Browse Articles

Projections of the probability of the “Elfsteden” ice skating tour under future climate scenarios using lake models

The influence of contact body size on superficial and deep tissue pain perception for development of a collision dummy

Abstract The use of robots is playing an increasingly important role in industrial manufacturing. Collaborative robots operated alongside humans can pose a hazard. Pain perception as an injury risk indicator is contingent on several factors, especially contact surface, anatomical region and the difference between superficial and deep tissue pain. In this article, we have conducted an experimental load study with human subjects in which we have analyzed these factors to enable the development of a pain-sensitive dummy for testing collaborative robot against biomechanical thresholds. For the load test, we used our established algometer setup with three different contact bodies (7 × 7 mm 2 , 14 × 14 mm 2 and 24 × 24 mm 2 ) to evaluate six anatomical regions of the arm. Subjects were instructed to activate a switch whenever they sensed pain after starting the test procedure. The algometer increased the force in 5 N/s increments. We used topical anesthetic to distinguish between superficial and deep tissue pain. We had 11 male subjects for measurements. The greatest force had to be applied with the medium-sized contact body to induce pain perception. Local anesthetization made it necessary to increase the force of the small and large contact bodies significantly. Our finding that peak force is a reliable indicator of the perception of pain caused by the medium-sized contact body indicates that the contact body’s size affects peak force magnitude significantly.

Fabrication of atomically flat cleavage planes with ultrafast laser scribing

Abstract The preparation of extensive, atomically flat surfaces remains a central challenge in modern quantum materials research, as many crystals lack natural cleavage planes suitable for advanced surface-sensitive investigations. Here, we demonstrate that laser scribing guided by an ultrafast laser can be applied to facilitate easy cleavage along a desired crystallographic plane under ultra-high vacuum. The method is validated on two brittle materials, $$\textrm{SrTiO}_3$$ and Si. The technique allows precise spatial localization of the cleaving site and produces extensive, uniformly oriented, and atomically flat surfaces, as verified by scanning electron microscopy (SEM) and atomic force microscopy (AFM). When applied to $$\textrm{SrTiO}_3$$ , the technique enables angle-resolved photoemission spectroscopy (ARPES) measurements of surface electronic states characteristic of the two-dimensional electron liquid (2DEL) hosted at its bare (100) surface. Moreover, ultrafast laser scribing is significantly faster than focused ion beam (FIB) techniques for preparing cleavable planes, offering a more accessible and efficient approach. Owing to its broad applicability, this method establishes a powerful and general framework to prepare high-quality surfaces for advanced photoemission and microscopic investigations of quantum phenomena.

Explainable artificial intelligence for groundwater quality prediction and hydrochemical interpretation

A novel node and edge cyclic embedding graph convolutional network for skeleton-based two-person interaction recognition

Impact of cancer on mortality in critically ill patients with sepsis: a propensity score-matched analysis

CYSTSCAN–PKD: a comprehensive pipeline for automatic cyst segmentation and counting on µCT scans from PKD animal models

Pratenol B, Eriodictyol, Losbanine, and Isookanin, as potential EGFR and HRAS inhibitors in oral squamous cell carcinoma

Abstract Oral squamous cell carcinoma (OSCC) is the most prevalent form of head and neck cancer and remains highly prevalent in developing countries. Disease management is challenged by the increasing prevalence of risk factors, limited treatment modalities, adverse side effects, and growing drug resistance, highlighting the need for safer and more effective treatment. This study investigates the potential of phytochemicals as targeted inhibitors of key dysregulated biomarkers in OSCC patients. RNA sequencing and pathway analysis identified the MAPK signaling pathway as the most significantly altered, with EGFR and HRAS emerging as critical therapeutic targets within this pathway. Due to limited availability of HRAS inhibitors and limitation of existing EGFR therapies, a natural product–based drug discovery strategy was employed. Molecular docking simulations of 17,000 phytochemicals identified Pratenol B, Eriodictyol, Losbanine, and Isookanin as promising candidates, with Pratenol B demonstrating dual inhibition of EGFR and HRAS. All the compounds demonstrated strong binding affinities, favorable interactions with active site residues, good pharmacokinetic profiles, high bioavailability, and low predicted toxicity. Molecular dynamics simulation confirmed highest stability of Pratenol B with both target proteins. Plant-derived molecules provide a cost-effective and low-toxicity approach for OSCC therapy, warranting further pre-clinical, and clinical validation.

Development of a consistent body mass index

Genome-wide association identifies and validates genomic region controlling grain yield and agronomic traits in extra-early orange maize inbred lines under drought

Correction: The downregulation of ubiquitin-specific peptidase 2 indicates a poor prognosis and promotes the progression of gastric cancer through focal adhesion and ECM pathway signaling

Psychological problems as the mediator for the relationship between social support and internet addiction among students during Covid-19 pandemic

Improving preoperative risk stratification in colorectal liver metastases: a multi-institutional evaluation of multimodal prediction models

Prescribed fire is unlikely to reduce net PM _2.5 emissions in most locations

Wildfire smoke poses a growing global health risk, largely from fine particulate matter (PM 2.5 ) emissions. Prescribed fires, which are critical for maintaining resilient forests in many locations, can also reduce wildfire emissions in treated areas that later burn. However, prescribed fires also produce smoke, creating a tradeoff in their net impact on PM 2.5 emissions. We develop a mathematical framework showing that, under most current conditions globally, prescribed fire emissions are rarely offset by reduced wildfire emissions and therefore increase PM 2.5 emissions overall. An analysis of 73 prior study sites reveals that reported net emission reductions are often based on unrealistic assumptions, for example that all treatments are subsequently encountered by wildfire during their effective lifespan. Using empirically based estimates of expected encounter rates, prescribed fire increases net emissions at 99% of sites, with a median 10-year increase of 210% (IQR: 70 to 475%), and only 0.06 tons of wildfire emissions avoided per ton of prescribed fire emissions (IQR: 0.03 to 0.15). However, prescribed fires are intentionally conducted under favorable meteorological conditions, allowing smoke to disperse more safely than during wildfires. Thus, whether prescribed burning can reduce health risks while increasing emissions—and whether its emissions impact can be lowered beyond current practice—is a critical area for future study. Regardless, prescribed fires have multiple objectives and benefits; they remain essential for forest management and hazard reduction.

Integrating 3-D thermal videography, ultrasonic acoustics, and weather radar to characterize bird and bat activity at wind turbines

Wind turbines intersect airspace used by both migratory birds and bats, yet most monitoring approaches rely on single sensing modalities that capture only part of this system. Here, we integrate synchronized three-dimensional (3-D) thermal videography, ultrasonic acoustic monitoring, and regional weather radar data to characterize wildlife activity at two inland wind turbines during fall migration in Iowa, USA. Across 38 nights in late summer and autumn of 2022, we recorded 12,047 3-D flight tracks within rotor-swept altitudes, 2,249 bat echolocation sequences, and cumulative radar-derived migration traffic of approximately 2.0 million birds/km. Thermal video detections were strongly correlated with radar-derived migration intensity but not with acoustic detections. This pattern is consistent with birds comprising the majority of thermal video detections during peak migration. Thousands of flight trajectories occurred within 30–150 m above ground level and within 200 m of turbine monopoles, demonstrating frequent use of altitudes associated with collision risk. Fine-scale trajectory analysis revealed strong avoidance of flight paths directed toward the rotor-swept zone (RSZ), with targets 3.6 times more likely to fly toward the RSZ when turbines were stationary than when producing power. Angular divergence from turbine bearing also increased with decreasing distance, with a steeper avoidance gradient near operating turbines, consistent with birds actively responding to cues associated with blade rotation. Our results demonstrate how a sensor-fusion framework improves inference about taxonomic composition, airspace use, and behavioral responses near wind turbines. The strong relationship between regional radar activity and turbine-level detections highlights the potential of publicly available radar data to support wind energy siting by identifying areas with lower migratory bird traffic without extensive on-site monitoring. Understanding the sensory basis of the avoidance behavior documented here could also inform the design of deterrent systems for infrastructure where bird collisions are a concern. Integrating complementary monitoring technologies provides a scalable approach for understanding wildlife–turbine interactions and guiding responsible wind energy development.

Knowledge and practice of hemodialysis catheter care and associated factors among patients on maintenance hemodialysis: An analytical cross-sectional study

Background Effective care of hemodialysis catheters (HDCs) is essential for preventing complications, particularly catheter-related infections, among patients receiving maintenance hemodialysis. However, gaps in patient knowledge and practice may compromise optimal catheter care. This study assessed the level of knowledge and practice of HDC care and identified factors associated with these outcomes among patients on maintenance hemodialysis. Methods A cross-sectional study was conducted among 97 patients undergoing maintenance hemodialysis. Data were collected using a structured questionnaire assessing socio-demographic and clinical characteristics, as well as knowledge and practice related to HDC care. Knowledge and practice scores were categorized using an 80% cutoff. Bivariate and multivariable logistic regression analyses were performed to identify factors associated with knowledge and practice. Model fitness and multicollinearity were assessed. Results Overall, 51.5% of respondents demonstrated satisfactory knowledge, while 45.4% reported satisfactory HDC care practices. Knowledge was highest regarding the purpose and insertion site of the catheter but lower for aspects related to infection recognition and dressing care. In multivariable analysis, female sex (AOR: 3.09; 95%CI: 1.12, 8.54; p  = 0.030) and shorter duration on hemodialysis (<2 years) (AOR: 3.66; 95%CI: 1.28, 10.50; p  = 0.016) were independently associated with satisfactory knowledge. Regarding practice, knowledge emerged as the only independent predictor, with respondents having satisfactory knowledge demonstrating significantly higher odds of satisfactory practice (AOR: 47.39; 95%CI: 11.14, 201.64; p  < 0.001). Other variables were not significantly associated with practice after adjustment. Conclusion Less than half of patients demonstrated satisfactory HDC care practices despite moderate levels of knowledge. Knowledge was strongly associated with practice, highlighting its potential importance in supporting optimal catheter care behaviors. These findings indicate that targeted and sustained patient education may be important in improving knowledge and supporting better catheter care practices. Further multicenter and longitudinal studies using more robust measurement approaches are recommended to better clarify factors influencing HDC care practices and related outcomes.

Aggregation processes in customer rating systems - Insights from an economic decision experiment

The aggregation of rating metrics in reputation systems is crucial for mitigating information overload by condensing customer rating distributions into singular valence scores. While platforms typically employ technical aggregation functions, such as the arithmetic mean to capture product quality, it remains unclear whether these functions align with customers’ innate aggregation patterns. To address this knowledge gap, we designed a controlled economic decision experiment to elicit customers’ aggregation principles by analyzing their product ranking decisions and contrasting these with various reference functions. Our findings indicate that the majority of customers aggregate rating information in accordance with the arithmetic mean. However, a granular analysis at the individual level reveals significant heterogeneity in aggregation behavior, with a substantial cluster exhibiting binary patterns that focus equally on negative (1–2 star) and positive (4–5 star) ratings. Additional clusters concentrate on negative feedback, particularly 1-star ratings or 1–2 star ratings collectively. Notably, these inherent aggregation patterns exhibit stability across variations in numerical information presentation and are largely not significantly influenced by individual characteristics, such as online shopping experience or demographics. The only exception is a plausible relationship between risk attitudes and aggregation functions that focus on extreme ratings, such as 1-star, 1–2 star, and 5-star ratings. Our findings suggest that while the arithmetic mean captures the behavior of the majority of consumers, platforms could benefit from offering customizable aggregation options to better cater to diverse user preferences for processing rating distributions. By doing so, platforms can enhance the effectiveness of their reputation systems and improve the overall quality of decision-making for consumers.

Neuromuscular ultrasound as a biomarker in the SOD1 mouse model of amyotrophic lateral sclerosis

A progression marker that indicates early disease-related changes and treatment responses in the to date incurable neurodegenerative disease amyotrophic lateral sclerosis (ALS) is highly desirable. Translation of therapeutics that have been successful in in vivo models into trials in human patients has proven difficult in recent decades. This failure can be attributed, at least in part, to the lack of specific biomarkers for ALS diagnosis and progression in human ALS patients as well as in in vivo models. Neuromuscular ultrasound is an easily accessible, non-invasive tool to support diagnosis of ALS in humans. Our current study shows for the first time that the disease can be detected in an ALS mouse model with the help of neuromuscular ultrasound. We characterized disease progression regarding changes in the peripheral nerves and muscles of the hind limb in the SOD1 G93A mouse model of ALS using different techniques (neuromuscular ultrasound, electroneurography, motor function tests, phenotypic assessments and histology). By neuromuscular ultrasound, we measured the cross-sectional area and diameter of the sciatic nerve and analyzed hind limb muscle texture and thickness. Our results show that motor neuron loss and muscle atrophy – analogous to ALS in humans – can be measured by ultrasound in the SOD1 G93A mouse model. Changes in nerve and muscle morphology appear at the same time or even before changes in the established tests (including electroneurographic measurements) performed in vivo in this model. Correlations with histologic features of disease progression make neuromuscular ultrasound a sensitive, non-invasive outcome marker for preclinical studies.

Using natural language processing to support digital communication in adolescents and young adults with autism spectrum disorder

Background Autism Spectrum Disorder (ASD) is characterized by social communication challenges, including difficulties interpreting figurative language, understanding conversational context, and expressing thoughts clearly. This feasibility study examined an NLP-based communication assistance tool for adolescents and young adults with ASD. The tool combined sentiment analysis, intent identification, and situational simplification to provide real-time feedback in digital communication. Methods Participants were 16 individuals with ASD (aged 15–28 years) and 16 neurotypical conversation partners in a within-subjects, mixed-methods design. The intervention lasted eight weeks. The tool was used in both structured tasks and naturalistic conversations. Results Preliminary data suggest associations between tool use and improved outcomes. Communication clarity increased from 3.12 to 3.89 (d = 0.74, 95% CI [0.28, 1.20], p < 0.001 after Bonferroni correction). Anxiety decreased by 2.3 points on a 7-point scale (d = 1.21, 95% CI [0.62, 1.80], p < 0.001). Confidence scores improved by 42% from baseline (d = 1.15, 95% CI [0.56, 1.74], p < 0.001). Qualitative thematic analysis (Braun & Clarke, 2006; inter-coder reliability, κ = 0.81) identified five themes: reduced communication burden, learning through use, empowerment and autonomy, context-specific value, and desire for customization. These preliminary findings suggest that NLP-driven assistive technologies warrant further investigation in controlled trials. Conclusion This study demonstrates feasibility and provides preliminary evidence for NLP-based communication support, though causal conclusions cannot be drawn due to the absence of a control group, small sample size (N = 16), and short duration.

Decision tree model to predict one-year survival in ambulatory patients with advanced cancer

Background An accurate prognostication is crucial for end-of-life decision-making in advanced cancer care. While existing prognostic tools focus on short-term survival (weeks/months), there is a paucity of studies that have examined the long-term prediction at one year. A one-year timeframe is regarded as a general indicator of palliative care referral; however, there are many uncertain issues. This study aimed to develop a one-year survival prediction model using objective parameters for patients with advanced cancer. Methods This was a secondary analysis of data from a Korean prospective cohort study. Participants, with clinician-predicted survival of ≤1 year, were assessed using clinical data, performance status, laboratory data and chemotherapy response. Recursive partitioning analyses (RPA) were used to identify the prognostic factors and build a prediction model. Results Of the 200 advanced cancer patients (mean age 64.4, 36% female; 33.5% lung cancer), the median survival was 228 days. Using three variables (chemotherapy response, C reactive protein -Albumin Ratio, and lactate dehydrogenase level), we developed a 4-node survival tree. The model demonstrated an optimism-corrected area under the curve of 0.749 (95% confidence interval: 0.696–0.800) at one year, after 200 bootstrap resampling. The Brier score was 0.161, and the calibration slope was 0.99, indicating high predictive accuracy. Conclusions We developed an RPA model to facilitate one-year survival prediction in patients with advanced cancer. The 4-leaf model incorporated only three readily available variables. Following external validation, this model may prove valuable in assisting clinicians with one-year survival prognostication.