Browse Articles

Using internal air injection for fuel mixing enhancement of annular hydrogen jet behind an inclined strut in a supersonic combustor: computational study

Automated cardiac MRI analysis for robust profiling of heart failure models in mice

Diagnostic efficacy of targeted next-generation sequencing in suspected lower respiratory tract infections of patients with cancer: a retrospective study

A glasses-free 3D display uses AI to direct images straight to the viewer’s eyes

Research on the influence of different heat release rates on the throttling effect and critical wind speed in tunnel engineering

Tailored Interphase Chemistry Enables Ultra-Stable O3-Type Sodium Layered Oxide Cathodes

New insights into recently emerged Leucocytozoon caulleri infection in Egyptian broiler flocks through clinical, pathological, hematological, and molecular investigation

Abstract Leucocytozoonosis is a vector-borne blood protozoal disease that affects birds worldwide. Although Egypt has a large-scale poultry industry, data on Leucocytozoon infections in broilers remain limited. This study reports the first detection of leucocytozoonosis in 24 broiler farms (180,000 birds) in El-Sharkia governorate, Egypt. The affected broiler flocks were raised in open poultry farms located near agricultural rice fields and water sources during the late summer season, which are conditions favorable for vector proliferation. Clinically, infected birds exhibited depression, anorexia, weight loss, and anemia, with mortality rates ranging from 0.5% to 2%. Postmortem examination revealed widespread hemorrhages across multiple organs and a large pool of blood in the abdominal cavity. Microscopic examination of blood smears and cell block preparations showed the presence of various gametocytes within leukocytes and erythrocytes. Histopathological examination identified characteristic megaloschizonts in multiple organs, including the kidneys, liver, lungs, muscles, heart, intestine, pancreas, and spleen, accompanied by hemorrhages, necrosis, and mononuclear cell infiltration, primarily lymphocytes and macrophages. Molecular identification using real-time PCR confirmed L. caulleryi infection, corroborating the hematological and pathological findings. This study provides key insights into L. caulleryi diagnosis, promotes awareness among veterinarians and poultry producers, and highlights the need for improved biosecurity.

Malaria pigment hemozoin drives M1 pro-inflammatory macrophage polarization in vitro

Biomarker studies to predict outcomes of patients with COVID-19 related acute respiratory distress syndrome measured pre and post initiation of veno venous extracorporeal membrane oxygenation

Abstract Veno-venous extracorporeal membrane oxygenation (V-V ECMO) is a resource intensive and expensive therapy increasingly used to treat a select group of patients with severe acute respiratory distress syndrome (ARDS). High levels of Interleukin-10 (IL-10) are reported to be predictive of mortality in non-COVID ARDS patients receiving ECMO. In this study we evaluated if levels of biomarkers including IL-10 can predict mortality if measured prior to ECMO initiation in patients with COVID-19 ARDS. A total of sixteen patients with COVID-related ARDS undergoing treatment with ECMO were included in the pre-ECMO biomarker analysis, while samples from a total of 22 patients with post-ECMO samples were analyzed. Nine of sixteen patients with pre-ECMO samples (56%) survived to hospital discharge. There was no difference in baseline demographics between survivors and non-survivors. In univariate analysis, survivors had no difference in IL-10 levels prior to ECMO initiation although we identified several other biomarkers (Ang 1 and TNF-alpha with logistical regression, Mitochondrial DNA and IP-10 with Kaplan–Meier analysis) that differed significantly between survivors and non-survivors. Moreover, as opposed to a prior study, area under the curve (AUC) analysis showed that IL-10 levels were not predictive of survival. We advise caution for utilizing a single biomarker as outcome predictor in this highly complex population with often long clinical courses despite our promising results.

Combined in vitro and computational studies on the antioxidant and anticancer effects of Caesalpinia digyna (Rottl.) fruit extracts

Si-Miao-Yong-An decoction suppresses necroptosis by regulating the p53 pathway in myocardial ischemia/reperfusion injury

Pseudomonas aeruginosa-derived DnaJ functions as a novel immunomodulator inducing IFNβ via CME–SGK1–IRF3 axis in macrophages

Abstract Type I interferons (IFNs), particularly IFNβ, play a pivotal role in coordinating innate and adaptive immune responses during microbial infections. Pseudomonas aeruginosa ( P. aeruginosa ), a clinically significant opportunistic pathogen, is able to induce IFNβ expression; however, the specific microbial factors responsible for this induction remain poorly characterized. In this study, we identify DnaJ, a heat shock protein 40 (HSP40) homolog derived from P. aeruginosa , as a novel microbial inducer of IFNβ expression in macrophages. Among the bacterial HSP homologs tested, DnaJ elicits the most robust IFNβ production via a mechanism dependent on Toll-like receptor 4 (TLR4) and the TRIF–TBK1–IRF3 signaling axis. Mechanistic analysis revealed that clathrin-mediated endocytosis (CME) is required for DnaJ-induced IRF3 activation, and that serum/glucocorticoid regulated kinase 1 (SGK1) functions downstream of CME to promote IRF3 phosphorylation and subsequent IFNβ expression. Consistent with these findings, human HSP40 similarly induced IFNβ expression through the conserved CME–SGK1–IRF3 pathway, indicating that both bacterial and host-derived HSP40 proteins can serve as immune modulators. Collectively, these findings identify P. aeruginosa DnaJ as a potent immunomodulatory ligand capable of inducing IFNβ expression. DnaJ may therefore represent a promising candidate for therapeutic modulation of innate immunity or as an adjuvant in antimicrobial immunotherapy.

Photobombing satellites could ruin the night sky for space telescopes

Evaluating the larvicidal effect of Bacillus Thuringiensis M-H-14 on Aedes aegypti larvae under laboratory and semi-field conditions in Southern Iran

Electrochemical and colorimetric sensing of P-xylene using doped C60 fullerenes: a dual approach to medical and environmental applications

Abstract P-xylene is a type of aromatic hydrocarbon that has growing biomedical and environmental importance. It has been identified as a putative biomarker for prostate cancer and its fast and selective detection in biological fluids (especially urine and blood) is critical in the diagnosis and monitoring of the disease. Similarly, removal of p-xylene from industrial effluents and wastewater is an important environmental consideration due to its toxicity and persistence. These reasons emphasize the importance of developing and calculating the performance of efficient adsorbent and electrochemical sensors for p-xylene. Here, the adsorption and sensing performance of three fullerene-based nanostructures (C 60 , BC 59 , NC 59 ) were computationally studied for p-xylene using electronic structure calculations, charge transport analysis, dipole moment calculations, and non-covalent interaction (NCI/RDG) maps. There were significant changes in electrical conductivity induced by adsorption and transduction was strongly analyte-dependent: C 60 from 1.92 × 10 − 5 to 2.93 × 10 − 4 S/m (C 60 @ p-xylene), BC 59 from 1.18 × 10 − 2 to 1.81 × 10 − 1 S/m (BC 59 @ p-xylene), NC 59 from 3.68 × 10 − 3 to 2.07 × 10 − 3 S/m (NC 59 @ p-xylene). Recovery times were ultrafast for all three complexes with the fastest recovery time being for NC 59 @ p-xylene (3.5 × 10 − 7 s), C 60  = 9.8 × 10 − 7 , and BC 59 @ p-xylene (1.9 × 10 − 5 s). Dipole-moment analysis showed significant polarization upon adsorption for the doped systems. The dipole moment increased from 1.50 to 3.91 D in BC 59 and from 1.39 to 3.05 D in NC 59 . NCI and RDG analyses found that C 60 @p-xylene is mainly affected by weak van der Waals forces. BC 59 @p-xylene shows stronger π-π interactions. NC 59 @p-xylene has intermediate but improved attractive interactions because of nitrogen doping. These trends are consistent with the adsorption energy ranking BC 59  > C 60  > NC 59 and highlight the changes in sensor response and recovery behavior. This study shows that BC 59 has the strongest adsorption and is suitable for environmental adsorption and removal of p-xylene. NC 59 , on the other hand, has extremely high conductivity modulation and ultrafast recovery, along with reasonable adsorption. This makes NC 59 the most promising candidate for detecting p-xylene in early prostate cancer detection.

Theoretical investigation and practical validation of AI based fault detection in BJT amplifier circuits

Abstract Analog circuits are essential to electronic systems due to their variety of critical functions. Catastrophic faults, such as short- and open-circuit faults, can stop the circuit from functioning. Meanwhile, parametric faults such as drift due to temperature fluctuations, noise in components, and power supply issues cause the circuit to operate beyond its range. Such faults can harm the circuit and its constituent parts. Therefore, it is crucial to identify and address the issues brought on by these faults. Common traditional fault diagnosis methods that rely on manual testing and visual assessment are lacking in complex electronic systems and faults. Such methods can catch obvious problems but struggle with subtle or rare faults. Herein, AI has emerged as a strong tool in enhancing fault detection processes, providing capabilities for analyzing vast amounts of data, recognizing patterns, and making decisions based on that data with improved accuracy and speed in fault diagnosis. Accordingly, the common emitter circuit—DC configuration was considered with 15 patterns. The first pattern represents normal conditions, whereas the others represent faulty conditions. The faulty patterns included probable fluctuations in collector and emitter resistor values, power supply internal resistance, and operating temperature. A dataset covering the study parameters and circuit patterns was established, considering both simulation and experimental readings. Supervised machine learning, primarily using random forest classifiers, was employed to detect faults. The hybrid dataset model was trained and validated on that basis. The system achieved a 98.7% success rate in the fault detection condition, demonstrating its efficiency and robust performance. To further investigate the physics behind various fault patterns, a comprehensive analytical model was developed and linked to the machine learning results, highlighting the effectiveness of each feature.

Experimental vaccine prevents deadly allergic reactions in mice

Alterations in serum metabolomics predict drug-resistant epilepsy

‘Anyone hired a student before?’ How a group of novice lab leaders are supporting each other

Novel R-CNN and transformer models for pollution impacts and land cover changes around iconic heritage sites in developing countries: a case study

Abstract Irreversible Land Use Land Cover changes around iconic heritage sites in south Asian countries, precipitated by an ever-increasing urban density build up, is a serious cause of concern. Some of the monuments are UNESCO world heritage sites and are currently under threat. Heavy vehicular traffic and unregulated encroachments over several south Asian cities, including Indian cities have led to particulate pollution build up around iconic heritage structures. This study shows that emissions from diesel engines operating around a network of roads around the 2000-year-old Meenakshi Amman Temple complex, emit soot and black carbon particles in the accumulation mode (0.2–0.5 μm) which remain airborne for over a week and eventually soil and mar the beauty of the temple’s iconic tower gates (Gopurams). Over this size range the scavenging efficiency from natural rain showers is ~ 20%. Efficient scavenging depends not only on the rain drop size distribution, but also on the collected aerosol particle size distribution. The region of Madurai remains largely dry for 9 months in a year and municipal authorities must look for the use of pressure calibrated sprinklers optimised with the drop-size distribution of the airborne particles. Quantifying particle size distribution requires the use of in situ scanning mobility particle sizer spectrometers-SMPS -which is prohibitively expensive for developing nations. Creating artificial rain showers is an even more expensive process and is resorted to only in dire situations. From an analysis of European data repositories and with the use of the state-of-the-art Weather Research and Forecasting (WRF) model we show (i) a progressive loss of vegetative cover (and hence a decrease in the leaf area index, LAI) (ii) decrease in precipitation (iii) decrease in soil moisture and (iv) an increase in fractions of non-precipitating clouds over the region over a time period of 1940–2050. It is therefore clear that any economically affordable engineered solution must involve smart roadside sprinklers yielding a fine spray mist which will be able to capture these particles through efficient collision and coalescence until non-polluting vehicles are used. It is shown that when vehicles are in an idling mode (as is very often the case) the median size is at its broadest and progressively decreases with increasing vehicular operating loads. This study proposes a quick AI mediated method to specify the range of Sauter Mean Radii (SMR) for varying vehicular operations, completely circumventing the use of SMPS and geoengineered artificial rain showers. The drop size distribution (DSD) range will help in the choice of sprinkler attributes—a sprinkler yielding an assortment of millimetre sized droplets should scavenge much of the soot particles with moderate spray durations of the order of a few hours. Mask Recurrent-Convolutional Neural Networks(R-CNN) modelling was used to ascertain particle SMR. Future predictions spanning the years 2020–2050 using the Stacked Transformer model show a progressive decrease in the LAI and soil moisture and an aggravated increase in heat stress during the Representation Concentration Pathway (RCP) 8.5 high emissions scenario. Simulations are contrasted for business-as-usual scenarios (using polluting vehicles), with scenarios using a phased-out transition to electric vehicles (EVs) and it was ascertained that if municipal agencies replace diesel vehicles with EVs, then the above indicators significantly improved. These quick protocols can be used by government officials and other stakeholders to provide smart forecasts for Impact analyses (EIA) and cost savings assessments for calculating paybacks resulting from a partial transition to cleaner vehicular transport in the interim period. Estimates suggest benefits to the tune of millions of USD over a 5-year period vis-à-vis facade restoration ravaged by soot deposition.