Search Results (2,433)

Introduction: Given the impact of aneurysmal subarachnoid hemorrhage (aSAH) on patients’ health, preclinical research is substantial to understand its pathophysiology and improve treatment strategies, which necessitates reliable and comprehensive animal models. Traditionally, aSAH models utilize iliac or subclavian access for angiography, requiring invasive procedures that are associated with significant risks and animal burden. This pilot study explores a less invasive method of digital subtraction angiography (DSA) by using the auricular artery (AA) as an alternative access point. Our aim was to demonstrate the feasibility of this refined technique, with the intention of reducing procedural risks, providing shorter operation times with enhanced neurological recovery, and simplifying the process for both researchers and animals. Materials and Methods: In this study, six female New Zealand white rabbits (3.2–4.1 kg body weight) underwent experimental induction of aSAH via a subclavian-cisternal shunt. The initial steps of this procedure followed traditional techniques, consisting of subclavian access through microsurgical preparation, followed by DSA to analyze retrograde filling of the basilar artery (BA). To evaluate the alternative method, on day 3 after induction of aSAH, DSA was performed via the AA instead of the traditional subclavian or femoral access. A catheter was placed in the AA to allow retrograde filling of the BA. This approach aimed to simplify the procedure while maintaining comparable imaging quality. Results: All rabbits survived until the study endpoint. Postoperatively, two rabbits showed signs of hemisyndrome, which significantly improved by the time of follow-up. No additional morbidities were observed. Upon euthanasia and necropsy, all animals showed clear subarachnoid bleeding patterns. DSA via the AA produced strong contrasting of the BA comparable to the traditional method. Conclusions: This technical note presents an initial evaluation of AA access as a feasible and potentially advantageous method for DSA in a rabbit model of blood shunt subarachnoid hemorrhage. The method shows promise in reducing invasiveness and procedural complexity, but further studies are required to fully establish its efficacy and safety. Future research should focus on expanding the sample size, refining the anatomical understanding of the AA, and continuing to align with ethical considerations regarding animal welfare. Full article

Alperujo, a solid by-product from the two-phase olive oil extraction process, poses significant environmental challenges due to its high organic load, phytotoxicity, and phenolic content. At the same time, it represents a promising feedstock for recovering value-added compounds such as phenols and volatile fatty acids (VFAs). When used as a substrate for white rot fungi (WRF), it also produces ligninolytic enzymes. This study explores the use of two native WRF, Anthracophyllum discolor and Stereum hirsutum, for the biotransformation of alperujo under solid-state fermentation conditions, with and without supplementation of copper and manganese, two cofactors known to enhance fungal enzymatic activity. S. hirsutum stood out for its ability to release high concentrations of phenolic compounds (up to 6001 ± 236 mg gallic acid eq L⁻¹) and VFAs (up to 1627 ± 325 mg L⁻¹) into the aqueous extract, particularly with metal supplementation. In contrast, A. discolor was more effective in degrading phenolic compounds within the solid matrix, achieving a 41% reduction over a 30-day period. However, its ability to accumulate phenolics and VFAs in the extract was limited. Both WRF exhibited increased enzymatic activities (particularly Laccase and Manganese Peroxidase) with the addition of Cu-Mn, highlighting the potential of the aqueous extract as a natural source of biocatalysts. Phytotoxicity assays using Solanum lycopersicum seeds confirmed a partial detoxification of the treated alperujo. However, none of the fungi could entirely eliminate inhibitory effects on their own, suggesting the need for complementary stabilization steps before agricultural reuse. Overall, the results indicate that S. hirsutum, especially when combined with metal supplementation, is better suited for valorizing alperujo through the recovery of bioactive compounds. Meanwhile, A. discolor may be more suitable for detoxifying the solid phase strategies. These findings support the integration of fungal pretreatment into biorefinery schemes that valorize agroindustrial residues while mitigating environmental issues. Full article

Background/Objectives: Sudden cardiac arrest (SCA) is a major global health concern with high mortality despite advances in resuscitation techniques. Achieving return of spontaneous circulation (ROSC) represents merely the initial step in the extensive rehabilitation journey. This review highlights the critical role of structured, multidisciplinary rehabilitation following ROSC, emphasizing the necessity of integrated physiotherapy, neurocognitive therapy, and psychosocial support to enhance quality of life and societal reintegration in survivors. Methods: This narrative review analyzed peer-reviewed literature from 2020–2025, sourced from databases such as PubMed, Scopus, Web of Science, and Google Scholar. Emphasis was on clinical trials, expert guidelines (e.g., European Resuscitation Council 2021, American Heart Association 2020), and high-impact journals, with systematic thematic analysis across rehabilitation phases. Results: The review confirms rehabilitation as essential in addressing Intensive Care Unit–acquired weakness, cognitive impairment, and post-intensive care syndrome. Early rehabilitation (0–7 days post-ROSC), focusing on parameter-guided mobilization and cognitive stimulation, significantly improves functional outcomes. Structured interdisciplinary interventions encompassing cardiopulmonary, neuromuscular, and cognitive domains effectively mitigate long-term disability, facilitating return to daily activities and employment. However, access disparities and insufficient randomized controlled trials limit evidence-based standardization. Discussion: Optimal recovery after SCA necessitates early and continuous interdisciplinary engagement, tailored to individual physiological and cognitive profiles. Persistent cognitive fatigue, executive dysfunction, and emotional instability remain significant barriers, underscoring the need for holistic and sustained rehabilitative approaches. Conclusions: Comprehensive, individualized rehabilitation following cardiac arrest is not supplementary but fundamental to meaningful recovery. Emphasizing early mobilization, neurocognitive therapy, family involvement, and structured social reintegration pathways is crucial. Addressing healthcare disparities and investing in rigorous randomized trials are imperative to achieving standardized, equitable, and outcome-oriented rehabilitation services globally. Full article

Novel networking paradigms such as the Internet of Things (IoT) have expanded their usage and deployment to various application domains. Consequently, unseen critical security vulnerabilities such as zero-day attacks have emerged in such deployments. The design of intrusion detection systems for IoT networks is often challenged by a lack of labeled data, which complicates the development of robust defenses against adversarial attacks. As deep learning-based network intrusion detection systems, network intrusion detection systems (NIDS) have been used to counteract emerging security vulnerabilities. However, the deep learning models used in such NIDS are vulnerable to adversarial examples. Adversarial examples are specifically engineered samples tailored to a specific deep learning model; they are developed by minimal perturbation of network packet features, and are intended to cause misclassification. Such examples can bypass NIDS or enable the rejection of regular network traffic. Research in the adversarial example detection domain has yielded several prominent methods; however, most of those methods involve computationally expensive retraining steps and require access to labeled data, which are often lacking in IoT network deployments. In this paper, we propose an unsupervised method for detecting adversarial examples that performs early detection based on the intrinsic characteristics of the deep learning model. Our proposed method requires neither computationally expensive retraining nor extra hardware overhead for implementation. For the work in this paper, we first perform adversarial example generation on a deep learning model using autoencoders. After successful adversarial example generation, we perform adversarial example detection using the intrinsic characteristics of the layers in the deep learning model. A robustness analysis of our approach reveals that an attacker can easily bypass the detection mechanism by using low-magnitude log-normal Gaussian noise. Furthermore, we also test the robustness of our detection method against further compromise by the attacker. We tested our approach on the Kitsune datasets, which are state-of-the-art datasets obtained from deployed IoT network scenarios. Our experimental results show an average adversarial example generation time of 0.337 s and an average detection rate of almost 100%. The robustness analysis of our detection method reveals a reduction of almost 100% in adversarial example detection after compromise by the attacker. Full article

Roses are one of the most essential ornamental flowers in the world. At present, traditional techniques such as cross breeding are mainly used in rose breeding. The inefficiency of the in vitro regeneration system has become the limiting step for the innovation and genetic improvement of rose germplasm resources. A tissue culture rapid propagation system of Rosa ‘Pompon Veranda’ was established using the stem segments with shoots as the initial experimental material. The results showed that the best disinfection method was to soak the explants in 75% ethanol for 1 min, and then soak them in 15% sodium hypochlorite solution for 15 min. The contamination rate was only about 6%. The best rooting medium for tissue culture seedlings was 1/2MS with 0.1 mg∙L⁻¹ NAA, and the rooting rate can reach around 95%. On this basis, calluses were induced by using leaflets of tissue-cultured seedlings as explants. The results showed that the optimal medium for inducing callus tissue was MS + 5.0 mg∙L⁻¹ 2,4-D, with an induction rate of 100%. The calluses were cultured in the medium of MS with 0.01 mg∙L⁻¹ NAA, 1.5 mg∙L⁻¹ TDZ and 0.1 mg∙L⁻¹ GA₃ for 12 days in the dark and then transferred to light conditions. The differentiation rate of callus was 10.87%. On the medium of MS with 0.5 mg∙L⁻¹ 6-BA, 0.004 mg∙L⁻¹ NAA and 0.1 mg∙L⁻¹ GA₃, the shoots could regenerate into whole plants. This study has established an in vitro regeneration system of R. ‘Pompon Veranda’, which is a key perquisite for the subsequent establishment of its genetic transformation system. Moreover, this method will also be an important reference for studies on quality traits such as floral scent and prickles of Rosa plants. Full article

In this study, a magnet-integrated fabric phase sorptive extraction (MI-FPSE) protocol was developed in combination with high pressure liquid chromatography—diode array detection (HPLC-DAD) for the simultaneous determination of five phenylurea pesticides (i.e., chlorbromuron, diuron, linuron, metoxuron, monuron) in environmental water samples. To produce the MI-FPSE device, two individual sol-gel coated carbowax 20 M (CW 20 M) cellulose membranes were fabricated and stitched to each other, while a magnetic rod was inserted between them to give the resulting device the ability to spin and serve as a stand-alone microextraction platform. The adsorption and desorption step of the MI-FPSE protocol was optimized to achieve high extraction efficiency and the MI-FPSE-HPLC-DAD method was validated in terms of linearity, sensitivity, selectivity, accuracy, and precision. The limits of detection (LODs) were found to be 0.3 μg L⁻¹. The relative recoveries were 85.2–110.0% for the intra-day and 87.7–103.2% for the inter-day study. The relative standard deviations were better than 13% in all cases. The green character and the practicality of the developed procedure were assessed using ComplexGAPI and Blue Analytical Grade Index metric tools, showing good method performance. Finally, the developed method was successfully used for the analysis of tap, river, and lake water samples. Full article

Phycocyanin is a natural, non-toxic, blue pigment-protein with many commercial applications. Its exploitation in various biotechnological sectors strongly depends on its purity grade (P). Phycocyanin is largely used in food industry where a low purity grade is required, while its widespread use in sectors requiring a higher purity is hampered by the cost of large-scale industrial production. Industry, in fact, needs simple, easily scalable and cost-effective procedures to ensure sustainable production of high-quality pigment. In this work we applied the innovative two-step ultrasound-based process UltraBlu to the pilot-scale production of phycocyanin. A total of 50 L of biomass suspension of commercial Spirulina were processed in batch mode. The pigment extract was obtained in one day, including the biomass harvesting. Food/cosmetic grade (P = 1.41–1.76) and a good yield (Y = 59.2–76.1%) were achieved. The initial results obtained suggest that UltraBlu can be an effective scalable process suitable to produce phycocyanin also on an industrial scale. Full article

Vacuum degassing (VD) is a critical refining step in electric arc furnace (EAF) steelmaking for producing clean steel with reduced nitrogen and hydrogen content. This study develops an Effective Equilibrium Reaction Zone (EERZ) model focused on denitrogenation (de-N) by simulating interfacial reactions at the bubble–steel interface (Z1). The model incorporates key process parameters such as argon flow rate, vacuum pressure, and initial nitrogen and sulfur concentrations. A robust empirical correlation was established between de-N efficiency and the mass of Z1, reducing prediction time from a day to under a minute. Additionally, the model was further improved by incorporating a dynamic surface exposure zone (Z_eye) to account for transient ladle eye effects on nitrogen removal under deep vacuum (<10 torr), validated using synchronized plant trials and Python-based video analysis. The integrated approach—combining thermodynamic-kinetic modeling, plant validation, and image-based diagnostics—provides a robust framework for optimizing VD control and enhancing nitrogen removal control in EAF-based steelmaking. Full article

LLMs are trained using many thousands of GPUs in well-known conventional models. It is necessary to address numerous issues in the training process, such as manual data collection organization, data parallel, model parallel, evaluation, testing, deployment, transferring large data streams, detecting errors, ongoing maintenance, and project management. A team of dozens of engineers is required to handle system problems in the training process. Therefore, it is time-consuming and expensive to build an efficient and fault-tolerant system based on Kubernetes. This paper develops SimpleScale for building LLMs based on FSDP and Slurm, which is a simple and efficient training system that includes the training agent, the efficient parallel strategy, the optimal step of checkpoint, and so on. Using the proposed system enables us to significantly accelerate the process of building the LLM without incurring substantial time spent on various manual issues. The proposed 1024-GPU cluster was tested on TinyLlama, which has 1.1 billion parameters and 300 billion tokens. For example, utilizing a 16-H100 GPU cluster accelerated the traditional TinyLlama training time costs from 89.05 days to 39.05 days. In the future, the project team plans to integrate Flash-Attention3, aiming for an MFU of more than 60% while maintaining the acceleration achieved in the present work. Full article

Background: Infectious proctitis remains an underrecognized entity, although sexually transmitted diseases, especially bacterial infections, exhibit a marked increase in their incidence. Methods: Here, we report a case of a 44-year-old man who presented to the emergency department with lower abdominal and rectal pain, tenesmus, fever and night sweats for the past 6 days. Results: The computed tomography initially revealed a high suspicion of metastatic rectal cancer. The endoscopic findings showed a 5 cm rectal mass, suggestive of malignancy. The histologic examination showed, however, no signs of malignancy and lacked the classical features of an inflammatory bowel disease, so an infectious proctitis was further suspected. The patient reported to have had unprotected receptive anal intercourse, was tested positive for Treponema pallidum serology and received three doses of intramuscular benzathine penicillin G. A control rectosigmoidoscopy, imaging at 3 months and histological evaluation after antibiotic treatment showed a complete resolution of inflammation. Conclusions: Syphilitic proctitis may mimic various conditions such as rectal cancer or inflammatory bowel disease and requires a high degree of suspicion. Clinicians need to be aware of infectious proctitis in high-risk populations, while an appropriate thorough medical history may guide the initial diagnostic steps. Full article

Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral demands—such as water supply, irrigation, and hydropower scheduling—while also mitigating flood and drought risks. To address this need, in this study, we propose a novel time-variant encoder–decoder (ED) model designed specifically to improve multi-step reservoir inflow forecasting, enabling accurate predictions of reservoir inflows up to seven days ahead. Unlike conventional ED-LSTM and recursive ED-LSTM models, which use fixed encoder parameters or recursively propagate predictions, our model incorporates an adaptive encoder structure that dynamically adjusts to evolving conditions at each forecast horizon. Additionally, we introduce the Expected Baseline Integrated Gradients (EB-IGs) method for variable importance analysis, enhancing interpretability of inflow by incorporating multiple baselines to capture a broader range of hydrometeorological conditions. The proposed methods are demonstrated at several diverse reservoirs across the United States. Our results show that they outperform traditional methods, particularly at longer lead times, while also offering insights into the key drivers of inflow forecasting. These advancements contribute to enhanced reservoir management through improved forecasting accuracy and practical decision-making insights under complex hydroclimatic conditions. Full article

Accurate modeling of ionospheric variability is critical for space weather forecasting and GNSS applications. While machine learning approaches have shown promise, progress is hindered by the absence of standardized benchmarking practices and narrow test periods. In this paper, we take the first step toward fostering rigorous and reproducible evaluation of AI models for ionospheric forecasting by introducing IonoBench: a benchmarking framework that employs a stratified data split, balancing solar intensity across subsets while preserving 16 high-impact geomagnetic storms (Dst ≤ $-100$ nT) for targeted stress testing. Using this framework, we benchmark a field-specific model (DCNN) against state-of-the-art spatiotemporal architectures (SwinLSTM and SimVPv2) using the climatological IRI 2020 model as a baseline reference. DCNN, though effective under quiet conditions, exhibits significant degradation during elevated solar and storm activity. SimVPv2 consistently provides the best performance, with superior evaluation metrics and stable error distributions. Compared to the C1PG baseline (the CODE 1-day forecast product), SimVPv2 achieves a notable RMSE reduction up to $32.1\%$ across various subsets under diverse solar conditions. The reported results highlight the value of cross-domain architectural transfer and comprehensive evaluation frameworks in ionospheric modeling. With IonoBench, we aim to provide an open-source foundation for reproducible comparisons, supporting more meticulous model evaluation and helping to bridge the gap between ionospheric research and modern spatiotemporal deep learning. Full article

Background/Objectives: This study aimed to determine whether the movement patterns and mental health of university teachers changed after returning to on-site class activities following the COVID-19 lockdown. Specifically, it compared levels of physical activity (PA), sedentary behavior time (SBT), active breaks (ABs), and symptoms of depression, anxiety, and stress among university teachers during online and on-site teaching periods. We also analyzed the association between movement patterns with psychological and anthropometric variables. Methods: University teachers who engaged in online teaching activities because of the COVID-19 restrictions and returned to on-site classes were included. Each teacher wore an accelerometer and answered the Depression Anxiety Stress Scales. The following parameters were assessed: SBT, light (LPA), moderate (MPA), and vigorous (VPA) (min/day); moderate–vigorous PA (MVPA) (min/week); steps/day and ABs/day. Results: Thirty-seven teachers with complete data from both phases were included. Once the on-site teaching activities resumed, LPA (9 min/day), MPA (6 min/day), total PA (20 min/day), MVPA (49 min/week), and steps/day (1100) significantly increased. While SBT showed no changes, ABs/day bouts increased. Depression and stress symptoms improved upon returning to on-site teaching activities. A positive association was identified between SBT and waist circumference (WC). There were negative associations between steps/day and MVPA with body mass index (BMI), steps/day with WC, and LPA with stress symptoms. Conclusions: Upon returning to on-site teaching activities, PA levels, steps/day, and ABs/day bouts all increased, although SBT remained elevated compared with during the lockdown. The teachers’ psychological symptoms improved. PA was associated with better health markers, while SBT was associated with increased WC. Full article

The 100-Day Mission, coordinated by the Coalition for Epidemic Preparedness Innovations (CEPI) and endorsed by significant international stakeholders, aims to shorten the timeframe for developing and implementing vaccines to 100 days after the report of a new pathogen. This ambitious goal is outlined as an essential first step in improving pandemic preparedness worldwide. This review highlights the mission’s implementation potential and challenges by examining it through the lens of low- and middle-income countries (LMICs), which often face barriers to equitable vaccine access. This article explores the scientific, economic, political, and social aspects that could influence the mission’s success, relying on lessons learned from previous pandemics, such as the Spanish flu, H1N1, and COVID-19. We also examined important cornerstones like prototype vaccine libraries, accelerated clinical trial preparedness, early biomarkers identification, scalable manufacturing capabilities, and rapid pathogen characterization. The review also explores the World Health Organization (WHO) Pandemic Agreement and the significance of Phase 4 surveillance in ensuring vaccine safety. We additionally evaluate societal issues that disproportionately impact LMICs, like vaccine reluctance, health literacy gaps, and digital access limitations. Without intentional attempts to incorporate under-resourced regions into global preparedness frameworks, we argue that the 100-Day Mission carries the risk of exacerbating already-existing disparities. Ultimately, our analysis emphasizes that success will not only rely on a scientific innovation but also on sustained international collaboration, transparent governance, and equitable funding that prioritizes inclusion from the beginning. Full article

The growing need for developing safer and more effective methods for obtaining enantiomers of chiral compounds, particularly those with pharmacological activity, highlights the potential of biocatalysis as an appropriate pharmaceutical research direction. However, low catalytic activity and stability of free enzymes are often among the substantial limitations to the wide application of biocatalysis. Therefore, to overcome these obstacles, new technological procedures are being designed. In this study, we present optimized protocols for the immobilization of Candida antarctica lipase B (CALB) on an octyl- agarose support, ensuring high enantioselectivity in an organic reaction medium. The immobilization procedures (with drying step), including buffers with different pH values and concentrations, as well as the study of the influence of temperature and immobilization time, were presented. It was found that the optimal conditions were provided by citrate buffer with a pH of 4 and a concentration of 300 mM. The immobilized CALB on the octyl-agarose support exhibited high catalytic activity in the kinetic resolution of (R,S)-1-phenylethanol via enantioselective transesterification with isopropenyl acetate in 1,2-dichloropropane (DCP), as a model reaction for lipase activity monitoring on an analytical scale. HPLC analysis demonstrated that the (R)-1-phenylethyl acetate was obtained in an enantiomeric excess of ee_p > 99% at a conversion of approximately 40%, and the enantiomeric ratio was E > 200. Thermal and storage stability studies performed on the immobilized CALB octyl-agarose support confirmed its excellent stability. After 7 days of thermal stability testing at 65 °C in a climatic chamber, the (R)-1-phenylethyl acetate was characterized by enantiomeric excess of ee_p > 99% at a conversion of around 40% (similar values of catalytic parameters to those achieved using a non-stored lipase). The documented high catalytic activity and stability of the developed CALB-octyl-agarose support allow us to consider it as a useful tool for enantioselective transesterification in organic medium. Full article