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This study aimed to validate a modified Statistics Anxiety Scale for students in the United States taking university courses. Modifications were made by changing the wording of several items to be consistent with American English, and to accommodate students taking statistics courses in various formats. Items were added to investigate anxiety toward the use of statistical packages, and peer mentoring. Data from 352 participants and exploratory factor analyses were used to analyze the original 24-item SAS (SAS-O) and a version of the SAS with six additional items (SAS-M). The three-factor structure for SAS-O was consistent with the original validation study, explaining about 64% of the items' variance. The factor structure for SAS-M contained an additional two factors, that explained a total of 68 % of the items' variance. The factors were internally consistent, correlated with one another, and negatively correlated with Wise's Attitude Toward Statistics scale. Male students generally had lower application anxiety and examination anxiety than female students, and lower asking for help anxiety than non-traditional students.

Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue failure. These tools have been important in biomechanics research where measuring in vitro and in vivo tissue deformations are important for evaluating tissue health and disease progression. However, accurately measuring tissue deformation in vivo is particularly challenging due to limited image signal-to-noise ratio. Therefore, we created a novel deep-learning approach for measuring deformation from a sequence of images collected in vivo called StrainNet. Utilizing a training dataset that incorporates image artifacts, StrainNet was designed to maximize performance in challenging, in vivo settings. Artificially generated image sequences of human flexor tendons undergoing known deformations were used to compare benchmark StrainNet against two conventional image-based strain measurement techniques. StrainNet outperformed the traditional techniques by nearly 90%. High-frequency ultrasound imaging was then used to acquire images of the flexor tendons engaged during contraction. Only StrainNet was able to track tissue deformations under the in vivo test conditions. Findings revealed strong correlations between tendon deformation and applied forces, highlighting the potential for StrainNet to be a valuable tool for assessing rehabilitation strategies or disease progression. Additionally, by using real-world data to train our model, StrainNet was able to generalize and reveal important relationships between the effort exerted by the participant and tendon mechanics. Overall, StrainNet demonstrated the effectiveness of using deep learning for image-based strain analysis in vivo.

Mycoplasma genitalium causes a sexually transmitted infection and is also emerging as an important antimicrobial resistant pathogen. Data on M. genitalium infections among men who have sex with men (MSM) in low-resource settings are sparse.From January to December 2022, participants in an HIV pre-exposure prophylaxis (PrEP) program in Hanoi, Vietnam were enrolled into the study. Demographic, behavioral, and clinical characteristics were collected. Self-collected urine, rectal, and pharyngeal specimens were tested for M. genitalium using the Alinity m STI Assay (Abbott Molecular, USA). Univariate and multivariate logistic regression were performed to assess for factors associated with infections.Among 477 participants, the median age was 25.3 years (21.7-29.6) and 92.2% (n = 440) identified as MSM; 48.6% had ≥2 sex partners and 38.1% reported condomless anal sex in the prior month. The overall prevalence of M. genitalium infection was 10.9% (52/477); 7.3% (34/464) rectal, 3.2% (15/476) urethral, and 1.9% (9/476) pharyngeal. Infections were asymptomatic in 71.2% (37/52). Among those with M. genitalium, 30.7% (16/52) were co-infected with either N. gonorrhoeae or C. trachomatis. Among those reporting rectal (n = 51) or urethral (n = 35) symptoms, but without C. trachomatis or N. gonorrhoeae co-infections, five (9.8%) had rectal infections and one (2.9%) had urethral infection. Participants with M. genitalium were more likely to be asymptomatic than participants without M. genitalium (aOR 1.93; 95% CI 1.01-3.71).M. genitalium infections were common among primarily MSM engaged in an HIV PrEP program in Vietnam. The prevalence was highest in rectal specimens and nearly three quarters of M. genitalium infections were asymptomatic. Testing for M. genitalium infections among those with symptoms is important to enable pathogen-directed therapy. Additional research on antimicrobial resistance and treatment strategies for M. genitalium in low-resource settings is needed.

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinsons disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.

Prime editing enables the precise modification of genomes through reverse transcription of template sequences appended to the 3' ends of CRISPR-Cas guide RNAs1. To identify cellular determinants of prime editing, we developed scalable prime editing reporters and performed genome-scale CRISPR-interference screens. From these screens, a single factor emerged as the strongest mediator of prime editing: the small RNA-binding exonuclease protection factor La. Further investigation revealed that La promotes prime editing across approaches (PE2, PE3, PE4 and PE5), edit types (substitutions, insertions and deletions), endogenous loci and cell types but has no consistent effect on genome-editing approaches that rely on standard, unextended guide RNAs. Previous work has shown that La binds polyuridine tracts at the 3' ends of RNA polymerase III transcripts2. We found that La functionally interacts with the 3' ends of polyuridylated prime editing guide RNAs (pegRNAs). Guided by these results, we developed a prime editor protein (PE7) fused to the RNA-binding, N-terminal domain of La. This editor improved prime editing with expressed pegRNAs and engineered pegRNAs (epegRNAs), as well as with synthetic pegRNAs optimized for La binding. Together, our results provide key insights into how prime editing components interact with the cellular environment and suggest general strategies for stabilizing exogenous small RNAs therein.

Up, down. Over and under. It's really basic.' Jeremy Frey is talking about basketry. He's devoted his life to this art form, going deep into its possibilities, all the while [...]

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

BACKGROUND: A rapid and affordable point-of-care test is a priority for Neisseria gonorrhoeae control. WHO and Foundation for Innovative New Diagnostics (FIND) have a target product profile for a non-molecular N gonorrhoeae rapid point-of-care test that requires a clinical sensitivity of greater than 80% and a specificity over 95% to be considered useful in syndromic management; test turnaround time should be 30 min or under, and the test should cost less than US$3. A novel lateral flow assay (LFA) was developed to achieve that profile. METHODS: In this cross-sectional study we evaluated the performance of the novel N gonorrhoeae lateral flow assay (NG-LFA) at the primary health-care level in South Africa. Male patients with urethral discharge syndrome and female patients with vaginal discharge syndrome were recruited from five primary health-care facilities in the Buffalo City Metropolitan Municipality health district of South Africa. First-void urine specimens and nurse-collected vaginal swabs were tested in-facility with the NG-LFA and Xpert CT/NG PCR assay. N gonorrhoeae multi-antigen sequence typing (NG-MAST) was performed on all LFA positive specimens. FINDINGS: Between March 7, and Sept 19, 2022, we enrolled 200 male patients with urethral discharge and 200 female patients with vaginal discharge. The median age of male patients was 24 years (IQR 21-31 years), and the median age of female patients was 25 years (IQR 21-32 years). In addition, 23 male patients and 12 female patients who presented at the facility with a partner notification slip were enrolled of whom one (4%) and five (42%) were symptomatic, respectively. NG-LFA and Xpert results were available for all participants. In urine specimens, NG-LFA sensitivity was 96·1% (Wilson 95% CI 91·2-98·3; 123 LFA-positive among 128 PCR-positive specimens) and 91·7% in vaginal swab specimens (78·2-97·1; 33 LFA-positive among 36 PCR-positive). The specificity was 97·2% in urine specimens (90·4-99·2; 70 LFA-negative among 72 PCR-negative) and 96·3% in vaginal specimens (92·2-98·3; 158 LFA-negative among 164 PCR-negative). In 156 LFA-positive specimens, NG-MAST showed 93 different sequence types. INTERPRETATION: The novel NG-LFA had excellent clinical sensitivity and specificity in symptomatic male and female patients. The test met the optimal requirement for sensitivity and the minimal requirement for specificity specified in the target product profile. NG-LFA could provide an important tool to optimise clinical management and reduce excess antibiotic use in settings without direct access to laboratory testing. FUNDING: Global Antimicrobial Resistance Innovation Fund (GAMRIF) via FIND and National Institutes of Health.

The Exascale Computing Project software deployment effort developed and advanced DevOps capabilities. One goal was to enable robust continuous integration (CI) workflows that span the protected high-performance computing environments found within many of the U.S. Department of Energy's (DOE's) national laboratories. This article highlights several challenges encountered with enabling automation, such as charging models for CI jobs and meeting individualized security requirements that revolve around strongly associating running code with a human identity. It also describes how the Jacamar CI tool evolved to meet later requirements and became a key aspect of the solutions currently offered. Derived from this experience, we offer a conceptual framework for understanding current and future CI challenges at DOE facilities and offer suggestions for long-term solutions.

With dramatic global expansion of secondary schooling there has been significant research interest in how education is related to future aspirations, with important calls to acknowledge connections within processes of aspiring to young people's social, economic and cultural circumstances. This paper presents findings from thematic analysis of interview, participant observation and classroom observation data from an ethnographic study in two secondary schools in Tanzania. It argues that an important, and often overlooked, aspect of this complex process is the way in which aspirations for the future are connected not only to present realities, but also aspirations in the present. Focusing on students' aspirations relating to 'being a "good" student' and being able to "'soma kwa bidii'" or 'study hard', this paper uses the conceptual language of the capability approach to assert the importance of considering aspirations for 'being' in education in conjunction with future aspirations for 'becoming'.

This study explores the views of young people attending within-school 'alternative' provision to support their return to mainstream classes. Q-methodology was used to explore pupil views about what helps and inhibits successful on-site 'reintegration'. Eighteen pupils aged 13-16 years with experience of attending school inclusion centres in three high schools participated. Protective factors supporting reintegration and mitigating the risk of further exclusion from school included the need to be seen and heard, and to have somewhere or someone to go to for support. The study highlights the importance of pupil voice and a need for greater clarity in terminology used to describe the shifting terrain of inclusion. Further research is needed on the context and factors driving the rise in in-school alternative provision in England, including blind spots in official data on the extent of pupil moves and subsequent opportunities within mainstream settings.

The goal of equity in education in England is damaged by regional disparities in outcomes and a marked social gradient in school exclusion. The most vulnerable groups are disproportionately represented in in-year transfers. Drawing on 24 interviews with school leaders and education decision-makers in a socioeconomically deprived area, this study examined institutional strategies to promote inclusion by reducing pupil mobility in an area-based initiative. The analysis highlights the interaction of administrative, professional and market logics, and the significance of the "middle tier" in mediating inter-local tensions. Further research is needed on "hidden" pupil moves and diverse forms of within-school segregation-reintegration.

There aren't too many round museums out there --it's not easy to hang paintings on curved walls. But maybe there should be more. The two most conspicuous examples in the [...]

Antimicrobial resistance in Neisseria gonorrhoeae is an urgent global health issue. The objectives of the study were to use a global collection of 12,936 N. gonorrhoeae genomes from the PathogenWatch database to evaluate different machine learning models to predict ceftriaxone susceptibility/decreased susceptibility using 97 mutations known to be associated with ceftriaxone resistance. We found the random forest classifier model had the highest performance. The analysis also reported the relative contributions of different mutations within the ML model predictions, allowing for the identification of the mutations with the highest importance for ceftriaxone resistance. A machine learning model retrained with the top five mutations performed similarly to the model using all 97 mutations. These results could aid in the development of molecular tests to detect resistance to ceftriaxone in N. gonorrhoeae. Moreover, this approach could be applied to building and evaluating machine learning models for predicting antimicrobial resistance in other pathogens.

Abstract Background Implementation of the World Health Organization (WHO) recommended Advanced HIV Disease screening package, remains poor in most settings with limited resources. More than 50% of newly diagnosed-HIV clients are missed on screening as a result of implementation barriers. It is important to mitigate the existing barriers and leverage enablers’ inorder to maximize uptake of the advanced HIV disease screening. This study aimed to identify strategies for scaling up implementation of advanced HIV disease screening among newly HIV-diagnosed clients in pre-ART phase using a Consolidated Framework for Implementation Research-Expert Recommendation for Implementing Change (CFIR-ERIC) guiding tool. Methods A qualitative study was conducted at Rumphi district hospital in Malawi (August – September, 2023). Two sessions of Focus group discussions (FDGs) involving key stakeholders were facilitated to identify specific strategies following the initial study on exploration of barriers and facilitators of advanced HIV disease screening package. Participants comprised healthcare providers, purposively selected from key hospital departments. A deductive approach was used to analyze FDG transcripts where emerging themes were mapped with ERIC list of strategies. CFIR-ERIC Matching tool version 1.0, was used to generate an output of the most to least expert-endorsed Level 1 and Level 2 strategies. Findings About 25 key healthcare workers participated in FDGs. Overall, 6 Level 1 strategies (≥ 50% expert endorsement score) and 4 Level 2 strategies (≥ 20%, ≤ 49% expert endorsement score) were identified, targeting barriers associated with availability of resources, intervention complexity, access to knowledge and information, communication; and implementation leads. Most of the reported strategies were cross-cutting and aimed at enhancing clinical knowledge of the intervention (distributing training materials, educational meetings), developing stakeholders’ interrelations (network weaving) as well as improving clinical workflow (environmental restructuring). Use of evaluative and iterative strategies such as monthly data collection for evaluation were also recommended as part of continuous improvement while an AHD coordinator was recommended to be formally appointed inorder to spearhead coordination of AHD screening services. Conclusion Through the involvement of key stakeholders and the use of CFIR-ERIC matching tool, this study has identified cross-cutting strategies that if well implemented, can help to mitigate contextual barriers and leverage enablers for an improved delivery of AHD screening package.

Abstract Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue failure. These tools have been important in biomechanics research where measuring in vitro and in vivo tissue deformations are important for evaluating tissue health and disease progression. However, accurately measuring tissue deformation in vivo is particularly challenging due to limited image signal-to-noise ratio. Therefore, we created a novel deep-learning approach for measuring deformation from a sequence of images collected in vivo called StrainNet. Utilizing a training dataset that incorporates image artifacts, StrainNet was designed to maximize performance in challenging, in vivo settings. Artificially generated image sequences of human flexor tendons undergoing known deformations were used to compare benchmark StrainNet against two conventional image-based strain measurement techniques. StrainNet outperformed the traditional techniques by nearly 90%. High-frequency ultrasound imaging was then used to acquire images of the flexor tendons engaged during contraction. Only StrainNet was able to track tissue deformations under the in vivo test conditions. Findings revealed strong correlations between tendon deformation and applied forces, highlighting the potential for StrainNet to be a valuable tool for assessing rehabilitation strategies or disease progression. Additionally, by using real-world data to train our model, StrainNet was able to generalize and reveal important relationships between the effort exerted by the participant and tendon mechanics. Overall, StrainNet demonstrated the effectiveness of using deep learning for image-based strain analysis in vivo.

Abstract Interleukin-1α is a suggested dual-function cytokine that diverged from interleukin-1β in mammals potentially by acquiring additional biological roles that relate to highly conserved regions in the pro-domain of interleukin-1α, including a nuclear localisation sequence and histone acetyltransferase-binding domains. Why evolution modified pro-interleukin-1α’s subcellular location and protein interactome, and how this shaped interleukin-1α’s intracellular role, is unknown. Here we show that TurboID proximity labelling with pro-interleukin-1α suggests a nuclear role for pro-interleukin-1α that involves interaction with histone acetyltransferases, including EP300. We also identify and validate inactivating mutations in the pro-interleukin-1α nuclear localisation sequence of multiple mammalian species, including toothed whales, castorimorpha and marsupials. However, histone acetyltransferase-binding domains are conserved in those species that have lost pro-interleukin-1α nuclear localisation. Together, these data suggest that histone acetyltransferase binding and nuclear localisation occurred together, and that while some species lost the nuclear localisation sequence in their pro-interleukin-1α, histone acetyltransferase binding ability was maintained. The nuclear localisation sequence was lost from several distinct species at different evolutionary times, suggesting convergent evolution, and that the loss of the nuclear localisation sequence confers some important biological outcome.