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A novel multiple directional shearing interferometry system with synchronous polarization phase shifting
Observation of magnetic skyrmion lattice in Cr0.82Mn0.18Ge by small-angle neutron scattering
Abstract Incommensurate magnetic phases in chiral cubic crystals are an established source of topological spin textures such as skyrmion and hedgehog lattices, with potential applications in spintronics and information storage. We report a comprehensive small-angle neutron scattering (SANS) study on the B20-type chiral magnet Cr $$_{0.82}$$ Mn $$_{0.18}$$ Ge, exploring its magnetic phase diagram and confirming the stabilization of a skyrmion lattice under low magnetic fields. Our results reveal a helical ground state with a decreasing pitch from 40 to 35 nm upon cooling, and a skyrmion phase stable in applied magnetic fields of 10–30 mT, and over an unusually wide temperature range for chiral magnets of 6 K ( $$\sim T_\text {C}/2< T < T_\text {C}$$ , $$T_\text {C}=13$$ K). The skyrmion lattice forms a standard two-dimensional hexagonal coordination that can be trained into a single domain, distinguishing it from the three-dimensional hedgehog lattice observed in MnGe-based systems. Additionally, we demonstrate the persistence of a metastable SkL at 2 K, even at zero field. These findings advance our understanding of magnetic textures in Cr-based B20 compounds, highlighting Cr0.82Mn0.18Ge as a promising material for further exploration in topological magnetism.
CT-based skeletal muscle loss predicts long term prognosis in patients with distal cholangiocarcinoma undergone pancreaticoduodenectomy
Spectroscopic investigation of two xanthane dyes and design of a FRET based pesticide sensor
Perioperative treatment with cilostazol reverses steatosis and improves liver regeneration after major hepatectomy in a steatotic rat model
Abstract Cilostazol has previously been shown to reduce liver steatosis and enhance hepatic perfusion. We investigated the effects of cilostazol after major hepatectomy in a steatotic rat model. Six weeks prior to surgery, Sprague–Dawley rats were fed with a high-fructose diet. The treatment group received daily 5 mg/kg cilostazol. Seven days following the cilostazol treatment, all animals underwent 70% liver resection (PHX). Analysis of hepatic blood flow and microcirculation and immunohistochemical examinations were conducted 30 min after PHX (postoperative day [POD] 0) as well as on POD 1, POD 3 and POD 7. The weight of cilostazol-treated animals was significantly reduced compared to untreated controls after completion of the 6-week high-FRC diet. Furthermore, 41% macrovesicular steatosis was found in the control group compared to 8% in the cilostazol group. Hepatic arterial and portal venous perfusion were increased in the cilostazol group on POD 7. Lower liver enzyme release was found postoperatively in cilostazol-treated animals. Moreover, apoptosis and neutrophil infiltration were reduced after cilostazol treatment. Proliferation of hepatocytes and liver regeneration after PHX were significantly increased in the cilostazol group. Consequently, cilostazol should be evaluated as a novel strategy to reduce the rate of liver failure after PHX in steatotic liver.
Vehicle speed measurement method using monocular cameras
Abstract This paper proposes a method for fast and accurate vehicle speed measurement based on a monocular camera. Firstly, by establishing a new camera imaging model, the calibration method for variable focal lengths is optimized, simplifying the transformation process between the four coordinate systems in traditional camera imaging models, and the method does not need to restore the pixel coordinates to dedistortion. Secondly, based on the camera imaging model, a two-dimensional positioning algorithm is proposed. By leveraging the characteristics of the speed measurement problem, the complex three-dimensional positioning problem is simplified into a two-dimensional model, reducing the overall computational complexity of the positioning problem. Finally, the algorithm is combined with You Only Look Once version 7 (YOLOv7) and Deep Simple Online and Realtime Tracking (DeepSORT) algorithms, integrating multiple model structures to optimize the network, achieving precise multi-target speed measurement. Experiments show that under frame-by-frame measurement conditions, the minimum and average accuracies of this method reach 95.1% and 97.6%, respectively. Compared with other methods, it has significant advantages in speed measurement accuracy and computational efficiency. Therefore, this research outcome is expected to play an important role in intelligent transportation systems and road safety management.
Evaluation of ecological geological environment carrying capacity and analysis of driving mechanisms based on normal cloud model and geodetector model
The link between tinnitus and menstrual cycle disorders in premenopausal women
Identification of prognostic biomarkers of sepsis and construction of ceRNA regulatory networks
Identifying key genes in cancer networks using persistent homology
Abstract Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the high-dimensional interactions that cancer genes have within cancer networks. This study presents a novel method using Persistent Homology to analyze the role of driver genes in higher-order structures within Cancer Consensus Networks derived from main cellular pathways. We integrate mutation data from six cancer types and three biological functions: DNA Repair, Chromatin Organization, and Programmed Cell Death. We systematically evaluated the impact of gene removal on topological voids ( $$\beta _2$$ structures) within the Cancer Consensus Networks. Our results reveal that only known driver genes and cancer-associated genes influence these structures, while passenger genes do not. Although centrality measures alone proved insufficient to fully characterize impact genes, combining higher-order topological analysis with traditional network metrics can improve the precision of distinguishing between drivers and passengers. This work shows that cancer genes play an important role in higher-order structures, going beyond pairwise measures, and provides an approach to distinguish drivers and cancer-associated genes from passenger genes.
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.