Browse Articles

Strength training intervention for hybrid workers: a randomised pilot feasibility trial

Abstract There is a major productivity and economic loss due to poor mental and physical health of the workforce. These factors can be improved by muscle strengthening exercise, which few people currently do. This study investigated the effect of a simple, time-efficient, resistance band training intervention on physical function and work-related outcomes in hybrid-working employees. In this pilot randomised controlled trial, untrained hybrid-working employees were randomised to a four-week resistance band training intervention (15 min 3x per week) or control group. Written and video instructions on the exercises were provided. The primary outcomes were physical function; 30s sit to stand and 30s push-up test, with secondary outcomes of perceived stress, work engagement and productivity. Outcome data was collected via an online survey. Fifty participants (age 46 ± 11 years; 41 female) were recruited to the study, with 46 completing the trial. Compared to the control group, the intervention group (adherence 89%) saw significant improvement in sit-to-stand (mean difference 5.34 ± 7.88 (SD), 95% CI [1.94, 8.75]; p = 0.04) and 30s push-up (mean difference 5.00 ± 3.37 (SD), 95% CI [3.54, 6.46]; p < 0.001) test score. Significant improvements were also found for perceived stress (p = 0.002), total work engagement (p = 0.008) and total productivity (p = 0.004). Four weeks of a simple, time-efficient resistance band training intervention improved lower and upper body physical function, stress, work engagement and productivity. This intervention may be a practical and simple strategy to improve adherence to muscle strengthening guidelines in hybrid working employees, whilst improving physical health and work-related outcomes.

Continuous targeted hypermutation with a tunable mutation window

Pathogen-focused metagenomic analysis reveals predominance of human rotavirus genotypes G3 and G12 in Zambian pediatric diarrhea cases

Triptolide ameliorates LPS-induced acute lung injury in Balb/c mice through gut-lung axis-mediated regulation of bile acid metabolism and gut microbiota

An antimicrobial peptide as a potential therapy for bacterial pneumonia that alleviates antimicrobial resistance

Data augmentation alters feature importance in XGBoost for CVD prediction

Enhanced range doppler mapping algorithm for passive GNSS based radar aerial target detection

Remission spectroscopy resolves the mechanism of action of bedaquiline within living mycobacteria

Abstract Bedaquiline, an ATP synthase inhibitor, is the spearhead of transformative therapies against drug-resistant Mycobacterium tuberculosis . Here, we use remission spectroscopy to measure the energy-transducing cytochromes within unperturbed, respiring suspensions of mycobacterial and human cells, allowing spectroscopic measurements of electron transport chains as they power living cells and respond to bedaquiline. No evidence is found for protonophoric or ionophoric uncoupling. Rather, by directly inhibiting ATP synthase, bedaquiline slows the respiratory supercomplex (Qcr:Cta; bcc : aa 3 ) by increasing the proton-motive force, causing sub-second redirection of electron flux through the cytochrome bd oxidase (CydAB) to O 2 . Electron flux redirection explains the idiosyncratic bedaquiline-induced increase in O 2 consumption rates previously observed. Redirection occurs as CydAB is present even in cells grown in plentiful O 2 . Applying the same approach to human cells did not detect bedaquiline-induced inhibition of mitochondrial function despite such inhibition being seen in isolated systems. Overall, we clarify how bedaquiline works, why different models for its action developed, and the mechanisms underlying the synergy of bedaquiline in combination regimes.

Design and analysis of a GaN-based 2D photonic crystal biosensor integrated with machine learning techniques for detection of skin diseases

Abstract Photonic crystals are prevalent in the detection of assorted diseases and malignancies such as vitiligo and cutis laxa. A 2D photonic crystal utilizing GaN is demonstrated to detect skin diseases, highlighting its substantial relevance to the photonic sensing community. Various parameters analysed are quality factor, wavelength sensitivity, FWHM, figure of merit and detection limit. The analysis of sensor characteristics demonstrated that GaN is a highly suitable material for detecting vitiligo and cutis laxa. Topology of design is crucial to focus the light on to the sensing region. Opti FDTD tool was used for the design and simulation of the sensor. The photonic band gap was simulated and it was observed that it contained one band gap region. The design provided high transmission efficiency and sensitivity. The various skin abnormalities related to vitiligo and cutis laxa could be easily detected from the results. Machine learning models such as K-nearest neighbor, Random Forest, Support Vector Machine and Multi-Layer Perceptron were adopted to enhance the sensor system to classify the data with higher accuracy.

Reduced Fc-mediated antibody responses after COVID-19 mRNA vaccination in a cohort of people living with HIV-1

Abstract HIV-1 infection has been associated with increased COVID-19-related hospitalization and greater SARS-CoV-2 shedding. People living with HIV-1 (PLWH) also have higher risks to other respiratory infections and lower response rates to influenza and pneumococcal vaccines, even after antiretroviral therapy. This observational study evaluated serum antibody responses after mRNA COVID-19 vaccination in elderly male cohorts of PLWH on antiretroviral therapy and people without HIV-1 (PWOH). Specifically we measured the titers, isotypes/subtypes, and functions of antibodies after the third dose of mRNA COVID-19 vaccines. SARS-CoV-2-specific total immunoglobulin (Ig) titers were significantly higher in blood samples from PLWH vs. PWOH. Notably, PLWH had higher levels of IgG2 and IgG4, two IgG subtypes with minimal Fc activities. Correspondingly, lower Fc capacities were displayed by PLWH, and they correlated inversely with SARS-CoV-2-specific IgG4 levels. Our data point to changes in SARS-CoV-2-specific antibody distribution elicited in PLWH after mRNA COVID-19 vaccinations, resulting in the curtailments in the Fc functionalities that may contribute to suboptimal antiviral responses.

The AML cellular state space unveils NPM1 immune evasion subtypes with distinct clinical outcomes

Abstract Acute myeloid leukemia is a genetically and cellularly heterogeneous disease. We characterize 120 AMLs using genomic and transcriptomic analyses, including single-cell RNA sequencing. Our results reveal an extensive cellular heterogeneity that distorts the bulk transcriptomic profiles. Selective examination of the transcriptional signatures of >90,000 immature AML cells identifies four main clusters, thereby extending current genomic classification of AML. Notably, NPM1 -mutated AML can be stratified into two clinically relevant classes, with NPM1 class I associated with downregulation of MHC class II and excellent survival following hematopoietic stem cell transplantation. NPM1 class II is instead associated with resistance to allogeneic T cells in an ex vivo co-culture assay, and importantly, dismal survival following hematopoietic stem cell transplantation. These findings provide insights into the cellular state space of AML, define diagnostic entities, and highlight potential therapeutic intervention points.

A fusion approach of YOLOv8 and CNN-Transformer for End-to-End road anomaly detection

Abstract Surveillance cameras are common in both the private and public sectors for security and monitoring, and closed-circuit television (CCTV) systems are used for surveillance, generating large amounts of video data that cannot be manually monitored 24/7. The traditional approach to analysis is time-consuming and inefficient, and there is a growing need for automated surveillance systems that can recognize and classify anomalies. The research area that has been the most challenging to solve is AD systems that detect anomalies in data that is not structured according to the normal patterns. RNNs are slow and have difficulty identifying anomalies in the road that occur in multiple frames at the same time, whereas CNNs are limited in extracting temporal features from objects and generally disregard the background noise in video frames. In this study, a new framework for background removal is presented that removes the irrelevant background elements during object recognition. This framework saves temporal and spatial information over frames and uses YOLOv8 and a spatial-temporal adaptive fusion method with an end-to-end model based on a CNN encoder and a Transformer decoder for parallel video investigation. The proposed method was tested on the UCF Crime dataset and a custom Road Anomaly Dataset (RAD), and the accuracy of the framework was 89.90% on the UCF Crime dataset and 98.28% on the RAD dataset.

Depth-estimation of stiffness singularity in an elastic object via directional touch sensing using microfinger with tactile sensor

Abstract Understanding object information during robotic hand grasping is a key goal in robotics. Researchers have integrated tactile sensors to replicate artificial haptics on humanoid robot fingertips, but robotic grasping has yet to be fully applied in palpation-based medical diagnosis. Current techniques, such as vibration-based ultrasound-assisted surgeries, face limitations in diagnosis due to anatomical constraints or surgical access issues. To address this, we explored palpation-assisted surgeries using a microfinger, a miniaturized version of a human finger. We developed micromachine-based palpation techniques for advanced minimally invasive diagnosis using endoscopes. Specifically, we developed a microfinger with artificial muscle and tactile sensors, designed to detect stiffness singularities in pseudo-biological tissues. Our microfinger, thin and small, exerted a pushing force greater than 1 N and performed directional palpation. Next, we proposed an algorithm for estimating three-dimensional coordinates, thus transcending the existing two-dimensional singularity-estimation method. Consequently, we achieved touch sensing on silicone gel blocks using a small rigid ball, with depth-estimation of approximately ± 1.3 mm at a depth of 15 mm. The directivity of the microfinger enabled three-dimensional positional estimation of the singular point. We present a breakthrough for microfinger-based palpation technology for medical diagnosis, accelerating the advancement of robotics-based palpation-driven minimally invasive techniques.

Ecological similarities and dissimilarities between donor and recipient regions shape global plant naturalizations

Abstract A central question in ecology is why alien species naturalize successfully in some regions but not in others. While some hypotheses suggest aliens are more likely to naturalize in environments similar to donor regions, others suggest they thrive in regions where certain characteristics are different. Using the native (i.e., donor) and recipient distributions of 11,604 naturalized alien plant species across 650 regions globally, we assess whether plants are more likely to naturalize in regions that are ecologically similar or dissimilar to their donor regions. Our results show that species are more likely to naturalize in recipient regions where climates are similar and native floras are phylogenetically similar to those of their donor regions, indicating that pre-adaptation to familiar biotic and abiotic conditions facilitates naturalization. However, naturalization is also more likely in regions with lower native flora diversity and more intense human modification than in the species’ native range. Among all predictors, climate similarity and difference in native flora diversity emerge as the strongest predictors of naturalization success. In conclusion, ecological similarity in some factors but dissimilarity in others between donor and recipient regions promote the naturalization of alien plants and contribute to their uneven global distribution patterns.

Global, regional, and national burden of ischemic heart disease in young and middle-aged population from 1990 to 2021

Evaluation of performance, essential oil composition, and genetic parameters of Grammosciadium platycarpum populations toward developing high-linalool industrial cultivars

General-purpose mechanical computing enabled by origami circuit reconfiguration with robotic addressing and activation

Parametric analysis of biodiesel synthesis from palm oil using homogenous base catalyst: experimental and numerical investigation

OptiNet-B3: a lightweight explainable deep learning model for multiclass classification of fruit and leaf diseases

Abstract Early and accurate detection of diseases is very important for the health of crops and ensuring sustainable agricultural productivity. This paper proposes OptiNet-B3, a novel approach and an efficient deep model for the multiclass classification of fruit and leaf diseases for apples, bananas, and oranges. Through two diverse and comprehensive image datasets, the model performs well for both fruit 13,602 images and leaf 11,199 images classification. OptiNet-B3 optimizes learning in low computational budget by integrating Mish activation, Convolutional Block Attention Module (CBAM), Group Normalization, and knowledge distillation. Great care in preprocessing and augmenting data was taken to improve generalization. Comparison with state-of-the-art models-including DenseNet121, ResNet50, MobileNetV3, and InceptionV3-based models-reveals that OptiNet-B3 substantially outperforms in terms of accuracy, with 98.12% and 99.23% on the fruit and leaf datasets, respectively. Due to its light-weight architecture, real-time deployment for in-field diagnosis on mobile and edge devices is much more feasible. The results underscore the potential of explainable, AI-driven tools in transforming plant disease management practices.

A lignin-enrichment approach for processing natural wood into highly NIR-selective optical filters