A rapid and easy method for the DNA extraction from Cryptococcus neoformans

DNA isolation from C. neoformans is difficult due to a thick and resistant capsule. We have optimized a new and rapid DNA isolation method for Cryptococcus using a short urea treatment followed by a rapid method using a chelex resin suspension. This procedure is simpler than previously reported methods

Extreme Idiopathic gigantomastia

Gigantomastia is a rare mastopathy of unknown cause. Due to mechanical and psychological complications related to excessive breast weights and volume, effective surgical treatment is required. Most cases of gigantomastia in the literature are associated with pregnancy or puberty and very rare cases of spontaneous gigantomastia have been reported We report a 38 years old woman with an idiopathic gigantomastia treated successfully with Thorek technique

A large scale hearing loss screen reveals an extensive unexplored genetic landscape for auditory dysfunction

The developmental and physiological complexity of the auditory system is likely reflected in the underlying set of genes involved in auditory function. In humans, over 150 non-syndromic loci have been identified, and there are more than 400 human genetic syndromes with a hearing loss component. Over 100 non-syndromic hearing loss genes have been identified in mouse and human, but we remain ignorant of the full extent of the genetic landscape involved in auditory dysfunction. As part of the International Mouse Phenotyping Consortium, we undertook a hearing loss screen in a cohort of 3006 mouse knockout strains. In total, we identify 67 candidate hearing loss genes. We detect known hearing loss genes, but the vast majority, 52, of the candidate genes were novel. Our analysis reveals a large and unexplored genetic landscape involved with auditory function

Nat Genet

The function of the majority of genes in the mouse and human genomes remains unknown. The mouse embryonic stem cell knockout resource provides a basis for the characterization of relationships between genes and phenotypes. The EUMODIC consortium developed and validated robust methodologies for the broad-based phenotyping of knockouts through a pipeline comprising 20 disease-oriented platforms. We developed new statistical methods for pipeline design and data analysis aimed at detecting reproducible phenotypes with high power. We acquired phenotype data from 449 mutant alleles, representing 320 unique genes, of which half had no previous functional annotation. We captured data from over 27,000 mice, finding that 83% of the mutant lines are phenodeviant, with 65% demonstrating pleiotropy. Surprisingly, we found significant differences in phenotype annotation according to zygosity. New phenotypes were uncovered for many genes with previously unknown function, providing a powerful basis for hypothesis generation and further investigation in diverse systems.Comment in : Genetic differential calculus. [Nat Genet. 2015] Comment in : Scaling up phenotyping studies. [Nat Biotechnol. 2015

Experimental and Numerical Modelling of Thermo-Forming of Anisotropic Thin Sheet

International audienceCoupled constitutive equations, formulated in the framework of the thermodynamics of irreversible processes accounting for isotropic hardening as well as the isotropic ductile damage are used to simulate numerically, by the Finite Element Analysis, 3D metal hydroforming processes. The experimental study is dedicated to the identification of stress-strain flow and damage parameters by using the Nelder-Mead simplex algorithm optimization from the global measure of displacement and force. Applications are made to the simulation of thin sheet thermohydroforming using different die geometry to show the efficiency of the proposed methodology and to localize plastic instability, thinning of sheet and damage initiation under different forming conditions

Experimental and Numerical Studies of Welded Tubes Formability

International audienceThis paper presents a numerical methodology which aims to improve 3D welded tube hydroforming formability. This methodology is based on elastoplastic constitutive equations accounting for non-linear anisotropic hardening. The experimental study is dedicated to the identification of material parameters (the parent and the heat-affected zones) using the global measure response of tube displacement, thickness evolution and internal pressure expansion. Applications are made to study numerically the effect of the anisotropic parameters, the hardening flow and the heat-affected zone shape on the hydro-formability of welded tubes

Voice Pathology Detection Using a Two-Level Classifier Based on Combined CNN–RNN Architecture

The construction of an automatic voice pathology detection system employing machine learning algorithms to study voice abnormalities is crucial for the early detection of voice pathologies and identifying the specific type of pathology from which patients suffer. This paper’s primary objective is to construct a deep learning model for accurate speech pathology identification. Manual audio feature extraction was employed as a foundation for the categorization process. Incorporating an additional piece of information, i.e., voice gender, via a two-level classifier model was the most critical aspect of this work. The first level determines whether the audio input is a male or female voice, and the second level determines whether the agent is pathological or healthy. Similar to the bulk of earlier efforts, the current study analyzed the audio signal by focusing solely on a single vowel, such as /a/, and ignoring phrases and other vowels. The analysis was performed on the Saarbruecken Voice Database,. The two-level cascaded model attained an accuracy and F1 score of 88.84% and 87.39%, respectively, which was superior to earlier attempts on the same dataset and provides a steppingstone towards a more precise early diagnosis of voice complications

An Ensemble of Transfer Learning Models for the Prediction of Skin Cancers with Conditional Generative Adversarial Networks

Skin cancer is one of the most severe forms of the disease, and it can spread to other parts of the body if not detected early. Therefore, diagnosing and treating skin cancer patients at an early stage is crucial. Since a manual skin cancer diagnosis is both time-consuming and expensive, an incorrect diagnosis is made due to the high similarity between the various skin cancers. Improved categorization of multiclass skin cancers requires the development of automated diagnostic systems. Herein, we propose a fully automatic method for classifying several skin cancers by fine-tuning the deep learning models VGG16, ResNet50, and ResNet101. Prior to model creation, the training dataset should undergo data augmentation using traditional image transformation techniques and Generative Adversarial Networks (GANs) to prevent class imbalance issues that may lead to model overfitting. In this study, we investigate the feasibility of creating dermoscopic images that have a realistic appearance using Conditional Generative Adversarial Network (CGAN) techniques. Thereafter, the traditional augmentation methods are used to augment our existing training set to improve the performance of pre-trained deep models on the skin cancer classification task. This improved performance is then compared to the models developed using the unbalanced dataset. In addition, we formed an ensemble of finely tuned transfer learning models, which we trained on balanced and unbalanced datasets. These models were used to make predictions about the data. With appropriate data augmentation, the proposed models attained an accuracy of 92% for VGG16, 92% for ResNet50, and 92.25% for ResNet101, respectively. The ensemble of these models increased the accuracy to 93.5%. A comprehensive discussion on the performance of the models concluded that using this method possibly leads to enhanced performance in skin cancer categorization compared to the efforts made in the past

Extreme Idiopathic gigantomastia

Gigantomastia is a rare mastopathy of unknown cause. Due to mechanical and psychological complications related to excessive breast weights and volume, effective surgical treatment is required. Most cases of gigantomastia in the literature are associated with pregnancy or puberty and very rare cases of spontaneous gigantomastia have been reported We report a 38 years old woman with an idiopathic gigantomastia treated successfully with Thorek technique