Les poissons de la Grande Grotte d'Arcy-sur-Cure (Yonne)

Un original método de investigacion, mediante adelgazamiento de la calcita, utilizado por primera vez en una cueva con decoración paleolítica ha permitido sacar a la luz, entre otras cosas, cuatro peces pintados en negro sobre las paredes de la Grande Grotte d'Arcy-sur-Cure (Yonne, France). Dichos peces, salmónidos y lucio, confirman el carácter excepcional el bestiario de esta gruta constituido por animales que no están presentes en las demás, a excepción de la de la cueva Chauvet (Ardechè) ; mamuts, rinocerontes, osos, felino, aves, megáceros. Los vestigios, bien conservados, encontrados sobre su suelo nos han permitido determinar que fue utilizada en la época auriñaciense gravetiense

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

Women’s dissatisfaction with inappropriate behavior by health care workers during childbirth care in France: A survey study

Background As part of a decades-long process of restructuring primary care, independent (also known as community) healthcare workers are being encouraged to work in groups to facilitate their coordination and continuity of care in France. French independent midwives perform about half of the early prenatal interviews that identify mothers' needs during pregnancy and then refer them to the appropriate resources. The French government, however, structured the COVID-19 pandemic response around public health institutions and did not directly mobilise these community healthcare workers during the lockdown phase. These responses have raised questions about their role within the healthcare system in crises. This survey’s main objectives were to estimate the proportion of independent midwives who experienced new difficulties in referring women to healthcare facilities or other caregivers and in collaborating with hospitals during the first stage of this pandemic. The secondary objective was to estimate the proportion, according to their mode of practice, of independent midwives who considered that all the women under their care had risked harm due to failed or delayed referral to care. Methods We conducted an online national survey addressed to independent midwives in France from 29 April to 15 May 2020, around the end of the first lockdown (17 March–11 May, 2020). Results Of the 5264 registered independent midwives in France, 1491 (28.3%) responded; 64.7% reported new or greater problems during the pandemic in referring women to health facilities or care-providers, social workers in particular, and 71.0% reported new difficulties collaborating with hospitals. Nearly half (46.2%) the respondents considered that all the women in their care had experienced, to varying degrees, a lack of or delay in care that could have affected their health. This proportion did not differ according to the midwives’ form of practice: solo practice, group practice with other midwives only, or group practice with at least two types of healthcare professionals. Conclusions The pandemic has degraded the quality of pregnant women’s care in France and challenged the French model of care, which is highly compartmentalised between an almost exclusively independent primary care (community) sector and a predominantly salaried secondary care (hospital) sector