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Abstract Background Imprinted genes play important functions in placentation and pregnancy; however, research on their roles in different placental diseases is limited. It is believed that epigenetic alterations, such as DNA methylation, of placental imprinting genes may contribute to the different pathological features of severe placental diseases, such as pre-eclampsia (PE) and placenta accreta spectrum disorders (PAS). Results In this study, we conducted a comparative analysis of the methylation and expression of placental imprinted genes between PE and PAS using bisulfite sequencing polymerase chain reaction (PCR) and quantitative PCR, respectively. Additionally, we assessed oxidative damage of placental DNA by determining 8-hydroxy-2′-deoxyguanosine levels and fetal growth by determining insulin-like growth factor 2 (IGF2) and cortisol levels in the umbilical cord blood using enzyme-linked immunosorbent assay. Our results indicated that methylation and expression of potassium voltage-gated channel subfamily Q member 1, GNAS complex locus, mesoderm specific transcript, and IGF2 were significantly altered in both PE and PAS placentas. Additionally, our results revealed that the maternal imprinted genes were significantly over-expressed in PE and significantly under-expressed in PAS compared with a normal pregnancy. Moreover, DNA oxidative damage was elevated and positively correlated with IGF2 DNA methylation in both PE and PAS placentas, and cortisol and IGF2 levels were significantly decreased in PE and PAS. Conclusions This study suggested that DNA methylation and expression of imprinted genes are aberrant in both PE and PAS placentas and that PE and PAS have different methylation profiles, which may be linked to their unique pathogenesis.

The boundary condition is a crucial factor affecting the permeability variation due to suffusion. An experimental investigation on the permeability of gap-graded soil due to horizontal suffusion considering the boundary effect is conducted, where the hydraulic head difference (ΔH) varies, and the boundary includes non-loss and soil-loss conditions. Soil samples are filled into seven soil storerooms connected in turn. After evaluation, the variation in content of fine sand (ΔRf) and the hydraulic conductivity of soils in each storeroom (Ci) are analyzed. In the non-loss test, the soil sample filling area is divided into runoff, transited, and accumulated areas according to the negative or positive ΔRf values. ΔRf increases from negative to positive along the seepage path, and Ci decreases from runoff area to transited area and then rebounds in accumulated area. In the soil-loss test, all soil sample filling areas belong to the runoff area, where the gentle-loss, strengthened-loss, and alleviated-loss parts are further divided. ΔRf decreases from the gentle-loss part to the strengthened-loss part and then rebounds in the alleviated-loss part, and Ci increases and then decreases along the seepage path. The relationship between ΔRf and Ci is different with the boundary condition. Ci exponentially decreases with ΔRf in the non-loss test and increases with ΔRf generally in the soil-loss test.

Abstract Urban microbiome plays crucial roles in human health and are related to various diseases. The MetaSUB Consortium has conducted the most comprehensive global survey of urban microbiomes to date, profiling microbial taxa/functional genes across 60 cities worldwide. However, the influence of environmental/demographic factors on urban microbiome remains to be elucidated. We collected 35 environmental and demographic characteristics to examine their effects on global urban microbiome diversity/composition by PERMANOVA and regression models. PM10 concentration was the primary determinant factor positively associated with microbial α-diversity (observed species: p = 0.004, β = 1.66, R 2 = 0.46; Fisher’s alpha: p = 0.005, β = 0.68, R 2 = 0.43), whereas GDP per capita was negatively associated (observed species: p = 0.046, β = −0.70, R 2 = 0.10; Fisher’s alpha: p = 0.004, β = −0.34, R 2 = 0.22). The β-diversity of urban microbiome was shaped by seven environmental characteristics, including Köppen climate type, vegetation type, greenness fraction, soil type, PM2.5 concentration, annual average precipitation and temperature (PERMANOVA, p

Potato starch is widely utilized in the food industry. Gamma irradiation is a cost-effective and environmentally friendly method for starch modification. Nevertheless, there is a scarcity of comprehensive and consistent knowledge regarding the physicochemical characteristics of high-dose gamma-irradiated potato starch, retrogradation properties in particular. In this study, potato starch was exposed to gamma rays at doses of 0, 30, 60, 90, and 120 kGy. Various physicochemical properties, including retrogradation characteristics, were investigated. Generally, the apparent amylose content (AAC), water absorption, gel viscosity, gel hardness, and gumminess decreased as the doses of gamma irradiation increased. Conversely, the resistant starch (RS), amylose content evaluated by the concanavalin A precipitation method, water solubility, and enthalpy of gelatinization were increased. Additionally, swelling power, crystalline structure, and amylopectin branch chain length distribution either remained stable or exhibited only minor changes. Notably, the degree of retrogradation of potato starches on day 7 was positively correlated with the doses of gamma irradiation.

In the context of rapid advancement in automation and increasing global warming, understanding the impact of artificial intelligence (AI) on carbon emissions (CES) is a cutting-edge research topic. However, there is limited focus in existing research on the nonlinear carbon reduction effect (CRE) of AI. This paper first theoretically elaborates the dual impact mechanisms of AI on CES and illuminates the nonlinear carbon reduction mechanisms of AI. Then, this study employs panel data encompassing 30 Chinese provinces between 1997 and 2019 to empirically test the net effect of AI on CES and the nonlinear carbon reduction effect of AI through econometric models. The results are as follows: first, although AI can both reduce and increase CES, AI primarily helps decrease CES. This conclusion holds true even after considering robustness, endogeneity, and spatial heterogeneity. Secondly, relative to the central and western regions, AI has significant achievement in reducing carbon intensity and per capita CES in the eastern region. However, there is still room for improvement in terms of reducing the total CES in the eastern region. Thirdly, improving the AI development level (AIDL) can magnify the marginal CRE of AI and lead to a nonlinear CRE of AI. Lastly, even if the AIDL remains constant, improving the level of marketization, human capital, digital infrastructure, economic development, openness, and government intervention can also amplify the marginal CRE of AI and lead to a nonlinear CRE of AI. To fully harness the potential of AI for green development, concerted efforts should be directed towards enhancing the innovation and application of AI technologies with carbon reduction potential.

Abstract Background Placenta accreta spectrum (PAS) disorder is a major cause of postpartum hemorrhage-associated maternal and fetal death, and novel methods for PAS screening are urgently needed for clinical application. Methods The purpose of this study was to develop new methods for PAS screening using serum biomarkers and clinical indicators. A total of 95 PAS cases and 137 controls were enrolled in a case–control study as cohort one, and 44 PAS cases and 35 controls in a prospective nested case–control study were enrolled as cohort two. All subjects were pregnant women of Chinese Han population. Biomarkers for PAS from maternal blood samples were screened based on high-throughput immunoassay and were further validated in three phases of cohort one. Screening models for PAS were generated using maternal serum biomarkers and clinical indicators, and were validated in two cohorts. The expression levels of biomarkers were analyzed using histopathological and immunohistochemical (IHC) techniques, and gene expression was examined by QPCR in the human placenta. Binary logistic regression models were built, and the area under the curve (AUC), sensitivity, specificity, and Youden index were calculated. Statistical analyses and model building were performed in SPSS and graphs were generated in GraphPad Prism. The independent-sample t test was used to compare numerical data between two groups. For nonparametric variables, a Mann–Whitney U test or a X 2 test was used. Results The results demonstrated that the serum levels of matrix metalloproteinase-1 (MMP-1), epidermal growth factor (EGF), and vascular endothelial growth factor-A (VEGF-A) were consistently higher, while the level of tissue-type plasminogen activator (tPA) was significantly lower in PAS patients compared with normal term controls and patients with pre-eclampsia (PE) and placenta previa (PP). IHC and QPCR analysis confirmed that the expression of the identified biomarkers significantly changed during the third trimester in human placenta. The generated screening model combining serum biomarkers and clinical indicators detected 87% of PAS cases with AUC of 0.94. Conclusions Serum biomarkers can be used for PAS screening with low expense and high clinical performance; therefore, it may help to develop a practicable method for clinical prenatal PAS screening.

Thermocatalytic decomposition is an efficient purification technology that is potentially applicable to degrading chemical warfare agents and industrial toxic gases. In particular, ZrO2 has attracted attention as a catalyst for the thermocatalytic decomposition of dimethyl methylphosphonate (DMMP), which is a simulant of the nerve gas sarin. However, the influence of the crystal phase and morphology on the catalytic performance of ZrO2 requires further exploration. In this study, monoclinic- and tetragonal-phase ZrO2 (m- and t-ZrO2, respectively) with nanoparticle, flower-like shape and hollow microsphere morphologies were prepared via hydrothermal and solvothermal methods, and their thermocatalytic decomposition of DMMP was systematically investigated. For a given morphology, m-ZrO2 performed better than t-ZrO2. For a given crystalline phase, the morphology of hollow microspheres resulted in the longest protection time. The exhaust gases generated by the thermocatalytic decomposition of DMMP mainly comprised H2, CO2, H2O and CH3OH, and the by-products were phosphorus oxide species. Thus, the deactivation of ZrO2 was attributed to the deposition of these phosphorous oxide species on the catalyst surface. These results are expected to help guide the development of catalysts for the safe disposal of chemical warfare agents.

Abstract Stratospheric airships are long‐endurance flight platforms that utilize renewable energy systems. The solar array in these systems cannot generate electricity at night; only an energy storage battery pack can be used to power the entire system. Therefore, accurate modeling and estimation of the output characteristics and operating conditions of battery packs have become the focus of current research. During the actual flight of an airship, uneven temperature distribution causes changes in the output characteristics and differences in the cell voltage of the battery packs. To address this problem, this study establishes an output characteristic model, series–parallel structure coupled with a temperature field model (SPTM), for a battery pack based on the series–parallel structure of a single cell. In addition, the influence of temperature distribution was considered in the model. Then, a flight verification test was conducted, and the test data and simulation data were compared. The average absolute error of the SPTM was maintained at approximately 0.61 V, which accurately reflected the output characteristics of the battery pack. The simulation model was then used to study the cell voltage difference under changes in the temperature distribution and demonstrate the relationship between the cell voltage difference and temperature difference.

At present, small-scale agriculture is dominating in China.How to develop appropriate smart agriculture for agricultural management with small farmers and small plots is quite challenging.A distributed agricultural AI framework combining federal learning and blockchain technology was proposed, which can achieve the purpose of the training model and establish the incentive mechanism for participants without data sharing.This framework helped make full use of agricultural multi-source heterogeneous data, reducing user data requirements, developing decision-making models according to local conditions, and promoting the connection of production and marketing of small-scale agriculture.

Tailings ponds are places for storing industrial waste. The saturation line is the crucial factor in quantifying the safety of tailings ponds. Existing saturation line time-series prediction methods are mainly based on statistical models or shallow machine learning models. Although these models aim to capture the time dependence of the sequence data, the channel and temporal are even unavailable in principle. To mitigate this problem, in this paper, we present a two-stage forecasting method, which embeds the channel and temporal attention into a hybrid CNN-LSTM model to predict the saturation line. The channel and temporal attention are utilized to capture subtle high-dimensional time-series dependence. In the first stage, the discrete wavelet transform (DWT) is applied to capture the refined sequence information. In the second stage, the CNN-LSTM model is utilized to learn the basic spatial and temporal features in the time series. Furthermore, the channel and temporal attention model are embedded into the CNN-LSTM model to enhance the feature-extracting ability in the channel and temporal dimensions. Consequently, our proposed model is shown to outperform classic models on multiple real-world datasets in terms of RMSE, MAPE, R2 and MAE, respectively.

Current stratospheric airships generally employ photovoltaic cycle energy systems. Accurately calculating their power generation is significant for airships’ overall design and mission planning. However, the power generation of solar arrays on stratospheric airships is challenging to model and calculate due to the dynamic nature of the airships’ flight, resulting in continuously changing radiation conditions on the curved surface of the airships. The power generated by the airship solar array was modeled herein through a combination of the flight attitude, spatial position, time, and other influencing factors. Additionally, the model was modified by considering the variation in photovoltaic conversion efficiency based on the radiation incidence angle, as well as the state of charge and power consumption of the energy storage battery pack. This study compared the measurement data of power generation in real flight tests with the calculation results of the model. The comparison showed that the results of the calculated model were highly consistent with the actual measured data. An average normalized root-mean-square error of 2.47% validated the accuracy of the newly built model. The generalizability and rapidity of the model were also tested, and the results showed that the model performed well in both metrics.

The catalytic performances of the catalysts and decomposition mechanisms of dimethyl methylphosphonate (DMMP), a commonly used nerve agent simulant, are well understood based on previous studies. However, the effects of the morphology of the catalyst on DMMP decomposition performance and mechanisms remain unexplored. Thus, in this work, experimental studies were conducted on the thermocatalytic decomposition of DMMP on CeO2 nanomaterials with different morphologies, e.g., irregular nanoparticles, nanorods, and nanocubes. From the performance evaluation, CeO2 nanorods exhibited higher DMMP thermocatalytic decomposition performance as compared to irregular nanoparticles and nanocubes. The primary reaction pathways were the same on all three morphologies of materials, according to in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) study, whereas side reaction paths showed variable behaviors. According to the catalytic reaction mechanism study, the surface lattice oxygen played a vital role in the thermocatalytic decomposition of DMMP and the accumulation of phosphates, carbonates, and formates were the main factors for deactivation of the catalyst. The behavior of CeO2 catalyst with different morphologies in the thermocatalytic decomposition of DMMP was revealed in this work, and this will be useful for the future design of high-performing catalysts for the efficient degradation of chemical toxicant.

Chemical warfare agents (CWAs) are highly toxic and fast-acting and are easy to cause large-scale poisoning to humans and livestock after being released. The activated carbon used for CWAs adsorption has disadvantages of limited adsorption capacity, easy aging and deactivation. Metal oxides have environmental stability, and they are characterized by long lasting and broad spectrum when used for thermal catalytic decomposition. Therefore, in this study, the supported copper–cerium catalyst CuO-CeO2/γ-Al2O3 was prepared using an equal volume impregnation method. The thermal catalytic decomposition performance was studied using sarin CWAs simulant dimethyl methyl phosphonate (DMMP) as the target compound. The results show that the CuO-CeO2/γ-Al2O3 catalyst with a CeO2 loading of 5% exhibited better thermal catalytic decomposition performance of DMMP. The catalyst provided protection against DMMP for 237 min at 350 °C; CuO was highly dispersed on CuO-5% CeO2/γ-Al2O3, and there was a strong interaction between Cu and Ce on CuO-5% CeO2/γ-Al2O3, which promoted the generation of surface-adsorbed oxygen, leading to a better thermal catalytic decomposition performance of DMMP. This study is expected to provide a reference for the study of catalysts for the thermal catalytic decomposition of CWAs.

The stratospheric airship is a kind of aircraft that completely relies on the cycle of photovoltaic energy systems to achieve long duration flight. The accurate estimation of the operating temperature of solar cell modules on stratospheric airship is extremely important for the design of photovoltaics system (PV system), the output power calculation of PV system, and the calculation of energy balance. However, the related study has been rarely reported. A support vector machine prediction method based on particle swarm optimization algorithm (PSO-SVM) was established to predict the operating temperature of solar cell modules on stratospheric airship. The PSO algorithm was used to dynamically optimize the SVM’s parameters between the operating temperature of the solar cell modules and the measured data such as atmospheric pressure, solar radiation intensity, flight speed, and ambient temperature. The operating temperature data of the two sets of solar cell modules measured in the flight test were used to verify the accuracy of the temperature prediction model, and the prediction results were compared with a back propagation neural network (BPNN) method and the simulation results calculated by COMSOL Multiphysics of COMSOL, Inc., Columbus, MA, USA. The results shown that the PSO-SVM model realized the accurate prediction of the operating temperature of solar cell modules on stratospheric airship, which can guide the design of PV system, the output power calculation of PV system, and the calculation of energy balance.