A robust ensemble deep learning framework for accurate diagnoses of tuberculosis from chest radiographs.

Introduction: Tuberculosis (TB) stands as a paramount global health concern, contributing significantly to worldwide mortality rates. Effective containment of TB requires deployment of cost-efficient screening method with limited resources. To enhance the precision of resource allocation in the global fight against TB, this research proposed chest X-ray radiography (CXR) based machine learning screening algorithms with optimization, benchmarking and tuning for the best TB subclassification tasks for clinical application. Methods: This investigation delves into the development and evaluation of a robust ensemble deep learning framework, comprising 43 distinct models, tailored for the identification of active TB cases and the categorization of their clinical subtypes. The proposed framework is essentially an ensemble model with multiple feature extractors and one of three fusion strategies-voting, attention-based, or concatenation methods-in the fusion stage before a final classification. The comprised de-identified dataset contains records of 915 active TB patients alongside 1,276 healthy controls with subtype-specific information. Thus, the realizations of our framework are capable for diagnosis with subclass identification. The subclass tags include: secondary tuberculosis/tuberculous pleurisy; non-cavity/cavity; secondary tuberculosis only/secondary tuberculosis and tuberculous pleurisy; tuberculous pleurisy only/secondary tuberculosis and tuberculous pleurisy. Results: Based on the dataset and model selection and tuning, ensemble models show their capability with self-correction capability of subclass identification with rendering robust clinical predictions. The best double-CNN-extractor model with concatenation/attention fusion strategies may potentially be the successful model for subclass tasks in real application. With visualization techniques, in-depth analysis of the ensemble model's performance across different fusion strategies are verified. Discussion: The findings underscore the potential of such ensemble approaches in augmenting TB diagnostics with subclassification. Even with limited dataset, the self-correction within the ensemble models still guarantees the accuracies to some level for potential clinical decision-making processes in TB management. Ultimately, this study shows a direction for better TB screening in the future TB response strategy.

The role of novel protein acylations in cancer.

Novel protein acylations are a class of protein post-translational modifications, such as lactylation, succinylation, crotonylation, palmitoylation, and ß-hydroxybutyrylation. These acylation modifications are common in prokaryotes and eukaryotes and play pivotal roles in various key cellular processes by regulating gene transcription, protein subcellular localization, stability and activity, protein-protein interactions, and protein-DNA interactions. The diversified acylations are closely associated with various human diseases, especially cancer. In this review, we provide an overview of the distinctive characteristics, effects, and regulatory factors of novel protein acylations. We also explore the various mechanisms through which novel protein acylations are involved in the occurrence and progression of cancer. Furthermore, we discuss the development of anti-cancer drugs targeting novel acylations, offering promising avenues for cancer treatment.

The role of circadian gene CLOCK in cancer.

Circadian Locomotor Output Cycles Kaput (CLOCK) is one of the circadian clock genes and is considered to be a fundamental regulatory gene in the circadian rhythm, responsible for mediating several biological processes. Therefore, abnormal expression of CLOCK affects its role in the circadian clock and its more general function as a direct regulator of gene expression. This dysfunction can lead to severe pathological effects, including cancer. To better understand the role of CLOCK in cancer, we compiled this review to describe the biological function of CLOCK, and especially highlighted its function in cancer development, progression, tumor microenvironment, cancer cell metabolism, and drug resistance.

Parent-teacher relationship and students' learning engagement in China: A longitudinal study considering urban-suburban differences.

BACKGROUND: The importance of parent-teacher relationships has been well-discussed in Western contexts. It's still unclear whether and how parent-teacher relationships affect students' academic development, especially in the context of China's uneven development between urban and suburban areas. AIMS: This study examined the urban-suburban differences in the influence of the parent-teacher relationships on students' learning engagement during the last 3 years of primary school to contribute to related policy and practice. SAMPLE AND METHODS: Data were collected annually during the summer period in three waves from Grades 4 to 6. The sample included 1408 students (Mage = 10.35, SD = .48; 49.1% boys) paired with their mothers (Mage = 38.82, SD = 3.54), with 643 pairs from urban areas and 765 pairs from suburban areas in China. In each wave, the parents reported the frequency of contact with their child's teacher during the previous school year as well as the quality of their relationship, and the students reported their learning engagement. Latent growth models were used to examine the hypotheses. RESULTS: A statistically significant decline in learning engagement was seen only in suburban students, while the parent-teacher relationships seemed to increase for both suburban and urban participants. Latent growth modelling showed that the increasing levels of relationship quality reduced the decline in the suburban students' learning engagement, although the contact between parents and teachers did not play such a positive role for both urban and suburban students. CONCLUSIONS: The findings reveal a compensatory effect of the quality of the parent-teacher relationship on suburban students' learning engagement. Considering the difference in students' learning engagement between urban and suburban students, increased effort on high-quality parent-teacher relationships for suburban students is required to better support their academic development.

Highly-Aligned All-Fiber Actuator with Asymmetric Photothermal-Humidity Response and Autonomous Perceptivity.

Soft robots adapt to complex environments for autonomous locomotion, manipulation, and perception are attractive for robot-environment interactions. Strategies to reconcile environment-triggered actuation and self-powered sensing responses to different stimuli remain challenging. By tuning the in situ vapor phase solvent exchange effect in continuous electrospinning, an asymmetric highly-aligned all-fiber membrane (HAFM) with a hierarchical "grape-like" nanosphere-assembled microfiber structure (specific surface area of 13.6 m2 g-1) and excellent mechanical toughness (tensile stress of 5.5 MPa, and fracture toughness of 798 KJ m-3) is developed, which shows efficient asymmetric actuation to both photothermal and humidity stimuli. The HAFM consists of a metal-organic framework (MOF)-enhanced moisture-responsive layer and an MXene-improved photothermal-responsive layer, which achieves substantial actuation with a bending curvature up to ≈7.23 cm-1 and a fast response of 0.60 cm-1 s-1. By tailoring the fiber alignment and bi-layer thickness ratio, different types of micromanipulators, automatic walking robots, and plant robots with programmable structures are demonstrated, which are realized for self-powered information perception of material type, object moisture, and temperature by integrating the autonomous triboelectric effect induced by photothermal-moisture actuation. This work presents fiber materials with programable hierarchical asymmetries and inspires a common strategy for self-powered organism-interface robots to interact with complex environments.

Complex dislocation loop networks as natural extensions of the sink efficiency of saturated grain boundaries in irradiated metals.

The development of radiation-tolerant structural materials is an essential element for the success of advanced nuclear energy concepts. A proven strategy to increase radiation resistance is to create microstructures with a high density of internal defect sinks, such as grain boundaries (GBs). However, as GBs absorb defects, they undergo internal transformations that limit their ability to capture defects indefinitely. Here, we show that, as the sink efficiency of GBs becomes exhausted with increasing irradiation dose, networks of irradiation loops form in the vicinity of saturated or near-saturated GB, maintaining and even increasing their capacity to continue absorbing defects. The formation of these networks fundamentally changes the driving force for defect absorption at GB, from "chemical" to "elastic." Using thermally-activated dislocation dynamics simulations, we show that these networks are consistent with experimental measurements of defect densities near GB. Our results point to these networks as a natural continuation of the GB once they exhaust their internal defect absorption capacity.

Dual-Responsive MXene-Functionalized Wool Yarn Artificial Muscles.

Fiber-based artificial muscles are promising for smart textiles capable of sensing, interacting, and adapting to environmental stimuli. However, the application of current artificial muscle-based textiles in wearable and engineering fields has largely remained a constraint due to the limited deformation, restrictive stimulation, and uncomfortable. Here, dual-responsive yarn muscles with high contractile actuation force are fabricated by incorporating a very small fraction (<1 wt.%) of Ti3C2Tx MXene/cellulose nanofibers (CNF) composites into self-plied and twisted wool yarns. They can lift and lower a load exceeding 3400 times their own weight when stimulated by moisture and photothermal. Furthermore, the yarn muscles are coiled homochirally or heterochirally to produce spring-like muscles, which generated over 550% elongation or 83% contraction under the photothermal stimulation. The actuation mechanism, involving photothermal/moisture-mechanical energy conversion, is clarified by a combination of experiments and finite element simulations. Specifically, MXene/CNF composites serve as both photothermal and hygroscopic agents to accelerate water evaporation under near-infrared (NIR) light and moisture absorption from ambient air. Due to their low-cost facile fabrication, large scalable dimensions, and robust strength coupled with dual responsiveness, these soft actuators are attractive for intelligent textiles and devices such as self-adaptive textiles, soft robotics, and wearable information encryption.