Search

Abstract Leaf rust (LR) caused by Puccinia hordei is a serious disease of barley worldwide, causing significant yield losses and reduced grain quality. Discovery and incorporation of new sources of resistance from gene bank accessions into barley breeding programs is essential for the development of leaf rust resistant varieties. To identify Quantitative Trait Loci (QTL) conferring LR resistance in the two barley subsets, the Generation Challenge Program (GCP) reference set of 142 accessions and the leaf rust subset constructed using the Focused Identification of Germplasm Strategy (FIGS) of 76 barley accessions, were genotyped to conduct a genome-wide association study (GWAS). The results revealed a total of 59 QTL in the 218 accessions phenotyped against barley leaf rust at the seedling stage using two P. hordei isolates (ISO-SAT and ISO-MRC), and at the adult plant stage in four environments in Morocco. Out of these 59 QTL, 10 QTL were associated with the seedling resistance (SR) and 49 QTL were associated with the adult plant resistance (APR). Four QTL showed stable effects in at least two environments for APR, whereas two common QTL associated with SR and APR were detected on chromosomes 2H and 7H. Furthermore, 39 QTL identified in this study were potentially novel. Interestingly, the sequences of 27 SNP markers encoded the candidate genes (CGs) with predicted protein functions in plant disease resistance. These results will provide new perspectives on the diversity of leaf rust resistance loci for fine mapping, isolation of resistance genes, and for marker-assisted selection for the LR resistance in barley breeding programs worldwide.

Abstract Efficiency of evidence accumulation (EEA), an individual’s ability to selectively gather goal-relevant information to make adaptive choices, is thought to be a key neurocomputational mechanism associated with cognitive functioning and transdiagnostic risk for psychopathology. However, the neural basis of individual differences in EEA is poorly understood, especially regarding the role of largescale brain network dynamics. We leverage data from 5198 participants from the Human Connectome Project and Adolescent Brain Cognitive Development Study to demonstrate a strong association between EEA and flexible adaptation to cognitive demand in the “task-positive” frontoparietal and dorsal attention networks. Notably, individuals with higher EEA displayed divergent task-positive network activation across n-back task conditions: higher activation under high cognitive demand (2-back) and lower activation under low demand (0-back). These findings suggest that brain networks’ flexible adaptation to cognitive demands is a key neural underpinning of EEA.

This epidemiological model forecasted reductions in recurrences and recurrence treatment cost savings with adjuvant atezolizumab vs best supportive care among Canadians with stage II-IIIA non-small cell lung cancer (NSCLC) at national and provincial levels. The population had resected, programmed cell death 1 ligand 1 (PD-L1)–high (≥50%), EGFR−, ALK−, stage II-IIIA NSCLC eligible for adjuvant treatment. Patients with recurrence or death and the costs of treating recurrences were estimated for those receiving adjuvant atezolizumab or best supportive care each year (2024–2034). Proportions of patients expected to be event free up to 10 years after treatment initiation were extrapolated with parametric survival analyses. In the base case analysis, 240 fewer recurrences were estimated to occur over 10 years (2024–2034) with adjuvant atezolizumab vs best supportive care across Canada, with 136 (57%) and 104 (43%) fewer locoregional and metastatic recurrences, respectively. Projected costs of treated recurrences were CAD 33.2 million less over 10 years with adjuvant atezolizumab at a national level (adjuvant atezolizumab, CAD 135.8 million; best supportive care, CAD 169.0 million). This model predicts a considerable long-term reduction in recurrences and substantial treatment cost savings with adjuvant atezolizumab vs best supportive care for patients with PD-L1–high early-stage NSCLC in Canada.

Acoustic signal analysis has been employed in various medical devices. However, studies involving cough sound analysis to screen the potential pulmonary tuberculosis (PTB) suspects are very few. The main objective of this cross-sectional validation study was to develop and validate the Swaasa AI platform to screen and prioritize at risk patients for PTB based on the signature cough sound as well as symptomatic information provided by the subjects. The voluntary cough sound data was collected at Andhra Medical College-India. An Algorithm based on multimodal convolutional neural network architecture and feedforward artificial neural network (tabular features) was built and validated on a total of 567 subjects, comprising 278 positive and 289 negative PTB cases. The output from these two models was combined to detect the likely presence (positive cases) of PTB. In the clinical validation phase, the AI-model was found to be 86.82% accurate in detecting the likely presence of PTB with 90.36% sensitivity and 84.67% specificity. The pilot testing of model was conducted at a peripheral health care centre, RHC Simhachalam-India on 65 presumptive PTB cases. Out of which, 15 subjects truly turned out to be PTB positive with a positive predictive value of 75%. The validation results obtained from the model are quite encouraging. This platform has the potential to fulfil the unmet need of a cost-effective PTB screening method. It works remotely, presents instantaneous results, and does not require a highly trained operator. Therefore, it could be implemented in various inaccessible, resource-poor parts of the world.

Objective: Although it is well known that exercise has many benefits, many people encounter barriers to following a regular fitness regime. The Exercise Benefits and Barriers Scale (EBBS) had been developed in the English language to understand the perceptions of people about the benefits and barriers of exercise participation. The main intent of the current study was to translate the EBBS into the Telugu language and assess the psychometric properties of cross-culturally adapting the scale in people with Type 2 diabetes in Telangana State of India. Material and Methods: According to the translation guidelines, forward and backward translation was carried out and the final Telugu version of the questionnaire was prepared and distributed. A total of 103 responses were received from Type 2 diabetes patients in Telugu-speaking states of India from March 2022 to June 2022. Reliability and validity were assessed using Intra class Correlation Coefficient and Cronbach’s alpha was calculated to evaluate the internal consistency. Results: The reliability of the complete EBBS and its subscales were significantly high, with Cronbach’s alpha of more than 0.7 (0.946 for the Exercise Benefits subscale and 0.879 for the Exercise Barriers subscale) for all items in the Telugu version showing good internal consistency. Conclusion: The results of the translated and cross-culturally adapted Telugu version of EBBS demonstrated satisfactory reliability and validity in a Type 2 Diabetes population, and therefore can be used in the Telugu-speaking population.

Les nématodes à kystes des céréales (Heterodera avenae) constituent une menace importante pour la production de blé en Afrique du Nord. Cette étude a évalué la résistance de 100 lignées élites de blé (51 lignées de blé dur et 49 lignées de blé tendre) du Maroc contre H. avenae. En utilisant une seule population du pathogène de la région de Zaers, la résistance a été évaluée en fonction des facteurs de reproduction des nématodes, classés en cinq catégories allant de résistant à hautement sensible. Trois lignées de blé dur (L7, L19 et L41) et six lignées de blé tendre (L8, L12, L14, L29, L38 et L44) ont montré une résistance modérée. Ces résultats fournissent des informations précieuses sur les sources potentielles de résistance au sein du germoplasme de blé marocain, qui peuvent contribuer au développement de variétés améliorées pour une production durable des céréales dans la région. Des recherches supplémentaires sont nécessaires pour identifier et utiliser les gènes et mieux comprendre les mécanismes spécifiques responsables de la résistance observée dans ces lignées prometteuses de blé.

The prognosis of early non-small-cell lung cancer (eNSCLC) remains poor. An understanding of current therapies and outcomes can provide insights into how novel therapies can be integrated into clinics. We conducted a large, retrospective, population-based cohort study of patients with de novo eNSCLC (stages IB, IIA, IIB, and IIIA) diagnosed in Alberta, Canada, between 2010 and 2019. The primary objectives were to describe treatment patterns and survival outcomes among patients with eNSCLC. A total of 5126 patients with eNSCLC were included. A total of 45.3% of patients were referred to a medical oncologist, ranging from 23.7% in stage IB to 58.3% in IIIA. A total of 23.6% of patients initiated systemic therapy (ST), ranging from 3.5% in stage IB to 38.5% in IIIA. For stage IIB and IIIA individuals who received surgery, adjuvant ST was associated with a decreased likelihood of death (hazard ratios (HR) of 0.77 (95% CI: 0.56–1.07) and 0.69 (95% CI: 0.54–0.89), respectively). In a Canadian real-world setting, stage IIB and IIIA patients who received adjuvant ST tended to have better survival than patients who did not, but future studies that provide adjustment of additional confounders are warranted. Examining referral pathways that account for disparities based on age, sex, and comorbidities in the real world would also provide further insights.

Within the European Union (EU), new plant varieties to be included in the Common catalog of a member state have to be registered on the national list after plant variety testing processes to establish whether the candidate variety is distinguishable, uniform, and stable (DUS) and meets the cultivation or use value requirement (VCU). Technical development, climate change, and changing consumer needs, including the detection of GMOs, necessitate the innovation of plant variety testing methods. In our study, we assessed new characters, testing methods, and inclusion of additional data for the potential to benefit the DUS and VCU protocols. To achieve our goal, we asked experts to fill in questionnaires for the DUS and VCU methods currently used for a selection of common crops, including potato, maize, lentil, oilseed rape, and perennial grass. Within the EU-funded “InnoVar” project, partners sent out questionnaires to 19 European Countries and to 3 countries outside Europe. Surveys were aimed at analyzing the strengths, weaknesses, opportunities, and threats (SWOT) of the current methods. With their help, it is possible to look for a new direction, opportunity, and strategy to incorporate, together with the innovative new techniques, into the development of the new methods. Our study demonstrated that the SWOT analysis could be used to achieve the set goals. Results obtained after evaluation of surveys confirmed that introduction of new characters such as cold tolerance, nitrogen and water efficiency, etc. has become necessary, as has the inclusion of new test methods (molecular markers, precision techniques, organic farming). The development of high-yielding, disease and/or pest-resistant plant varieties with good adaptability and the accurate evaluation of genotypes play a crucial role in ensuring that farmers can access high-performing plant varieties and contribute to sustainable food production.

Faults in photovoltaic (PV) modules may occur due to various environmental and physical factors. To prevent faults and minimize investment losses, fault diagnosis is crucial to ensure uninterrupted power production, extended operational lifespan, and a high level of safety in PV modules. Recent advancements in inspection techniques and instrumentation have significantly reduced the cost and time required for inspections. A novel stacking-based ensemble approach was performed in the present study for the accurate classification of PV module visible faults. The present study utilizes AlexNet (a pre-trained network) to extract image features from the aerial images of PV modules with the aid of MATLAB software. Furthermore, J48 algorithm was applied to perform the feature selection task to determine the most relevant features. The features derived as output from the J48 algorithm were passed onto train eight base classifiers namely, Naïve Bayes, logistic regression (LR), J48, random forest (RF), multilayer perceptron (MLP), logistic model tree (LMT), support vector machines (SVM) and k-nearest neighbors (kNN). The best performing five classifiers on the front run with higher classification accuracies were selected to formulate three categories of stacking ensemble groups as follows: (i) three-class ensemble (SVM, kNN, and LMT), (ii) four-class ensemble (SVM, kNN, LMT, and RF), and (iii) five-class ensemble (SVM, kNN, LMT, RF, and MLP). A comparison in the performance of the aforementioned stacked ensembles was evaluated with different meta classifiers. The obtained results infer that the four-class stacking ensemble model (SVM, kNN, LMT, and RF) with RF as the predictor achieved the highest possible classification accuracy of 99.04%.

Abstract The Advent of Artificial Intelligence (AI) has led to the use of auditory data for detecting various diseases, including COVID-19. SARS-CoV-2 infection has claimed more than six million lives to date and therefore, needs a robust screening technique to control the disease spread. In the present study we created and validated the Swaasa AI platform, which uses the signature cough sound and symptoms presented by patients to screen and prioritize COVID-19 patients. We collected cough data from 234 COVID-19 suspects to validate our Convolutional Neural Network (CNN) architecture and Feedforward Artificial Neural Network (FFANN) (tabular features) based algorithm. The final output from both models was combined to predict the likelihood of having the disease. During the clinical validation phase, our model showed a 75.54% accuracy rate in detecting the likely presence of COVID-19, with 95.45% sensitivity and 73.46% specificity. We conducted pilot testing on 183 presumptive COVID subjects, of which 58 were truly COVID-19 positive, resulting in a Positive Predictive Value of 70.73%. Due to the high cost and technical expertise required for currently available rapid screening methods, there is a need for a cost-effective and remote monitoring tool that can serve as a preliminary screening method for potential COVID-19 subjects. Therefore, Swaasa would be highly beneficial in detecting the disease and could have a significant impact in reducing its spread.

Abstract Background Approximately ten percent of US military veterans suffer from posttraumatic stress disorder (PTSD). Cognitive processing therapy (CPT) is a highly effective, evidence-based, first-line treatment for PTSD that has been widely adopted by the Department of Veterans Affairs (VA). CPT consists of discrete therapeutic components delivered across 12 sessions, but most veterans (up to 70%) never reach completion, and those who discontinue therapy receive only four sessions on average. Unfortunately, veterans who drop out prematurely may never receive the most effective components of CPT. Thus, there is an urgent need to use empirical approaches to identify the most effective components of CPT so CPT can be adapted into a briefer format. Methods The multiphase optimization strategy (MOST) is an innovative, engineering-inspired framework that uses an optimization trial to assess the performance of individual intervention components within a multicomponent intervention such as CPT. Here we use a fractional factorial optimization trial to identify and retain the most effective intervention components to form a refined, abbreviated CPT intervention package. Specifically, we used a 16-condition fractional factorial experiment with 270 veterans (N = 270) at three VA Medical Centers to test the effectiveness of each of the five CPT components and each two-way interaction between components. This factorial design will identify which CPT components contribute meaningfully to a reduction in PTSD symptoms, as measured by PTSD symptom reduction on the Clinician-Administered PTSD Scale for DSM-5, across 6 months of follow-up. It will also identify mediators and moderators of component effectiveness. Discussion There is an urgent need to adapt CPT into a briefer format using empirical approaches to identify its most effective components. A brief format of CPT may reduce attrition and improve efficiency, enabling providers to treat more patients with PTSD. The refined intervention package will be evaluated in a future large-scale, fully-powered effectiveness trial. Pending demonstration of effectiveness, the refined intervention can be disseminated through the VA CPT training program. Trial registration ClinicalTrials.gov NCT05220137. Registration date: January 21, 2022.

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making.

Abstract Childhood attention-deficit/hyperactivity disorder (ADHD) symptoms are believed to result from disrupted neurocognitive development. However, evidence for the clinical and predictive value of neurocognitive assessments in this context has been mixed, and there have been no large-scale efforts to quantify their potential for use in generalizable models that predict individuals’ ADHD symptoms in new data. Using data drawn from the Adolescent Brain Cognitive Development Study (ABCD), a consortium that recruited a diverse sample of over 10,000 youth (ages 9–10 at baseline) across 21 U.S. sites, we develop and test cross-validated machine learning models for predicting youths’ ADHD symptoms using neurocognitive abilities, demographics, and child and family characteristics. Models used baseline demographic and biometric measures, geocoded neighborhood data, youth reports of child and family characteristics, and neurocognitive tests to predict parent- and teacher-reported ADHD symptoms at the 1-year and 2-year follow-up time points. Predictive models explained 15–20% of the variance in 1-year ADHD symptoms for ABCD Study sites that were left out of the model-fitting process and 12–13% of the variance in 2-year ADHD symptoms. Models displayed high generalizability across study sites and trivial loss of predictive power when transferred from training data to left-out data. Features from multiple domains contributed meaningfully to prediction, including neurocognition, sex, self-reported impulsivity, parental monitoring, and screen time. This work quantifies the information value of neurocognitive abilities and other child characteristics for predicting ADHD symptoms and provides a foundational method for predicting individual youths’ symptoms in new data across contexts.

Abstract Background Septoria tritici blotch (STB) remains a significant obstacle to durum wheat cultivation on a global scale. This disease remains a challenge for farmers, researchers, and breeders, who are collectively dedicated to reduce its damage and improve wheat resistance. Tunisian durum wheat landraces have been recognized as valuable genetic ressources that exhibit resistance to biotic and abiotic stresses and therefore play a crucial role in breeding program aimed at creating new wheat varieties resistant to fungal diseases as STB, as well as adapted to climate change constraints. Results A total of 366 local durum wheat accessions were assessed for resistance to two virulent Tunisian isolates of Zymoseptoria tritici Tun06 and TM220 under field conditions. Population structure analysis of the durum wheat accessions, performed with 286 polymorphic SNPs (PIC > 0.3) covering the entire genome, identified three genetic subpopulations (GS1, GS2 and GS3) with 22% of admixed genotypes. Interestingly, all of the resistant genotypes were among GS2 or admixed with GS2. Conclusions This study revealed the population structure and the genetic distribution of the resistance to Z. tritici in the Tunisian durum wheat landraces. Accessions grouping pattern reflected the geographical origins of the landraces. We suggested that GS2 accessions were mostly derived from eastern Mediterranean populations, unlike GS1 and GS3 that originated from the west. Resistant GS2 accessions belonged to landraces Taganrog, Sbei glabre, Richi, Mekki, Badri, Jneh Khotifa and Azizi. Furthermore, we suggested that admixture contributed to transmit STB resistance from GS2 resistant landraces to initially susceptible landraces such as Mahmoudi (GS1), but also resulted in the loss of resistance in the case of GS2 susceptible Azizi and Jneh Khotifa accessions.

Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from resting-state functional magnetic resonance imaging (rs-fMRI) recordings of subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain CFC based on directed graphical modeling in a time series setting. We compared the CFC outcome of TPC with that of other related approaches in the literature. Then, we used the CFC outcomes of TPC and performed an exploratory analysis of the difference in strengths of CFC edges between Alzheimer's and cognitively normal groups, based on edge-wise p-values obtained by Welch's t-test. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.

Family poverty has been associated with altered brain structure, function, and connectivity in youth. However, few studies have examined how disadvantage within the broader neighborhood may influence functional brain network organization. The present study leveraged a longitudinal community sample of 538 twins living in low-income neighborhoods to evaluate the prospective association between exposure to neighborhood poverty during childhood (6–10 y) with functional network architecture during adolescence (8–19 y). Using resting-state and task-based fMRI, we generated two latent measures that captured intrinsic brain organization across the whole-brain and network levels – network segregation and network segregation-integration balance. While age was positively associated with network segregation and network balance overall across the sample, these associations were moderated by exposure to neighborhood poverty. Specifically, these positive associations were observed only in youth from more, but not less, disadvantaged neighborhoods. Moreover, greater exposure to neighborhood poverty predicted reduced network segregation and network balance in early, but not middle or late, adolescence. These effects were detected both across the whole-brain system as well as specific functional networks, including fronto-parietal, default mode, salience, and subcortical systems. These findings indicate that where children live may exert long-reaching effects on the organization and development of the adolescent brain.

Objective: The ability to choose one's work-location can influence burnout from employment. We sought to evaluate potential associations between autonomy in work-location decision and burnout. Methods: We used 2020 Veterans Health Administration Annual All Employee Survey data from behavioral health providers at 129 medical facilities. Based on responses to items in a COVID-19 module related to teleworking frequency and reasons for not teleworking both pre- and during COVID-19, we generated six groups representing potentially differing levels of work-location decision autonomy. We hypothesized that greater autonomy is associated with less burnout. Results: Psychologists reported highest burnout (40.1%, 1801/4494), followed by psychiatrists (35.6%, 703/1976) and social workers (31.0%, 3421/11,051). Adjusting for covariates including workload, providers newly approved for telework during COVID-19 showed 1.28 (95% CI=1.03–1.59), 1.71 (1.25–2.35), and 1.35 (0.86–2.12) times higher odds of burnout among social workers, psychologists, and psychiatrists, respectively, compared to providers chose not to telework during COVID-19. Conclusions: Providers who chose to keep coming into work in person reported less burnout than providers who previously could not telework and are teleworking during the pandemic. Greater autonomy in work-location decision might have greater importance in reducing burnout in behavioral health providers than having the ability to telework per se.

Public health measures necessary to counteract the coronavirus disease 2019 (COVID-19) pandemic have resulted in dramatic changes in the physical and social environments within which children grow and develop. As our understanding of the pathways for viral exposure and associated health outcomes in children evolves, it is critical to consider how changes in the social, cultural, economic, and physical environments resulting from the pandemic could affect the development of children. This review article considers the environments and settings that create the backdrop for children's health in the United States during the COVID-19 pandemic, including current threats to child development that stem from: A) change in exposures to environmental contaminants such as heavy metals, pesticides, disinfectants, air pollution and the built environment; B) changes in food environments resulting from adverse economic repercussion of the pandemic and limited reach of existing safety nets; C) limited access to children's educational and developmental resources; D) changes in the social environments at the individual and household levels, and their interplay with family stressors and mental health; E) social injustice and racism. The environmental changes due to COVID-19 are overlaid onto existing environmental and social disparities. This results in disproportionate effects among children in low-income settings and among populations experiencing the effects of structural racism. This article draws attention to many environments that should be considered in current and future policy responses to protect children's health amid pandemics.