Echinogammarus lusitanicus (Amphipoda), a New Intermediate Host of Echinorhynchus truttae (Acanthocephala: Echinorhynchidae).

Purpose: The infection of brown trout (Salmo trutta) by the acanthocephalan parasite Echinorhynchus truttae is initiated by the ingestion of gammarid crustaceans harbouring the cystacanth form. Gammarus pulex has been reported as the common intermediate host of this parasite species. The absence of G.

Patient Perceptions on Telepsychiatry as an In-Consult Alternative During COVID-19 Pandemic: Peruvian Adaptation of the Telehealth Usability Questionnaire.

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Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells.

RNA velocity has been rapidly adopted to guide interpretation of transcriptional dynamics in snapshot single-cell data; however, current approaches for estimating RNA velocity lack effective strategies for quantifying uncertainty and determining the overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty.

An empirical Bayes method for differential expression analysis of single cells with deep generative models.

Detecting differentially expressed genes is important for characterizing subpopulations of cells. In scRNA-seq data, however, nuisance variation due to technical factors like sequencing depth and RNA capture efficiency obscures the underlying biological signal. Deep generative models have been extensively applied to scRNA-seq data, with a special focus on embedding cells into a low-dimensional latent space and correcting for batch effects.

The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans.

Molecular characterization of cell types using single-cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression.

PeakVI: A deep generative model for single-cell chromatin accessibility analysis.

Single-cell ATAC sequencing (scATAC-seq) is a powerful and increasingly popular technique to explore the regulatory landscape of heterogeneous cellular populations. However, the high noise levels, degree of sparsity, and scale of the generated data make its analysis challenging. Here, we present PeakVI, a probabilistic framework that leverages deep neural networks to analyze scATAC-seq data.

Cell2location maps fine-grained cell types in spatial transcriptomics.

Spatial transcriptomic technologies promise to resolve cellular wiring diagrams of tissues in health and disease, but comprehensive mapping of cell types in situ remains a challenge. Here we present сell2location, a Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single-cell and spatial transcriptomics with higher sensitivity and resolution than existing tools.

Mapping single-cell data to reference atlases by transfer learning.

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches).

Joint probabilistic modeling of single-cell multi-omic data with totalVI.

The paired measurement of RNA and surface proteins in single cells with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, combining these paired views into a unified representation of cell state is made challenging by the unique technical characteristics of each measurement. Here we present Total Variational Inference (totalVI; https://scvi-tools.

Enhancing scientific discoveries in molecular biology with deep generative models.

Generative models provide a well-established statistical framework for evaluating uncertainty and deriving conclusions from large data sets especially in the presence of noise, sparsity, and bias. Initially developed for computer vision and natural language processing, these models have been shown to effectively summarize the complexity that underlies many types of data and enable a range of applications including supervised learning tasks, such as assigning labels to images; unsupervised learning tasks, such as dimensionality reduction; and out-of-sample generation, such as de novo image synthesis. With this early success, the power of generative models is now being increasingly leveraged in molecular biology, with applications ranging from designing new molecules with properties of interest to identifying deleterious mutations in our genomes and to dissecting transcriptional variability between single cells.

Author Correction: Characterization of cell fate probabilities in single-cell data with Palantir.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Characterization of cell fate probabilities in single-cell data with Palantir.

Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state.

Stress-Adaptive Responses Associated with High-Level Carbapenem Resistance in KPC-Producing .

Carbapenem-resistant Enterobacteriaceae (CRE) organisms have emerged to become a major global public health threat among antimicrobial resistant bacterial human pathogens. Little is known about how CREs emerge. One characteristic phenotype of CREs is heteroresistance, which is clinically associated with treatment failure in patients given a carbapenem.

Impact of laparoscopic cholecystectomy on the management of cholelithiasis in children with sickle cell disease.

Children with sickle cell disease, well known to have a high incidence of cholelithiasis, are frequently admitted to the hospital for episodes of abdominal pain. Before the advent of laparoscopy, few children with sickle cell and cholelithiasis underwent cholecystectomy unless absolutely necessary, because of the high morbidity of an open cholecystectomy (OC). We reviewed our records of all children with sickle cell disease and cholelithiasis treated from 1985 to 1992 to investigate the impact of laparoscopic cholecystectomy (LC).

[Loco-regional fibrinolysis with urokinase in the treatment of deep venous thromboses].

The importance of the fibrinolytic treatment in deep venous thrombosis, to avoid the mortal pulmonary embolism, among other complications, is commented on. The results in 32 patients, presenting deep venous thrombosis and treated with loco-regional Urokinase, are presented. Author carries out some commentaries about surgical and medical treatment.