Browse Articles

Flexural behavior of steel-UHPC composite bridge slab under different curing conditions

Investigating the mechanism of Gentiopicroside in rheumatoid arthritis through network pharmacology, molecular docking, and experimental validation

Toward the standardization of big datasets of urine output for AKI analysis: a multicenter validation study

Abstract Acute kidney injury (AKI) is a prevalent condition in ICU patients. However, inconsistencies in urine charting and guideline interpretations hinder accurate diagnosis and research. This study aimed to derive and validate a standardization for the processing of big urine output datasets to improve consistency in AKI diagnosis and staging. Using a derivation cohort from 14 ICUs at Beth Israel Deaconess Medical Center (2008–2019) and a validation cohort from an academic center in Amsterdam (2003–2016), we developed and validated an algorithm for computing hourly urine output rates and identifying oliguric AKI across its definitions. Peak AKI stages computed using the method were significantly associated with all clinical outcomes, including severity scores, serum creatinine levels, ICU and hospital lengths of stay, renal replacement therapy requirements, and hospital mortality (all p < 0.001). Adjusted 30-day mortality odds ratios for AKI stages 1–3 were 1.58, 2.93, and 5.24 in the derivation cohort and 2.91, 5.16, and 13.59 in the validation cohort (all p < 0.001). Tested on over 85,000 multinational ICU admissions, this approach demonstrated robust performance and consistent results across diverse settings; it has the potential to enhance clinical practice through e-alerts and support future AKI and fluid balance research, including ML model training and inference. Supported by open-source code, the proposed method advances the standardization of AKI diagnostic criteria and can be applied to other EHR-based databases.

Structure stability of (U, Pu) C and (U, Pu) N compositions

Abstract Atomic scale computer simulations based on density functional theory (DFT) are used to calculate the formation energies and structures associated with phases in the U–N, Pu–N, U–C and Pu–C systems. Stable phases across the compositional spaces, from the metal to the nitrogen gas or graphite end members, are identified using convex hull analysis. Many predicted phases correspond to those known from experimental phase diagrams (e.g. UN, U2N3; PuN; UC, U2C3; Pu2C3). However, many phases only sit on the convex hull upon inclusion of a suitably characterised Hubbard parameter (i.e. DFT + U). A nonstoichiometric composition of UN2−x is identified on the U–N convex hull but others, including stoichiometric UN2, are close to the line. A stoichiometric structure for Pu3C2 with $$R\overline{3}c$$ symmetry is identified, alongside which a nonstoichiometric PuC1−x phase has a similar energy.

Emotion recognition with multiple physiological parameters based on ensemble learning

A prospective randomized study that compares three different dressings for the prevention of surgical site infections following major heart surgery

Depletion of γ-glutamyl cyclotransferase suppresses the proliferation, migration and invasion of breast cancer cells accompanied by the activation of PI3K/AKT/mTOR pathway

UANV: UNet-based attention network for thoracolumbar vertebral compression fracture angle measurement

Identifying common trends and ecosystem states to inform Gulf of Alaska ecosystem-based fisheries management

Ecosystem-based fisheries management requires the successful integration of ecosystem information into the fisheries management process. In the Northeast Pacific Ocean, ecosystem data collection and accessibility have achieved successful milestones, yet application to the harvest specification process remains challenging. The synthesis, interpretation, and application of ecosystem information to groundfish fisheries management in the Gulf of Alaska (GOA) can be supported by the identification of common ecosystem trends and ecosystem states across a diverse set of indicators. In this study, we used Dynamic Factor Analysis (DFA) and hidden Markov models (HMM) to analyze 92 indicators in climate, lower-trophic, mid-trophic, and seabird models for the western and eastern GOA marine ecosystems. Time series ranged from 25 to 52 years in length, analyzed through 2022. The DFA identified common trends across indicators and groups of covarying indicators (e.g., biomass of zooplankton species), highlighting opportunities to streamline communication of these data to management. Non-stationarity analyses revealed past changes in relationships, and can provide early warnings in future annual updates if previously identified correlations change. The HMM identified two to three ecosystem states in each sub-model that largely aligned with previously observed long- and short-term shifts in ecosystem dynamics in the region (i.e., shifts starting in 1975, 1988, and 2014). Annually updating these analyses, within an existing framework of reporting ecosystem information to management bodies, can streamline communication and improve early warning of changes in ecosystem dynamics. These tools can provide ecosystem support to management decisions relative to groundfish productivity and resulting harvest specifications.

Enhanced etch characteristics of EUV PR masked SiON through the ion beam grid pulsing technique

Abstract EUV lithography technology, applied in nano-patterning processes, enables the creation of fine patterns below 10 nm. However, issues still remain due to the reduced etch selectivity and increased line edge roughness (LER) caused by the thin thickness and weakness of organic EUV photoresist (PR). In this study, research was conducted to improve the low etch selectivity and high LER using a novel grid pulsed ion beam etching technique. In this system, Ar/H2 plasma is generated in the inductively coupled plasma (ICP) source, and the Ar+/Hx + ion beam is irradiated while fluorocarbon gas is injected into the process chamber. Grid pulsing technique increases the etch selectivity of SiON over EUV PR while improving LER. With a 50% duty ratio and optimal gas flow conditions (Ar:H2 ratio = 1:3 to the ion beam source and CF4:C4F8 = 1:1), the etch selectivity of SiON over EUV PR approached ∞ while maintaining the LER close to the reference.

Leptospira seroprevalence and associated risk factors among cattle in Bor County, South Sudan

Leptospirosis is a bacterial zoonotic disease that is distributed globally. In livestock, leptospirosis often presents as a subclinical disease that results in significant reproductive and production losses, which could in turn have detrimental economic consequences, particularly in countries like South Sudan that rely on livestock farming for livelihood. Leptospirosis often presents as a subclinical disease in which case the animal may be a maintenance host for a specific serovar. Recent increases in unexplained abortions have prompted us to investigate Leptospira exposure and associated risk factors among cattle in Bor County South Sudan. A cross-sectional study was conducted between 22nd January to 15th February 2023. Blood samples were collected from 357 cattle in four of the six cattle camps in the County at that time. Seropositivity was determined by detecting anti-Leptospira antibodies in the serum samples by microscopic agglutination test (MAT) based on a panel of 12 serovars representing 12 serogroups. Data on risk factors were obtained using pre-tested questionnaires administered to the owner or herdsman of each sampled herd. Of the 357 cattle sampled, 66.95% (95% CI = 61.91–71.62) were seropositive (cut-off titer ≥100). Seventy-six of the seropositive cattle (21.65%) had MAT titer ≥800, indicating a probable recent infection at the time of sampling. The most prevalent serogroups were L. borgpetersenii Tarassovi (59.83%) and L. borgpetersenii Ballum (17.38%). In the robust Poisson regression model, only the age of cattle was a significant risk factor to Leptospira seroprevalence. The prevalence in adult cattle was 1.43 times higher than in young ones (95% CI 1.09–1.92; P-value = 0.012). The extremely high seroprevalence indicates that leptospirosis may be endemic in cattle in South Sudan, and potentially one of the etiologies for the recently increasing abortion reports. This may require confirmation of the infection status among the aborting cattle.

Computational insights into wide bandgap lead free perovskite solar cells for silicon based tandem configurations

Impact of self-perceived discomfort in critically ill patients on the occurrence of psychiatric symptoms in post-intensive care syndrome (PICS): A prospective observational study

Background Mental health impairments after intensive care unit (ICU) discharge include anxiety, depression, and post-traumatic stress disorder [PTSD], forming part of the post-intensive care syndrome (PICS). We assessed the effects of discomfort on the occurrence of psychiatric symptoms as a part of PICS. Methods This prospective observational study conducted from September 2022 to June 2023 included all patients aged ≥ 18 years who survived an ICU stay of ≥3 days. To assess patient discomfort during the ICU stay, we used the Inconforts des Patients de REAnimation (IPREA) questionnaire. The primary outcome was the occurrence of anxiety, depression, or PTSD after ICU discharge. Secondary outcomes were the quality of life in ICU survivors and the clinical impression of physicians and psychologists to predict post-ICU psychiatric symptoms. Results Of the 173 patients included initially, 109 were finally analysed. An IPREA score ≥ 13 was strongly associated with an increased risk of post-ICU psychiatric symptoms (odds ratio: 3.8, 95% confidence interval: 1.4–10.3, p = 0.008). The patients with post-ICU psychiatric symptoms had a reduced quality of life. The clinical impression of physicians and psychologists at ICU discharge for the risk of psychiatric symptoms 3 months after the ICU stay was not selective. Conclusions Self-perceived discomfort in ICU survivors was the most predictive factor of the development of post-ICU psychiatric symptoms.

Predicting emergency department visits for non-traumatic dental-related conditions among Medicaid beneficiaries

MTSA-SC: A multi-task learning approach for individual trip destination prediction with multi-trajectory subsequence alignment and space-aware loss functions

Individual Trip Destination Prediction aims to accurately forecast an individual’s future travel destinations by analyzing their historical trajectory data, holding significant application value in intelligent navigation, personalized recommendations, and urban traffic management. However, challenges such as data sparsity, low quality, and complex spatiotemporal volatility pose substantial difficulties for prediction tasks. Existing studies exhibit notable limitations in insufficient integration of sparsity handling and prediction tasks, constrained modeling capability for local volatility, and inadequate exploration of fine-grained spatial dependencies, struggling to balance global patterns and local features in trajectory data. To address these issues, this paper proposes an individual trip destination prediction method that integrates multi-task learning, a multi-trajectory subsequence alignment attention mechanism, and a spatially consistent constrained cross-entropy loss function. Leveraging a multi-task learning framework(MTSA-SC), our approach collaboratively addresses trajectory recovery and prediction tasks, enhancing prediction accuracy while improving robustness to missing data. The multi-trajectory subsequence alignment attention mechanism incorporates sliding windows and convolutional operations to dynamically capture local volatility and diverse patterns in trajectories. The spatially consistent constrained loss function strengthens spatial feature learning through differential error penalty adjustments. Experimental results on public datasets from Shenzhen and Xiamen demonstrate recall rates of 0.722 and 0.6 under complete and sparse trajectory scenarios, respectively, outperforming state-of-the-art baselines by an average of 15.64%. This research provides robust technical support for intelligent travel recommendations and traffic management.

Isolation and characterization of human cKIT positive amniotic fluid stem cells obtained from pregnancies with spina bifida

The tumor suppressor FAT1 controls YAP/TAZ protein degradation and tumor cell proliferation through E3 ligase MIB2

FAT1 is a tumor suppressor gene encoding the protocadherin FAT1, which has been found to be mutated in different types of human cancers with the highest frequency in head and neck squamous cell carcinoma (HNSCC). However, through which mechanisms mutations of FAT1 lead to tumor progression is incompletely understood. Here, we report that loss of FAT1 in various tumor cells, including HNSCC cells, resulted in increased protein levels of the transcriptional regulators YAP and TAZ. This was sufficient to lead to increased expression of YAP/TAZ target genes and to increased tumor cell proliferation. We found that elevated YAP/TAZ activity after loss of FAT1 was due to decreased YAP/TAZ protein degradation, which could be rescued by expression of the intracellular part of FAT1. When analyzing the interactome of the cytoplasmic part of FAT1 in tumor cells, we identified the E3 ubiquitin ligase Mind Bomb-2 (MIB2) as an interaction partner. Suppression of MIB2 expression in various tumor cells led to same effects as loss of FAT1 expression, including a decrease in YAP and TAZ ubiquitination, and degradation as well as an increase in YAP/TAZ protein levels and expression of YAP/TAZ target genes. Similarly, Hela cells or HNSCCs with suppressed MIB2 expression resembled FAT1 defective tumor cells showing faster proliferation in vitro as well as increased tumor growth in vivo compared to control cells. Our study identifies a mechanism by which YAP/TAZ levels are kept low through FAT1/MIB2-mediated protein degradation and shows that tumor progression resulting from mutation of tumor suppressor FAT1 involves loss of MIB2-dependent degradation of YAP and TAZ.

Mediational effect analysis of childhood emotional abuse on prodromal psychotic symptoms in self-taught examination students

Active disturbance rejection control based on soft computing techniques for electric power steering to improve system performance

Electric Power Steering (EPS) systems enhance driving comfort and safety. However, their performance often degrades under varying operating conditions due to external disturbances and modeling uncertainties. Traditional control methods, which typically rely on fixed parameters or neglect disturbance dynamics, struggle to maintain robustness and adaptability across diverse scenarios. This article presents an improved control strategy integrating Active Disturbance Rejection Control (ADRC) with advanced soft computing techniques to address these challenges. The proposed method introduces two key innovations: optimizing the tracking differentiator’s speed factor using a genetic algorithm and dynamically tuning state feedback control parameters through a fuzzy inference system. This hybrid approach enhances the disturbance rejection capability of ADRC and significantly improves system adaptability and tracking accuracy. Simulation results validate the effectiveness of the proposed controller, demonstrating low tracking errors (1.875% at low speed and 1.373% at high speed) and disturbance estimation accuracy exceeding 90%. Compared to conventional controllers, the proposed method exhibits superior robustness, reduced steady-state error, and improved performance across a wide range of operating conditions. These results confirm the potential of integrating ADRC with intelligent optimization techniques for advanced control in automotive mechatronic systems.

Short-wall roadway backfill method for sustainable mining