An young female with secondary infertility underwent diagnostic hysterolaparoscopy under general anaesthesia which showed flimsy adhesions over uterus with beaded appearance of bilateral fallopian tubes. Methylene blue dye was injected transcervically to check tubal patency which confirmed bilateral tubal blockage with no spillage of dye. At 3 min of post injection,

This short communication develops a variational formulation for modeling a silicon semiconductor design utilizing phosphorus atoms as a dopant substance. The results are based on standard tools of calculus of variations and optimization theory.

Rubber plantations biomass is a crucial indicator for assessing carbon storage and ecological functions within Rubber plantation ecosystems. However, improving the accuracy of biomass estimation remains a key research focus. This study focuses on rubber plantations of different ages in complex mountainous regions. The biomass of plots were then integrated with UAV multispectral remote sensing imagery, and 10 typical machine learning models, including deep learning models(Multiple Linear Regression, MLR; Support Vector Regression, SVR;K-Nearest Neighbors, KNN; Random Forest, RF; Extreme Gradient Boosting, XGBoost; Partial Least Squares Regression, PLSR; Gradient Boosting, GB; Artificial Neural Network, ANN; Convolutional Neural Network, CNN; Backpropagation Neural Network, BPNN), were used to estimate Rubber plantations biomass. To further optimize model accuracy, topographic factors (slope, aspect) were incorporated to enhance model performance. The results show that slope and aspect factor make the R 2 values of 7/10 models improved, and the RMSE of 8/10 models significantly decreased. The R 2 value of the Extreme Gradient Boosting (XGBoost) model increased from 0.9882 to 0.9887, and the RMSE decreased from 19.280 to 18.970, maintaining the highest accuracy among the 10 models. Ultimately, the XGBoost model was selected to estimate Rubber plantations biomass. The results showed that the average biomass of young rubber plantations was 279.176 t/hm 2, middle-aged plantations 342.110 t/hm 2, mature plantations 301.660 t/hm 2, and over-mature plantations 415.222 t/hm 2. Overall, this study achieved high-precision model construction, developing a set of ”spectral+ texture + topographic factor” estimation techniques for Rubber plantations regions, providing a basis for accurate biomass estimation in rubber plantations.

A document by Gidion Edwin. Click on the document to view its contents.

Specific diets are green and efficient method to obtained enhanced robustness silkworm silk, which has been attracted particular research interest. Graphene oxide is widely used for biomedicine and reinforcement. Thus, in this work, we reported enhanced mechanically silk directly collected by feeding Bombyx mori silkworms with graphene oxide and its physiology of silkworm was explored. These results suggested that feeding graphene oxide can promote silkworm growth and development slightly. And cocoon weight was also increased by feeding graphene oxide. Furthermore, it was found that the activity of alkaline phosphatase related to absorption and metabolism, the content of reduced glutathione related to antioxidant capacity were increased with graphene oxide incorporation. The stability of silk protein in silk glands was also changed with graphene oxide incorporation. And the content of amino acids in the hemolymph and silk were affected. In addition, it suggested that feeding graphene oxide has a great influence on the secondary structure of silk, resulting in the reduction of β-sheet structure, crystallinity, crystal size, and the increase of orientation, polar amino acid content, intermolecular force. Thus, mechanically processed silk was obtained, demonstrating significant potential for utilization in the fields of reinforced composites and biomedicine.

[Key Clinical Message]: In pediatric leukemia patients undergoing prolonged corticosteroid therapy, avascular necrosis may involve less typical weight-bearing sites such as the distal femoral condyles. While MRI remains the preferred diagnostic modality for AVN, bone scintigraphy and SPECT/CT may provide complementary data—especially when evaluating unexplained pain—to help identify areas at increased risk of structural compromise.

Under low snapshot conditions, a novel DOA estimation method that integrates covariance matrix reconstruction with deep learning is proposed in this letter. We reconstruct a structured covariance matrix using a reference-auxiliary subarray model combined with diagonal loading. The reconstructed matrix is transformed into a two-channel input and fed into the proposed squeeze-and-excitation multi-scale deep convolutional network (SE-MSDCN). DOA estimates are obtained via a sub-grid peak detection strategy. Simulation results demonstrate that the proposed approach significantly outperforms traditional methods and existing deep learning techniques in terms of accuracy and resolution, particularly under low snapshot and SNR conditions.

Changes in land use and landcover, combined with disturbances from human activities, can impact biodiversity in water. In this study, we investigated the effects of human land uses and human constructions—specifically the Ubol Ratana hydroelectric dam—on water quality and the distribution of aquatic macroinvertebrates in the Phong River in northeastern Thailand. Fieldwork was conducted six times in 2024—three in the dry season and three in the rainy season—across 12 stations along the Phong River from its headwaters downstream covering a gradient from natural to heavily impacted land uses. Seven stations located upstream of the Ubol Ratana Dam (PO1–PO7) while five located downstream (PO8–PO12). At each station, an unmanned aerial vehicle was used to document surrounding land cover, and biological, physical, and chemical parameters were measured. Furthermore, we collected aquatic macroinvertebrates according to rapid bioassessment protocols using a D-frame aquatic dip net. We categorized the stations into three groups based on the dominant land use/land cover type: forest (2 stations), agriculture (8 stations) and residential (2 stations). We observed significant differences in temperature, electrical conductivity and total dissolved solids among three land use types (one-way ANOVA, p-value < 0.05). The level of total suspended solids, total dissolved solids, electrical conductivity, nitrate, ammonia, and orthophosphate were significantly lower after the dam, while water transparency was higher (independent t-test, p-value < 0.05). The constrained correspondence analysis (CCA) revealed that aquatic macroinvertebrate distribution was associated with ten environmental factors, i.e., temperature, depth, transparency, pH, dissolved oxygen, electrical conductivity, total dissolved solids, ammonia, nitrate, and orthophosphate. The results suggest that human land conversion and construction in the Phong River have impacted water quality and aquatic macroinvertebrate assemblages highlighting their potential as indicators of land use impacts in the Phong River.

A document by John Doe. Click on the document to view its contents.

The recovery of spent lithium-ion batteries is essential for the sustainable development of renewable resources and environmental protection. Low lithium recovery efficiency and high energy consumption are the main problems in the current recycling technologies for black mass of spent NCM batteries. In this work, flash joule heating (FJH) method was innovatively applied to preferentially recover lithium from the actual raw material in industry. In particular, we firstly revealed the Li-phase conversion behavior in a nonequilibrium and high temperature state. Under the optimal FJH condition (1400 ℃, 90 s), the main Li phases in the product are converted into Li2O and Li5AlO4, while the transition metals are reduced to elemental forms and low valence oxides. The Li-phase conversion behavior and FJH characteristics matched excellently, so 91.8% of Li could be selectively extracted by water. Ni, Co, and Mn are recovered by sulfuric acid leaching with the efficiencies over 98%. This innovative method combines the phase transition behavior of lithium with the advantages of rapid joule heating, offering a new pathway for recycling spent LIBs in both theory and technology, and is extremely appealing owing to its energy conservation and high recovery efficiency.

Liquid metals (LMs) are the functional materials with both metallic and liquid properties, remaining in a liquid state at room temperature. The fluidity, electrical conductivity, printability, and self-healing capabilities provide liquid metals with significant advantages for applications in electronics. However, liquid metals generally exhibit a silver-white coloration, the single physical appearance restricted their applicability. Fortunately, the metal halide perovskites serve as fluorescent materials characterized by their abundant fluorescence properties. The atomic-level halogenation transformation of liquid metals surface will endow them with unique fluorescence properties, and providing groundbreaking opportunities for applications. Herein, the aluminum (Al) was dissolved and then dispersed in eGaSn to produce the eGaSn-Al materials. Subsequently, the solutions of KX + HX (where X = Cl, Br, I) were dropped onto the surface of the eGaSn-Al. Then, the metal halide fluorescent shells with different colors were formed, and endowing them with abundant fluorescent properties. Particularly, through the selection, regulation and combination of halogen elements, resulting colorful liquid metals with cyan, cyan-green, and yellow-green. This finding presents innovative concepts for the interface modification and special functional applications of liquid metals, and expands their application potential in electronic information devices, such as lighting and displays, anti-counterfeiting measures, sensing, and chameleon robotics.

Poly(vinylidene fluoride) (PVDF)-based electrolytes exhibit a strong potential for applications in high-energy-density solid-state lithium (Li) metal batteries, owing to their superior ionic conductivity, wide electrochemical window, and excellent flexibility. However, their practical performance is so far limited by challenges at the Li/PVDF interface. Specifically, the porosity of the PVDF interface induces uneven ion deposition, which facilitates Li dendrite growth, while residual N,N-dimethylformamide (DMF) solvent at the interface triggers severe side reactions with Li. Here, we demonstrate that introducing an in-situ cured highly elastic layer stabilizes and dynamically tunes the Li/PVDF interface. In situ polymerization of thermoplastic polyurethane (TPU)-containing 1,3-dioxolane (DOL) enables intimate interfacial contact. The TPU within the cured layer increases its elastic modulus, empowering dynamic regulation of pressure distribution and contact retention at the Li/PVDF interface during cycling. The engineered interface enables a dense and flat Li deposition morphology to prolong the cycling life. Furthermore, the interfacial layer prevents side reactions between the DMF solvent residues and the Li anode, maintaining an electrochemically stable contact. Our findings demonstrate that introducing the highly flexible TPU/P-DOL interfacial layer is a large-scale manufacturable approach, which overcomes the main barrier hindering the performance of PVDF electrolytes, significantly improving the effectiveness of solid-state batteries.

AI-SCAN is a CNN-based scalable Intrusion Detection System (IDS) that detects known and unknown cyber-attacks with minimal false positives. AI-SCAN is created to solve contemporary cybersecurity challenges, employing a systematic approach involving data acquisition, preprocessing, feature selection, class balancing, model design, training, and evaluation. The model utilizes the CSE-CICIDS2018 dataset, a benchmark dataset mimicking real-world cloud network traffic with varied attack patterns, to train and test its performance. Using techniques like Z-score normalization, SMOTE class balancing (Synthetic Minority Oversampling Techniques), and a customized CNN architecture that distinguishes between malicious and legitimate network traffic, the model detects attacks with state-of-the-art accuracy. Measures of accuracy, precision, recall, and F1-score demonstrate that AI-SCAN outperformed the current IDS models with a 97.5% accuracy in detecting attacks and high sensitivity to uncommon and novel attack patterns. Balancing strategies and architecture guarantee scalability, robustness, and applicability for deployment in dynamic cloud environments.

One of the major challenges of the digital era is information overload, which makes it difficult for users to structure information and find actionable insights based on dispersed sources of knowledge. Conventional, static knowledge management systems are ineffective in handling the dynamic nature of contemporary information streams. With the integration of AI, the process transforms into an engaging personal assistant. This AI-powered system serves as a ”Big Brain” by tapping into multiple streams of data through a web interface or browser extension in order to construct a rich, interconnected knowledge base. When user pose natural language questions to the system, the AI responds with contextually aware answers based on vector-based semantic search combined with LLM-driven reasoning, providing a personalized overview of knowledge. We evaluated the AI system using real-world data from digital platforms and web content, achieving a retrieval accuracy of 92% for semantic relevance (F1) and 73% for exact matches. Usability tests yielded a high user satisfaction rating of 4.6 out of 5. Unlike existing tools restricted to keyword-based search or rigid categorization, our method mimics human associative memory, effectively bridging the gap between fragmented data sources and actionable knowledge. This work advances personalized AI by providing a scalable, user-friendly, and privacy-centric solution for managing digital information, ultimately boosting productivity.

Recent developments in natural language processing (NLP) using large language models (LLMs) have transformed information retrieval systems. Problems still exist, however, in high stakes use cases where high accuracy is an essential requirement. A key issue is the hallucination problem, where models generate information unsupported by the underlying data, potentially leading to dangerous misinformation. This paper introduces a new approach addressing this gap by combining large language models (LLMs) with Query Refinement Technique and knowledge graphs (KGs) to improve question-answering system accuracy and credibility. Our approach employs LLMs to transform natural language questions into Cypher queries and complements this with a three-phase query-checking Module. The Module enforces syntactic correctness, semantic compatibility with KG schemas, and logical relationship integrity to enable proper information extraction from a knowledge graph in order to mitigate errors such as hallucinations. Evaluating on MedQA and Custom biomedical dataset for various tasks, our method drastically reduced hallucinations and achieved F1 rates of 91.1% (MedQA) and 86.0% (our dataset) with domain-fine-tuned models like Llama-3.1-8B-UltraMedical. Importantly, KG-validated data coupled with domain-fine-tuned models performed best amongst other LLM methods. Our query checker addressed crucial errors in 85% of the cases, correcting node-type mismatching and reversed relationships. Open-source models significantly improved through prompt engineering and algorithmic optimization and approached accuracy of closed-source LLMs. By grounding responses on the Unified Medical Language System (UMLS) KG, our system illustrates how structure-based knowledge verification can balance LLM flexibility with clinical precision. The method presents a roadmap for building reliable AI systems in mission-critical applications.

Human encroachment into natural habitats is increasing encounters with wildlife, particularly large carnivores, leading to conflicts and sometimes attacks on humans. Understanding the correlates of these attacks is crucial for enhancing safety. This study examines how evolution has shaped the behavior of the eight extant bear species in human-bear interactions, focusing on ultimate and proximate factors. We review species-specific responses, such as distance-dependent reactions and predatory behaviors, and discuss how evolutionary histories can inform bear safety strategies. Bear behavior is influenced by genetics, cultural learning, individual experience, as well as a host of biotic and abiotic factors. Evolutionary factors like habitat type, competition, predation pressure, and escape options have helped shape species' threat responses. Bears in open habitats tend to adopt aggressive-defensive strategies, while those in forested habitats lean towards avoidance. These adaptations, essential for survival, now pose conservation challenges by increasing human-bear conflict potential.

1. Anthropogenic food subsidies can have profound influences on wildlife behavior and health, including exposure to parasites. In many host-macroparasite systems, parasite exposure is tied to foraging behavior, but how different distributions of food subsidy shape macroparasite encounter and population-level impacts is poorly understood. 2. Here we modify a mathematical model of macroparasite transmission to explore how food subsidies could change parasite encounter rates and between-host variation in parasite burdens, reflecting changes in host foraging and conspecific overlap. 3. Hosts experience the highest average parasite abundance, and associated reductions in population size, when food subsidies increase and homogenize parasite encounter rates, for example when hosts center their home ranges on a point food source and overlap with many conspecifics. Conversely, hosts experience the lowest parasite abundance and impacts when subsidies result in lower and more heterogeneous parasite encounter rates, for example when multiple, patchily-distributed subsidies subdivide host populations and increase host commute times to food at the expense of time spent foraging. Even when resources affect other processes such as improving host immunity or fecundity, the overall effect of subsidies on infection is more strongly driven by changes in parasite encounter rates through altered foraging behavior. These patterns are robust to different effect sizes of resource subsidy on foraging and non-foraging parameters. 4. The finding that host foraging behavior strongly influences infection outcomes in environmentally-acquired parasites has implications for the provisioning of wildlife for recreation, conservation, and management. We advocate for distributing food subsidies in ways that reduce artificial aggregation of hosts and the time animals spend at feeding stations, or that reduce environmental parasite encounters while feeding.

This meta-analysis of 15 randomized controlled trials with 19,447 patients compared to IVUS-guided versus angiography-guided PCI for left main coronary artery disease. IVUS-guided PCI significantly reduced mortality, major adverse cardiac events, myocardial infarction, stent thrombosis, and target vessel revascularization, highlighting its superiority. These findings support prioritizing IVUS in LMCA interventions, despite procedural challenges, with further research needed to optimize its use.

Purpose: A primary challenge that cardiac sonography students experience during the echocardiography learning process is correlating between the anatomical structures of the heart to transducer created two-dimensional (2D) ultrasound images. Simulation-based training using a manikin may enhance learning, but evidence supporting its effectiveness in Transthoracic Echocardiography (TTE) education remains limited. This study evaluates the effectiveness of simulation-based versus traditional didactic teaching of cardiac anatomy and corresponding TTE images. Methods: A simulation-based learning lab was introduced in Fall 2023 within the Cardiac Anatomy TTE course at our institution. Thirty-five first-year cardiac sonography students (with no prior TTE training) completed the traditional didactic course, including pre- and post-course assessments, and participated in a hands-on simulation lab. The session included real-time scanning of a manikin for Parasternal Long Axis, Parasternal Short Axis, Apical 4 Chamber, and Apical 2 Chamber views, alongside an interactive three-dimensional (3D) cardiac anatomy review. The outcomes were compared to a control group (Spring 2023, n=42) that completed the didactic course without simulation. Results: Post-course assessment scores were significantly higher in the simulation cohort (p<0.0001), with an increase in mean scores from 72.12% (control) to 88.23% (simulation). The knowledge gain was more consistent in the simulation group (SD=3.26%) compared to the control (SD=13.38%). The lowest score in the simulation cohort was 76.47%, compared to 41.18% in the control. Conclusion: Simulation-based teaching significantly enhances knowledge acquisition and retention in cardiac anatomy TTE training of cardiac sonography students. Our findings support integrating structured simulation into echocardiography education to improve the learning process.

Immune Checkpoint Inhibitors: Think beyond cardiotoxicity! Is physical deconditioning a new marker of disease?

Background Equine Herpes Myeloencephalopathy (EHM) is a rare manifestation of Equine Herpesvirus-1 (EHV-1) infection. EHM outbreaks have been documented in Europe, North America, and New Zealand, no confirmed cases have been reported in India. Objectives The study presents the virological, clinic-pathological, and outcome data from two EHM outbreaks in Punjab, India. Study design Descriptive epidemiology Methods Two outbreaks of Equine Herpes Myeloencephalopathy (EHM) were reported from distinct agro-climatic zones of Punjab, India. This study documents the clinical, serological, molecular, and histopathological findings of the reported outbreaks. Blood and serum samples were used for virus neutralization test (VNT) to assess serological responses, and quantitative PCR (qPCR) for molecular detection of EHV-1. Additionally, nasal swabs were collected for virus isolation, and tissue samples from necropsied horses were tested for both virus isolation and qPCR. Relative quantification of EHV-1 DNA was performed using real-time PCR targeting gB gene (ORF33) Results In the first outbreak, three stallions showed variable clinical signs, including facial nerve paralysis, hind limb paresis, prolapsed penis, fever, hind limb swelling, and episodic seizures. The outbreak was confirmed serologically, with VNT titers greater than 1:4. The second outbreak began with a 7-year-old stallion showing fever, ataxia, and paresis, which collapsed. EHV-1 was detected in nasal and ocular swabs, and tissue samples of the dead animal. qPCR amplification using specific primers for the EHV-1 gB gene confirmed EHV-1 infection, distinguishing it from EHV-4. No other horses on the farm showed clinical signs of EHM, but one of four horses, a 6-year-old stallion, tested positive for EHV-1 with a VNT titer of 1:8. Main Limitation Small sample size Conclusion This study describes two sporadic outbreaks of Equine Herpesvirus Myeloencephalopathy (EHM) in India, which were limited to a small number of horses. Serological, molecular, virus isolation, and histopathological analyses confirmed the presence of EHV-1.

Earth’s most complex and biodiverse ecosystems are characterised by high habitat complexity. On coral reefs, habitat complexity is influenced by the diverse morphology and composition of hard corals, shaping reef structure and shelter provision for many species. Various metrics are used to quantify reef complexity, yet it remains unclear how well metrics capture ecological functions such as shelter provision. The diversity of coral communities means there is no ‘one metric fits all’ solution. We used a published dataset of 13 distinct coral community types generated using a 3D functional-structural model, to investigate the redundancy, complementarity and ecological relevance of 11 habitat complexity metrics (four general structural and seven ecologically meaningful shelter metrics). We were especially interested in the extent to which structural metrics can predict shelter metrics, potentially reducing the need for more complicated direct shelter measurements. We used Pearson’s correlations to compare metrics in (i) one pooled analysis from all community types, and (ii) 13 individual analyses for each community type. In the pooled analysis, structural metrics were strongly correlated while the shelter metrics formed two distinct groups – ‘pelagic’ and ‘benthic’ – where the metrics were highly correlated (i.e. redundant) within the groups but showed weak correlations between (complementary). Certain coral morphologies influenced the redundancy or complementarity of these metrics, where structural metrics can be useful predictors of shelter metrics when community type is known: e.g. surface rugosity was a stronger predictor of shelter volume, particularly for tabular and digitate coral communities. Fractal dimension was highly complementary to other metrics, but further investigation is needed to identify its ecological relevance. We highlight that there is no universal metric, and it is important to consider a range of suitable habitat complexity metrics and morphological community composition with our findings also relevant to ecosystems with morphologically distinct biogenic habitat formers.

The CASP16 Evaluation of Model Accuracy (EMA) experiment assessed the ability of predictors to estimate the accuracy of predicted models, with a particular emphasis on multimeric assemblies. Expanding on the CASP15 framework, CASP16 introduced a new evaluation mode (QMODE3) focused on selecting high-quality models from large-scale AlphaFold2-derived model pools generated by MassiveFold. Three primary evaluation tasks were therefore conducted: QMODE1 assessed global structure accuracy, QMODE2 focused on the accuracy of interface residues, and QMODE3 tested model selection performance. Predictors were evaluated using a diverse set of OpenStructure-based metrics, and a novel penalty-based ranking scheme was developed for QMODE3 to handle score interdependence and varying prediction quality distributions. Additionally, we explored the accuracy and utility of predicted local confidence measuresnow made available on a per-atom basis by methods that invoke AlphaFold3. Results showed that methods incorporating AlphaFold3-derived features – particularly per-atom pLDDT – performed best in estimating local accuracy and in utility for experimental structure solution. For QMODE3, performance varied significantly across monomeric, homomeric, and heteromeric target categories, and underscored the ongoing challenge of evaluating complex assemblies.

Background: A novel Cryoballoon (n-CB) system offers new and unique features to potentially improve safety, feasibility and efficacy as well as 12-month clinical outcome of CB based pulmonary vein isolation (PVI). In particular, the variable sizing of the novel CB (n-CB) ranging from 28 to 31 mm aims to fit different pulmonary vein (PV) anatomies as well as a more stable balloon positioning. The aim of this study was to assess the safety, feasibility and efficacy as well as 12-month clinical outcome of the n-CB system compared to the conventional CB (c-CB) without a variable diameter. Methods and results: We included 104 patients receiving CB-based PVI using the n-CB. The control groups consisted of 52 patients undergoing the procedure using the c-CB (n = 52). The two groups were analysed in terms of baseline and procedural data as well as 1 year follow up. A total of 104 consecutive patients with paroxysmal (n=43) and persistent (n=61) atrial fibrillation were analyzed. The mean age was 66.2 ± 11.5 years (n-CB) and 67.4 ± 11.6 (c-CB). A total of 208 (n-CB) and 205 (c-CB) PV were identified. Acute PVI was achieved in all patients in both groups. The median procedure time was 54.1 ± 18.8 min in the n-CB group and 53.6 ± 11.9 in the c-CB group (p=0.708). No differences in mean fluoroscopy time and severe adverse events were observed. No statistical differences were observed regarding the 12-month follow up between the groups. Conclusion: The n-CB showed similar safety, efficacy and a 12-months recurrence-free survival compared to the c-CB. Significantly shorter fluoroscopy duration and lower fluoroscopy dose was observed int the n-CB group. Although lower nadir balloon temperatures were noted in the c-CB group, lower esophageal temperatures at the left-sided PVs were observed in the n-CB group.