Spearfishing-induced behavioral changes of an unharvested species inside and outside a marine protected area.

By prohibiting fishing, marine protected areas (MPAs) provide a refuge for harvested species. Humans are often perceived as predators by prey and therefore respond fearfully to humans. Thus, fish responses to humans inside and outside of an MPA can provide insights into their perception of humans as a predatory threat. Previous studies have found differences in the distance that harvested species of fish initiate flight (flight initiation distance-FID) from humans inside and outside an MPA, but less is known about unharvested species. We focused on whether the lined bristletooth Ctenochaetus striatus, an unharvested surgeonfish, can discriminate between a snorkeler and a snorkeler with a spear gun inside and outside of a no-take MPA in Mo'orea, French Polynesia. Additionally, we incorporated starting distance (the distance between the person and prey at the start of an experimental approach), a variable that has been found to be important in assessing prey escape decisions in terrestrial species, but that has not been extensively studied in aquatic systems. Lined bristletooth FID was significantly greater in the presence of a spear gun and varied depending on if the spear gun encounter was inside or outside of the MPA. These results imply a degree of sophistication of fish antipredator behavior, generate questions as to how a nontargeted species of fish could acquire fear of humans, and demonstrate that behavioral surveys can provide insights about antipredator behavior

Niveau de préparation des diplômés à une carrière en médecine interne générale avant et après la reconnaissance de la surspécialité : objectifs atteints et besoins évolutifs dans le programme d’études

Background: A survey of General Internal Medicine (GIM) graduates published in 2006 revealed large training gaps that informed the development of the first national GIM objectives of training in 2010. The first recognized GIM certification examination was written by candidates in 2014. The landscape is again changing with the introduction in 2019 of competency-by-design (CBD) to GIM training. This study aims to examine pre-existing and emerging training gaps with standardization of GIM curricula and identify new training needs to inform CBD curricula.  Methods: GIM graduates from all 16 Canadian programs from 2014 -2019 were emailed a survey modeled after the original study published in 2006. Graduates were asked about their preparedness and importance ratings for various elements of practice. Results: Many of the previously identified gaps (difference between importance and preparedness ratings) have been resolved in specific clinical areas (obstetrical and perioperative medicine) and skills (exercise stress testing) although some still require ongoing work in areas such as substance use disorders. Importantly, gaps still exist in preparedness for some intrinsic roles (e.g. managerial skills). Conclusions:  The development of a national GIM curriculum has helped close some educational gaps but some still exist. Our study provides data needed to meet the evolving needs of our graduates.Contexte : Une enquête auprès des diplômés en médecine interne générale (MIG), publiée en 2006, a révélé d’importantes lacunes dans leur formation, menant à l’élaboration des premiers objectifs nationaux de formation en MIG en 2010. Le premier examen de certification en MIG a été organisé en 2014. Le paysage est à nouveau en train de changer avec l’introduction en 2019 de la compétence par conception (CPC) dans la formation en MIG. Cette étude vise à examiner les lacunes de formation préexistantes et émergentes avec la normalisation de la formation en MIG et à identifier les nouveaux besoins de formation pour éclairer la définition des programmes de formation selon l’approche fondée sur les compétences.  Méthodes : Les diplômés des 16 programmes canadiens en MIG entre 2014 et 2019 ont reçu par courriel un sondage inspiré de l’étude originelle publiée en 2006. Les diplômés ont été interrogés sur leur état de préparation et sur l’importance qu’ils accordaient à divers éléments de la pratique. Résultats : Un grand nombre des lacunes décelées précédemment (différence entre les cotes d’importance et de préparation) ont été comblées dans des domaines cliniques spécifiques (médecine obstétrique et périopératoire) et par rapport à des compétences spécifiques (tests de stress à l’effort); dans certains domaines, comme les troubles liés à l’utilisation de substances psychoactives, les efforts doivent être poursuivis. Il est important de noter que des lacunes subsistent dans la préparation à certains rôles intrinsèques (par exemple, les compétences de gestionnaire). Conclusion : L’élaboration d’un programme national de formation en MIG a permis de combler certaines lacunes en matière de formation, mais des carences subsistent. Notre étude fournit les données nécessaires pour répondre aux besoins évolutifs de nos diplômés

Une histoire méconnue : celle des immigrants qui se marient dans la vallée du Saint-Laurent pendant la première moitié du XVIIIe siècle

Cet article a pour sujet les immigrants qui se marient dans la vallée du Saint-Laurent entre 1700 et 1750, période où l’immigration a toujours été décrite comme quasi-inexistante. Or, ils ne sont pas quelques centaines mais 2 227 hommes et femmes. Le portrait de ces immigrants et leurs parcours dans la colonie montrent des différences avec ceux des immigrants du XVIIe siècle. Ces différences concernent leurs origines géographiques, sociales, professionnelles mais aussi leur mobilité post-matrimoniale.This article deals with immigrants who married in the St. Lawrence valley between 1700 and 1750, when immigration was described as non-existent. Yet, they were 2 227 men and women, not just a few hundred as often suggested. Their life and their fate in the colony show some differences with the 17th-century immigrants. These differences concern their geographical, social and professional origins, but also their post-matrimonial mobility

Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches

Deep learning plays an important role in modern agriculture, especially in plant pathology using leaf images where convolutional neural networks (CNN) are attracting a lot of attention. While numerous reviews have explored the applications of deep learning within this research domain, there remains a notable absence of an empirical study to offer insightful comparisons due to the employment of varied datasets in the evaluation. Furthermore, a majority of these approaches tend to address the problem as a singular prediction task, overlooking the multifaceted nature of predicting various aspects of plant species and disease types. Lastly, there is an evident need for a more profound consideration of the semantic relationships that underlie plant species and disease types. In this paper, we start our study by surveying current deep learning approaches for plant identification and disease classification. We categorise the approaches into multi-model, multi-label, multi-output, and multi-task, in which different backbone CNNs can be employed. Furthermore, based on the survey of existing approaches in plant pathology and the study of available approaches in machine learning, we propose a new model named Generalised Stacking Multi-output CNN (GSMo-CNN). To investigate the effectiveness of different backbone CNNs and learning approaches, we conduct an intensive experiment on three benchmark datasets Plant Village, Plant Leaves, and PlantDoc. The experimental results demonstrate that InceptionV3 can be a good choice for a backbone CNN as its performance is better than AlexNet, VGG16, ResNet101, EfficientNet, MobileNet, and a custom CNN developed by us. Interestingly, empirical results support the hypothesis that using a single model can be comparable or better than using two models. Finally, we show that the proposed GSMo-CNN achieves state-of-the-art performance on three benchmark datasets.Comment: Jianping and Son are joint first authors (equal contribution

Machine Learning for Leaf Disease Classification: Data, Techniques and Applications

The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular

Mesenchymal Stem Cell Use in the Treatment of Osteoarthritis: A Literature Review

Mesenchymal stem cell (MSC) therapies have been growing in popularity in research due to their anti-inflammatory, immunomodulatory, and regenerative properties. Many ongoing clinical trials are investigating the safety and efficacy of MSC therapies to treat osteoarthritis, also known as “wear and tear” arthritis. As the average life expectancy increases, with age people are more prone to developing this disease, therefore, increasing its prevalence. This condition is progressive and will lead to functional decline, decreased quality of life, and increased medical costs. Our focus is to discuss the efficacy of mesenchymal stem cell injections in alleviating pain, improving functionality, and slowing the disease progression of osteoarthritis in adults. We systematically reviewed studies through multiple databases, including PubMed, ScienceDirect, AccessMedicine, and Iceberg using the search terms mesenchymal stem cells, osteoarthritis, stem cell therapy, and degenerative joint disease. We limited searches to 2018 and newer, studies in English, and human trials. A total of 20 studies that met the criteria out of 65 full-text studies were included in this review. Clinical outcomes such as pain, functionality, and tissue regeneration were assessed using WOMAC, KOOS, and other validated clinical outcome scales, and resonance imaging were used for disease progression rating. Studies reviewing mesenchymal stem cell injections in arthritic joints have shown positive clinical outcomes with results showing pain level, joint function and regeneration. To realize stem cell injections outside of studies, long-term and larger-scale randomized clinical trials are required to strengthen the interpretations and validity of current studies

Abstract Competition

Articlehttp://deepblue.lib.umich.edu/bitstream/2027.42/97009/1/UMURJ-Issue09_2012-StudentAbstractCompetition.pd

Naturally Segregating Variation at Ugt86Dd Contributes to Nicotine Resistance in Drosophila melanogaster

Identifying the sequence polymorphisms underlying complex trait variation is a key goal of genetics research, since knowing the precise causative molecular events allows insight into the pathways governing trait variation. Genetic analysis of complex traits in model systems regularly starts by constructing QTL maps, but generally fails to identify causative sequence polymorphisms. Previously we mapped a series of QTL contributing to resistance to nicotine in a Drosophila melanogaster multiparental mapping resource and here use a battery of functional tests to resolve QTL to the molecular level. One large-effect QTL resided over a cluster of UDP-glucuronosyltransferases, and quantitative complementation tests using deficiencies eliminating subsets of these detoxification genes revealed allelic variation impacting resistance. RNAseq showed that Ugt86Dd had significantly higher expression in genotypes that are more resistant to nicotine, and anterior midgut-specific RNA interference (RNAi) of this gene reduced resistance. We discovered a segregating 22-bp frameshift deletion in Ugt86Dd, and accounting for the InDel during mapping largely eliminates the QTL, implying the event explains the bulk of the effect of the mapped locus. CRISPR/Cas9 editing of a relatively resistant genotype to generate lesions in Ugt86Dd that recapitulate the naturally occurring putative loss-of-function allele, leads to a large reduction in resistance. Despite this major effect of the deletion, the allele appears to be very rare in wild-caught populations and likely explains only a small fraction of the natural variation for the trait. Nonetheless, this putatively causative coding InDel can be a launchpad for future mechanistic exploration of xenobiotic detoxification

University of Michigan Undergraduate Research Journal, Issue 9, Winter 2012

Articlehttp://deepblue.lib.umich.edu/bitstream/2027.42/97010/1/UMURJ-Issue09_2012.pd