Search Results (682)

Background: Empirical antibacterial therapy for febrile neutropenia requires adaptation to local epidemiology, a process that is often complex, time-consuming, and prone to human error. This study aims to address this challenge by developing a practical, data-driven tool to efficiently evaluate and adapt treatment protocols. Methods: We developed a novel, open-source computational script in Python (version 3.10), aided by large language models for code revision, to analyze antibiotic susceptibility data. The script was validated using a retrospective dataset of 237 Gram-negative bloodstream infections (BSIs) from 2015 to 2024 in cancer or hematopoietic stem cell transplant recipients at a tertiary care pediatric hospital in Italy. The script calculates efficacy metrics for both single agents and two-drug combinations. Results: Among the Gram-negative BSI strains analyzed, meropenem monotherapy demonstrated the highest efficacy (median 95.4%). In contrast, piperacillin/tazobactam and cefepime showed lower efficacy (80.3% and 81.8%, respectively). On the contrary, combination therapy, particularly with amikacin, significantly increased the efficacy of beta-lactams, elevating their effectiveness to a level comparable to meropenem. Conclusions: The developed script is a valuable tool for antimicrobial stewardship programs, offering a rapid and accessible method to validate international guidelines against local epidemiological data. While meropenem shows high efficacy, its broad use should be limited to prevent resistance. The combination of piperacillin–tazobactam and amikacin is identified as a robust and effective empirical treatment choice. Full article

YouTube is a de facto learning environment for athletes seeking fast, actionable nutritional guidance, yet platform dynamics may favor simplified or testimonial narratives over evidence-aligned messages. This study maps Spanish-language sports-nutrition videos to clarify who is most visible, how advice is framed, and what users encounter first. We conducted a cross-sectional, mixed-methods study of 558 YouTube videos on pre/post-exercise nutrition and supplementation. Data was coded for video types (divulgation/testimonial), claim presence, evidence links, and creator status (professional/non-professional). Exposure-adjusted metrics (View Ratio, Viewer Interaction) and nonparametric tests summarized distributions. An undirected network generated centrality rankings to select qualitative samples. Thematic analysis of titles and descriptions identified recurring rhetorical patterns and discourse modes. Divulgation videos predominated (97.3%). Evidence links were rare (0.2%). Exposure and interaction were right-skewed, indicating concentrated visibility. Non-professionals produced most videos, with older uploads and higher daily view accrual; however, interaction per view was similar across groups. Qualitative synthesis revealed two dominant discourse modes, scientific–cautious and experience–testimonial. Oversimplification and motivational cues clustered in testimonial/non-professional items; instructional language and scarce evidence links concentrated in professional/divulgation items. In Spanish sports-nutrition content, visibility is concentrated, and creator identity shapes advice framing. Evidence-aligned messages can compete when expressed with clear athletic framing, explicit caveats, and links to trustworthy sources. Full article

Background: Comparing biological properties among related proteins has traditionally benefited research in areas such as biomedicine, phylogeny and evolution. Moreover, these kinds of properties have significantly increased as a result of the development of open-access resources and databases. In this context, the multiple sequence alignment (MSA) methods continue to be extensively applied to compare protein sequences and to identify evolutionarily conserved regions. Methods: In this work, we present INPROF, a novel web server designed to centralize and automate the computation of a large collection of features derived from protein sequences. This tool allows us to deeply analyze protein relationships and their conserved information by comparing them through their MSA. Specifically, this platform computes up to 46 different metrics including information at several proteomic levels (categories) like sequences, structures, domains or ontological terms. INPROF was designed to enable bioinformaticians and researchers to create a complete catalogue of features for subsequent prediction and artificial intelligence modeling based on proteins. The INPROF web server and source code are freely available. Results: INPROF were validated by predicting disease’s severity in several RNA-Seq datasets from COVID-19 patients. Specifically, INPROF were extracted from canonical proteins related to differentially expressed genes. Classification performance proved that INPROF were able to accurately classify COVID-19 severity, even outperforming classical classification with expression data. Conclusions: INPROF web server is a flexible platform designed to compute 46 quantitative metrics describing protein interactions which provide biologically meaningful characteristics applicable to downstream classification and prediction algorithms. Full article

Quantum computing has the potential to transform modern computation by offering exponential advantages in areas such as cryptography, optimization, and intelligent data processing. To effectively realize these advantages, particularly in fault-tolerant and Noisy Intermediate-Scale Quantum (NISQ) environments, quantum circuits must be both resource-efficient and error-resilient. This paper presents a novel Binary-Coded Decimal (BCD) to Excess-3 code converter designed exclusively using the Clifford+T gate set, which is widely supported by fault-tolerant quantum hardware. The proposed design eliminates conventional 4-bit reversible adder-based implementations and instead employs an optimized logic structure based on Clifford+T-decomposed Peres gates. By leveraging Temporary Logical-AND gates and CNOT operations, the circuit achieves reduced T-count, circuit depth, and quantum cost as key metrics in fault-tolerant quantum computation. Functional correctness is verified through IBM Qiskit, Version 2.1 simulations for all valid BCD inputs. The proposed converter serves as a scalable and hardware-compatible arithmetic building block for resource-aware and AI-oriented quantum architectures. Full article

High-quality commit messages are essential software artifacts because they succinctly communicate the intent and scope of code changes, yet large language models (LLMs) often fail to reflect project-specific writing conventions when used in a zero-shot setting without contextual signals. This study investigates not whether retrieval helps, but how the same retrieved example, when represented differently in the prompt, quantitatively changes generation outcomes. We implement a retrieve-then-generate framework where the target commit’s diff is used as a query for BM25 (Best Matching 25)-based sparse retrieval over a commit-level database, and the top-1 similar commit is optionally injected as an example context. We compare a no-context condition (K = 0) against a minimal-context condition (K = 1) under three context representations: Diff-only, Message-only, and Diff + Message pair. Using Qwen-7B on 8000 evaluation samples with a fixed prompt skeleton, deterministic decoding, and identical post-processing across conditions, we observe negligible differences at K = 0 (BLEU-4 1.14, ROUGE-L 7.47–7.48, METEOR 4.88–4.91), establishing a stable baseline. At K = 1, the same top-1 retrieved case yields systematically different metric responses depending on how it is represented (Diff-only, Message-only, or Diff + Message), even under an identical prompt skeleton, deterministic decoding, and identical post-processing. This indicates that “context representation” is not a cosmetic formatting choice but a first-class prompt-design variable in retrieval-augmented in-context learning for commit message generation. Accordingly, practitioners should select the representation based on the intended objective (e.g., lexical/style alignment vs. change-intent grounding), rather than assuming a universally optimal format. Full article

Commit messages are essential for understanding software evolution and maintaining traceability of projects; however, their quality varies across repositories. Recent Large Language Models provide a promising path to automate this task by generating concise context-sensitive commit messages directly from code diffs. This paper provides a comparative study of three paradigms of large language models: zero-shot prompting, retrieval-augmented generation, and fine-tuning, using the large-scale CommitBench dataset that spans six programming languages. We assess the performance of the models with automatic metrics, namely BLEU, ROUGE-L, METEOR, and Adequacy, and a human assessment of 100 commits. In the latter, experienced developers rated each generated commit message for Adequacy and Fluency on a five-point Likert scale. The results show that fine-tuning and domain adaptation yield models that perform consistently better than general-purpose baselines across all evaluation metrics, thus generating commit messages with higher semantic adequacy and clearer phrasing than zero-shot approaches. The correlation analysis suggests that the Adequacy and BLEU scores are closer to human judgment, while ROUGE-L and METEOR tend to underestimate the quality in cases where the models generate stylistically diverse or paraphrased outputs. Finally, the study outlines a conceptual integration pathway for incorporating such models into software development workflows, emphasizing a human-in-the-loop approach for quality assurance. Full article

This study addresses the challenge of identifying heavy Building Information Modeling (BIM) library components that disproportionately degrade model performance. While BIM has become standard in the construction industry, heavy components characterized by excessive geometric complexity, numerous instances, or inefficient optimization—cause extended file loading times, interface lag, and coordination difficulties, particularly in large cross-industry projects. Current identification methods rely primarily on designer experience and manual inspection, lacking systematic evaluation frameworks. This research develops a multi-criteria evaluation method based on Multi-Criteria Decision Analysis (MCDA) that quantifies component performance impact through five weighted criteria: instance count (20%), geometry complexity (30%), face count (20%), edge count (10%), and estimated file size (20%). These metrics are aggregated into a composite Weight Score, with components exceeding a threshold of 200 classified as requiring optimization attention. The method was implemented as HeavyFamilies, a pyRevit plugin for Autodesk Revit featuring a graphical interface with tabular results, CSV export functionality, and direct model visualization. Validation on three real BIM projects of varying scales (133–680 families) demonstrated effective identification of heavy components within 8–165 s of analysis time. User validation with six BIM specialists achieved 100% task completion rate, with automatic color coding and direct model highlighting particularly valued. The proposed approach enables a shift from reactive troubleshooting to proactive quality control, supporting routine diagnostics and objective prioritization of optimization efforts in federated and multi-disciplinary construction projects. Full article

Background/Objective: The primary cilium is the sensory organelle of a cell and a dynamic membrane protrusion during the cell cycle. It originates from the centriole at G₀/G₁ and undergoes disassembly to release centrioles for spindle formation before a cell enters mitosis, thereby serving as a cell cycle checkpoint. Cancer cells that undergo rapid cell cycle and replication have a low ciliation rate. In this study, we aimed to identify cilia-promoting drugs that can accelerate ciliation and decelerate replication of cancer cells. Methods: To perform a comprehensive and efficient literature search on drugs that can promote ciliation, we developed an intelligent process that integrates either the GPT 4 Turbo, Gemini 1.5 Pro, or Claude 3.5 Haiku application programming interfaces (APIs) into a PubMed scraper that we coded, enabling the large language models (LLMs) to directly query articles for predefined user questions. We evaluated the performance of this intelligent literature search based on metrics and tested the effect of two candidate drugs on ciliation and proliferation of medulloblastoma cells. Results: Gemini was the best model overall, as it balanced high accuracy with solid precision and recall scores. Among the top candidate drugs identified are Alvocidib and Alisertib, small-molecule inhibitors of CILK1 and AURKA, respectively. Here, we show that both kinase inhibitors can effectively increase cilia frequency and significantly decrease the replication of medulloblastoma cells. Conclusions: The results demonstrated the potential of using cilia-promoting drugs, such as Alvocidib and Alisertib, to suppress cancer cell replication. Additionally, it shows the massive benefits of integrating accessible large language models to conduct sweeping, rapid, and accurate literature searches. Full article

Background: Port digital transition is central to competitiveness and sustainability, yet existing frameworks devoted to such transition toward smart port are descriptive, technology-centered, or weak on data governance. This study designs and empirically refines a comprehensive and novel ten-step roadmap relative to existing Port/Industry 4.0 models, synthesized from 14 partial frameworks that each cover only subsets of the transition, by considering data governance and consolidating cost, time, and impact in the selection step. Methods: We synthesized recent Industry 4.0 and smart port-related frameworks into a normalized sequence of steps embedded in the so-called roadmap, then examined it in an exploratory case of a technology deployment project in a Canadian port using stakeholder interviews and project documents. Evidence was coded with a step-aligned scheme, and stakeholder feedback and implementation observations assessed whether each step’s outcomes were met. Results: The sequence proved useful yet revealed four recurrent hurdles: limited maturity assessment, uneven stakeholder engagement, ad hoc technology selection and integration, and under-specified data governance. The refined roadmap adds a diagnostic maturity step with target-state setting and gap analysis, a criteria-based selection worksheet, staged deployment with checkpoints, and compact indicators of transformation performance, such as reduced logistics delays, improved energy efficiency, and technology adoption. Conclusions: The work couples theory-grounded synthesis with empirical validation and provides decision support to both ports and public authorities to prioritize investments, align stakeholders, propose successful policies and digitalization supporting programs, and monitor outcomes, while specifying reusable steps and indicators for multi-port testing and standardized metrics. Full article

This study treats initial building modal planning as the organizing unit for tropical neighborhood design and unifies three pedestrian-scale objectives: perimeter daylight at 1.5 m (S), grey-space wind (W), and ground-plane visibility (V)—within a typology-aware, two-void layout grammars for Haikou. Using α-referenced deviations (|ΔMean| + 0.25|ΔIQR| per metric) and multi-objective simulated annealing over 16 morphotypes plus two VOIDs, we obtained a Pareto archive of 4000 layouts. A thick knee emerges: mid-field paired voids with bar–court compositions consistently suppress W and V deviations while keeping S close to α; the central spine and cross-breath prototypes dominate among the top solutions, and the 80-layout atlas enables direct selection. The configuration and α baselines were fixed for full reproducibility, supporting policy-grade traceability. All evaluations were performed at the human interface with metric-specific aggregation (S over 14 non-VOID blocks; w/v over all 16), coupling building morphotypes, pedestrian-layer analytics, and archive-aware Multi-Objective Simulated Annealing (MOSA). Collectively, these results provide evidence-backed rules—site two voids near the middle, composed of tempered courts and bars, and provide strong support for near-term tropical planning codes and schematic design decisions. Full article

Background/Objectives: Narrative differences in autism spectrum disorder (ASD) and subtle and parallel differences among their first-degree relatives suggest potential genetic liability to this critical social-communication skill. Effective social-communication relies on coordinating signals across modalities, which is often disrupted in ASD. Therefore, the current study examined the coordination of fundamental skills—gaze and speech—as a potential mechanism underlying narrative and broader pragmatic differences in ASD and their first-degree relatives. Methods: Participants included 35 autistic individuals, 41 non-autistic individuals, 90 parents of autistic individuals, and 34 parents of non-autistic individuals. Participants narrated a wordless picture book presented on an eye-tracker, with gaze and speech simultaneously recorded and subsequently coded. Time series analyses quantified their temporal coordination (i.e., the temporal lead of gaze to speech) and content coordination (i.e., the amount of gaze-speech content correspondence). These metrics were then compared between autistic and non-autistic groups and between parent groups and examined in relation to narrative quality and conversational pragmatic language skills. Results: Autistic individuals showed reduced temporal coordination but increased content coordination relative to non-autistic individuals with no significant differences found between parent groups. In both autistic individuals, and parent groups combined, increased content coordination and reduced temporal coordination were linked to reduced narrative quality and pragmatic language skills, respectively. Conclusions: Reduced temporal and increased content coordination may reflect a localized strategy of labeling items upon visualization. This pattern may indicate more limited visual, linguistic, and cognitive processing and underlie differences in higher-level social-communicative abilities in ASD. To our knowledge, this study is the first to identify multimodal skill coordination as a potential mechanism contributing to higher-level social-communicative differences in ASD and first-degree relatives, implicating mechanism-based interventions to support pragmatic language skills in ASD. Full article

Underwater optical images are the primary carriers of underwater scene information, playing a crucial role in marine resource exploration, underwater environmental monitoring, and engineering inspection. However, wavelength-dependent absorption and scattering severely deteriorate underwater images, leading to reduced contrast, chromatic distortions, and loss of structural details. To address these issues, we propose a U-shaped underwater image enhancement framework that integrates Swin-Transformer blocks with lightweight attention and residual modules. A Dual-Window Multi-Head Self-Attention (DWMSA) in the bottleneck models long-range context while preserving fine local structure. A Global-Aware Attention Map (GAMP) adaptively re-weights channels and spatial locations to focus on severely degraded regions. A Feature-Augmentation Residual Network (FARN) stabilizes deep training and emphasizes texture and color fidelity. Trained with a combination of Charbonnier, perceptual, and edge losses, our method achieves state-of-the-art results in PSNR and SSIM, the lowest LPIPS, and improvements in UIQM and UCIQE on the UFO-120 and EUVP datasets, with average metrics of PSNR 29.5 dB, SSIM 0.94, LPIPS 0.17, UIQM 3.62, and UCIQE 0.59. Qualitative results show reduced color cast, restored contrast, and sharper details. Code, weights, and evaluation scripts will be released to support reproducibility. Full article

Translation of evidence-based resuscitation practices into clinical settings remains slow and inconsistent, a gap that significantly impacts survival and neurological outcomes. Implementation science offers a structured approach to accelerate adoption by identifying context-specific barriers—such as dispatcher workload, team choreography, and resource constraints—and tailoring strategies to overcome them. This paper applies the Knowledge-to-Action (KTA) framework to resuscitation, emphasizing stakeholder engagement, iterative monitoring, and sustainability. We provide detailed guidance across key resuscitation settings, including dispatch-assisted cardiopulmonary resuscitation (DA-CPR), in-hospital code teams, and emergency medical services (EMS). The manuscript introduces a comprehensive outcomes framework encompassing implementation, service/system, and patient-level metrics, and illustrates practical application through case examples such as DA-CPR and real-time feedback devices. To enhance scientific utility, we also present a decision-oriented table for pilot testing, offering healthcare institutions a roadmap for sustainable integration of evidence-based resuscitation protocols. Full article

In modern sports, straight-line sprinting alone is insufficient for assessing overall sprint performance, as athletes must also decelerate and change direction efficiently. Existing methods lack a single metric that integrates all abilities, enabling holistic assessment. This study aimed to develop a comprehensive and novel measurement of multidirectional sprinting ability. Fifty-four university athletes (21.0 ± 1.5 years; 69.6 ± 9.1 kg; 172.6 ± 7.8 cm) performed linear sprints, decelerations, and 45°, 90°, and 135° change of direction (COD) tests in both directions over 30 m. Sprint accelerations and decelerations were recorded using a Stalker ATS II radar gun, while COD times were measured with stationary time gates. Sprint velocities were used to generate a multidirectional sprint area (MDSA), which was divided into forward, backward, left, and right sections. The MDSA method is calculated as the area of the octagonal polygon created by plotting eight velocity vectors from different angles of sprints. Paired t-tests compared area differences across directions, and ANOVA tests were used to compare sporting codes and sex. The resulting model reported differences across sporting codes (p < 0.001), sex (p < 0.001), the total area value (p < 0.001), and total area percentage (p < 0.001). The results showed a significant difference between forward and backward accelerations (p < 0.001), but no significant difference between left and right movements (p = 0.244). The MDSA method offers a reliable, quantitative intra-session approach for assessing athletes’ multidirectional sprint abilities by calculating the octagonal area on the basis of velocity data. This holistic analysis identifies asymmetries and performance weaknesses, providing valuable insights for coaches. Full article

The COVID-19 pandemic has highlighted the importance of rapid clinical decision-making to facilitate the efficient usage of healthcare resources. Over the past decade, machine learning (ML) has caused a tectonic shift in healthcare, empowering data-driven prediction and decision-making. Recent research demonstrates how ML was used to respond to the COVID-19 pandemic. This paper puts forth new computer-aided COVID-19 disease screening techniques using six classes of ML algorithms (including penalized logistic regression, random forest, artificial neural networks, and support vector machines) and evaluates their performance when applied to a real-world clinical dataset containing patients’ demographic information and vital indices (such as sex, ethnicity, age, pulse, pulse oximetry, respirations, temperature, BP systolic, BP diastolic, and BMI), as well as ICD-10 codes of existing comorbidities, as attributes to predict the risk of having COVID-19 for given patient(s). Variable importance metrics computed using a random forest model were used to reduce the number of important predictors to thirteen. Using prediction accuracy, sensitivity, specificity, and AUC as performance metrics, the performance of various ML methods was assessed, and the best model was selected. Our proposed model can be used in clinical settings as a rapid and accessible COVID-19 screening technique. Full article