Browse Articles

Comparison of two plates and screw osteosynthesis configurations in a rat model of critical sized femoral defects to reduce implant related failures

Evaluating cutinase from Fusarium oxysporum as a biocatalyst for the degradation of nine synthetic polymer

Effects of HIV exposure on anemia and vitamin D nutritional status in children aged 6–24 months: a hospital-based cross-sectional study

Association between a body shape index and bone mineral density in US adults based on NHANES data

Comparative analysis of correlation and causality inference in water quality problems with emphasis on TDS Karkheh River in Iran

Abstract Water quality management is a critical aspect of environmental sustainability, particularly in arid and semi-arid regions such as Iran where water scarcity is compounded by quality degradation. This study delves into the causal relationships influencing water quality, focusing on Total Dissolved Solids (TDS) as a primary indicator in the Karkheh River, southwest Iran. Utilizing a comprehensive dataset spanning 50 years (1968–2018), this research integrates Machine Learning (ML) techniques to examine correlations and infer causality among multiple parameters, including flow rate (Q), Sodium (Na+), Magnesium (Mg2+), Calcium (Ca2+), Chloride (Cl−), Sulfate (SO4 2−), Bicarbonates (HCO3 −), and pH. For modeling the causation, the “Back door linear regression” approach has been considered which establishes a stable and interpretable framework in causal inference by focusing on clear assumptions. Predictive modeling was used to show the difference between correlation and causation along with interpretability modeling to make the predictive model transparent. Predictive modeling does not report the causality among the variables as it showed Mg is not contributing to the target (TDS) while the findings reveal that TDS is predominantly positive influenced by Mg, Na, Cl, Ca and SO4, with HCO3 and pH exerting negative (inverse) effects. Unlike correlations, causal relationships demonstrate directional and often unequal influences, highlighting Mg as a critical driver of TDS levels. This novel application of ML-based causal inference in water quality research provides a cost-effective and time-efficient alternative to traditional experimental methods. The results underscore the potential of ML-driven causal analysis to guide water resource management and policy-making. By identifying the key drivers of TDS, this study proposes targeted interventions to mitigate water quality deterioration. Moreover, the insights gained lay the foundation for developing early warning systems, ensuring proactive and sustainable water quality management in similar hydrological contexts.

From node to network: weaving a global perspective on efficacy and costs of non-pharmaceutical interventions

A gamma-ray spectrometer based on MAPD-3NM-2 and LaBr3(ce) and LSO scintillators for hydrogen detection on planetary surfaces

Abstract The presented work is dedicated to the detection of hydrogen, using detectors based on a MAPD (Micropixel Avalanche Photodiode) array based on new MAPD-3NM-2 type photodiodes and two different scintillators (LaBr3(Ce) and LSO(Ce)). The physical parameters of the MAPD photodiode used in the study and the intrinsic background of the scintillators were investigated. For the 2.223 MeV energy gamma-ray indicating the presence of hydrogen, the energy resolution was 6.89% with the MAPD array and LSO scintillator-based detector, and the number of events corresponding to this energy was 4817. With the MAPD array and LaBr3(Ce) scintillator, the energy resolution for the 2.223 MeV gamma-ray was 3.55%, and the number of events corresponding to this energy was 3868. The LSO scintillator-based detector allowed for the detection of 24.5% more 2.223 MeV energy gamma-rays compared to the LaBr3(Ce) scintillator. For the 2.223 MeV gamma-ray associated with hydrogen, the energy resolution with the LaBr3(Ce) scintillator was 48.5% better than with the LSO scintillator. The lower energy resolution compared to the LSO is due to the higher light output of LaBr3(Ce). The obtained results experimentally demonstrate that it is possible to obtain information about the presence of hydrogen in the target using both detectors.

Electrochemical deposition of bimetallic sulfides on novel BDD electrode for bifunctional alkaline seawater electrolysis

Publisher Correction: Predicting high sensitivity C-reactive protein levels and their associations in a large population using decision tree and linear regression

Enhancing social functioning using multi-user, immersive virtual reality

Association of domain-specific physical activity with nocturia: a population-based study

Comprehensive RNA-seq analysis of benign prostatic hyperplasia (BPH) in rats exposed to testosterone and estradiol

Hydrogenotrophic methanogenesis at 7–12 mbar by Methanosarcina barkeri under simulated martian atmospheric conditions

Analysis of retinal sublayers in patients with systemic COVID-19 illness with varying degrees of severity

Growth-rate distributions of gut microbiota time series

Abstract Logarithmic growth-rates are fundamental observables for describing ecological systems and the characterization of their distributions with analytical techniques can greatly improve their comprehension. Here a neutral model based on a stochastic differential equation with demographic noise, which presents a closed form for these distributions, is used to describe the population dynamics of microbiota. Results show that this model can successfully reproduce the log-growth rate distribution of the considered abundance time-series. More significantly, it predicts its temporal dependence, by reproducing its kurtosis evolution when the time lag $$\tau$$ is increased. Furthermore, its typical shape for large $$\tau$$ is assessed, verifying that the distribution variance does not diverge with $$\tau$$ . The simulated processes generated by the calibrated stochastic equation and the analysis of each time-series, taken one by one, provided additional support for our approach. Alternatively, we tried to describe our dataset by using a logistic neutral model with an environmental stochastic term. Analytical and numerical results show that this model is not suited for describing the leptokurtic log-growth rates distribution found in our data. These results support an effective neutral model with demographic stochasticity for describing the considered microbiota.

Author Correction: Effectiveness of physical activity interventions on reducing perceived fatigue among adults with chronic conditions: a systematic review and meta-analysis of randomised controlled trials

When did recombination suppression events occur in bird ZW sex chromosomes?

Development and validation of a machine learning-based prediction model for hepatorenal syndrome in liver cirrhosis patients using MIMIC-IV and eICU databases

Chemical tools to define and manipulate interferon-inducible Ubl protease USP18

Abstract Ubiquitin-specific protease 18 (USP18) is a multifunctional cysteine protease primarily responsible for deconjugating the interferon-inducible ubiquitin-like modifier ISG15 from protein substrates. Here, we report the design and synthesis of activity-based probes (ABPs) that incorporate unnatural amino acids into the C-terminal tail of ISG15, enabling the selective detection of USP18 activity over other ISG15 cross-reactive deubiquitinases (DUBs) such as USP5 and USP14. Combined with a ubiquitin-based DUB ABP, the USP18 ABP is employed in a chemoproteomics screening platform to identify and assess inhibitors of DUBs including USP18. We further demonstrate that USP18 ABPs can be utilized to profile differential activities of USP18 in lung cancer cell lines, providing a strategy that will help define the activity-related landscape of USP18 in different disease states and unravel important (de)ISGylation-dependent biological processes.

SLC1A5 is a key regulator of glutamine metabolism and a prognostic marker for aggressive luminal breast cancer

Abstract Cancer cells exhibit altered metabolism, often relying on glutamine (Gln) for growth. Breast cancer (BC) is a heterogeneous disease with varying clinical outcomes. We investigated the role of the amino acid transporter SLC1A5 (ASCT2) and its association with BC subtypes and patient outcomes. In large BC cohorts, SLC1A5 mRNA (n = 9488) and SLC1A5 protein (n = 1274) levels were assessed and correlated their expression with clinicopathological features, molecular subtypes, and patient outcomes. In vitro SLC1A5 knockdown and inhibition studies in luminal BC cell lines (ZR-75-1 and HCC1500) were used to further explore the role of SLC1A5 in Gln metabolism. Statistical analysis was performed using chi-squared tests, ANOVA, Spearman’s correlation, Kaplan–Meier analysis, and Cox regression. SLC1A5 mRNA and SLC1A5 protein expression were strongly correlated in luminal B, HER2 + and triple-negative BC (TNBC). Both high SLC1A5 mRNA and SLC1A5 protein expression were associated with larger tumour size, higher grade, and positive axillary lymph node metastases (P < 0.01). Importantly, high SLC1A5 expression correlated with poor BC-specific survival specifically in the highly proliferative luminal subtype (P < 0.001). Furthermore, SLC1A5 knockdown by siRNA or GPNA inhibition significantly reduced cell proliferation and glutamine uptake in ZR-75-1 cells. Our findings suggest SLC1A5 plays a key role in the aggressive luminal BC subtype and represents a potential therapeutic target. Further research is needed to explore SLC1A5 function in luminal BC and its association with Gln metabolism pathways.