Browse Articles

Invasion of glioma cells through confined space requires membrane tension regulation and mechano-electrical coupling via Plexin-B2

Seq2Seq-based GRU autoencoder for anomaly detection and failure identification in coal mining hydraulic support systems

CMA-Based design of a Novel structure for isolation enhancement and Radiation Pattern correction in MIMO antennas

Distinct roles of dentate gyrus and medial entorhinal cortex inputs for phase precession and temporal correlations in the hippocampal CA3 area

AbstractThe hippocampal CA3 subregion is a densely connected recurrent circuit that supports memory by generating and storing sequential neuronal activity patterns that reflect recent experience. While theta phase precession is thought to be critical for generating sequential activity during memory encoding, the circuit mechanisms that support this computation across hippocampal subregions are unknown. By analyzing CA3 network activity in the absence of each of its theta-modulated external excitatory inputs, we show necessary and unique contributions of the dentate gyrus (DG) and the medial entorhinal cortex (MEC) to phase precession. DG inputs are essential for preferential spiking of CA3 cells during late theta phases and for organizing the temporal order of neuronal firing, while MEC inputs sharpen the temporal precision throughout the theta cycle. A computational model that accounts for empirical findings suggests that the unique contribution of DG inputs to theta-related spike timing is supported by targeting precisely timed inhibitory oscillations. Our results thus identify a novel and unique functional role of the DG for sequence coding in the CA3 circuit.

Automatic detection, classification, and segmentation of sagittal MR images for diagnosing prolapsed lumbar intervertebral disc

Spatial heterogeneity of keyhole imagery coverage in China and imagery dataset cost estimation

DPFunc: accurately predicting protein function via deep learning with domain-guided structure information

Use of the Da Vinci SP surgical system in robot-assisted nipple-sparing mastectomy: a single-center, retrospective study

Varying pixel resolution significantly improves deep learning-based carotid plaque histology segmentation

Dengue virus IgG and neutralizing antibody titers measured with standard and mature viruses are protective

A multicenter study on TROP2 as a potential targeted therapy for extramammary Paget disease in Japan

Efficacy and safety of propranolol tablets vs. oral solution in neonates with severe infantile hemangiomas: a retrospective study in China

A new class of natural anthelmintics targeting lipid metabolism

Association of triglyceride-glucose index with Helicobacter pylori infection in the 1999–2000 NHANES cross-sectional study

SAILOR: perceptual anchoring for robotic cognitive architectures

AbstractSymbolic anchoring is an important topic in robotics, as it enables robots to obtain symbolic knowledge from the perceptual information acquired through their sensors and maintain the link between that knowledge and the sensory data. In cognitive-based robots, this process of transforming sub-symbolic data generated by sensors to obtain and maintain symbolic knowledge is still an open problem. To address this issue, this paper presents SAILOR, a framework for symbolic anchoring integrated into ROS 2. SAILOR aims to maintain the link between symbolic data and perceptual data in real robots over time. It provides a semantic world modeling approach using two deep learning-based sub-symbolic robotic skills: object recognition and matching function. The object recognition skill allows the robot to recognize and identify objects in its environment, while the matching function enables the robot to decide if new perceptual data corresponds to existing symbolic data. This paper describes the proposed method and the development of the framework, as well as its integration in MERLIN2 (a hybrid cognitive architecture fully functional in robots running ROS 2) and the validation of SAILOR using public datasets and a real-world scenario.

Structures of Saccharolobus solfataricus initiation complexes with leaderless mRNAs highlight archaeal features and eukaryotic proximity

AbstractThe archaeal ribosome is of the eukaryotic type. TACK and Asgard superphyla, the closest relatives of eukaryotes, have ribosomes containing eukaryotic ribosomal proteins not found in other archaea, eS25, eS26 and eS30. Here, we investigate the case of Saccharolobus solfataricus, a TACK crenarchaeon, using mainly leaderless mRNAs. We characterize the small ribosomal subunit of S. solfataricus bound to SD-leadered or leaderless mRNAs. Cryo-EM structures show eS25, eS26 and eS30 bound to the small subunit. We identify two ribosomal proteins, aS33 and aS34, and an additional domain of eS6. Leaderless mRNAs are bound to the small subunit with contribution of their 5’-triphosphate group. Archaeal eS26 binds to the mRNA exit channel wrapped around the 3’ end of rRNA, as in eukaryotes. Its position is not compatible with an SD:antiSD duplex. Our results suggest a positive role of eS26 in leaderless mRNAs translation and possible evolutionary routes from archaeal to eukaryotic translation.

Lossless and reference-free compression of FASTQ/A files using GeneSqueeze

Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images

AbstractDeep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current approaches were either applied only to 2D cross-sectional images, addressed few structures, or were validated on small datasets, which limit the application in large-scale databases. This study aimed to validate an improved deep learning model for volumetric MSK segmentation of the hip and thigh with uncertainty estimation from clinical computed tomography (CT) images. Databases of CT images from multiple manufacturers/scanners, disease status, and patient positioning were used. The segmentation accuracy, and accuracy in estimating the structures volume and density, i.e., mean HU, were evaluated. An approach for segmentation failure detection based on predictive uncertainty was also investigated. The model has improved all segmentation accuracy and structure volume/density evaluation metrics compared to a shallower baseline model with a smaller training database (N = 20). The predictive uncertainty yielded large areas under the receiver operating characteristic (AUROC) curves (AUROCs ≥ .95) in detecting inaccurate and failed segmentations. Furthermore, the study has shown an impact of the disease severity status on the model’s predictive uncertainties when applied to a large-scale database. The high segmentation and muscle volume/density estimation accuracy and the high accuracy in failure detection based on the predictive uncertainty exhibited the model’s reliability for analyzing individual MSK structures in large-scale CT databases.

Targeting pleuro-alveolar junctions reverses lung fibrosis in mice

Abstract Lung fibrosis development utilizes alveolar macrophages, with mechanisms that are incompletely understood. Here, we fate map connective tissue during mouse lung fibrosis and observe disassembly and transfer of connective tissue macromolecules from pleuro-alveolar junctions (PAJs) into deep lung tissue, to activate fibroblasts and fibrosis. Disassembly and transfer of PAJ macromolecules into deep lung tissue occurs by alveolar macrophages, activating cysteine-type proteolysis on pleural mesothelium. The PAJ niche and the disassembly cascade is active in patient lung biopsies, persists in chronic fibrosis models, and wanes down in acute fibrosis models. Pleural-specific viral therapeutic carrying the cysteine protease inhibitor Cystatin A shuts down PAJ disassembly, reverses fibrosis and regenerates chronic fibrotic lungs. Targeting PAJ disassembly by targeting the pleura may provide a unique therapeutic avenue to treat lung fibrotic diseases.

Clinical characteristics and prognosis of dialysis patients with metastatic calcification diagnosed by 99mTc-MDP bone scintigraphy