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Collaboration has become an important way for organizations to leverage human resources to create shared organizational goals. Schools, as organizations, thrive on positive collegial partnerships among educators, with effective educator collaborations linked to improved school effectiveness and student outcomes. However, not all collaborations are successful. The current study draws heavily from Coleman's (1988) social capital framework to understand with whom educators choose to interact and how patterns of interaction facilitate educators' social capital development. Using an equal status, sequential mixed methods design, K-12 educators' interview and survey data were iteratively analyzed to explore how educators describe their school-based collaborations, how collaborations reveal distinct social network structures, and how school-based collaborations and network structures lead to educators' social capital. Findings illuminated four distinct profiles of collaboration that emerged as educators described their school-based collaboration, which were linked to different social network structures and qualities of social capital. These results can be used to inform professional development practices for school administrators and human resources teams through consideration of profiles that may inform responsive school-based collaborations. We discuss additional implications for educator hiring and retention as well as policy around PK-12 teacher standards.

Scholarship focused on Black male students in school counseling has been intermittent despite being well documented in the larger field of education and other disciplines. In this article, we conducted a systematic review of the school counseling literature that focused on Black male students. We used critical race theory (CRT) to examine the programs and interventions that have been published with Black male participants in school settings within the school counseling literature and examined the role that school counselors took when supporting Black male students' academic, social emotional, college and career identity development. We reconceptualize the Achieving Success Everyday (ASE) group model (Steen et al., 2014) and call for others to use the ASE group model to combat racism and foster Black excellence.

Effective treatment of infection in chronic wounds is critical to improve patient outcomes and prevent severe complications, including systemic infections, increased morbidity, and amputations. Current treatments, including antibiotic administration and antimicrobial dressings, are challenged by the increasing prevalence of antibiotic resistance and patients’ sensitivity to the delivered agents. Previous studies have demonstrated the potential of a new antimicrobial agent, Gallium maltolate (GaM); however, the high burst release from the GaM-loaded hydrogel gauze required frequent dressing changes. To address this need, we developed a hydrogel foam-based wound dressing with GaM-loaded microspheres for sustained infection control. First, the minimal inhibitory and bactericidal concentrations (MIC and MBC) of GaM against two Staphylococcus aureus strains isolated from chronic wounds were identified. No significant adverse effects of GaM on dermal fibroblasts were shown at the MIC, indicating an acceptable selectivity index. For the sustained release of GaM, electrospraying was employed to fabricate microspheres with different release kinetics. Systematic investigation of loading and microsphere size on release kinetics indicated that the larger microsphere size and lower GaM loading resulted in a sustained GaM release profile over the target 5 days. Evaluation of the GaM-loaded hydrogel dressing demonstrated cytocompatibility and antibacterial activities with a zone of inhibition test. An equine distal limb wound model was developed and utilized to demonstrate the efficacy of GaM-loaded hydrogel foam in vivo. This antimicrobial hydrogel foam dressing displayed the potential to combat methicillin-resistant S. aureus (MRSA) infection with controlled GaM release to improve chronic wound healing.

IntroductionAdvancements in machine learning (ML) algorithms that make predictions from data without being explicitly programmed and the increased computational speeds of graphics processing units (GPUs) over the last decade have led to remarkable progress in the capabilities of ML. In many fields, including agriculture, this progress has outpaced the availability of sufficiently diverse and high-quality datasets, which now serve as a limiting factor. While many agricultural use cases appear feasible with current compute resources and ML algorithms, the lack of reusable hardware and software components, referred to as cyberinfrastructure (CI), for collecting, transmitting, cleaning, labeling, and training datasets is a major hindrance toward developing solutions to address agricultural use cases. This study focuses on addressing these challenges by exploring the collection, processing, and training of ML models using a multimodal dataset and providing a vision for agriculture-focused CI to accelerate innovation in the field.MethodsData were collected during the 2023 growing season from three agricultural research locations across Ohio. The dataset includes 1 terabyte (TB) of multimodal data, comprising Unmanned Aerial System (UAS) imagery (RGB and multispectral), as well as soil and weather sensor data. The two primary crops studied were corn and soybean, which are the state's most widely cultivated crops. The data collected and processed from this study were used to train ML models to make predictions of crop growth stage, soil moisture, and final yield.ResultsThe exercise of processing this dataset resulted in four CI components that can be used to provide higher accuracy predictions in the agricultural domain. These components included (1) a UAS imagery pipeline that reduced processing time and improved image quality over standard methods, (2) a tabular data pipeline that aggregated data from multiple sources and temporal resolutions and aligned it with a common temporal resolution, (3) an approach to adapting the model architecture for a vision transformer (ViT) that incorporates agricultural domain expertise, and (4) a data visualization prototype that was used to identify outliers and improve trust in the data.DiscussionFurther work will be aimed at maturing the CI components and implementing them on high performance computing (HPC). There are open questions as to how CI components like these can best be leveraged to serve the needs of the agricultural community to accelerate the development of ML applications in agriculture.

Background: Breast cancer is a major public health problem. Covid-19 pandemic impacted all areas of daily life, including medical care. In particular, delivering care for patients with cancer or suspected cancer during the crisis was challenging given the competing risks of death from untreated cancer versus serious complications from SARS-CoV-2, and the likely higher lethality of Covid-19 in immunocompromised patients. This study aims to assess the efficacy and immunogenicity of SARS-COV-2 vaccines in breast cancer patients by evaluating antispike antibodies in vaccinated and non-vaccinated patients and also through assessing the infection with Covid-19 after vaccination in vaccinated patients and non-vaccinated patients. The study also aims to assess the safety of SARS-COV-2 vaccine in breast cancer patients (local and systemic toxicity). Methods: Our population consisted of 120 female patients diagnosed with early and locally advanced breast cancer (60 vaccinated and 60 unvaccinated against Covid-19), in the breast cancer unit, clinical oncology department, Ain Shams University Hospitals. All were on oncological systemic therapy (neoadjuvant or adjuvant treatment). The cutoff value of SARS COV-2 antibody is 50 AU/mL (≥ 50 seropositive,

The adoption of grain amaranth (Amaranthus hypochondriacus L.) as a climate-smart crop due to its hardiness and drought escape mechanisms in sub-Saharan Africa is under threat from the lack of pure seed due to contamination by weedy relatives, such as pigweed (A. hybridus L.). Due to limited land availability, proper isolation distances to attain seed purity in the small-scale farming sector are impossible. A study investigating the cross-pollination rate between grain amaranth and pigweed at distances between 3 and 150 m was conducted at three sites in Zimbabwe during the 2020/2021 and 2021/2022 rainy seasons. Grain amaranth was planted at radial distances of 3 to 150 m from the pure pigweed plot. Meteorological conditions, such as temperature and wind speed and direction, were measured during the anthesis period. The cross-pollination rate in grain amaranth was influenced by distance from the pollen source. A maximum cross-pollination rate of 12.9% was observed within a 10 m radius from the pollen donor plot, and a minimum outcrossing rate of 0.45% was recorded at 150 m. The complete removal of weedy relatives within an extrapolated distance of 325 m is recommended to maintain the genetic purity of grain amaranth in small-scale farming systems.

Underscored by a long overdue call to challenge racism in teacher education (TE), we set forth to examine our own university TE classrooms to understand how we are both perpetuating and disrupting systemic racism and decentering whiteness, such that we can move toward sustained antiracist pedagogy for ourselves, our institutions, teacher candidates, and school communities. Undergirded by Critical Race Theory, this paper presents the development of an "Antiracist Pedagogy Course Audit" -- a tool to develop instructor capacity to engage in critically reflective practices in five key areas: 1) Instructor Critical Consciousness; 2) Understanding Students' Backgrounds and Experiences; 3) Course Readings and Content; 4) Classroom Learning Environment; and 5) Assignments and Assessments. We describe early implementation, process-oriented mechanisms for adaption, and applications of the tool in an early childhood education teacher education course.

Students who experience research as undergraduates tend to remain in science, technology, engineering, and mathematics fields. Since course-based undergraduate research experiences (CUREs) are often expensive to implement, we developed a relatively inexpensive first-semester research course for majors entitled "Freshman Research in Biochemistry," in which approximately 100 students participated in an ongoing research project each year. During this course, students conducted laboratory work related to multiple research veins, and graduate teaching assistants (TAs) and the instructor ensured that sufficient data from the laboratory were made available to the students to analyze. During three unique course iterations, students worked on faculty research projects that included connecting contigs of the draft genome of a native Oklahoma bacterial Arhodomonas isolate capable of degrading petroleum hydrocarbons, cloning antibiotic resistance genes from the genus Elizabethkingia, and analyzing RNAseq data from Elizabethkingia anopheles challenged with cell wall-active antibiotics to determine gene function, gene differential expression, and relative gene genome location. During the course, students scored results, analyzed data, maintained laboratory notebooks, and presented posters in a symposium to faculty and graduate TAs. To determine positive impacts of the course on students, they completed the Laboratory Course Assessment Survey in two course sections. Survey data indicated/students agreed that the course included elements of Iteration, Collaboration, and Discovery and Relevance. Students also reported that they gained scientific skills but were ambivalent about whether instructor feedback was harsh. Examination of Student Survey of Instruction responses showed students thought positively about the course, but some considered it too difficult. Biochemistry and Molecular Biology and Biochemistry major cohort tracking revealed no effect of Freshman Research in Biochemistry on student persistence in the major or institution. Nonetheless, we consider the course a cost-conscious model for biochemistry and molecular biology departments for introducing research to first-year students.

Mycobacterium tuberculosis (Mtb), the aetiologic agent of tuberculosis (TB), stores triacylglycerol (TAG) in the form of intrabacterial lipid inclusions (ILI) to survive and chronically persist within its host. These highly energetic molecules represent a major source of carbon to support bacterial persistence and reactivation, thus playing a leading role in TB pathogenesis. However, despite its physiological and clinical relevance, ILI metabolism in Mtb remains poorly understood. Recent discoveries have suggested that several ILI‐associated proteins might be widely conserved across TAG‐producing prokaryotes, but still very little is known regarding the nature and the biological functions of these proteins. Herein, we performed an in silico analysis of three independent ILI‐associated proteomes previously reported to computationally define a potential core ILI‐associated proteome, referred to as ILIome. Our investigation revealed the presence of 70 orthologous proteins that were strictly conserved, thereby defining a minimal ILIome core. We further narrowed our analysis to proteins involved in lipid metabolism and discuss here their putative biological functions, along with their molecular interactions and dynamics at the surface of these bacterial organelles. We also highlight the experimental limitations of the original proteomic investigations and of the present bioinformatic analysis, while describing new technological approaches and presenting biological perspectives in the field. The in silico investigation presented here aims at providing useful datasets that could constitute a scientific resource of broad interest for the mycobacterial community, with the ultimate goal of enlightening ILI metabolism in prokaryotes with a special emphasis on Mtb pathogenesis.

BackgroundFabry disease (FD) causes cold-evoked pain and impaired cold perception through small fiber damage, which also occurs in polyneuropathies (PNP) of other origins. The integrity of thinly myelinated fibers and the spinothalamic tract is assessable by cold-evoked potentials (CEPs). In this study, we aimed to assess the clinical value of CEP by investigating its associations with pain, autonomic measures, sensory loss, and neuropathic signs.MethodsCEPs were examined at the hand and foot dorsum of patients with FD (n = 16) and PNP (n = 21) and healthy controls (n = 23). Sensory phenotyping was performed using quantitative sensory testing (QST). The painDETECT questionnaire (PDQ), FabryScan, and measures for the autonomic nervous system were applied. Group comparisons and correlation analyses were performed.ResultsCEPs of 87.5% of the FD and 85.7% of the PNP patients were eligible for statistical analysis. In all patients combined, CEP data correlated significantly with cold detection loss, PDQ items, pain, and autonomic measures. Abnormal CEP latency in FD patients was associated with an abnormal heart frequency variability item (r = −0.684; adjusted p = 0.04). In PNP patients, CEP latency correlated significantly with PDQ items, and CEP amplitude correlated with autonomic measures (r = 0.688, adjusted p = 0.008; r = 0.619, adjusted p = 0.024). Furthermore, mechanical pain thresholds differed significantly between FD (gain range) and PNP patients (loss range) (p = 0.01).ConclusionsAbnormal CEPs were associated with current pain, neuropathic signs and symptoms, and an abnormal function of the autonomic nervous system. The latter has not been mirrored by QST parameters. Therefore, CEPs appear to deliver a wider spectrum of information on the sensory nervous system than QST alone.