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The vital role of honeybees in pollination and their high rate of mortality in the last decade have raised concern among beekeepers and researchers alike. As such, robust and remote sensing of beehives has emerged as a potential tool to help monitor the health of honeybees. Over the last decade, several monitoring systems have been proposed, including those based on in-hive acoustics. Despite its popularity, existing audio-based systems do not take context into account (e.g., environmental noise factors), and thus the performance may be severely hampered when deployed. In this paper, we investigate the effect that three different environmental noise factors (i.e., nearby train rail squealing, beekeeper speech, and rain noise) can have on three acoustic features (i.e., spectrogram, mel frequency cepstral coefficients, and discrete wavelet coefficients) used in existing automated beehive monitoring systems. To this end, audio data were collected continuously over a period of three months (August, September, and October) in 2021 from 11 urban beehives located in downtown Montréal, Québec, Canada. A system based on these features and a convolutional neural network was developed to predict beehive strength, an indicator of the size of the colony. Results show the negative impact that environmental factors can have across all tested features, resulting in an increase of up to 355% in mean absolute prediction error when heavy rain was present. [ABSTRACT FROM AUTHOR]

Background: In 2020, the World Health Organization introduced cardiovascular diseases as the first cause of death. One of the common causes of this category of diseases is Myocardial Infarction. Myocardial Infarction is necrosis of the myocardium caused by an obstruction of the blood supply to the heart. The advancement of hybrid technologies such as machine learning and m-health provides a significant capacity for managing the treatment of this disease. Objective: In this study, we determine how m-health can benefit from machine learning to manage Myocardial Infarction. Material and Methods: This study is a rapid review. We applied MeSH Terms such as: Machine Learning, Telemedicine and Myocardial Infarction by Boolean operators in databases including: IEEE Xplore, ISI-web of science, Pub Med, Scopus, Science Direct and Google Scholar in recently five years. The obtained articles synthesized in accordance with the study goal. Results: Our findings showed that m-health used these models of machine learning to manage/monitor Myocardial Infarction: A) statistical learning models for processing and analyzing clinical data B) data mining algorithms for classifying clinical data and training the databases. Conclusion: The development of applications using machine learning on mobile health platforms warns more efficiently and effectively the prognosis of Myocardial Infarction and since it is accessible at any time and place, it leads to the realization of the goals of health justice in communities. [ABSTRACT FROM AUTHOR]

Supported molybdic acid on nano-Fe3O4@TiO2 (Fe3O4@TiO2@CH2)3OMoO3H) has been successfully prepared, characterized and applied as a catalyst for the synthesis of sulfonamide containing coumarin moieties. The prepared Fe3O4 nanoparticles by coprecipitation of Fe2+ and Fe3+ ions were treated with tetraethyl orthotitanate to obtain Fe3O4@TiO2. By anchoring 3-chloropropyltriethoxysilan on Fe3O4@TiO2 followed by reacting with molybdic acid, the desired catalyst was produced. The synthesized catalyst was characterized using XRD, SEM, EDS, FT-IR and VSM analysis. Fe3O4@TiO2@ CH2)3OMoO3H was used as a catalyst for the synthesis of sulfonamide containing coumarin moieties via a three-component reaction of aryl aldehydes, para-toluenesulfonamide and 4-hydroxycoumarin or 5,7-dihydroxy-4-methylcoumarin. The catalyst recovery test showed the catalyst is highly reusable without losing its activity. [ABSTRACT FROM AUTHOR]

Background: The current trend of heart transplantation in recent years has taken a quantum leap forward. We decided to look back at our experience in this center., Objectives: Here, we focus on the diagnostic pitfalls and challenges in these biopsies., Patients and Methods: Forty two patients based on the standard protocol of heart transplantation group, yielded 63 biopsy samples over a period of 33 months (April 2010 - December 2012). The mean age was 30.4 years (ranging from 16 to 58 years) with 51 males (81%) and 12 females (19%). All the patients were examined periodically and biopsy samples were taken from the right ventricular wall., Results: Rarely fewer than three pieces of myocardial samples were procured. Scar, adipose tissues and blood clots may be seen instead. Quilty effect (nodular endocardial lesions composed of inflammatory cell infiltrates) was seen in 8 cases (12.7%). Other findings not directly related to rejection including early ischemic injury, Quilty effect and post-transplant lymphoproliferative disorders (PTLD) were not encountered., Conclusions: Specimen inadequacy was not a major problem in our center. It poses a great limitation, because suboptimal specimens sometimes mislead the pathologist. Other findings especially Quilty effect were within the range defined for this finding.