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In recent years, the development of computer technology has promoted the informatization and intelligentization of hospital management systems and thus produced a large amount of medical data. These medical data are valuable resources for research. We can obtain inducers and unknown symptoms that can help discover diseases and make earlier diagnoses. Hypertensive disorder in pregnancy (HDP) is a common obstetric complication in pregnant women, which has severe adverse effects on the life safety of pregnant women and fetuses. However, the early and mid-term symptoms of HDP are not obvious, and there is no effective solution for it except for terminating the pregnancy. Therefore, detecting and preventing HDP is of great importance. This study aims at the preprocessing of pregnancy examination data, which serves as a part of HDP prediction. We found that the problem of missing data has a large impact on HDP prediction. Unlike general data, pregnancy examination data have high dimension and a high missing rate, are in a time series, and often have many non-linear relations. Current methods are not able to process the data effectively. To this end, we propose an improved bi-LSTM-based missing value imputation approach. It combines traditional machine learning and bidirectional LSTM to deal with missing data of pregnancy examination data. Our missing value imputation method obtains a good effect and improves the accuracy of the later prediction of HDP using examination data.

The G2019S mutation in the leucine-rich repeat kinase 2 (LRRK2) gene is associated with late-onset Parkinson’s disease (PD). Although PD affects men and women differently, longitudinal studies examining sex- and age-dependent alterations in mice carrying the G2019S mutation are limited. We examined if behavioral and neurochemical dysfunctions, as well as neurodegeneration, occur in male and female BAC LRRK2-hG2019S (G2019S) mice, compared to their age-matched wild type littermates, at four age ranges. In the open field test, hyperlocomotion was observed in 10–12 month old male and 2–4.5 months old female G2019S mice. In the pole test, motor coordination was impaired in male G2019S mice from 15 months of age and in 20–21 months old female G2019S mice. In the striatum of G2019S male and female mice, the amounts of tyrosine hydroxylase (TH), measured with Western blotting, were unaltered. However, we found a decreased expression of the dopamine transporter in 20–21 month old male G2019S mice. The number of TH-positive neurons in the substantia nigra compacta was unaltered in 20–21 month old male and female G2019S mice. These results identify sex- and age-dependent differences in the occurrence of motor and neurochemical deficits in BAC LRRK2-hG2019S mice, and no degeneration of DA neurons.

Reliable markers to predict or diagnose anastomotic leakage (AL) of stapled circular anastomoses following colorectal resections are an important clinical need. Here, we aim to quantitatively investigate the morphology of anastomotic rings as an early available prognostic marker for AL and compare them to established inflammatory markers. We perform a prospective single-center cohort study, including patients undergoing stapled circular anastomosis between August 2020 and August 2021. The predictive value of the anastomotic ring configuration and the neutrophil-to-lymphocyte ratio (NLR) regarding anastomotic leakage is examined by ROC analyses and compared to the C-reactive protein (CRP) as an established marker. We included 204 patients, of which 19 suffered from anastomotic leakage (LEAK group), while in 185 patients the anastomoses healed well (HEAL group). The minimal height of the anastomotic rings as a binary classifier had a good ROC-AUC of 0.81 but was inferior to the NLR at postoperative day (POD) 5, with an excellent ROC-AUC of 0.93. Still, it was superior to the NLR at POD 3 (0.74) and the CRP at POD 3 (ROC-AUC 0.54) and 5 (ROC-AUC 0.70). The minimal height of the anastomotic rings as indicator for technically insufficient anastomoses is a good predictor of AL, while postoperatively the NLR was superior to the CRP in prediction of AL.

Crop models are important for understanding and regulating agroecosystems. Although the CERES-Wheat model is an important tool for winter wheat research, it has some limitations under water stress conditions. To narrow the gap, this study aimed to improve the performance of the CERES-Wheat model under water stress in arid and semi-arid regions based on the winter wheat experimental data from 2012 to 2014. The Priestley–Taylor (PT) and FAO56 Penman–Monteith (PM) equations were used to calculate the reference crop evapotranspiration and further modified the crop coefficient of the CERES wheat model to improve the simulation accuracy of crop yield and evapotranspiration under water stress conditions. The results showed that: water stress before jointing seriously affected the accuracy of the CERES-Wheat model in simulating biomass and grain yield, so it was necessary to improve the original model. In the original and improved models, the accuracy of the PM equation was lower than that of PT. In addition, the simulation accuracy of the improved model was higher than that of the original model (the average RMAE and RRMSE are less than 30%). In general, among the four scenarios, the PT equation for calculating crop reference evapotranspiration and crop coefficient had the best performance. Water stress occurred at the heading and grain filling stages, and the simulated biomass was in good agreement with the observed results, which better simulated the soil water content under water stress at the later growth stages. Therefore, the change in water stress response function had positive effects on winter wheat growth under simulated water stress conditions. This study provided a reference for applying the CERES-Wheat model in arid and semi-arid areas.

Drought has great negative impacts on crop growth and production. In order to select appropriate drought indices to quantify drought influences on crops to minimize the risk of drought-related crops as much as possible, climate and spring wheat yield-related data from eight sites in the Qinghai Province of China were collected for selecting better drought index between standardized precipitation evapotranspiration index (SPEI, denoting meteorological drought) and soil moisture deficit index (SMDI, denoting agricultural drought) as well as the key parameters (timescale and month) in denoting drought impacts on spring wheat yields. The spring wheat yields during 1961–2018 were simulated by the DSSAT–CERES–Wheat model. Pearson correlations were used to investigate the relationship between SPEI and SMDI and between spring wheat yields and drought indices at different timescales. The results showed that: (1) SMDI reflected more consistent dry/wet conditions than SPEI when the timescales changed and (2) There were one- and two-month lags in SMDI compared to SPEI (with the higher correlation coefficients values of 0.35–0.68) during May to August and (3) May (the jointing period of spring wheat) and the two-month timescale of SMDI0–10 (with the higher correlation coefficients values of 0.21–0.37) were key parameters denoting drought influences on spring wheat yield and (4) The correlations between the linear slopes of spring wheat yield reduction rate and linear slopes of SMDI0–10 in May at the studied eight sites were considerable between 1961–2018 (r = 0.85). This study provides helpful references for mitigating the drought risk of spring wheat.

China has a marked continental monsoon climate characterized by dry and wet hazards that have destructive impacts on grape yields and quality. The purpose of this study was to analyze the spatiotemporal characteristics of dryness/wetness in the wine regions of China and explore the links between these variations and large-scale climatic factors. The crop-specific standardized precipitation evapotranspiration index (SPEI) was used to characterize the dryness/wetness using meteorological data collected at 168 meteorological stations located in or near the wine regions from 1981–2015. Results showed that most wine regions of China experienced a wetting trend. The drought and wet event characteristics were region- and site-specific. The main wine regions of China (e.g., Xinjiang, Helan Mountain and Hexi Corridor) were characterized by relatively high drought severity; the extreme drought frequencies of the three regions were higher as well (11.5%, 3.3%, and 3.6%, respectively). Xinjiang was also characterized by a high wetness severity and an extremely high wetness frequency of 16%, but the wetness severity decreased over time. A 4–6-year periodical oscillation was commonly detected over the wine regions. The dryness/wetness characteristics were highly associated with the Southern Oscillation Index, Niño 3.4 and the Indian Ocean Dipole, with highest correlation coefficients of −0.40, 0.36 and 0.43 at lag times of 11, 8, and 11 months, respectively. The serious dry and wet events that occurred in 2001 and 1998, respectively, were speculated to be associated with anomalous atmospheric circulation patterns. These results can be used to inform grapevine stakeholders at various levels (e.g., farmer and industry) for developing strategies to mitigate and adapt dryness/wetness events in the wine regions of China. It is expected that the approach proposed in this study can also be applicable to wine regions of other countries.

We used the APSIM-Maize model to simulate maize potential yield (Yp) and rain-fed yield (Yw) when adaptation options of sowing date and planting density were adopted under Representative Concentration Pathway (RCP) 4.5 and 8.5 in the Guanzhong Plain of China. The results showed that Yp would decrease by 10.6–14.9% and 15.0–31.4% under RCP4.5 and RCP8.5 for summer maize, and 13.9–19.7% and 18.5–36.3% for spring maize, respectively. The Yw would decrease by 17.1–19.0% and 23.6–41.1% under RCP4.5 and RCP8.5 for summer maize, and 20.9–24.5% and 27.8–45.5% for spring maize, respectively. The loss of Yp and Yw could be reduced by 2.6–9.7% and 0–9.9%, respectively, under future climate for summer maize through countermeasures. For spring maize, the loss of Yp was mitigated by 14.0–25.0% and 2.0–21.8% for Yw. The contribution of changing sowing date and plant density on spring maize yield was more than summer maize, and the optimal adaptation options were more effective for spring maize. Additionally, the influences of changing sowing date and planting density on yields become weak as climate changes become more severe. Therefore, it is important to investigate the potential of other adaptation measures to cope with climate change in the Guanzhong Plain of China.

The regional and national scales variation and propagation characteristics of different types of droughts are critical for improving drought resilience, while information is limited in China. The objective of this research was to investigate the evolution and propagation characteristics of three types of droughts using standardized indices at multi-timescales in different sub-regions of China. The indices included Standardized Precipitation/Soil Moisture/Runoff Index (SPI/SSI/SRI) using the optimal probability density function, representing meteorological, agricultural, and hydrological droughts based on precipitation, soil water storage, and baseflow-groundwater runoff, respectively. Wavelet analysis was used to reveal their periodical characteristics. Modified Mann-Kendall trend test was used to compare the trend among drought indices. Correlation coefficients between SPI and SSI/SRI were calculated to identify the time-lags of SPI with SSI and SRI. In general, droughts indicated by SPI agreed well with the historical drought events at different sub-regions. The main periods of SSI were closer to SPI than SRI, indicating stronger connections of agricultural drought with meteorological drought. A weaker connection between meteorological and agricultural/hydrological droughts at shorter timescales was observed in northwestern arid and semi-arid regions. The propagation from meteorological to agricultural or hydrological droughts were well denoted by the lagged time (months) from SPI to SSI or SRI at a timescale ranged from 0 (mostly located in south China) to 5 months (mostly located in northeastern China) for 1-, 3-, 6-, 12-, or 24-month timescale; this was a new finding for China. The methods of wavelet combining trend test and Pearson coefficient showed meaningful power for revealing the drought propagation characteristics and the obtained results can be a good reference for other regions of the world since this study compared different climate zones from arid to humid conditions. The study provides crucial information and guidance to develop drought management strategies at regional to national scale and their critical time of action.

Natural gas dehydration is a critical process in natural gas extraction and transportation, and the membrane separation method is the most suitable technology for gas dehydration. In this paper, based on molecular dynamics theory, we investigate the performance of a metal-organic composite membrane (ZIF-90 membrane) in natural gas dehydration. The paper elucidates the adsorption, diffusion, permeation, and separation mechanisms of water and methane with the ZIF-90 membrane, and clarifies the influence of temperature on gas separation. The results show that (1) the diffusion energy barrier and pore size are the primary factors in achieving the separation of water and methane. The diffusion energy barriers for the two molecules (CH 4 and H 2 O) are ΔE(CH 4 ) = 155.5 meV and ΔE(H 2 O) = 50.1 meV, respectively. (2) The ZIF-90 is more selective of H 2 O, which is mainly due to the strong interaction between the H 2 O molecule and the polar functional groups (such as aldehyde groups) within the ZIF-90. (3) A higher temperature accelerates the gas separation process. The higher the temperature is, the faster the separation process is. (4) The pore radius is identified as the intrinsic mechanism enabling the separation of water and methane in ZIF-90 membranes.