In this study, we focused on the transformative potential of machine learning in the engineering of genetically encoded fluorescent indicators (GEFIs), protein-based sensing tools that are critical for real-time monitoring of biological activity. GEFIs are complex proteins with multiple dynamic states, rendering optimization by trial-and-error mutagenesis a challenging problem. We applied an alternative approach using machine learning to predict the outcomes of sensor mutagenesis by analyzing established libraries that link sensor sequences to functions.
View Article and Find Full Text PDFNeglected tropical diseases are a group of 20 disabling diseases, which, in particular, are the most common chronic infections in the most vulnerable people. This study aimed to characterize the infection by intestinal parasites (IPs) in dwellings from a peri-urban neighborhood in Pampa del Indio, Chaco (Argentina), and its association with socioeconomic and environmental variables. Single stool samples were collected from all individuals older than 1 year through household visits and processed using coprological sedimentation and flotation techniques.
View Article and Find Full Text PDFAmyotrophic lateral sclerosis (ALS) is associated with impaired energy metabolism, including weight loss and decreased appetite which are negatively correlated with survival. Neural mechanisms underlying metabolic impairment in ALS remain unknown. ALS patients and presymptomatic gene carriers have early hypothalamic atrophy.
View Article and Find Full Text PDFIntroduction: The granulocyte colony-stimulating factor receptor (G-CSFR), encoded by the gene, is involved in the production and function of neutrophilic granulocytes. Somatic mutations in leading to truncated G-CSFR forms are observed in acute myeloid leukemia (AML), particularly those subsequent to severe chronic neutropenia (SCN), as well as in a subset of patients with other leukemias.
Methods: This investigation introduced equivalent mutations into the zebrafish gene genome editing and used a range of molecular and cellular techniques to understand the impact of these mutations on immune cells across the lifespan.
We report on an opto-mechanical metal mirror design for highly dynamic, diffraction-limited focus shifting. Here, the mechanical geometry of the membrane is of crucial interest as it must provide sufficient optical performance to allow for diffraction limited focussing and have a high mechanical eigenfrequency to provide dynamic motions. The approach is the analytical consideration of the plate theory and provides the basis for a parameterized finite element model.
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