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High-throughput and label-free protein nanopore-based sensors are extensively used in DNA sequencing, single-protein analysis, molecular sensing and chemical catalysis with single channel recording. These technologies show great potential for identifying various harmful substances linked to public health by addressing the limitations of current portability and the speed of existing techniques. In this review, we provide an overview of the fundamental principles of nanopore sensing, with a focus on chemical modification and genetic engineering strategies aimed at enhancing the detection sensitivity and identification accuracy of protein nanopores. The engineered protein nanopores enable direct sensing, while the introduction of aptamers and substrates enables indirect sensing, translating the physical structure and chemical properties of analytes into readable signals. These scientific discoveries and engineering efforts have provided new prospects for detecting and monitoring trace hazardous substances. [ABSTRACT FROM AUTHOR]

The golden snub-nosed monkey (Rhinopithecus roxellana) is a typical arboreal group-living Old World primate. While limb preference has been extensively studied in this species, limb preference consistency has not yet been explored. Here, based on 26 R. roxellana adults, we investigated whether individuals exhibit consistent motor biases in manual- (e.g., unimanual feeding and social grooming) and foot-related (e.g., bipedal locomotion) tasks and whether limb preference consistency is influenced by increased social interactions during social grooming. Results showed no consistency in the direction or strength of limb preference among tasks, except for lateral strength in handedness for unimanual feeding and footedness in the initiation of locomotion. Population-level foot preference was only found among right-handers. Marked lateral bias was found in unimanual feeding, indicating that it may be a sensitive behavioural measure for assessing manual preference, especially for provisioned populations. This study not only improves our understanding of the relationship between hand and foot preference in R. roxellana but also reveals potential differential hemispheric regulation of limb preference and the influence of increased social interaction on handedness consistency. [ABSTRACT FROM AUTHOR]

There is a great deal of spatial and temporal variation in the availability and nutritional quality of foods eaten by animals, particularly in temperate regions where winter brings lengthy periods of leaf and fruit scarcity. We analyzed the availability, dietary composition, and macronutrients of the foods eaten by the northern‐most golden snub‐nosed monkey (Rhinopithecus roxellana) population in the Qinling Mountains, China to understand food choice in a highly seasonal environment dominated by deciduous trees. During the warm months between April and November, leaves are consumed in proportion to their availability, while during the leaf‐scarce months between December and March, bark and leaf/flower buds comprise most of their diet. When leaves dominated their diet, golden snub‐nosed monkeys preferentially selected leaves with higher ratios of crude protein to acid detergent fiber. While when leaves were less available, bark and leaf/flower buds that were high in nonstructural carbohydrates and energy, and low in acid detergent fiber were selected. Southern populations of golden snub‐nosed monkey can turn to eating lichen, however, the population studied here in this lichen‐absent area have adapted to their cool deciduous habitat by instead consuming buds and bark. Carbohydrate and energy rich foods appear to be the critical resources required for the persistence of this species in temperate habitat. The dietary flexibility of these monkeys, both among seasons and populations, likely contributes to their wide distribution over a range of habitats and environments. [ABSTRACT FROM AUTHOR]

Objective: To explore the clinical value of 3D Echocardiography (3DE) in evaluating the changes of left atrial volume and pulmonary vein structure in patients with Atrial Fibrillation (AF)., Methods: Clinical data were collected from 54 AF patients. Left Atrial Anteroposterior Diameter (LADap), Left Atrial left and right Diameter (LADml), and Left Atrial upper and lower Diameter (LADsi) were measured; the maximum Left Atrial Volume (LAVmax), minimum Left Atrial Volume (LAVmin), left atrial presystolic volume (LAVpre), and Cross-Sectional Area (CSA) of each pulmonary vein were analyzed. Passive Ejection Fraction (LAPEF) was calculated. The differences in left atrial volume and pulmonary vein structure between patients with AF and healthy people were compared, and the correlation between the indexes was analyzed. The diagnostic value of the above indicators for AF patients was analyzed., Results: LADap, LADml, LADsi, LAVmax, LAVmin, LAVpre, LAPEF, LSPV CSA, LIPV CSA, RSPV CSA, and RIPV CSA of AF patients were significantly higher. There was a significant positive correlation between left atrial diameter and pulmonary vein structure. There was a significant positive correlation between left atrial volume and pulmonary vein structure. There was a negative correlation between LAPEF and pulmonary vein structure. LADap, LADml, LADsi, LAVmax, LAVmin, LAVpre, LAPEF, LSPV CSA, LIPV CSA, RSPV CSA, and RIPV CSA had a diagnostic value for AF patients., Conclusion: 3DE is applicable for evaluating left atrial volume and pulmonary vein structure in patients with AF., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 HCFMUSP. Published by Elsevier España, S.L.U. All rights reserved.)

Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.