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Abstract How organisms respond to environmental stress is a key topic in evolutionary biology. This study focused on the genomic evolution of Laburnicola rhizohalophila, a dark-septate endophytic fungus from roots of a halophyte. Chromosome-level assemblies were generated from five representative isolates from structured subpopulations. The data revealed significant genomic plasticity resulting from chromosomal polymorphisms created by fusion and fission events, known as dysploidy. Analyses of genomic features, phylogenomics, and macrosynteny have provided clear evidence for the origin of intraspecific diploid-like hybrids. Notably, one diploid phenotype stood out as an outlier and exhibited a conditional fitness advantage when exposed to a range of abiotic stresses compared with its parents. By comparing the gene expression patterns in each hybrid parent triad under the four growth conditions, the mechanisms underlying growth vigor were corroborated through an analysis of transgressively upregulated genes enriched in membrane glycerolipid biosynthesis and transmembrane transporter activity. In vitro assays suggested increased membrane integrity and lipid accumulation, as well as decreased malondialdehyde production under optimal salt conditions (0.3 M NaCl) in the hybrid. These attributes have been implicated in salinity tolerance. This study supports the notion that hybridization-induced genome doubling leads to the emergence of phenotypic innovations in an extremophilic endophyte.

Abstract Background One of the most common, but least studied, diabetic complication is diabetic bladder dysfunction. Current therapies include glucose control and symptom-based interventions. However, efficacy of these therapies is mixed and often have undesirable side effects. Diabetes is now known to be a chronic inflammatory disease. Specialized pro-resolving mediators are a class of compounds that promote the resolution of inflammation and have been shown to be effective in treating chronic inflammatory conditions. In this study we examine the ability of resolvin E1 to improve signs of diabetic bladder dysfunction. Methods Male Akita mice (Type 1 diabetic) develop hyperglycemia at 4 weeks and signs of bladder underactivity by 15 weeks. Starting at 15 weeks, mice were given one or two weeks of daily resolvin E1 and compared to age-matched wild type and untreated Akita mice. Results Resolvin E1 did not affect diabetic blood glucose after one week, although there was a slight decrease after two weeks. Diabetes decreased body weight and increased bladder weights and this was not affected by resolvin E1. Evan’s blue dye extravasation (an indirect index of inflammation) was dramatically suppressed after one week of resolvin E1 treatment, but, surprisingly, had returned to diabetic levels after two weeks of treatment. Using cystometry, untreated Akita mice showed signs of underactivity (increased void volumes and intercontraction intervals). One week of resolvin E1treatment restored these cystometric findings back to control levels. After two weeks of treatment, cystometric changes were changed from controls but still significantly different from untreated levels, indicating a durable treatment effect even in the presence of increased inflammation at 2 weeks. Conclusions Resolvin E1 has a beneficial effect on diabetic bladder dysfunction in the type 1 diabetic male Akita mouse model.

Abstract Background Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood-stabilizing effects help reduce the long-term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have beneficial effects on disease-associated conditions, including sleep and cardiovascular disorders. However, the individual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP-I and BP-II) according to the clinical presentation. Moreover, long-term Li treatment has been linked to adverse side-effects that are a cause of concern and non-adherence, including the risk of developing chronic medical conditions such as thyroid and renal disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged the ConLiGen cohort (N = 2064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing particular emphasis on identifying differences between BP-I and BP-II. Results We found that clinical response to Li treatment, measured with the Alda scale, was associated with a diminished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP-I and, in patients with BP-II, of depression only. Our genetic analyses showed that a stronger clinical response to Li was modestly related to lower polygenic load for diabetes and hypertension in BP-I but not BP-II. Moreover, our results suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the polygenic load for comorbid conditions, including diabetes, hypertension and hypothyroidism. Conclusions Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP are partially modulated by common genetic factors, with differential effects between BP-I and BP-II.

Abstract Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/ .

Abstract Background Innate immune responses can be activated by pathogen-associated molecular patterns (PAMPs), danger signals released by damaged tissues, or the absence of self-molecules that inhibit immunity. As PAMPs are typically conserved across broad groups of pathogens but absent from the host, it is unclear whether they allow hosts to recognize parasites that are phylogenetically similar to themselves, such as parasitoid wasps infecting insects. Results Parasitoids must penetrate the cuticle of Drosophila larvae to inject their eggs. In line with previous results, we found that the danger signal of wounding triggers the differentiation of specialized immune cells called lamellocytes. However, using oil droplets to mimic infection by a parasitoid wasp egg, we found that this does not activate the melanization response. This aspect of the immune response also requires exposure to parasite molecules. The unidentified factor enhances the transcriptional response in hemocytes and induces a specific response in the fat body. Conclusions We conclude that a combination of danger signals and the recognition of nonself molecules is required to activate Drosophila’s immune response against parasitic insects.

Infectious fungi have been an increasing global concern in the present era. A promising approach to tackle this pressing concern involves utilizing Antifungal peptides (AFP) to develop an antifungal drug that can selectively eliminate fungal pathogens from a host with minimal toxicity to the host. Accordingly, identifying precise therapeutic antifungal peptides is crucial for developing effective drugs and treatments. This study proposed MLAFP-XN, a neural network-based strategy for accurately detecting active AFP in sequencing data to achieve this objective. In this work, eight feature extraction techniques and the XGB feature selection strategy are utilized together to present an enhanced methodology. A total of 24 classification models were evaluated, and the most effective four have been selected. Each of these models demonstrated superior accuracy on independent test sets, with respective scores of 97.93 %, 99.47 %, and 99.48 %. Our model outperforms current state of the art methods. In addition, we created a companion website to demonstrate our AFP recognition process and use SHAP to identify the most influential properties.

Background and aims: The single nucleotide polymorphisms (SNPs) in SLC30A8 gene have been recognized as contributing to type 2 diabetes (T2D) susceptibility and colorectal cancer. This study aims to predict the structural stability, and functional impacts on variations in non-synonymous SNPs (nsSNPs) in the human SLC30A8 gene using various computational techniques. Materials and methods: Several in silico tools, including SIFT, Predict-SNP, SNPs&GO, MAPP, SNAP2, PhD-SNP, PANTHER, PolyPhen-1,PolyPhen-2, I-Mutant 2.0, and MUpro, have been used in our study. Results: After data analysis, out of 336 missenses, the eight nsSNPs, namely R138Q, I141N, W136G, I349N, L303R, E140A, W306C, and L308Q, were discovered by ConSurf to be in highly conserved regions, which could affect the stability of their proteins. Project HOPE determines any significant molecular effects on the structure and function of eight mutated proteins and the three-dimensional (3D) structures of these proteins. The two pharmacologically significant compounds, Luzonoid B and Roseoside demonstrate strong binding affinity to the mutant proteins, and they are more efficient in inhibiting them than the typical SLC30A8 protein using Autodock Vina and Chimera. Increased binding affinity to mutant SLC30A8 proteins has been determined not to influence drug resistance. Ultimately, the Kaplan-Meier plotter study revealed that alterations in SLC30A8 gene expression notably affect the survival rates of patients with various cancer types. Conclusion: Finally, the study found eight highly deleterious missense nsSNPs in the SLC30A8 gene that can be helpful for further proteomic and genomic studies for T2D and colorectal cancer diagnosis. These findings also pave the way for personalized treatments using biomarkers and more effective healthcare strategies.

The bacterial pathogen Staphylococcus aureus employs a thick cell wall for protection against physical and chemical insults. This wall requires continuous maintenance to ensure strength and barrier integrity, but also to permit bacterial growth and division. The main cell wall component is peptidoglycan. Accordingly, the bacteria produce so-called peptidoglycan hydrolases (PGHs) that cleave glycan strands to facilitate growth, cell wall remodelling, separation of divided cells and release of exported proteins into the extracellular milieu. A special class of PGHs contains so-called ‘cysteine, histidine-dependent amidohydrolase/peptidase’ (CHAP) domains. In the present study, we profiled the roles of 11 CHAP PGHs encoded by the core genome of S. aureus USA300 LAC. Mutant strains lacking individual CHAP PGHs were analysed for growth, cell morphology, autolysis, and invasion and replication inside human lung epithelial cells. The results show that several investigated CHAP PGHs contribute to different extents to extracellular and intracellular growth and replication of S. aureus, septation of dividing cells, daughter cell separation once the division process is completed, autolysis and biofilm formation. In particular, the CHAP PGHs Sle1 and SAUSA300_2253 control intracellular staphylococcal replication and the resistance to β-lactam antibiotics like oxacillin. This makes the S. aureus PGHs in general, and the Sle1 and SAUSA300_2253 proteins in particular, attractive targets for future prophylactic or therapeutic anti-staphylococcal interventions. Alternatively, these cell surface-exposed enzymes, or particular domains of these enzymes, could be applied in innovative anti-staphylococcal therapies.

Abstract Background Evidence-based care for acute myocardial infarction (AMI) reduces morbidity and mortality. Prior studies in Tanzania identified substantial gaps in the uptake of evidence-based AMI care. Implementation science has been used to improve uptake of evidence-based AMI care in high-income settings, but interventions to improve quality of AMI care have not been studied in sub-Saharan Africa. Methods Purposive sampling was used to recruit participants from key stakeholder groups (patients, providers, and healthcare administrators) in northern Tanzania. Semi-structured in-depth interviews were conducted using a guide informed by the Consolidated Framework for Implementation Research (CFIR). Interview transcripts were coded to identify barriers to AMI care, using the 39 CFIR constructs. Barriers relevant to emergency department (ED) AMI care were retained, and the Expert Recommendations for Implementing Change (ERIC) tool was used to match barriers with Level 1 recommendations for targeted implementation strategies. Results Thirty key stakeholders, including 10 patients, 10 providers, and 10 healthcare administrators were enrolled. Thematic analysis identified 11 barriers to ED-based AMI care: complexity of AMI care, cost of high-quality AMI care, local hospital culture, insufficient diagnostic and therapeutic resources, inadequate provider training, limited patient knowledge of AMI, need for formal implementation leaders, need for dedicated champions, failure to provide high-quality care, poor provider-patient communication, and inefficient ED systems. Seven of these barriers had 5 strong ERIC recommendations: access new funding, identify and prepare champions, conduct educational meetings, develop educational materials, and distribute educational materials. Conclusions Multiple barriers across several domains limit the uptake of evidence-based AMI care in northern Tanzania. The CFIR-ERIC mapping approach identified several targeted implementation strategies for addressing these barriers. A multi-component intervention is planned to improve uptake of evidence-based AMI care in Tanzania.

Abstract Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium. In this study, we replicated the results of our previous study using network propagation methods in a genome-wide association study of an independent sample of 2039 patients from the International Consortium on Lithium Genetics (ConLiGen) study. We identified functional enrichment in focal adhesion and PI3K-Akt pathways, but we did not find an association with the ECM pathway. Our results suggest that deficits in the neuronal growth cone and PI3K-Akt signaling, but not in ECM proteins, may influence response to lithium in BD.

BackgroundThe standard diagnosis of Ascaris lumbricoides and other soil-transmitted helminth (STH) infections relies on the detection of worm eggs by copromicroscopy. However, this method is dependent on worm patency and shows only limited accuracy in low-intensity infection settings. We aimed to decipher the diagnostic accuracy of different antibodies using various Ascaris antigens in reference to copromicroscopy and quantitative PCR (qPCR), four months after national STH preventative chemotherapy among school children in western Kenya.MethodologySTH infection status of 390 school children was evaluated via copromicroscopy (Kato-Katz and mini-FLOTAC) and qPCR. In parallel, Ascaris-specific antibody profiles against larval and adult worm lysates, and adult worm excretory-secretory (ES) products were determined by enzyme-linked immunosorbent assay. Antibody cross-reactivity was evaluated using the closely related zoonotic roundworm species Toxocara cati and Toxocara canis. The diagnostic accuracy of each antibody was evaluated using receiver operating curve analysis and the correspondent area under the curve (AUC).Principal findingsAscaris was the predominant helminth infection with an overall prevalence of 14.9% (58/390). The sensitivity of mini-FLOTAC and Kato-Katz for Ascaris diagnosis reached only 53.5% and 63.8%, respectively compared to qPCR. Although being more sensitive, qPCR values correlated with microscopic egg counts (R = -0.71, PConclusionIgG1 antibody responses against adult Ascaris-ES products hold a promising potential for complementing the standard fecal and molecular techniques employed for monitoring Ascaris infections. This is of particular importance in the context of deworming programs as the antibody diagnostic accuracy was independent of egg counts.

Classically, ATM is known for its role in sensing double-strand DNA breaks, and subsequently signaling for their repair. Non-canonical roles of ATM include transcriptional silencing, ferroptosis, autophagy and angiogenesis. Angiogenesis mediated by ATM signaling has been shown to be VEGF-independent via p38 signaling. Independently, p38 signaling has been shown to upregulate metalloproteinase expression, including MMP-2 and MMP-9, though it is unclear if this is linked to ATM. Here, we demonstrate ATM regulates aminopeptidase-N (CD13/APN/ANPEP) at the protein level. Positive correlation was seen between ATM activity and CD13 protein expression using both “wildtype” (WT) and knockout (KO) ataxia telangiectasia (AT) cells through western blotting; with the same effect shown when treating neuroblastoma cancer cell line SH-SY5Y, as well as AT-WT cells, with ATM inhibitor (ATMi; KU55933). However, qPCR along with publically available RNAseq data from Hu et al. (J. Clin. Invest., 2021, 131, e139333), demonstrated no change in mRNA levels of CD13, suggesting that ATM regulates CD13 levels via controlling protein degradation. This is further supported by the observation that incubation with proteasome inhibitors led to restoration of CD13 protein levels in cells treated with ATMi. Migration assays showed ATM and CD13 inhibition impairs migration, with no additional effect observed when combined. This suggests an epistatic effect, and that both proteins may be acting in the same signaling pathway that influences cell migration. This work indicates a novel functional interaction between ATM and CD13, suggesting ATM may negatively regulate the degradation of CD13, and subsequently cell migration.

Despite consensus by the international community on the need for setting targets for reducing emissions or enhancing removal of greenhouse gases, comprehensive strategies for enumerating smallholders’ roles are yet to be designed and tested. Moreover, although smallholders’ role as net greenhouse gas removers has been speculated, evidence of this fact is necessary. A survey of 380 smallholders involved in maize-farming system in four sub-counties of western Kenya representing heterogeneity in agro-ecological zones and postulated biomass cooking energy access and demand was undertaken. The objectives of the study included to i). determine the smallholders’ net role in GHGs’ removals; ii). assess the presence of differences in smallholders’ emission status for an identical system but in dissimilar agro-ecological zones iii). relate atmospheric GHG removers to households' socio-economic characteristics and iv). enumerate a cost-effective approach of estimating atmospheric greenhouse gases emissions and removal. Greenhouse gas emissions and removal activity data, default emission factors and outcomes of allometric equations based on existing secondary sources were incorporated into the primary database from the survey to derive smallholders’ roles as net GHG removers. Economic models were used to derived the drivers of GHG removal. Most smallholders were net CO2e removers with differences among them and the sampled sub-counties associated with intensity in adoption of the maize-agroforestry system and levels of biomass utilization. Net CO2e removers significantly (p < 0.05) reared more livestock, utilized more fertilizer at both planting and topdressing stages, reported higher maize yields and had planted more trees than those who were net dischargers. Factors influencing the probability (p < 0.05) of smallholder being a net carbon remover included household size, maize yield, land owned and adoption of energy saving cooking stoves. Emission mitigation practices reported were compatible with food security, agricultural commercialization and the welfare enhancing production operations. The approach adopted in enumerating greenhouse gases emission and removal could highly enhance developing countries reporting the National GHG's Inventories and pin-point intervention options.

Hand Gesture Recognition (HGR) using electromyography (EMG) signals is a challenging problem due to the variability and noise in the signals across individuals. This study addresses this challenge by examining the effect of incorporating a post-processing algorithm, which filters the sequence of predictions and removes spurious labels, on the performance of a HGR model based on spectrograms and Convolutional Neural Networks (CNN). The study also compares CNN vs CNN-LSTM to assess the influence of the memory cells on the model. The EMG-EPN-612 dataset, which contains measurements of EMG signals for 5 hand gestures from 612 subjects, was used for training and testing. The results showed that the post-processing algorithm increased the recognition accuracy by 41.86% for the CNN model and 24.77% for the CNN-LSTM model. The inclusion of the memory cells increased accuracy by 3.29%, but at the cost of 53 times more learnables. The CNN-LSTM model with post-processing achieved a mean recognition accuracy of 90.55% (SD=9.45%). These findings suggest new paths for research in HGR architectures beyond the traditional focus on the classification and feature extraction stages. For reproducibility purposes, we made publicly available the source code in Github.

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy.

There is an increasing need to improve the accuracy of extreme weather forecasts for life-saving applications and in support of various socioeconomic sectors in East Africa, a region with remarkable mesoscale systems due to its complex topography defined by sharp gradients in elevation, inland water bodies, and landuse conversions. This study sought to investigate the impacts of the Weather Research and Forecasting (WRF) model spatial resolution on resolving rainfall variability with topography utilizing nested domains at 12 and 2.4 km resolutions. The model was driven by the National Centers for Environmental Prediction (NCEP)-Global Data Assimilation System (GDAS) Global Forecast System (GFS) final (FNL) reanalysis to simulate the weather patterns over East Africa from 3rd April 2018 to 30th April 2018, which were evaluated against several freely available gridded weather datasets alongside rainfall data from the Kenya Meteorological Department (KMD) stations. The reference datasets and the model outputs revealed that the highlands had more rainfall events and higher maximum daily rainfall intensity compared to the surrounding lowlands, attributed to orographic lifting enhancing convection. Rainfall was inversely proportional to altitude from 500 m to 1,100 m above sea level (ASL) for both coarse and fine resolutions. The convection-permitting setup was superior in three aspects: resolving the inverse altitude-rainfall relationship for altitudes beyond 3000 m ASL, simulating heavy rainfall events over the lowlands, and resolution of the diurnal cycle of low-level wind. Although the coarse resolution setup reasonably simulated rainfall over large mountains, only the convection-permitting configuration could accurately resolve rainfall variability over contrasting topographical features. The study notes that high-resolution modeling systems and topography-sensitive bias correction techniques are critical for improving the quality of operational weather forecasts in East Africa.

BackgroundChronic myocardial injury is a condition defined by stably elevated cardiac biomarkers without acute myocardial ischemia. Although studies from high-income countries have reported that chronic myocardial injury predicts adverse prognosis, there are no published data about the condition in sub-Saharan Africa.MethodsBetween November 2020 and January 2023, adult patients with chest pain or shortness of breath were recruited from an emergency department in Moshi, Tanzania. Medical history and point-of-care troponin T (cTnT) assays were obtained from participants; those whose initial and three-hour repeat cTnT values were abnormally elevated but within 11% of each other were defined as having chronic myocardial injury. Mortality was assessed thirty days following enrollment.ResultsOf 568 enrolled participants, 81 (14.3%) had chronic myocardial injury, 73 (12.9%) had acute myocardial injury, and 412 (72.5%) had undetectable cTnT values. Of participants with chronic myocardial injury, the mean (± sd) age was 61.5 (± 17.2) years, and the most common comorbidities were CKD (n = 65, 80%) and hypertension (n = 60, 74%). After adjusting for CKD, thirty-day mortality rates (38% vs. 36%, aOR 1.03, 95% CI: 0.52-2.03, p = 0.931) were similar between participants with chronic myocardial injury and those with acute myocardial injury, but significantly greater (38% vs. 13.6%, aOR 3.63, 95% CI: 1.98-6.65, pConclusionIn Tanzania, chronic myocardial injury is a poor prognostic indicator associated with high risk of short-term mortality. Clinicians practicing in this region should triage patients with stably elevated cTn levels in light of their increased risk.

IntroductionMyocardial Infarction (MI) is a leading cause of death worldwide. In high income countries, quality improvement strategies have played an important role in increasing uptake of evidence-based MI care and improving MI outcomes. The incidence of MI in sub-Saharan Africa is rising, but uptake of evidence-based care in northern Tanzania is low. There are currently no published quality improvement interventions from the region. The objective of this study was to determine provider attitudes towards a planned quality improvement intervention for MI care in northern Tanzania.MethodsThis study was conducted at a zonal referral hospital in northern Tanzania. A 41-question survey, informed by the Theoretical Framework for Acceptability, was developed by an interdisciplinary team from Tanzania and the United States. The survey, which explored provider attitudes towards MI care improvement, was administered to key provider stakeholders (physicians, nurses, and hospital administrators) using convenience sampling.ResultsA total of 140 providers were enrolled, including 82 (58.6%) nurses, 56 (40.0%) physicians, and 2 (1.4%) hospital administrators. Most participants worked in the Emergency Department or inpatient medical ward. Providers were interested in participating in a quality improvement project to improve MI care at their facility, with 139 (99.3%) strongly agreeing or agreeing with this statement. All participants agreed or strongly agreed that improvements were needed to MI care pathways at their facility. Though their facility has an MI care protocol, only 88 (62.9%) providers were aware of it. When asked which intervention would be the single-most effective strategy to improve MI care, the two most common responses were provider training (n = 66, 47.1%) and patient education (n = 41, 29.3%).ConclusionProviders in northern Tanzania reported strongly positive attitudes towards quality improvement interventions for MI care. Locally-tailored interventions to improve MI should include provider training and patient education strategies.

The impact of environmental uncertainty on locomotor adaptation remains unclear. Environmental uncertainty could either aid locomotor adaptation by prompting protective control strategies that stabilize movements to assist learning or impede adaptation by reducing error sensitivity and fostering hesitance to pursue corrective movements. To explore this, we investigated participants' adaptation to a consistent force field after experiencing environmental uncertainty in the form of unpredictable balance perturbations. We compared the performance of this group (Perturbation) to the adaptive performance of a group that did not experience any unpredictable perturbations (Non-Perturbation). Perturbations were delivered using a cable-driven robotic device applying lateral forces to the pelvis. We assessed whole-body center of mass (COM) trajectory (COM signed deviation), anticipatory postural adjustments (COM lateral offset), and first step width. The Perturbation group exhibited larger disruptions in COM trajectory (greater COM signed deviations) than the Non-Perturbation group when first walking in the force field. While the COM signed deviations of both groups decreased towards baseline values, only the Non-Perturbation group returned to baseline levels. The Perturbation groups COM signed deviations remained higher, indicating they failed to fully adapt to the force field before the end. The Perturbation group also did not adapt their COM lateral offset to counter the predictable effects of the force field as the Non-Perturbation group did, and their first step width increased more slowly. Our findings suggest that exposure to unpredictable perturbations impeded future sensorimotor adaptations to consistent perturbations.

Both constitutive and inducible immune mechanisms are employed by hosts for defense against infection. Constitutive immunity allows for a faster response, but it comes with an associated cost that is always present. This trade-off between speed and fitness costs leads to the theoretical prediction that constitutive immunity will be favored where parasite exposure is frequent. We selected populations of Drosophila melanogaster under high parasite pressure from the parasitoid wasp Leptopilina boulardi. With RNA sequencing, we found the evolution of resistance in these populations was associated with them developing constitutively active humoral immunity, mediated by the larval fat body. Furthermore, these evolved populations were also able to induce gene expression in response to infection to a greater level, which indicates an overall more activated humoral immune response to parasitization. The anti-parasitoid immune response also relies on the JAK/STAT signaling pathway being activated in muscles following infection, and this induced response was only seen in populations that had evolved under high parasite pressure. We found that the cytokine Upd3, which induces this JAK/STAT response, is being expressed by immature lamellocytes. Furthermore, these immune cells became constitutively present when populations evolved resistance, potentially explaining why they gained the ability to activate JAK/STAT signaling. Thus, under intense parasitism, populations evolved resistance by increasing both constitutive and induced immune defenses, and there is likely an interplay between these two forms of immunity.

Active learning is essential for students in the construction and surveying disciplines due to the practical work nature and technical demands of these professions. This research study aims to identify and evaluate effective pedagogical strategies and critical success factors (CSFs) that can enhance active learning among undergraduate construction and surveying students. Customized e-learning materials based on the Technology, Pedagogy, Content, and Knowledge (TPACK) framework were adopted to improve the learning outcomes and effectiveness for semester-long construction-related research course students. Subsequently, an institutional student-based questionnaire survey was conducted and analyzed using mean score, exploratory factor analysis, and fuzzy synthetic evaluation (FSE). Accordingly, the top five active instructional learning strategies are using examples to reinforce understanding, case studies to encourage critical thinking, videos to improve understanding, connecting course contents to current community events, and creating classroom versions of interactive games. Exploratory factor analysis classified the CSFs into five major factor categories. The FSE results indicated that the top three CSF categories enhancing students’ active learning include electronic tools as learning aids, complementary learning and feedback, and model learning system development. This study provides essential learning environments and components needed to foster active learning among undergraduate construction and surveying students in Hong Kong and worldwide.

People living with HIV (PLWH) can exhibit impaired immune responses to vaccines. Accumulating evidence indicates that PLWH, particularly those receiving antiretroviral therapy, mount strong antibody responses to COVID-19 vaccines, but fewer studies have examined cellular immune responses to the vaccinations. Here, we used an activation-induced marker (AIM) assay to quantify SARS-CoV-2 spike-specific CD4+ and CD8+ T cells generated by two and three doses of COVID-19 vaccines in 50 PLWH receiving antiretroviral therapy, compared to 87 control participants without HIV. In a subset of PLWH, T-cell responses were also assessed after post-vaccine breakthrough infections and/or receipt of a fourth vaccine dose. All participants remained SARS-CoV-2 infection-naive until at least one month after their third vaccine dose. SARS-CoV-2 infection was determined by seroconversion to a Nucleocapsid (N) antigen, which occurred in 21 PLWH and 38 control participants after the third vaccine dose. Multivariable regression analyses were used to investigate the relationships between sociodemographic, health- and vaccine-related variables, vaccine-induced T-cell responses, and breakthrough infection risk. We observed that a third vaccine dose boosted spike-specific CD4+ and CD8+ T-cell frequencies significantly above those measured after the second dose (all p < 0.0001). Median T-cell frequencies did not differ between PLWH and controls after the second dose (p > 0.1), but CD8+ T-cell responses were modestly lower in PLWH after the third dose (p = 0.02), an observation that remained significant after adjusting for sociodemographic, health- and vaccine-related variables (p = 0.045). In PLWH who experienced a breakthrough infection, median T-cell frequencies increased even higher than those observed after three vaccine doses (p < 0.03), and CD8+ T-cell responses in this group remained higher even after a fourth vaccine dose (p = 0.03). In multivariable analyses, the only factor associated with an increased breakthrough infection risk was younger age, which is consistent with the rapid increase in SARS-CoV-2 seropositivity that was seen among younger adults in Canada after the initial appearance of the Omicron variant. These results indicate that PLWH receiving antiretroviral therapy mount strong T-cell responses to COVID-19 vaccines that can be enhanced by booster doses or breakthrough infection.

Background: Diabetic bladder dysfunction (DBD) is driven in part by inflammation which dysregulates prostaglandin release in the bladder. Precise inflammatory mechanisms responsible for such dysregulation have been elusive. Since prostaglandins impact bladder contractility, elucidating these mechanisms may yield potential therapeutic targets for DBD. In female Type 1 diabetic Akita mice, inflammation mediated by the nucleotide-binding domain, leucine-rich–containing family, pyrin domain–containing-3 (NLRP3) inflammasome is responsible for DBD. Here, we utilized female Akita mice crossbred with NLRP3 knock-out mice to determine how NLRP3-driven inflammation impacts prostaglandin release within the bladder and prostaglandin-mediated bladder contractions. Methods: Akita mice were crossbred with NLRP3-⁣/- mice to yield four groups of non-diabetics and diabetics with and without the NLRP3 gene. Females were aged to 30 weeks when Akitas typically exhibit DBD. Urothelia and detrusors were stretched ex vivo to release prostaglandins. Prostaglandin E2 (PGE2) and prostaglandin F2α (PGF2α) were quantified using enzyme linked immunosorbent assays (ELISA). In separate samples, ex vivo contractile force to PGE2 and PGF2α +/– the prostaglandin F (FP) receptor antagonist, AL8810, was measured. FP receptor protein expression was determined via western blotting. Results: Stretch-induced PGE2 release increases in urothelia but decreases in detrusors of diabetics. However, PGE2-mediated bladder contractions are not impacted. Conversely, diabetics show no changes in PGF2α release, but PGF2α-mediated contractions increase significantly. This is likely due to signaling through the FP receptors as FP receptor antagonism prevents this increase and diabetics demonstrate a four-fold increase in FP receptor proteins. Without NLRP3-mediated inflammation, changes in prostaglandin release, contractility, and receptor expression do not occur. Conclusion: NLRP3-dependent inflammation dysregulates prostaglandin release and prostaglandin-mediated bladder contractions in diabetic female Akita mice via FP receptor upregulation.

Tobacco use remains a leading preventable cause of morbidity and mortality, with pharmacotherapy and counseling recognized as effective cessation aids. Yet, the potential role of pharmacists and pharmacy technicians in tobacco cessation services is underutilized. This study explores the integration of such services in community pharmacies, identifying facilitators and barriers to their implementation. A qualitative study was conducted across seven community pharmacies in California that were affiliated with the Community Pharmacy Enhanced Services Network. Participants included 22 pharmacists and 26 pharmacy technicians/clerks who completed tobacco cessation training. Data were collected through semi-structured interviews, focusing on experiences with implementing cessation services. The analysis was guided by Rogers’ Diffusion of Innovations Theory. MAXQDA software was used for data management and thematic analysis. Sixteen pharmacy personnel participated in the study, highlighting key themes around the integration of cessation services. Compatibility with existing workflows, the importance of staff buy-in, and the crucial role of pharmacy technicians emerged as significant facilitators. Challenges included the complexity of billing for services, software limitations for documenting tobacco use and cessation interventions, and gaps in training for handling complex patient cases. Despite these barriers, pharmacies successfully initiated cessation services, with variations in service delivery and follow-up practices. Community pharmacies represent viable settings for delivering tobacco cessation services, with pharmacists and technicians playing pivotal roles. However, systemic changes are needed to address challenges related to billing, documentation, and training. Enhancing the integration of cessation services in community pharmacies could significantly impact public health by increasing access to effective cessation support.

Background: Uptake of evidence-based care for acute myocardial infarction (AMI) is suboptimal in Tanzania, but there are currently no published interventions to improve AMI care in sub-Saharan Africa. Objectives: Co-design a quality improvement intervention for AMI care tailored to local contextual factors. Methods: An interdisciplinary design team consisting of 20 physicians, nurses, implementation scientists, and administrators met from June 2022 through August 2023. Half of the design team consisted of representatives from the target audience, emergency department physicians and nurses at a referral hospital in northern Tanzania. The design team reviewed multiple published quality improvement interventions focusing on ED-based AMI care. After selecting a multicomponent intervention to improve AMI care in Brazil (BRIDGE-ACS), the design team used the ADAPT-ITT framework to adapt the intervention to the local context. Findings: The design team audited current AMI care processes at the study hospital and reviewed qualitative data regarding barriers to care. Multiple adaptations were made to the original BRIDGE-ACS intervention to suit the local context, including re-designing the physician reminder system and adding patient educational materials. Additional feedback was sought from topical experts, including patients with AMI. Draft intervention materials were iteratively refined in response to feedback from experts and the design team. The finalized intervention, Multicomponent Intervention to Improve Myocardial Infarction Care in Tanzania (MIMIC), consisted of five core components: physician reminders, pocket cards, champions, provider training, and patient education. Conclusion: MIMIC is the first locally tailored intervention to improve AMI care in sub-Saharan Africa. Future studies will evaluate implementation outcomes and efficacy.

The urgency to mitigate the effects of climate change necessitates an unprecedented global deployment of offshore renewable-energy technologies mainly including offshore wind, tidal stream, wave energy, and floating solar photovoltaic. To achieve the global energy demand for terawatt-hours, the infrastructure for such technologies will require a large spatial footprint. Accommodating this footprint will require rapid landscape evolution, ideally within two decades. For instance, the United Kingdom has committed to deploying 50 GW of offshore wind by 2030 with 90–110 GW by 2050, which is equivalent to four times and ten times more than the 2022 capacity, respectively. If all were 15 MW turbines spaced 1.5 km apart, 50 GW would require 7500 km ^2 and 110 GW would require 16 500 km ^2 . This review paper aims to anticipate environmental impacts stemming from the large-scale deployment of offshore renewable energy. These impacts have been categorised into three broad types based on the region (i.e. atmospheric, hydrodynamic, ecological). We synthesise our results into a table classifying whether the impacts are positive, negative, negligible, or unknown; whether the impact is instantaneous or lagged over time; and whether the impacts occur when the offshore infrastructure is being constructed, operating or during decommissioning. Our table benefits those studying the marine ecosystem before any project is installed to help assess the baseline characteristics to be considered in order to identify and then quantify possible future impacts.

Primitive meteorite groups such as the Vigarano, Mighei, and Karoonda carbonaceous chondrites have enigmatic patterns of elemental abundances, with moderately volatile elements—those that transition from vapor to condensate between ∼400 and ∼900 K—defining plateaus of subequal abundances despite a wide range in volatility. In detail, each group defines a plateau with distinctive nonmonotonic “chemical fingerprints” that have been attributed to combinations of mixing, vaporization/condensation, and fluid-mediated metasomatism—but the extent to which these processes can reproduce the observed variability has not been quantified. Starting with primitive Ivuna chondrite, a two-stage, two-component equilibrium condensation–vaporization model—with gravity implemented as Jeans escape—can explain large-scale plateaus in these chondrite groups, as well as more complex, nonmonotonic small-scale variations. For all three chondritic meteorite groups, models favor earlier high-temperature fractionation under low-gravity conditions followed by a low-temperature fractionation event that took place on a protoplanet at least as large as Ceres. The second fractionation event may represent the fractionation of incoming materials to the planetesimal during protracted pebble accretion. Models with only thermally driven volatile loss, gravity, and mixing can explain more than 80% of the observed compositional variability in these meteorite groups. In our five-parameter model, using only five randomly selected elements yields uselessly large ranges of planet sizes and temperatures, ranges that converge with increasing numbers of elements. These results suggest that even simple models are prone to generating inaccurate conclusions when constrained by too few observations, a fault likely held by more complex models as well.

Tobacco use remains a leading preventable cause of morbidity and mortality, with pharmacotherapy and counseling recognized as effective cessation aids. Yet, the potential role of pharmacists and pharmacy technicians in tobacco cessation services is underutilized. This study explores the integration of such services in community pharmacies, identifying facilitators and barriers to their implementation. A qualitative study was conducted across seven community pharmacies in California that were affiliated with the Community Pharmacy Enhanced Services Network. Participants included 22 pharmacists and 26 pharmacy technicians/clerks who completed tobacco cessation training. Data were collected through semi-structured interviews, focusing on experiences with implementing cessation services. The analysis was guided by Rogers’ Diffusion of Innovations Theory. MAXQDA software was used for data management and thematic analysis. Sixteen pharmacy personnel participated in the study, highlighting key themes around the integration of cessation services. Compatibility with existing workflows, the importance of staff buy-in, and the crucial role of pharmacy technicians emerged as significant facilitators. Challenges included the complexity of billing for services, software limitations for documenting tobacco use and cessation interventions, and gaps in training for handling complex patient cases. Despite these barriers, pharmacies successfully initiated cessation services, with variations in service delivery and follow-up practices. Community pharmacies represent viable settings for delivering tobacco cessation services, with pharmacists and technicians playing pivotal roles. However, systemic changes are needed to address challenges related to billing, documentation, and training. Enhancing the integration of cessation services in community pharmacies could significantly impact public health by increasing access to effective cessation support.

Batting or striking with a long-handled implement is an important fundamental motor skill used in several lifetime activities. This article outlines basic movement concepts that are developed through batting and provides recommendations that can serve as both curricular options for physical education and a potential homework idea for kids to explore at home.

Objective: We wanted to identify factors associated with pre-nursing student retention and success at a small, rural two-year institution in South Carolina. Method: Transcript analysis generated a dataset for pre-nursing students (N = 125). Both descriptive and inferential statistics were used to analyze high school and university data. Results: Our research identified predictors associated with student retention and/or success: high school performance, college readiness, and first semester performance. Ethnicity was not associated with student retention and success. Two intervention programs were evaluated: Opportunity Scholars Program, or OSP (a TRIO program), and University 101. Conclusions: The results for high school performance and pre-nursing student outcomes suggested that this predictor may be useful for identifying at-risk students. First semester performance was strongly associated with retention and success, which illustrated the importance of students' first semester at college. UNIV 101 does not appear to significantly improve pre-nursing students' retention or success. Student participation in OSP activities was associated with retention and success. Contributions: Our research has successfully identified predictors associated with student retention and success and evaluated two interventions. As a result, we demonstrated the value of collecting and analyzing student data at an institution.