INTRODUCTION Acute bacterial meningitis (ABM) is a serious infectious disease and medical emergency. Given the fact of its high mortality and morbidity, detecting prognostic factors is potentially useful in improving treatment strategies. This paper aims to determine prognostic factors of short-term outcomes of patients with ABM in a developing country - Bosnia and Herzegovina, measured by the Modified Rankin Scale (MRS) at discharge. METHODOLOGY In this retrospective cohort study, 56 patients treated at the Clinic of Infectious Diseases, Clinical Center University of Sarajevo, for 11 years (2012-2022) were included. Statistical analysis was performed using IBM SPSS Statistics version 29.0.1.0. RESULTS The subjects' average age was 31.6 ± 27.7 years, with the youngest patient being 4 months and the oldest 75 years old. Among those, 31 were male and 25 were female. Unfavorable outcomes had 16 (28.6%) patients, including fatal outcomes in four patients and severe disabilities in 12 patients (MRS: 2-6), while 40 (71.4%) patients had favorable outcomes (MRS: 0-1). In the multivariate analysis, predictors of unfavorable outcomes included age older than 60 years, duration of symptoms longer than 24 hours, presence of neurological defects at admission, impaired consciousness, respiratory distress, and no corticosteroid use during treatment. CONCLUSIONS There is certainly a window of opportunity for patients with ABM: The shorter the time between disease onset and treatment initiation, the better the disease outcome.

We analyze the stability of general nonlinear discrete-time stochastic systems controlled by optimal inputs that minimize an infinite-horizon discounted cost. Under a novel stochastic formulation of cost-controllability and detectability assumptions inspired by the related literature on deterministic systems, we prove that uniform semi-global practical recurrence holds for the closed-loop system, where the adjustable parameter is the discount factor. Under additional continuity assumptions, we further prove that this property is robust.

The integration of human-robot collaboration (HRC) into industrial and service environments demands efficient and adaptive robotic systems capable of executing diverse tasks, including pick-and-place operations. This paper investigates the application of Soft Actor-Critic (SAC) and Conservative Q-Learning (CQL)—two deep reinforcement learning (DRL) algorithms—for the learning and optimization of pick-and-place actions within HRC scenarios. By leveraging SAC’s capability to balance exploration and exploitation, the robot autonomously learns to perform pick-and-place tasks while adapting to dynamic environments and human interactions. Moreover, the integration of CQL ensures more stable learning by mitigating Q-value overestimation, which proves particularly advantageous in offline and suboptimal data scenarios. The combined use of CQL and SAC enhances policy robustness, facilitating safer and more efficient decision-making in continually evolving environments. The proposed framework combines simulation-based training with transfer learning techniques, enabling seamless deployment in real-world environments. The critical challenge of trajectory completion is addressed through a meticulously designed reward function that promotes efficiency, precision, and safety. Experimental validation demonstrates a 100 % success rate in simulation and an 80 % success rate on real hardware, confirming the practical viability of the proposed model. This work underscores the pivotal role of DRL in enhancing the functionality of collaborative robotic systems, illustrating its applicability across a range of industrial environments.

This paper seeks to answer the question: How did the COVID-19 pandemic affect the relationship between salary income and travel time to work? We expect our analysis will find that the COVID-19 pandemic caused a reversal in the relationship between commute time and salary income. This would mean that, following the pandemic, individuals with higher incomes would, on average, experience shorter commute times than individuals with lower incomes. Consistent with previous studies on the transition to remote work, our research found that the relationship between income and commute times changed following the COVID-19 pandemic. In addition, we found a reversal in the relationship between working from home and annual income, with higher-income individuals being more likely to work from home in 2021 following the pandemic.

Introduction: Sensory integration is the way in which the nervous system processes information from the senses. Irregularities or disturbances in brain function that make it difficult to integrate sensory input from stimuli lead to sensory disintegration. The proprioceptive sensory system provides information about joint and body movements, extent, strength, duration and direction of movement, position of the body or body parts in space, and muscle tone. The aim of this study is to investigate the prevalence of sensory integration disorders of the proprioceptive sensory system in children with intellectual disabilities and children without developmental disabilities and to determine whether the existing difference is statistically significant. Methods: The study was conducted on a sample of 60 respondents. The first subsample of respondents (n = 30) consisted of children with intellectual disabilities. The second subsample of respondents (n = 30) consisted of children without developmental disabilities of the same chronological age. The measuring instrument “Questionnaire for examining proprioceptive sensory sensitivity” was used. Data were collected by observing the respondents and interviewing the rehabilitator and the child’s parents. The frequencies and percentages of the respondents’ answers for all variables were calculated. To determine the statistical significance of differences, the Mann-Whitney U test and Wilcoxon W test were used at a statistical significance level of p < 0.05. Results: The results show that 81.4% of children with intellectual disabilities have difficulties with sensory integration of the sensations of the proprioceptive sensory system, manifested as hypersensitivity (37.6%), hyposensitivity (19.5%), and mixed sensory response (24.3%). Sensory integration difficulties are also experienced by 75.7% of children without developmental disabilities, manifested by hypersensitivity (17.62%), hyposensitivity (27.6%), and mixed sensory reactions (30.5%). There is a statistically significant difference in the variables: high-risk games, fine motor tasks, and activities requiring physical strength. For the other variables, the difference in sensory integration is not statistically significant. Conclusion: 81.4% of children with intellectual disabilities and 75.7% of children without developmental disabilities have difficulties in sensory integration of the proprioceptive sensory system. Children with intellectual disabilities show better integration of proprioceptive sensory input in activities requiring physical strength and in activities with eyes closed or covered. In all other activities, they show poorer sensory integration of proprioceptive sensations than children without developmental disabilities.

This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2025. The purpose of this volume is to provide an open access and curated anthology for the ToM and AI research community.

Origanum compactum, Melaleuca alternifolia, and Cinnamomum camphora essential oils are recognized for their therapeutic potential, including their selective cytotoxicity against cancer cell lines. Our research focused on examining the cytotoxic effects of these essential oils on three human carcinoma cell lines: lung carcinoma (H460), cervical adenocarcinoma (HeLa), and colorectal carcinoma (HCT116). The MTT-based cell viability assay was used to assess the cytotoxicity of essential oils. The results demonstrated that all three essential oils exhibited dose-dependent cytotoxic potential, with varying levels of growth inhibition across the cell lines. Notably, the highest sensitivity was observed in H460 cells, and the lowest sensitivity was found in HCT116 cells. Origanum compactum demonstrated the strongest cytotoxicity across all cell lines (GI50 73 - 154 nL/mL), making it the most promising candidate for further investigation, particularly for lung and cervical cancer treatment.

Machine-learning-based surrogate models offer significant computational efficiency and faster simulations compared to traditional numerical methods, especially for problems requiring repeated evaluations of partial differential equations. This work introduces the Geometry-Informed Neural Operator Transformer (GINOT), which integrates the transformer architecture with the neural operator framework to enable forward predictions on arbitrary geometries. GINOT employs a sampling and grouping strategy together with an attention mechanism to encode surface point clouds that are unordered, exhibit non-uniform point densities, and contain varying numbers of points for different geometries. The geometry information is seamlessly integrated with query points in the solution decoder through the attention mechanism. The performance of GINOT is validated on multiple challenging datasets, showcasing its high accuracy and strong generalization capabilities for complex and arbitrary 2D and 3D geometries.

We report the existence of two exotic compact objects in the leading relativistic model of modified Newtonian dynamics, namely aether-scalar-tensor theory. This model is consistent with precision cosmology and gravitational wave constraints on tensor speed. Black hole mimickers could subtly change observations: gravitational waves from their mergers might show unusual echoes or altered ringdown patterns, and images of their horizon-scale shadows might be slightly different from those of a true black hole. Shapiro-free lenses are massless objects that deflect light without any gravitational time delay, producing distinctive lensing events. These predictions connect to ongoing and future gravitational-wave searches, horizon-scale imaging, and time-domain lensing surveys.

Background and Objectives More than half of the endovascularly treated ischemic stroke patients with incomplete reperfusion (expanded Thrombolysis in Cerebral Infarction [eTICI] <3) show delayed reperfusion (DR) on 24-hour perfusion imaging, which is associated with favorable clinical outcome. The effect of intravenous thrombolysis (IVT) on the rates of DR remains unclear. This study aimed to assess the treatment effect of IVT on the occurrence of DR. Methods Pooled data from 3 randomized controlled trials (EXTEND-IA and EXTEND-IA TNK parts 1 and 2) and 2 comprehensive stroke centers (University Hospitals Graz and Bern) were analyzed. Only patients with a final reperfusion score of eTICI 2a-2c and available perfusion imaging at follow-up of 24 ± 12 hours were included. The primary outcome was the presence of DR on 24-hour follow-up CT/MRI perfusion imaging, defined as the absence of any focal perfusion deficit on perfusion imaging, despite incomplete reperfusion on the final angiography series during thrombectomy. For the secondary analysis, we explored the association between the primary outcome (DR) and the time elapsed between start of IVT and the end of an intervention. To address confounding in observational data, we performed a target trial emulation. Results Of 832 included patients with eTICI 2a-2c (median age 74 years, 49% female), 511 (61%) had DR. There was an independent treatment effect of IVT on DR (standardized risk ratio [sRR] 1.1, 95% CI 1.0–1.3; standardized risk difference [sRD] 8.2%, 95% CI 0.2%–16.1%), after adjusting for age, sex, atrial fibrillation, number of device passes, collateral score, and eTICI. Among those patients who have received IVT (n = 524/832, 63%), when adjusting for the aforementioned covariates, there was a causal effect of shorter time between administration of thrombolytics and end of the intervention on DR (sRR 0.93%, 95% CI 0.87–0.98; sRD −5.2%; 95% CI −9.1% to −1.3%, per hour increase). Discussion Exposure to thrombolytics showed independent treatment effect on the occurrence of DR among patients with incomplete reperfusion after thrombectomy who undergo perfusion imaging at the 24-hour follow-up. The effect of thrombolytics on DR was observed if there was a high chance of therapeutic concentrations of thrombolytics at the time point when the proximal vessel was recanalized, but distal occlusions persisted and/or occurred. Classification of Evidence This study is rated Class III because it is a nonrandomized study and there are substantial differences in baseline characteristics of the treatment groups.

Background/Objectives: The human kallikrein-related peptidase 6 (KLK6), a serine protease with trypsin-like properties, belongs to the 15-member kallikrein (KLK) gene family and is predominantly recognized for its role in oncogenesis, neurodegenerative disorders, and skin conditions. Aquaporins (AQPs) are integral membrane proteins that facilitate water transport across cell membranes. AQP1 is constitutively active in the kidneys and plays a crucial role in reabsorbing filtered water, while AQP2 is regulated by vasopressin and is essential for maintaining body fluid homeostasis. The primary objective of the present study is to investigate the spatio-temporal expression patterns of KLK6, AQP1, and AQP2 throughout normal human nephrogenesis and congenital kidney and urinary tract (CAKUT) abnormalities: duplex kidneys, horseshoe kidneys, and dysplastic kidneys. Methods: An immunofluorescence analysis of KLK6, AQP1, and AQP2 was performed on 37 paraffin-embedded fetal kidney samples. The area percentage of KLK6 in the kidney cortex was calculated in normal developing samples during developmental phases 2, 3, and 4 and compared with CAKUT samples. Results: KLK6 exhibits distinct spatiotemporal expression patterns during human kidney development, with consistent localization in proximal tubules. Its subcellular positioning shifts from the basolateral cytoplasm in early phases to the apical cytoplasm in later stages, which may be strategically positioned to act on its substrate in either the peritubular space or the tubular fluid. KLK6 expression followed a quadratic trajectory, peaking at Ph4. This marked increase in the final developmental phase aligns with its strong expression in mature kidneys, suggesting a potential role in proximal tubule differentiation and functional maturation through facilitating extracellular matrix remodeling and activating proteinase-activated receptors, modulating the signaling pathways that are essential for tubular development. In duplex kidneys, structural abnormalities such as ureteral obstruction and hydronephrosis may upregulate KLK6 as part of a reparative response, while its downregulation could impair epithelial remodeling and cytoskeletal integrity, exacerbating dysplastic phenotypes. Conclusions: These findings highlight the potential of KLK6 involvement in normal kidney development and the pathology of CAKUT.

Accurate Throughput Prediction (TP) represents a real challenge for reliable adaptive streaming in challenging mediums, such as cellular networks. State-of-the-art solutions adopt Deep Learning (DL) models to improve TP accuracy for various multimedia systems. This article illustrates that designing black-box TP engines that depend solely on the model’s capacity and power of learning does not achieve consistent accuracy across all throughput ranges. Additionally, we propose MATURE, a novel multistage DL-based TP model designed to capture network operating context to improve prediction accuracy. MATURE’s prediction involves characterizing the operating context before estimating the network throughput. We show that MATURE delivers consistent, accurate prediction for all throughput ranges in both 4G and 5G networks. We also show that light-weight MATURE models that use quantized parameters maintain their accuracy while featuring up to 100× faster inference, thus making them suitable for mobile implementation. Our real video streaming experiments further show that MATURE improves the average user Quality of Experience by up to 20% when compared to other TP methods.

Volume 3 of the FCC Feasibility Report presents studies related to civil engineering, the development of a project implementation scenario, and environmental and sustainability aspects. The report details the iterative improvements made to the civil engineering concepts since 2018, taking into account subsurface conditions, accelerator and experiment requirements, and territorial considerations. It outlines a technically feasible and economically viable civil engineering configuration that serves as the baseline for detailed subsurface investigations, construction design, cost estimation, and project implementation planning. Additionally, the report highlights ongoing subsurface investigations in key areas to support the development of an improved 3D subsurface model of the region. The report describes development of the project scenario based on the 'avoid-reduce-compensate' iterative optimisation approach. The reference scenario balances optimal physics performance with territorial compatibility, implementation risks, and costs. Environmental field investigations covering almost 600 hectares of terrain - including numerous urban, economic, social, and technical aspects - confirmed the project's technical feasibility and contributed to the preparation of essential input documents for the formal project authorisation phase. The summary also highlights the initiation of public dialogue as part of the authorisation process. The results of a comprehensive socio-economic impact assessment, which included significant environmental effects, are presented. Even under the most conservative and stringent conditions, a positive benefit-cost ratio for the FCC-ee is obtained. Finally, the report provides a concise summary of the studies conducted to document the current state of the environment.