpose of this study was to qualitatively examine how undergraduate engineering students describe the sources of their engineering self-efficacy and whether patterns in students' responses differed by gender. Design/Method: Participants (N = 654) were undergraduate engineering students attending two public, land-grant universities in the U.S. Open-ended survey questions were used to identify the events, social experiences, and emotions that students described as relevant to their engineering self-efficacy. Chi-square analyses were used to investigate whether response patterns varied by gender. Results: Students described enactive performances as their most salient source of self-efficacy, but interesting insights also emerged about how engineering students draw from social and emotional experiences when developing their self-efficacy. Women more often referred to social sources of self-efficacy and reported fewer positive emotions than did men. Conclusion: Findings suggest ways that educators can provide more targeted opportunities for students to develop their self-efficacy in engineering.

BackgroundThere is growing evidence of associations between the gut microbiota and anxiety disorders, where changes in gut microbiotas may affect brain function and behavior via the microbiota-gut-brain axis. However, population-level studies offering a higher level of evidence for causality are lacking. Our aim was to investigate the specific gut microbiota and associated metabolites that are closely related to anxiety disorders to provide mechanistic insights and novel management perspectives for anxiety disorders.MethodThis study used summary-level data from publicly available Genome-Wide Association Studies (GWAS) for 119 bacterial genera and the phenotype “All anxiety disorders” to reveal the causal effects of gut microbiota on anxiety disorders and identify specific bacterial genera associated with anxiety disorders. A two-sample, bidirectional Mendelian randomization (MR) design was deployed, followed by comprehensive sensitivity analyses to validate the robustness of results. We further conducted multivariable MR (MVMR) analysis to investigate the potential impact of neurotransmitter-associated metabolites, bacteria-associated dietary patterns, drug use or alcohol consumption, and lifestyle factors such as smoking and physical activity on the observed associations.ResultsBidirectional MR analysis identified three bacterial genera causally related to anxiety disorders: the genus Eubacterium nodatum group and genus Ruminococcaceae UCG011 were protective, while the genus Ruminococcaceae UCG011 was associated with an increased risk of anxiety disorders. Further MVMR suggested that a metabolite-dependent mechanism, primarily driven by tryptophan, tyrosine, phenylalanine, glycine and cortisol, which is consistent with previous research findings, probably played a significant role in mediating the effects of these bacterial genera to anxiety disorders. Furthermore, modifying dietary pattern such as salt, sugar and processed meat intake, and adjusting smoking state and physical activity levels, appears to be the effective approaches for targeting specific gut microbiota to manage anxiety disorders.ConclusionOur findings offer potential avenues for developing precise and effective management approaches for anxiety disorders by targeting specific gut microbiota and associated metabolites.

The human cytomegalovirus major immediate early gene (CMV) promoter is currently the most preferred promoter for recombinant therapeutic proteins (RTPs) production in CHO cells. To enhance the production of RTPs, five synthetic enhancers including multiple transcription factor regulatory elements (TFREs) were evaluated to enhance recombinant protein level in transient and stably transfected CHO cells. Compared with the control, four elements can enhance the report genes expression under both two transfected states. Further, the function of these four enhancers on human serum albumin (HSA) were investigated. We found that the transient expression can increase by up to 1.5 times, and the stably expression can maximum increase by up to 2.14 times. The enhancement of transgene expression was caused by the boost of their corresponding mRNA levels. Transcriptomics analysis was performed and found that transcriptional activation and cell cycle regulation genes were involved. In conclusion, optimization of enhancers in the CMV promoter could increase the production yield of transgene in transfected CHO cells, which has significance for developing high-yield CHO cell expression system.

Abstract Deep learning in bioinformatics is often limited to problems where extensive amounts of labeled data are available for supervised classification. By exploiting unlabeled data, self-supervised learning techniques can improve the performance of machine learning models in the presence of limited labeled data. Although many self-supervised learning methods have been suggested before, they have failed to exploit the unique characteristics of genomic data. Therefore, we introduce Self-GenomeNet, a self-supervised learning technique that is custom-tailored for genomic data. Self-GenomeNet leverages reverse-complement sequences and effectively learns short- and long-term dependencies by predicting targets of different lengths. Self-GenomeNet performs better than other self-supervised methods in data-scarce genomic tasks and outperforms standard supervised training with ~10 times fewer labeled training data. Furthermore, the learned representations generalize well to new datasets and tasks. These findings suggest that Self-GenomeNet is well suited for large-scale, unlabeled genomic datasets and could substantially improve the performance of genomic models.

Abstract Argonaute (AGO) proteins are highly conserved and widely distributed across various organisms. They mainly associate with small RNAs (sRNAs) and act as integral players of the RNA-induced silencing complex in the RNA interference (RNAi) pathway, regulating gene expression at transcriptional and post-transcriptional levels, thereby mounting diverse fine-tuning functions in a variety of biological processes. Since the discovery and functional characterization of the first AGO in Arabidopsis, our understanding of the functions of AGO proteins has grown rapidly throughout the plant kingdom. AGO1 attracts investigators’ attention because it forms an autoregulatory loop with miR168 and associates with other endogenous sRNAs and cross-kingdom exogenous sRNAs to relay all-round functions. AGO1 associates with endogenous sRNAs that form a complicated regulatory network via targeting a large body of downstream genes involved in growth, development, and stress-induced responses. Host AGO1 may also be exploited by cross-kingdom exogenous sRNAs generated by parasitic organisms to facilitate their colonization via suppressing host defense genes. Moreover, many pathogenic microbes directly target host AGO1 to facilitate their infection via suppression of the host RNAi pathway. Thus, we focus on plant AGO1 and provide an overview of our current understanding of the roles of AGO1 in the coordination of plant disease resistance with growth and development. We also discuss the perspectives in the dissection of the AGO1-mediated signal transduction pathway.

Abstract Decisional conflict might occur during shared decision-making (SDM) because immunotherapy is a rather novel treatment option for patients with cancer. To explore the prevalence and severity of physical and psychological symptoms and the effort invested in SDM in relation to decisional conflict among patients with cancer undergoing immunotherapy combined with chemotherapy or targeted therapy. This was a cross-sectional survey study. The SURE version of the Decisional Conflict Scale was used to screen cancer patients’ decisional conflict status. Demographic or clinical characteristics, physical symptoms and psychological distress; efforts invested in the SDM process were also assessed as potential factors related to decisional conflict. One hundred seventeen patients surveyed, the prevalence of fatigue (79.5%), sleep disturbance (78.6%), poor appetite (67.5%), and pain (58.1%) symptoms were high and the severity was at moderate levels. The prevalence of pruritus (40.2%), rash (34.2%), dry skin (41.9%), and diarrhea (17.1%) symptoms were low and the severity was at mild levels. 65.8% of patients reported uncertainty, with mild to moderate levels. Furthermore, 97.4% of the patients made some effort in SDM, and the effort level was moderate (mean: 5.56 ± 2.02). 64.1% of patients were certain that immunotherapy was the best option. Age, uncertainty, and effort in the SDM process were major factors related to decisional conflict. We observed that older patients (age: ≥ 65) and those with higher uncertainty levels and less effort in SDM reported higher levels of decisional conflict. Future studies should explore older patients’ decisional related needs of immunotherapy. Interventions should be designed to reduce the uncertainty experienced by patients with cancer and enhance their understanding of immunotherapy to enable them to take more effort in the SDM process.

In recent years, an increasing number of viruses have triggered outbreaks that pose a severe threat to both human and animal life, as well as caused substantial economic losses. It is crucial to understand the genomic structure and epidemiology of these viruses to guide effective clinical prevention and treatment strategies. Nanopore sequencing, a third-generation sequencing technology, has been widely used in genomic research since 2014. This technology offers several advantages over traditional methods and next-generation sequencing (NGS), such as the ability to generate ultra-long reads, high efficiency, real-time monitoring and analysis, portability, and the ability to directly sequence RNA or DNA molecules. As a result, it exhibits excellent applicability and flexibility in virus research, including viral detection and surveillance, genome assembly, the discovery of new variants and novel viruses, and the identification of chemical modifications. In this paper, we provide a comprehensive review of the development, principles, advantages, and applications of nanopore sequencing technology in animal and human virus research, aiming to offer fresh perspectives for future studies in this field.

Abstract Background Decades of research have examined what motivates students to pursue careers in science, technology, engineering, and mathematics (STEM) fields, but STEM careers are a broad category encompassing hundreds of distinct vocations. The present study examined high school students’ relative preferences for pursuing some types of STEM careers over others and explored what motivational beliefs (defined in accordance with situated expectancy value theory) most influenced students’ relative career preferences. A secondary goal was to examine whether there were differences in any patterns as a function of students’ intersecting gender and racial/ethnic identities. A large sample of high school students (N = 526) completed an online survey during class time about their beliefs regarding fifteen different STEM career categories. Results Students’ career preferences could be classified into four groups: appealing, unappealing, polarizing, or overlooked. The last category was the most common. Students primarily selected reasons related to utility and attainment value in influencing their choices of most- and least-preferred careers. However, within this category, beliefs about helping others were stronger influences on choosing most-preferred careers, whereas concerns about fitting in were more influential for choosing least-preferred careers. Gender and racial/ethnic comparisons suggest differentiation in how students think about the appeal of various career paths as early as high school. Implications Findings shed light on how students come to perceive some STEM career paths as relatively more appealing than others, with attention to gender and racial/ethnic differences in these processes. Findings also point to specific and actionable ideas for how teachers, counselors, and administrators can target career education to cultivate students’ interest in STEM career paths, where there are particular needs.