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A combination of calcium hydroxide and sodium hydrosulphate controls pathogens causing environmental mastitis in recycled manure solids
(2024-10-08) Praveen, Selladurai; Kataktalware, Mukund A.; Meena, Priyanka; Lavanya, Maharajan; Patoliya, Priyanka; Jeyakumar, Sakthivel; Ravindra, Menon R.; Chauhan, Mamta; Ramesha, K. P.; Devi, G. L.; Kastelic, John P.; Dhali, Arindam
Abstract Recycled manure solids (RMS) are dried cow dung processed using a manure dewatering machine and subsequently sun-dried to ~ 20% moisture. Benefits of RMS include abundant availability, low cost, and eco-friendliness, but its use as bedding material for cows is hindered by a moisture content that promotes microbial growth. This in vitro study evaluated impacts of calcium hydroxide (CH; 5 and 7.5%) and sodium hydrosulphate (SHS; 6 and 8%), independently and in combinations, at various depths of RMS, on physicochemical and microbial properties. The CH-treated groups had increased pH and reduced moisture on Day 0. Incorporating 7.5% CH + 6% SHS at 15–20 cm, and 7.5% CH + 8% SHS at all depths, effectively suppressed Escherichia coli and Klebsiella spp. Furthermore, a combination of 7.5% CH + 8% SHS at 20 cm inhibited coliform growth, whereas 7.5% CH with 6% SHS inhibited Streptococcus spp. In conclusion, a combination of 7.5% CH with either 6 or 8% SHS at a depth of 15 cm in RMS was particularly effective in controlling environmental mastitis-causing pathogens, specifically E. coli and Klebsiella spp. Graphical Abstract
Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma
(2024-10-10) Manoharan, Varsha T.; Abdelkareem, Aly; Gill, Gurveer; Brown, Samuel; Gillmor, Aaron; Hall, Courtney; Seo, Heewon; Narta, Kiran; Grewal, Sean; Dang, Ngoc H.; Ahn, Bo Y.; Osz, Kata; Lun, Xueqing; Mah, Laura; Zemp, Franz; Mahoney, Douglas; Senger, Donna L.; Chan, Jennifer A.; Morrissy, A. S.
Abstract Background Diffuse invasion of glioblastoma cells through normal brain tissue is a key contributor to tumor aggressiveness, resistance to conventional therapies, and dismal prognosis in patients. A deeper understanding of how components of the tumor microenvironment (TME) contribute to overall tumor organization and to programs of invasion may reveal opportunities for improved therapeutic strategies. Results Towards this goal, we apply a novel computational workflow to a spatiotemporally profiled GBM xenograft cohort, leveraging the ability to distinguish human tumor from mouse TME to overcome previous limitations in the analysis of diffuse invasion. Our analytic approach, based on unsupervised deconvolution, performs reference-free discovery of cell types and cell activities within the complete GBM ecosystem. We present a comprehensive catalogue of 15 tumor cell programs set within the spatiotemporal context of 90 mouse brain and TME cell types, cell activities, and anatomic structures. Distinct tumor programs related to invasion align with routes of perivascular, white matter, and parenchymal invasion. Furthermore, sub-modules of genes serving as program network hubs are highly prognostic in GBM patients. Conclusion The compendium of programs presented here provides a basis for rational targeting of tumor and/or TME components. We anticipate that our approach will facilitate an ecosystem-level understanding of the immediate and long-term consequences of such perturbations, including the identification of compensatory programs that will inform improved combinatorial therapies.
A randomized controlled trial of a “Small Changes” behavioral weight loss treatment delivered in cardiac rehabilitation for patients with atrial fibrillation and obesity: study protocol for the BE-WEL in CR-AF study
(2024-10-11) Williamson, Tamara M.; Rouleau, Codie R.; Wilton, Stephen B.; Valdarchi, A. B.; Moran, Chelsea; Patel, Stuti; Lutes, Lesley; Aggarwal, Sandeep G.; Arena, Ross; Campbell, Tavis S.
Abstract Background Atrial fibrillation (AF) represents a global epidemic. Although international AF practice guidelines indicate weight loss for patients with AF and comorbid obesity (BMI ≥ 30 kg/m2) to alleviate symptom burden and improve prognosis, few cardiac rehabilitation (CR) programs include targeted weight loss treatment. Aims This RCT protocol will evaluate the efficacy of a “Small Changes” behavioral weight loss treatment (BWLT) to produce clinically relevant (≥ 10%) weight loss among patients with AF and obesity undergoing CR, relative to CR alone. Secondary aims are to establish efficacy of CR + BWLT for improving AF symptoms, AF risk factors, and health-related quality of life. Methods Adults (18 +) with AF and obesity will be recruited and randomized to receive CR + BWLT (intervention) or CR-only (control). Controls will receive CR consisting of supervised exercise and risk factor self-management for 12 weeks. The intervention group will receive CR plus BWLT (12 weekly, group-based virtual sessions, followed by 12 weeks of follow-up support). Weight and AF-risk factors will be assessed at pre-randomization, 12 weeks, 24 weeks, and 52 weeks. AF burden will be assessed using 30-s ECGs recorded bidaily and with AF symptoms. The primary endpoint of weight loss will be calculated from baseline to 52 weeks as a percentage of starting weight. Intention-to-treat analyses will compare the proportion in each group achieving ≥ 10% weight loss. Assuming success rates of 5% and 30% among controls and intervention groups, respectively, and a 30% loss to follow-up, 120 patients (60 per group) will provide 80% power to detect a difference using a two-sided independent test of proportions (alpha = 5%). Impact This clinical trial will be the first to demonstrate that adding BWLT to CR promotes clinically meaningful weight loss among patients with AF and comorbid obesity. Findings will inform design and execution of a large efficacy trial of long-term (e.g., 5-year) clinical endpoints (e.g., AF severity, mortality). Implementing weight control interventions designed to target the AF substrate in CR could dramatically reduce morbidity and enhance quality of life among patients living with AF in Canada. Trial registration ClinicalTrials.gov registration number: NCT05600829. Registered October 31, 2022.
Playful(l) Literacies in a First Grade Classroom
(2024-03-27) Lenters, Kimberly; Mosher, Ronna
This video describes and animates a Canadian grade school teacher's approach to working with children's play in intentional and purposeful ways in her first grade classroom. The teacher was a part of the Playful(l) Literacies research project, funded by SSHRC and by the Canada Research Chairs program.
Federated Learning Model Aggregation in Heterogeneous Aerial and Space Networks
(2024-10-09) Dong, Fan; Drew, Steve; Leung, Henry; Drew, Steve; Leung, Henry; Ye, Qiang; Wang, Mea
Federated learning offers a promising solution for overcoming the challenges of networking and data privacy in aerial and space networks by harnessing large-scale private edge data and computing resources from drones, balloons, and satellites. Although existing research has extensively explored optimizing the learning process, improving computing efficiency, and reducing communication overhead, statistical heterogeneity remains a substantial challenge for federated learning optimization. While state-of-the-art algorithms have made progress, they often overlook diversity heterogeneity and fail to significantly improve performance in high-degree label heterogeneity conditions. In this thesis, statistical heterogeneity is further dissected into two categories: diversity heterogeneity and label heterogeneity, allowing for a more nuanced analysis. It also emphasizes the importance of addressing both diversity heterogeneity and high-degree label heterogeneity in aerial and space network applications. A theoretical analysis is provided to guide optimization in these two challenging scenarios. To tackle diversity heterogeneity, the WeiAvgCS algorithm is introduced to accelerate federated learning convergence. This algorithm employs weighted aggregation and client selection based on an estimated diversity measure, termed projection, enabling WeiAvgCS to outperform other benchmarks without compromising privacy. For high-degree label heterogeneity, the FedBalance algorithm is proposed, utilizing the label distribution information of each client. A novel metric, termed relative scarcity, is introduced to determine the aggregation weights assigned to clients. During the training process, fully homomorphic encryption is employed to protect clients’ label distributions. Additionally, two communication protocols are designed to facilitate training across different scenarios. Extensive experiments were conducted, demonstrating the effectiveness of WeiAvgCS and FedBalance in addressing the research gaps in diversity heterogeneity and high-degree label heterogeneity.