Browse Articles
Discover research articles across all indexed journals
Rational engineering of DNA-nanoparticle motor with high speed and processivity comparable to motor proteins
A machine learning based variable selection algorithm for binary classification of perinatal mortality
The identification of significant predictors with higher model performance is the key objective in classification domain. A machine learning-based variable selection technique termed as CARS-Logistic model is proposed by coupling competitive adaptive re-weighted sampling(CARS) and logistic regression for binary classification. Based on five assessment criteria, the proposed method is found to be more efficient than Forward selection logistic regression model. The CARS-Logistic model is executed to determine the significant factors of perinatal mortality in Pakistan. The identified hazards communicated social, cultural, financial, and health-related characteristics which contain key information about perinatal mortality in Pakistan for policymakers.
Control of water for high-yield and low-cost sustainable electrochemical synthesis of uniform monolayer graphene oxide
Abstract With the rapid development of graphene industry, low-cost sustainable synthesis of monolayer graphene oxide (GO) has become more and more important for many applications such as water desalination, thermal management, energy storage and functional composites. Compared to the conventional chemical oxidation methods, water electrolytic oxidation of graphite-intercalation-compound (GIC) shows significant advantages in environmental-friendliness, safety and efficiency, but suffers from non-uniform oxidation, typically ~50 wt.% yield with ~50% monolayers. Here, we show that water-induced deintercalation of GIC is responsible for the non-uniform oxidation of the water electrolytic oxidation method. Using in-situ experiments, the control principles of water diffusion governing electrochemical oxidation and deintercalation of GIC are revealed. Based on these principles, a liquid membrane electrolysis method was developed to precisely control the water diffusion to achieve a dynamic equilibrium between oxidation and deintercalation, enabling industrial sustainable synthesis of uniform monolayer GO with a high yield (~180 wt.%) and a very low cost (~1/7 of Hummers’ methods). Moreover, this method allows precise control on the structure of GO and the synthesis of GO by using pure water. This work provides new insights into the role of water in electrochemical reaction of graphite and paves the way for the industrial applications of GO.
RORγt inverse agonists demonstrating a margin between inhibition of IL-17A and thymocyte apoptosis
Multiple genetic associations suggest a causative relationship between Th17-related genes coding for proteins, such as IL-17A, IL-23 and STAT3, and psoriasis. Further support for this link comes from the findings that neutralizing antibodies directed against IL-17A, IL-17RA and IL-23 are efficacious in diseases like psoriasis, psoriatic arthritis and ankylosing spondylitis. RORγt is a centrally positioned transcription factor driving Th17 polarization and cytokine secretion and modulation of RORγt may thus provide additional benefit to patients. However, RORγt also plays a role in the normal development of T cells in the thymus and genetic disruption of RORγt in the mouse leads to the development of lymphoma originating in the thymus. Whilst it is not established that down-regulation of RORγt activity would lead to the same consequence in humans, further understanding of the thymus effects is desirable to support progress of this target as a potential treatment of Th17-driven disease. Herein we present the characterisation of recently disclosed RORγt inverse agonists demonstrating target engagement and efficacy in vitro and in vivo against Th17 endpoints but requiring higher concentrations in vitro to affect thymocyte apoptosis.
Shear localisation controls the dynamics of earthquakes
Abstract Earthquakes are produced by the propagation of rapid slip along tectonic faults. The propagation dynamics is governed by a balance between elastic stored energy in the surrounding rock, and dissipated energy at the propagating tip of the slipping patch. Energy dissipation is dictated by the mechanical behaviour of the fault, which is itself the result of feedbacks between thermo-hydro-mechanical processes acting at the mm to sub-mm scale. Here, we numerically simulate shear ruptures using a dual scale approach, allowing us to couple a sub-mm description of inner fault processes and km-scale elastodynamics, and show that the sudden localisation of shear strain within a shear zone leads to the emergence of classical cracks driven by a constant fracture energy. The fracture energy associated to strain localisation is substantially smaller than that predicted in theoretical and numerical models assuming uniform shearing within the shear zone. We show the existence of a unique scaling law between the localised shearing width and the rupture speed. Our results indicate that earthquakes are likely to be systematically associated to extreme strain localisation.
A co-produced mixed methods protocol: Exploring perceptions of oral health care and quality of life in people with mental health conditions
This planned mixed methods protocol is designed to explore oral health care for individuals living with serious mental health conditions (SMHC). It was co-produced by academics, people with lived experience of mental health conditions, and oral and mental health clinicians. The study seeks to explore oral health quality of life predictors and oral health care experiences of people diagnosed with serious mental health conditions (e.g. schizophrenia, bipolar disorder, major depression and general anxiety disorder) about their oral health care experiences. The research research will generate recommendations for creating positive oral healthcare experiences for people living with SMHC. Research co-production with people diagnosed with mental health conditions is a recommended strategy to improve the utility and relevance of health research, as well as empower a disenfranchised population.
Transducing chemical energy through catalysis by an artificial molecular motor
Abstract Cells display a range of mechanical activities generated by motor proteins powered through catalysis 1 . This raises the fundamental question of how the acceleration of a chemical reaction can enable the energy released from that reaction to be transduced (and, consequently, work to be done) by a molecular catalyst 2–7 . Here we demonstrate the molecular-level transduction of chemical energy to mechanical force 8 in the form of the powered contraction and powered re-expansion of a cross-linked polymer gel driven by the directional rotation of artificial catalysis-driven 9 molecular motors. Continuous 360° rotation of the rotor about the stator of the catalysis-driven motor-molecules incorporated in the polymeric framework of the gel twists the polymer chains of the cross-linked network around one another. This progressively increases writhe and tightens entanglements, causing a macroscopic contraction of the gel to approximately 70% of its original volume. The subsequent addition of the opposite enantiomer fuelling system powers the rotation of the motor-molecules in the reverse direction, unwinding the entanglements and causing the gel to re-expand. Continued powered twisting of the strands in the new direction causes the gel to re-contract. In addition to actuation, motor-molecule rotation in the gel produces other chemical and physical outcomes, including changes in the Young modulus and storage modulus—the latter is proportional to the increase in strand crossings resulting from motor rotation. The experimental demonstration of work against a load by a synthetic organocatalyst, and its mechanism of energy transduction 6 , informs both the debate 3,5,7 surrounding the mechanism of force generation by biological motors and the design principles 6,10–14 for artificial molecular nanotechnology.
Continuous photo-oxidation of methane to methanol at an atomically tailored reticular gas-solid interface
RETRACTED: Machine vision model for drip leakage detection of pipeline
The prevailing trend in industrial equipment development is integration, with pipelines as the lifeline connecting system components. Given the often harsh conditions of these industrial equipment pipelines, leakage is a common occurrence that can disrupt normal operations and, in severe cases, lead to safety accidents. Early detection of even minor drips at the onset of leakage can enable timely maintenance measures, preventing more significant leaks and halting the escalation of pipeline failures. In light of this, our study investigates a method for monitoring pipe drips in industrial equipment using machine vision technology. We propose a machine vision model specifically designed for pipe drip detection, aiming to facilitate monitoring of pipe system drips. The system designed to collect the image of the droplet side cross-section with a Charge charge-coupled device (CCD) industrial camera, is aided by the computer image processing system used to analyze and process the collected images. Image enhancement technology is applied to improve the visibility of the image and image filtering technology is applied to remove the noise of the image. With the help of image segmentation technology, target droplet identification and division are achieved. Morphological reconstruction and region-filling techniques are used to remove the noise caused by shooting in the side cross-section image, such as hollow, reflection, and irregular droplet edge, to upgrade the quality of the solution droplet edge. The mathematical model is established for boundary position points extracted from the droplet side cross-section image. Then, the fitting droplet image is drawn. The droplet volume is obtained by calculating the volume of the rotating body. The two-dimensional image of the target droplet is obtained dynamically through the camera capture technology. The droplet boundary extraction algorithm is proposed, and the three-dimensional model of the target droplet is established, so the volume calculation problem of the droplet is solved, which provides a way of thinking for drip leakage detection of the pipeline.
Pregnancy entails a U-shaped trajectory in human brain structure linked to hormones and maternal attachment
Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification
Skin cancer is considered globally as the most fatal disease. Most likely all the patients who received wrong diagnosis and low-quality treatment die early. Though if it is detected in the early stages the patient has fairly good chance and the aforementioned diseases can be cured. Consequently, diagnostic identification and management of the patient at this level becomes a rather enormous task. This paper offers a cutting-edge hybrid deep learning approach of better segmentation and classification of skin lesions. The proposed method incorporates three key stages: preprocessing, segmentation of lesions, and classification of lesions. By the stage of preprocessing, a morphology-based technique takes out hair so as to enhance the segmentation precision to use the cleansed images for subsequent analysis. Segmentation cuts off the lesion from the surrounding skin, giving the classification phase a dedicated area of interest and the ability to clear the background noise that may affect classification rates. The isolation enables the model to better analyze anatomical lesion features in order to achieve accurate benign and malignant classifications. Using ResUNet++, the cutting-edge deep learning architecture, we achieved accurate lesion segmentation. Next, we will modify and use an AlexNet-Random Forest (AlexNet-RF) based classifier for robust lesion classification. The proposed hybrid deep learning model is intensively validated on the Ham10000 data set which is one of the most popular datasets for skin lesions analysis. The obtained results show that the utilized approach, compared to the previous ones, is more effective, giving better segmentation and classification results. This method takes advantage of ResUNet++ strong classification skill and modified AlexNet-Random Forest robustness for more accurate segmentation. There is a high probability that ResUNet++, which is highly proficient at medical image segmentation, can produce better segmentation of lesions than the simpler models. The composition of AlexNet’s extraction of features with Random Forest ability to reduce overfitting possibly may be more precise in the classification when compared to using only one model.
Spontaneous curvature in two-dimensional van der Waals heterostructures
Correction: Defending Against Advanced Persistent Threats Using Game-Theory
Developing an alternative medium for in-space biomanufacturing
Protective effects of Euphorbia heterophylla against testicular degeneration in streptozotocin-induced diabetic rats in relation to phytochemical profile
Background Diabetes mellitus (DM) poses a major risk to human health due to an array of implications, one of which is a detrimental effect on the testicular and reproductive functions. Euphorbia heterophylla is widely recognized for its medicinal properties worldwide. Methods and findings The objective of this study was to profile E. heterophylla ethanol extract (EH-EtOH) and elucidate its protective role in oxidative stress, relieving inflammatory action of hyperglycemia-induced testicular degeneration and restoring the normal histological structure with physiological properties of testicular tissue in streptozotocin (STZ)-induced DM. High-resolution ultra-performance liquid chromatography-mass spectrometry (UHPLC-ESI-Orbitrap-MS) analysis was employed to analyze the compounds present in EH-EtOH. The protective effect of EH-EtOH against testicular degeneration in the rat model of DM was evaluated by measuring improvements in blood glucose levels, body weight, testicular inflammation, oxidative damage, testicular microcirculation impairment, and apoptosis of testicular cells induced by STZ. The chemical profiling of EH-EtOH revealed the presence of 52 compounds, including phenolic acids, flavonoids, coumarins, phloroglucinols, and triterpenes. Notably, this study identified isovitexin-C-hexoside, isorhamnetin-O-hexoside, diosmetin, and halfordin for the first time in Euphorbia species. Treatment with EH-EtOH effectively mitigated the damage caused by STZ, as evidenced by restored testosterone (T4) levels and antioxidant capacity, reduced expression of pro-inflammatory cytokines, improved testicular microcirculation, and inhibition of apoptosis in the testes. Conclusions These results emphasize the potential therapeutic effect of E. heterophylla on DM related to male infertility and reproductive dysfunctions via its antioxidant/angiogenetic /anti-apoptotic effect.
Removal of dissolved organic carbon in the West Pacific hadal zones
Correction: Identifying suitable tester for evaluating Striga resistant lines using DArTseq markers and agronomic traits
Author Correction: LLM-driven multimodal target volume contouring in radiation oncology
Novel deep reinforcement learning based collision avoidance approach for path planning of robots in unknown environment
Reinforcement learning is a remarkable aspect of the artificial intelligence field with many applications. Reinforcement learning facilitates learning new tasks based on action and reward principles. Motion planning addresses the navigation problem for robots. Current motion planning approaches lack support for automated, timely responses to the environment. The problem becomes worse in a complex environment cluttered with obstacles. Reinforcement learning can increase the capacity of robotic systems due to the reward system’s capability and feedback to the environment. This could help deal with a complex environment. Existing algorithms for path planning are slow, computationally expensive, and less responsive to the environment, which causes late convergence to a solution. Furthermore, they are less efficient for task learning due to post-processing requirements. Reinforcement learning can address these issues using its action feedback and reward policies. This research presents a novel Q-learning-based reinforcement algorithm with deep learning integration. The proposed approach is evaluated in a narrow and cluttered passage environment. Further, improvements in the convergence of reinforcement learning-based motion planning and collision avoidance are addressed. The proposed approach’s agent converged in 210th episodes in a cluttered environment and 400th episodes in a narrow passage environment. A state-of-the-art comparison shows that the proposed approach outperformed existing approaches based on the number of turns and convergence of the path by the planner.