AUTOMATING THE PROCESS OF PREPARING THE SCHOOL TIMETABLE

This paper presents a development that optimizes the process of educational scheduling by creating algorithms that automatically distribute the load of the educational process and take into account all the preferences of teachers. One of the currently popular platforms for deploying these developments is the google tables platform for creating google tables and creating code based on google app script. The peculiarity of the system implemented with the help of this platform is the ease of use and minimization of resources spent on software storage, which greatly simplifies interaction with the system. Purpose – create a tool for drawing up a study schedule, interacting with a database created in Google Tables, interacting with participants of an educational institution online, which saves personal time of teachers and employees of the dean’s office. Method or methodology of the work: the article discusses a way to optimize the creation of a weekly schedule, taking into account the convenience of the teacher in the distribution of pairs and taking into account the load of the educational process by creating algorithms based on Google app script. For implementation, online tables are used that are accessible to all teachers and employees of the dean’s office, updated in real time, Tables created in Google and the google app script programming language. Result: a unique tool has been developed that implements the functions of adding, storing, interacting and round-the-clock access to training schedule data. Scope of application of the results: the curriculum, stored in online tables and updated by the dean’s office staff, should be used for monitoring by each teacher, keeping track of their time of couples in educational institutions

Comparison of microbiological results obtained from per-wound bone biopsies versus transcutaneous bone biopsies in diabetic foot osteomyelitis: a prospective cohort study

International audienceTranscutaneous bone biopsy (TCB) is the gold standard for taking microbiological specimens in diabetic foot osteomyelitis (DFO), but this technique is not widely used in diabetic foot care centers. We aimed to evaluate the reliability of per-wound bone biopsy (PWB) cultures by comparing them with concomitant TCB cultures obtained through healthy skin. This is a prospective monocentric study including patients seen in consultation for clinical and radiological diabetic foot osteomyelitis with positive probe-bone tests between April 2015 and May 2018. Two bone biopsies were performed on each consenting patient: TCB through a cutaneous incision in healthy skin, and PWB, after careful debridement of the wound. A total of 46 paired cultures were available from 43 eligible patients. Overall, 16 (42%) of the PWB and TCB pairs had identical culture results, but the TCB cultures were sterile in 8 (17%) cases. For 38 paired cultures with positive TCB, the correlation between PWB results and TCB results was 58.4%. PWB revealed all microorganisms found in the transcutaneous specimen in 26/38 samples (68.5%). In patients with DFO, the culture results of specimens taken by per-wound biopsies did not correlate well with those obtained by TCB. PWB should be reserved for cases where the transcutaneous biopsy is sterile or not feasible

Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images

Lyme disease is one of the most common infectious vector-borne diseases in the world. In the early stage, the disease manifests itself in most cases with erythema migrans (EM) skin lesions. Better diagnosis of these early forms would allow improving the prognosis by preventing the transition to a severe late form thanks to appropriate antibiotic therapy. Recent studies show that convolutional neural networks (CNNs) perform very well to identify skin lesions from the image but, there is not much work for Lyme disease prediction from EM lesion images. The main objective of this study is to extensively analyze the effectiveness of CNNs for diagnosing Lyme disease from images and to find out the best CNN architecture for the purpose. There is no publicly available EM image dataset for Lyme disease prediction mainly because of privacy concerns. In this study, we utilized an EM dataset consisting of images collected from Clermont-Ferrand University Hospital Center (CF-CHU) of France and the internet. CF-CHU collected the images from several hospitals in France. This dataset was labeled by expert dermatologists and infectiologists from CF-CHU. First, we benchmarked this dataset for twenty-three well-known CNN architectures in terms of predictive performance metrics, computational complexity metrics, and statistical significance tests. Second, to improve the performance of the CNNs, we used transfer learning from ImageNet pre-trained models as well as pre-trained the CNNs with the skin lesion dataset “Human Against Machine with 10000 training images (HAM1000)”. In that process, we searched for the best performing number of layers to unfreeze during transfer learning fine-tuning for each of the CNNs. Third, for model explainability, we utilized Gradient-weighted Class Activation Mapping to visualize the regions of input that are significant to the CNNs for making predictions. Fourth, we provided guidelines for model selection based on predictive performance and computational complexity. Our study confirmed the effectiveness and potential of even some lightweight CNNs for building Lyme disease pre-scanner mobile applications to assist people with an initial diagnosis in the absence of an expert dermatologist. We also made all the trained models publicly available at https://dappem.limos.fr/download.html, which can be used by others for transfer learning and building pre-scanners for Lyme disease