Extremely preterm infants born outside a provincial tertiary perinatal center and transferred postnatally associated with poor outcomes: a real-world observational study

IntroductionExtremely preterm infants (EPIs) have high morbidity and mortality, and are recommended to be born in a tertiary perinatal center (inborn). However, many EPIs in central China are born in lower-level hospitals and transferred postnatally, the outcomes of which remain to be investigated.MethodsEPIs admitted to the Department of Neonatology, Maternal and Child Health Hospital of Hubei Province from January 2013 to December 2022 were retrospectively recruited and divided into the control (inborn) and transfer groups (born in other hospitals). The neonatal and maternal characteristics, neonatal outcomes, and the treatment of survival EPIs were analyzed.ResultsA total of 174 and 109 EPIs were recruited in the control and transfer groups, respectively. EPIs in the transfer group have a higher birth weight and a lower proportion of multiple pregnancies than the control group (all P < 0.05). The proportions of antenatal steroids, magnesium sulfate, cesarean delivery, premature rupture of membranes ≥18 h, gestational diabetes, and amniotic fluid abnormalities were lower in the transfer group (all P < 0.05). Survival rates (64.22% vs. 56.32%), proportions of severe periventricular-intraventricular hemorrhage (PIVH) (11.93% vs. 11.49%), severe bronchopulmonary dysplasia (sBPD) (21.05% vs. 20%), and severe retinopathy of prematurity (ROP) (24.77% vs. 20.11%) were similar in the transfer and control groups (all P > 0.05). However, the transfer group had higher proportions of severe birth asphyxia (34.86% vs. 13.22%, P < 0.001), PIVH (42.20% vs. 29.89%, P = 0.034), and extrauterine growth retardation (EUGR) (17.43% vs. 6.32%, P = 0.003). Less surfactant utilization was found in the transfer group among survival EPIs (70.00% vs. 93.88%, P < 0.001).ConclusionEPIs born outside a tertiary perinatal center and transferred postnatally did not have significantly higher mortality and rates of severe complications (severe PIVH, severe ROP, and sBPD), but there may be an increased risk of severe asphyxia, PIVH and EUGR. This may be due to differences in maternal and neonatal characteristics and management. Further follow-up is needed to compare neurodevelopmental outcomes, and it is recommended to transfer the EPIs in utero to reduce the risk of poor physical and neurological development

Dietary Isothiocyanates: Novel Insights into the Potential for Cancer Prevention and Therapy

Diet plays an important role in health. A high intake of plant chemicals such as glucosinolates/isothiocyanates can promote optimal health and decrease the risk of cancer. Recent research has discovered more novel mechanisms of action for the effects of isothiocyanates including the modulation of tumor microenvironment, the inhibition of the self-renewal of stem cells, the rearrangement of multiple pathways of energy metabolism, the modulation of microbiota, and protection against Helicobacter pylori. However, the hormetic/biphasic effects of isothiocyanates may make the recommendations complicated. Isothiocyanates possess potent anti-cancer activities based on up-to-date evidence from in vitro and in vivo studies. The nature of hormesis suggests that the benefits or risks of isothiocyanates largely depend on the dose and endpoint of interest. Isothiocyanates are a promising class of cancer-preventative phytochemicals, but researchers should be aware of the potential adverse (and hormetic) effects. In the authors’ opinion, dietary isothiocyanates are better used as adjunctive treatments in combination with known anti-cancer drugs. The application of nano-formulations and the delivery of isothiocyanates are also discussed in this review

ODDFUZZ: Discovering Java Deserialization Vulnerabilities via Structure-Aware Directed Greybox Fuzzing

Java deserialization vulnerability is a severe threat in practice. Researchers have proposed static analysis solutions to locate candidate vulnerabilities and fuzzing solutions to generate proof-of-concept (PoC) serialized objects to trigger them. However, existing solutions have limited effectiveness and efficiency. In this paper, we propose a novel hybrid solution ODDFUZZ to efficiently discover Java deserialization vulnerabilities. First, ODDFUZZ performs lightweight static taint analysis to identify candidate gadget chains that may cause deserialization vulner-abilities. In this step, ODDFUZZ tries to locate all candidates and avoid false negatives. Then, ODDFUZZ performs directed greybox fuzzing (DGF) to explore those candidates and generate PoC testcases to mitigate false positives. Specifically, ODDFUZZ applies a structure-aware seed generation method to guarantee the validity of the testcases, and adopts a novel hybrid feedback and a step-forward strategy to guide the directed fuzzing. We implemented a prototype of ODDFUZZ and evaluated it on the popular Java deserialization repository ysoserial. Results show that, ODDFUZZ could discover 16 out of 34 known gadget chains, while two state-of-the-art baselines only identify three of them. In addition, we evaluated ODDFUZZ on real-world applications including Oracle WebLogic Server, Apache Dubbo, Sonatype Nexus, and protostuff, and found six previously unreported exploitable gadget chains with five CVEs assigned.Comment: To appear in the Main Track of IEEE S&P 202

The Statistical Damage Constitutive Model of Longmaxi Shale under High Temperature and High Pressure

AbstractIn the exploitation of shale gas in deep and ultradeep formations, the mechanical properties of shale change under the action of high temperature and pressure. High-temperature stimulation can effectively release the damage of water phase trapping, which was caused during the drilling and completion of hydraulic fracturing of shale gas reservoirs. In this paper, the experiments have twelve groups of shale samples (three samples per group) under four target temperatures, 25, 200, 400, and 600°C as well as the confining pressure set as 0 MPa, 15 MPa, and 30 MPa. The servo testing machine is used to perform triaxial compression tests on the shale specimens that have undergone high temperature. The porosity, permeability, and velocity are also obtained under different temperatures. A statistical constitutive model of shale after temperature thermal damage under triaxial compression is established. Based on the characteristics of the random statistical distribution of rock strength and strain strength theory, apply relevant knowledge of damage mechanics as well as consider the failure of the microprotocol and the nonlinear relationship between elastic modulus and temperature. According to the test results, the relationship between the mechanical parameters of the shale and the temperature is discussed. The parameters of the statistical constitutive model considering temperature thermal damage are given also; a comparison with the results of uniaxial compression experiments shows the rationality and reliability. This work not only enriches the theory of shale failure pattern but also contributes to the deep shale development at high temperature

Simulation on dislocation’s contribution to metal strength and grain boundary’s role as irradiation defect sink

This series of works have been focused on materials response to various harsh enviroments, i.e. high stresses, high temperature and irradiation. The work is mainly achieved by using different computer simulation techniques across multiple time and space regimes. Multiphysics and multiscale simulations were carried out to try to reveal the true physics behind some phenomena never seen before, discovering new sciences and mechanisms that are promising to guide future generation materials design

Vacancy Energetics and Diffusivities in the Equiatomic Multielement Nb-Mo-Ta-W Alloy

In this work, we study vacancy energetics in the equiatomic Nb-Mo-Ta-W alloy, especially vacancy formation and migration energies, using molecular statics calculations based on a spectral neighbor analysis potential specifically developed for Nb-Mo-Ta-W. We consider vacancy properties in bulk environments as well as near edge dislocation cores, including the effect of short-range order (SRO) by preparing supercells through Metropolis Monte-Carlo relaxations and temperature on the calculation. The nudged elastic band (NEB) method is applied to study vacancy migration energies. Our results show that both vacancy formation energies and vacancy migration energies are statistically distributed with a wide spread, on the order of 1.0 eV in some cases, and display a noticeable dependence on SRO. We find that, in some cases, vacancies can form with very low energies at edge dislocation cores, from which we hypothesize the formation of stable 'superjogs' on edge dislocation lines. Moreover, the large spread in vacancy formation energies results in an asymmetric thermal sampling of the formation energy distribution towards lower values. This gives rise to effective vacancy formation energies that are noticeably lower than the distribution averages. We study the effect that this phenomenon has on the vacancy diffusivity in the alloy and discuss the implications of our findings on the structural features of Nb-Mo-Ta-W

A general solution for Stokes flow and its application to the problem of a rigid plate translating in a fluid

International audienc

An Ensemble Model-Based Estimation of Nitrogen Dioxide in a Southeastern Coastal Region of China

NO2 (nitrogen dioxide) is a common pollutant in the atmosphere that can have serious adverse effects on the health of residents. However, the existing satellite and ground observation methods are not enough to effectively monitor the spatiotemporal heterogeneity of near-surface NO2 concentrations, which limits the development of pollutant remediation work and medical health research. Based on TROPOMI-NO2 tropospheric column concentration data, supplemented by meteorological data, atmospheric condition reanalysis data and other geographic parameters, combined with classic machine learning models and deep learning networks, we constructed an ensemble model that achieved a daily average near-surface NO2 of 0.03° exposure. In this article, a meteorological hysteretic effects term and a spatiotemporal term were designed, which considerably improved the performance of the model. Overall, our ensemble model performed better, with a 10-fold CV R2 of 0.89, an RMSE of 5.62 µg/m3, and an MAE of 4.04 µg/m3. The model also had good temporal and spatial generalization capability, with a temporal prediction R2 and a spatial prediction R2 of 0.71 and 0.81, respectively, which can be applied to a wider range of time and space. Finally, we used an ensemble model to estimate the spatiotemporal distribution of NO2 in a coastal region of southeastern China from May 2018 to December 2020. Compared with satellite observations, the model output results showed richer details of the spatiotemporal heterogeneity of NO2 concentrations. Due to the advantages of using multi-source data, this model framework has the potential to output products with a higher spatial resolution and can provide a reference for downscaling work on other pollutants

Estimation of Near-Ground Ozone With High Spatio-Temporal Resolution in the Yangtze River Delta Region of China Based on a Temporally Ensemble Model

Recently, the near-ground ozone pollution has become an important factor restricting economic development and ecological environment protection. Due to the aging equipment of satellite sensors and the limitations of spatial resolution, the current approach utilizing satellite remote sensing observation faces challenges in effectively monitoring small-scale areas with sufficient data. Taking the near-ground ozone concentration as the research object, this article combined multiple classical machine learning (ML) methods based on tree models and developed a temporally ensemble model to achieve the estimation of near-surface ozone in the 1 km2 area of the Yangtze River Delta region in China. In the ensemble model, the coefficient of determination (R2) of the 10-fold cross-validation was 0.91, and the root-mean-square error was 9.21 μg/m3. All evaluation indicators confirm that our approach was more accurate than some conventional ML models. The predicted spatial errors were evenly distributed, which indicated the superior spatial stationarity of the ensemble model. On the temporal scale, the ozone distribution predicted by the model agreed well with the results of ground-based meteorological station monitoring, both showing distinct seasonal trends. On the spatial scale, the model output reflected well the refined spatial variation of near-ground ozone at a small scale and captured the “medium-high-low” trend of near-ground ozone concentration in Shanghai and the trend of “low-medium” in Hangzhou, China. In contrast, the satellite observation data cannot well reflect the differences in details. In the future, this model will have good application potential in the refined monitoring of polluting gases across the country