Browse Articles

Author Correction: Regulating triacylglycerol cycling for high-efficiency production of polyunsaturated fatty acids and derivatives

Theoretical error modeling and analysis of strapdown inertial navigation system alignment under zero-velocity conditions

A comparative gas chromatography-mass spectrometry (GC-MS) profiling of Egyptian and Indian ashwagandha (Withania somnifera) root extracts

Abstract Ashwagandha ( Withania somnifera ) is a woody shrub that grows up to 2 feet in height. Ashwagandha roots extract has been used as a traditional herbal medicine for centuries because it has phytochemicals. This study aimed to analyse the chemical composition of both the Egyptian and Indian ashwagandha roots’ extracts by Gas Chromatography-Mass Spectrometry (GC-MS). Both extracts analysis by GC-MS found that they contain essential compounds that have important health benefits. Both Egyptian and Indian ashwagandha extracts contain phytosterols and fatty acids with different area percentages. From the comparison between the ashwagandha extracts of the present study, it is revealed that the Egyptian ashwagandha extract contains higher area percentages of campesterol (28.70%), stigmasterol (16.11%), n-hexadecanoic acid (17.43%), octadecanoic acid (2.59%), 1-heptatriacotanol (1.64%) and atropine (1.49%) than those present in the Indian extract. On the other side, the Indian ashwagandha extract contains higher area percentages of ҫ-sitosterol (20.34%), oleic acid (9.14%), 9,12-octadecadienoic acid (Z, Z) (8.62%) and hexadecanoic acid, methyl ester (0.96%) than those present in the Egyptian extract. Depending on the analysis of the Egyptian and Indian ashwagandha extracts, it is concluded that both extracts contain essential bioactive compounds with vital effects on human health. The Egyptian ashwagandha extract is available, low cost and contains higher area percentages in the most bioactive compounds than were found in the Indian ashwagandha extract.

Topoisomerase IIb binding delineates localized mutational processes and driver mutations in cancer genomes

Comparison of metaheuristic algorithms set-point tracking-based weight optimization for model predictive control

Improved logistic regression combined with recursive feature elimination for investigating key influencing factors of Tornado Kick Turn in Wushu Routines

Psilocybin alters visual contextual computations

Enhanced blood parasite species identification using V4–V9 18S rDNA barcoding by universal primers on a nanopore platform

Fear of negative evaluation mediates and core self-evaluation moderates the relationship between social comparison orientation and social network addiction

Reconciling time and prediction error theories of associative learning

Pose estimation of differential drive robots using deep learning and raw sensor inputs

Abstract This paper presents an estimation method for determining the position and orientation of a real mobile robot using raw data from an Inertial Measurement Unit (IMU) sensor, alongside linear and angular velocities obtained from simulation. The dataset was collected using a real TurtleBot3 differential drive wheeled mobile robot in the ROS-Gazebo simulation environment, encompassing 2018 routes-2009 from simulation and 9 from real-world experiments-each consisting of five randomly generated waypoints. To improve the accuracy of the estimation models, noise from the real IMU sensor was incorporated into the input data, and velocities derived from the pure pursuit algorithm were also included. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gradient Boosting (GB), and Random Forest (RF) models were employed to estimate the robot’s position and orientation, and their performance was compared across both simulated and experimental scenarios. The results indicate that the CNN architecture consistently outperforms other models across all routes. Unlike many existing studies, this work directly utilizes raw sensor data without applying any feature extraction techniques, highlighting its novelty and contribution to the field.

Synergistic effects of graphene oxide loading and particle size in ice-templated carboxymethyl cellulose–based aerogels on dye removal

Tailoring conductive nanofiller alignment for high actuation strain and output force in electroactive polymers

Improved COOT optimization: An approach to multilevel thresholding in image segmentation

Abstract Image thresholding is one of the fastest and easiest approach for image segmentation and serves as a preprocessing step in computer vision and image processing applications, such as surveillance, image perception, scene understanding, artificial intelligence, augmented reality, biomedical imaging, remote sensing, image fusion, etc. Metaheuristic approaches have recently gained attention in the field of image segmentation. The standard COOT algorithm is a good alternative for solving complex optimization problems; however, it suffers from drawbacks, such as stagnation and insufficient balance between exploration and exploitation. This paper proposes the application of an improved COOT (ICOOT) optimization algorithm for multilevel image thresholding. In the proposed method, Lévy flights are incorporated to enhance the exploration capability of COOT, and quasi-opposition-based learning is introduced to improve the exploitation capacity and balance exploration and exploitation. To verify the efficiency of the ICOOT algorithm, it has been tested for solving complex optimization problems from the CEC’17 benchmark. The ICOOT algorithm is also employed to calculate the threshold values in image segmentation via Otsu’s entropy as an objective function for practical purposes. In addition to testing its performance in image thresholding, the proposed ICOOT algorithm has also been tested on benchmark images and computed tomography (CT) images from COVID-19 patients. The presented approach is compared with various state-of-the-art algorithms, and the ICOOT results outperform them.

Design of a textile-based UHF RFID sensor for high read range and multilevel diaper moisture sensing

Reconciling crop production, climate action and nature conservation in Europe by agricultural intensification and extensification

Abstract Agricultural production in areas characterized by low productivity, steep slopes, and high fragmentation is usually associated with higher-than-average management costs and environmental impacts. Abandoning this suboptimal cropland to vegetation regrowth, while optimizing crop production in other locations, is an attractive strategy for supporting climate and biodiversity targets without compromising food security. However, it has not yet been explored within the specific context of European agriculture. Here, we identify the area extent of suboptimal cropland in Europe and assess if crop production losses from its revegetation can be compensated by implementing scenarios of cropland intensification or extensification elsewhere. We found 24.2 million hectares of suboptimal cropland, of which 66% is at degradation risk and about 50% is within biodiversity priority areas. Reducing agricultural intensity in 16.4–30.9 million hectares of the remaining cropland by introducing parcels of trees into the agricultural landscape (extensification), together with strategic crop-switching optimization, can entirely offset crop production losses from revegetation of suboptimal cropland. This scenario has the potential to mitigate up to 40% of European agricultural emissions of greenhouse gases and reduce cropland pressure on biodiversity by 20%. In contrast, cropland intensification achieves lower carbon-biodiversity benefits, with risks that crop losses are not fully compensated.

Inhibition of FKBP5 alleviates obstetric antiphospholipid syndrome by regulating macrophage polarization

Abstract Immune homeostasis disturbance within the placental microenvironment plays a key role in the pathogenesis of obstetric antiphospholipid syndrome (OAPS), although the underlying mechanisms remain poorly understood. This study aimed to investigate the immune regulatory role of FK506-binding protein 5 (FKBP5) in OAPS-associated pathological pregnancies. RNA sequencing and immunocytotyping were performed on decidual tissues from OAPS patients and healthy controls. Bone marrow-derived macrophages (BMDMs) polarization assays and macrophage‒trophoblast coculture models were employed to explore the effects and mechanisms of FKBP5 on macrophage polarization at the maternal–fetal interface. We applied an animal model of OAPS and comprehensively evaluated the therapeutic effects of the FKBP5 inhibitor SAFit2 on OAPS. The results revealed significantly greater expression of FKBP5 and inflammation-related factors in OAPS patients than in healthy controls. An imbalance in macrophage polarization was observed, with an increase in M1 (iNOS + /CD86 + ) macrophages and a decrease in M2 (Arg-1 + /CD206 + ) macrophages in OAPS patients. In vitro studies demonstrated that FKBP5 may promote M1 polarization via the JAK1/STAT1 pathway and may inhibit M2 polarization through the PPARγ/STAT6 pathway. FKBP5-induced aberrant macrophage polarization impaired trophoblast migration, invasion, and proliferation. In vivo, FKBP5 knockdown in mice alleviated aPLs-induced placental injury, facilitated epithelial‒mesenchymal transition (EMT), and restored the M1/M2 macrophage balance. Furthermore, SAFit2 ameliorated pregnancy complications in the mouse OAPS model. FKBP5 regulates immune dysfunction associated with OAPS by modulating macrophage polarization, and its inhibition could mitigate aPLs-induced placental injury. These findings suggest that FKBP5 may serve as a promising therapeutic target for OAPS.

Normative data for insulin-like growth factor-1 and insulin-like growth factor binding protein-3 and their determinants in healthy adult males from india: the INDIIGo study

Variant-specific antibody correlates of protection against SARS-CoV-2 Omicron symptomatic and overall infections

Abstract Vaccination and prior infection elicit neutralizing antibodies targeting SARS-CoV-2, yet the quantitative relationship between serum antibodies and infection risk against viral variants remains uncertain, particularly in underrepresented regions. We investigated the protective correlation of pre-exposure serum neutralizing antibody levels, employing a panel of SARS-CoV-2 pseudoviruses (Omicron BA.1, Omicron BA.2, and ancestral D614G), and Spike-binding antibody levels, with symptomatic BA.1 or BA.2 SARS-CoV-2 infections and overall infection, in 345 household contacts from a SARS-CoV-2 household cohort study in Nicaragua. A four-fold increase in homotypic-neutralizing (e.g., BA.1-neutralizing vs. BA.1 exposure) titers was correlated with protection from symptomatic infections (BA.1 protection: 28% [95%CI 12–42%]; BA.2 protection: 43% [20–62%]), and ancestral-neutralizing titers were also correlated with protection from either variant, but only at higher average levels than homotypic. Mediation analyses revealed that homotypic and D614G-neutralizing antibodies mediated protection from infection and symptomatic infection both from prior infection and vaccination. These findings underscore the importance of monitoring variant-specific antibody responses and highlight that antibodies targeting circulating strains may be more predictive of protection from infection. Nevertheless, ancestral-strain-neutralizing antibodies remain relevant as a correlate of protection. Our study emphasizes the need for continued efforts to assess antibody correlates of protection.

Higher hospital level does not improve 30-day survival after road traffic accidents

Abstract Globally, road traffic accidents (RTAs) remain a major cause of death, particularly among individuals aged 15–30 years. While Sweden has been at the forefront of traffic safety through the Vision Zero initiative, in-hospital management remains crucial in determining RTA outcomes. Drawing on North American evidence suggesting improved survival at trauma centres, the Swedish healthcare system has increasingly emphasised trauma centralisation. However, comprehensive national data from Sweden are scarce. Given the country’s unique demographic and geographic characteristics, including vast sparsely populated areas, direct comparisons with other Western systems are challenging. We analysed the epidemiology and risk factors for 30-day mortality among 95,954 RTA-related hospital admissions in Sweden between 2008 and 2021. Predictors included the ICD-based Injury Severity Score (ICISS), age, sex, Charlson Comorbidity Index (CCI), year of event, and hospital level. Mortality risk was modelled using explainable artificial intelligence (XAI) via Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP), alongside conventional multivariable logistic regression for comparison. The most influential predictors of 30-day mortality, in descending order, were ICISS, age, CCI, event year, hospital level, and sex. A clear trend toward centralisation was observed, with Level 1 hospitals admitting the most severely injured patients. However, after risk adjustment, the hospital level was not independently associated with 30-day mortality. The XAI model outperformed logistic regression in both discrimination and calibration, confirming these findings. This study represents a comprehensive national analysis of in-hospital outcomes following RTAs in Europe. ICISS, age, sex, and comorbidity influenced mortality risk, while overall survival improved over time. The assumption that trauma centralisation confers a universal survival advantage does not appear to hold in the Swedish context. These findings underscore the need to re-evaluate trauma system design under Scandinavian conditions—ensuring that timely access to hospital care is not compromised by centralisation.