Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.</p

Deep-Learning based segmentation and quantification in experimental kidney histopathology

BACKGROUND: Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiased, reproducible, and efficient histopathologic analyses, which will require novel high-throughput tools. A deep learning technique, the convolutional neural network, is increasingly applied in pathology because of its high performance in tasks like histology segmentation. METHODS: We investigated use of a convolutional neural network architecture for accurate segmentation of periodic acid-Schiff-stained kidney tissue from healthy mice and five murine disease models and from other species used in preclinical research. We trained the convolutional neural network to segment six major renal structures: glomerular tuft, glomerulus including Bowman\u27s capsule, tubules, arteries, arterial lumina, and veins. To achieve high accuracy, we performed a large number of expert-based annotations, 72,722 in total. RESULTS: Multiclass segmentation performance was very high in all disease models. The convolutional neural network allowed high-throughput and large-scale, quantitative and comparative analyses of various models. In disease models, computational feature extraction revealed interstitial expansion, tubular dilation and atrophy, and glomerular size variability. Validation showed a high correlation of findings with current standard morphometric analysis. The convolutional neural network also showed high performance in other species used in research-including rats, pigs, bears, and marmosets-as well as in humans, providing a translational bridge between preclinical and clinical studies. CONCLUSIONS: We developed a deep learning algorithm for accurate multiclass segmentation of digital whole-slide images of periodic acid-Schiff-stained kidneys from various species and renal disease models. This enables reproducible quantitative histopathologic analyses in preclinical models that also might be applicable to clinical studies

Misogyny, racism, and Islamophobia: street harassment at the intersections

Veiled Muslim women are at an increased risk of street harassment in the current political and economic climate. Their visibility, combined with their popular portrayal as culturally dangerous or threatening means that they are vulnerable to receiving verbal and physical threats, which can be misogynistic and Islamophobic in nature. Drawing on 60 individual and 20 focus group interviews with Muslim women in the United Kingdom who wear the niqab (face veil) and had experienced harassment in public, this qualitative study details their lived experiences. It argues that an intersectional analysis is crucial to understanding the nuances of their lived experiences and the impact street harassment has on their lives. The findings demonstrate that street harassment can produce a hostile environment for veiled Muslim women, which can have a terrorizing effect, limiting their full participation in the public sphere

Veiled Muslim women’s responses to experiences of gendered Islamophobia in the UK

In a post-9/11 climate, Islamophobia has increased significantly in the UK and elsewhere in the West. ISIS-inspired terrorist attacks in the UK as well as in France, Belgium, Germany and more recently in Sri Lanka have triggered an increase in verbal and physical attacks against Muslims. Drawing on intersectionality (as a nexus of identities that work together to render certain individuals as ‘ideal’ targets to attack), veiled Muslim women are likely to experience gendered Islamophobia in the cyber world but also in ‘real’ life due to the intersections between their ‘visible’ Muslim identity and gender performance. In the British context, although Islamophobia is recorded as a hate crime nationally, and misogyny is recorded as a hate crime locally in some police forces, veiled Muslim women are unlikely to report their experiences to the police. Drawing on qualitative interviews with Muslim women who wear the niqab (face veil), the purpose of this paper is to examine the ways in which they respond to experiences of gendered Islamophobia as well as their reasons for not reporting their experiences to the police

Diagnostic de filtres à sable d'assainissement non collectif

International audienceOn-site domestic wastewater treatment (ODWT) systems are receiving renewed interest due to their simplicity of implementation, affordability and efficiency. The most widespread ODWT system is the sand filter with vertical drained flow (SFVDF). To optimise the design and maintenance of SFVDFs, it is important to be able to check the conformity of the installations, judge their treatment performance in service and estimate possible evolution and life expectancy. As yet, however, no methodology has been developed to perform these tasks. This paper proposes a new methodology for diagnosing SFVDF systems in service. The development of this methodology relies on the development of on-site characterisation of the filtering material based on the use of a light dynamic penetration test and image processing. From this, the paper proposes an automatic tool for detecting the presence of clogging in a filter and a methodology to estimate the hydraulic conductivity of the filtering material without destroying the filter. The global methodology for diagnosing SFVDF systems is presented and illustrated for a real case in order to demonstrate its advantages and efficiency

Méthode de diagnostic du fonctionnement d'un système d'épuration d'eau de type dispositif à milieu filtrant et outil de diagnostic adapté à cette méthode

L'invention concerne une méthode de diagnostic du fonctionnement d'un système d'épuration d'eau de type dispositif à milieu filtrant, comportant les étapes suivantes : - une étape de caractérisation d'au moins un paramètre physique et/ou mécanique du dispositif à milieu filtrant permettant d'établir une valeur de perméabilité Ks (m/s), - une étape de comparaison entre ladite valeur de perméabilité établie Ks et une plage de valeurs de perméabilité prédéterminée [k k ] représentative d'un état de fonctionnement satisfaisant du système d'épuration, et - une étape de diagnostic au cours de laquelle on attribue un indicateur du fonctionnement du dispositif à milieu filtrant en fonction du résultat de l'étape de comparaison. Application aux systèmes d'assainissement non collectifs (ANC) ou autonomes

Tackling stain variability using CycleGAN-based stain augmentation

Background: Considerable inter- and intra-laboratory stain variability exists in pathology, representing a challenge in development and application of deep learning (DL) approaches. Since tackling all sources of stain variability with manual annotation is not feasible, we here investigated and compared unsupervised DL approaches to reduce the consequences of stain variability in kidney pathology. Methods: We aimed to improve the applicability of a pretrained DL segmentation model to 3 external multi-centric cohorts with large stain variability. In contrast to the traditional approach of training generative adversarial networks (GAN) for stain normalization, we here propose to tackle stain variability by data augmentation. We augment the training data of the pretrained model by the stain variability using CycleGANs and then retrain the model on the stain-augmented dataset. We compared the performance of i/ the unmodified pretrained segmentation model with ii/ CycleGAN-based stain normalization, iii/ a feature-preserving modification to ii/ for improved normalization, and iv/ the proposed stain-augmented model. Results: The proposed stain-augmented model showed highest mean segmentation accuracy in all external cohorts and maintained comparable performance on the training cohort. However, the increase in performance was only marginal compared to the pretrained model. CycleGAN-based stain normalization suffered from encoded imperceptible information into the normalizations that confused the pretrained model and thus resulted in slightly worse performance. Conclusions: Our findings suggest that stain variability can be tackled more effectively by augmenting data by it than by following the commonly used approach of normalizing the stain. However, the applicability of this approach providing only a rather slight performance increase has to be weighted against an additional carbon footprint