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On 27 January 2021, at 21:00 (UTC + 8), a shallow loess landslide occurred in Heifangtai, Yongjing County, Gansu Province, northwest of the Chinese Loess Plateau. Fortunately, the independently developed global navigation satellite system predicted the landslide 7 h in advance. Although farmland and channels were buried and destroyed, no damage has been done to the lives and houses of residents. In order to explore the triggering factors and movement process of the landslide, based on the field investigation, we collected the precipitation and temperature data more than 1 year before the landslide and comprehensively used UAV photogrammetry, numerical simulation, and laboratory test for comprehensive research. It was found that as the temperature rose and freeze–thaw cycles, changes in mechanical properties of loess and unique stratum structure were the main factors triggering the landslide. The rise in temperature led to an increase in groundwater levels, and the strength of soil decreased gradually until shear liquefaction occurred. This landslide caused a substantial topographic change, which provided conditions for slope instability in the future. The process of landslide movement can be divided into three stages: start-up stage, severe sliding stage, and deceleration stage. Simulation results show that the maximum velocity was 22 m/s, and the maximum sliding distance was 393 m. The main movement period was 40 s, and the apparent friction angle was 5°. Finally, this study provides a reliable basis for studying the dynamic process and failure mechanism of loess landslides.

Background and ObjectivesCurrently, the prognostic performance of the staging systems proposed by the 8th edition of the American Joint Committee on Cancer (AJCC 8th) and the Liver Cancer Study Group of Japan (LCSGJ) in resectable intrahepatic cholangiocarcinoma (ICC) remains controversial. The aim of this study was to use machine learning techniques to modify existing ICC staging strategies based on clinical data and to demonstrate the accuracy and discrimination capacity in prognostic prediction.Patients and MethodsThis is a retrospective study based on 1,390 patients who underwent surgical resection for ICC at Eastern Hepatobiliary Surgery Hospital from 2007 to 2015. External validation was performed for patients from 2015 to 2017. The ensemble of three machine learning algorithms was used to select the most important prognostic factors and stepwise Cox regression was employed to derive a modified scoring system. The discriminative ability and predictive accuracy were assessed using the Concordance Index (C-index) and Brier Score (BS). The results were externally validated through a cohort of 42 patients operated on from the same institution.ResultsSix independent prognosis factors were selected and incorporated in the modified scoring system, including carcinoembryonic antigen, carbohydrate antigen 19-9, alpha-fetoprotein, prealbumin, T and N of ICC staging category in 8th edition of AJCC. The proposed scoring system showed a more favorable discriminatory ability and model performance than the AJCC 8th and LCSGJ staging systems, with a higher C-index of 0.693 (95% CI, 0.663–0.723) in the internal validation cohort and 0.671 (95% CI, 0.602–0.740) in the external validation cohort, which was then confirmed with lower BS (0.103 in internal validation cohort and 0.169 in external validation cohort). Meanwhile, machine learning techniques for variable selection together with stepwise Cox regression for survival analysis shows a better prognostic accuracy than using stepwise Cox regression method only.ConclusionsThis study put forward a modified ICC scoring system based on prognosis factors selection incorporated with machine learning, for individualized prognosis evaluation in patients with ICC.

Supplemental Material, sj-pdf-1-dos-10.1177_15593258211024393 for Celecoxib Alleviates Radiation-Induced Brain Injury in Rats by Maintaining the Integrity of Blood-Brain Barrier by Xiaoting Xu, Hao Huang, Yu Tu, Jiaxing Sun, Yaozu Xiong, Chenying Ma, Songbing Qin, Wentao Hu and Juying Zhou in Dose-Response

Motivation Warfarin is a widely used oral anticoagulant, but it is challenging to select the optimal maintenance dose due to its narrow therapeutic window and complex individual factor relationships. In recent years, machine learning techniques have been widely applied for warfarin dose prediction. However, the model performance always meets the upper limit due to the ignoration of exploring the variable interactions sufficiently. More importantly, there is no efficient way to resolve missing values when predicting the optimal warfarin maintenance dose. Methods Using an observational cohort from the Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine, we propose a novel method for warfarin maintenance dose prediction, which is capable of assessing variable interactions and dealing with missing values naturally. Specifically, we examine single variables by univariate analysis initially, and only statistically significant variables are included. We then propose a novel feature engineering method on them to generate the cross-over variables automatically. Their impacts are evaluated by stepwise regression, and only the significant ones are selected. Lastly, we implement an ensemble learning based approach, LightGBM, to learn from incomplete data directly on the selected single and cross-over variables for dosing prediction. Results 377 unique patients with eligible and time-independent 1173 warfarin order events are included in this study. Through the comprehensive experimental results in 5-fold cross-validation, our proposed method demonstrates the efficiency of exploring the variable interactions and modeling on incomplete data. The R2 can achieve 75.0% on average. Moreover, the subgroup analysis results reveal that our method performs much better than other baseline methods, especially in the medium-dose and high-dose subgroups. Lastly, the IWPC dosing prediction model is used for further comparison, and our approach outperforms it by a significant margin. Conclusion In summary, our proposed method is capable of exploring the variable interactions and learning from incomplete data directly for warfarin maintenance dose prediction, which has a great premise and is worthy of further research.

This paper discussed the application of nuclear magnetic resonance (NMR) logging in evaluating the pore structure of coal seams, physical properties, aquosity and sealing of the coal seam roof and floor. The study results show that T2 relaxation time spectrum could reflect the pore structure characteristics of the coal. The NMR transverse relaxation time (T2) of coal is 0.5~2.5, 20~50, and more than 100 ms, three spectral peaks represent meso pores, large pores and fractures. The larger the spectral peaks are where more pores and fractures develop. The T2 relaxation time spectrum of coal reservoirs in the research area is generally characterized by double-peak structure characteristics, which shows that the coal reservoir is microporous with meso pores. There are obvious gaps and poor continuities between the two peaks, and the break point is approximately 10 ms between the two peaks point. This indicates that the bound water and movable fluid in the coal reservoir cannot be connected effectively. NMR can reflect the level of porosity and permeability of the coal reservoir. Greater development of fracture and better permeability in the coal reservoir cause greater right peak of T2 relaxation time spectrum of the NMR. The effective porosity of coal reservoirs in the study area is generally less than 3%, which reflects the matrix pores of the coal seam. The fractures of large pore have little contribution to the porosity. The left peak of the T2 spectrum of a coal seam and its roof and floor are higher, which indicates that the meso pores and tiny pores develop, and there is less movable water. The low left peak and high right peak of T2 spectrum show that the meso and macro pores develop, and there is more movable water. In a well area with good sealing capability, the right peak of the NMR logging T2 spectrum of the coal seam roof and floor is low, the T2 spectrum peak amplitude is low, and there are low porosity and poor permeability. NMR logging technology can be used to evaluate the CBM (coalbed gas reservoir) effectively, and then correctly evaluate and effectively develop the CBM reservoir.

To effectively formulate a scheme for the development of gas reservoirs, the distribution of formation water in the Shan 23 member of the Zizhou gas field of the Ordos basin in China was studied in depth, making full use of data covering formation water, logging and production. The study concluded that the types of formation water of the Shan 23 member in the Zizhou gas field are edge (bottom) water, lenticular water and formation water residue. The edge (bottom) water in the Shan 23 member is mainly distributed in three regions with low structures in the west, south and southeast of the area, respectively, to the well areas of Y47-Y29-Y43, Y64-Y40 and Y69. This layer is generally interpreted as a water layer by well logging and produces a large amount of water discharge in the processes of gas testing and production. The lenticular water in the Shan 23 member is mainly scattered in the middle and southern parts of the area and is generally interpreted as a water layer by well logging, mainly in small water bodies. The typical production characteristics of gas wells that produce formation water residue in a gas reservoir are as follows: With less water production, the gas saturation is high, and there is no obvious information about the water layer in the logging curves. However, during production, there is trace formation water, and as production continues, this part of the water is taken out. The edge (bottom) water is distributed in the lower structure of the area and is mainly distributed in the southwest part of the area. It is clearly controlled by the structure, especially the low-amplitude structure; thus, structure is more important for the control of edge (bottom) water. Structural characteristics have some influence on the lenticular water and the formation water residue in gas reservoirs. The position of the lower structure is the main area that enriches water. In a relatively independent region containing gas, the position of the lower micro structure is also a common distribution area of water. In addition, a larger water body often forms at the pinchout and the bend of a sand body.

Metamorphic mechanism will result in impact when it is changing from one configuration to the next. Consequently, the internal impact force will bring about resonance for the mechanism. Therefore, it is necessary to study the nonlinear dynamics of the metamorphic mechanism when it is changing configuration. The controllable metamorphic palletizing robot mechanism is investigated in this paper. Based on the nonlinear dynamic model of the novel controllable metamorphic palletizing robot mechanism system of all configurations under the actual operating, the multiscale method is applied to analyze the nonlinear super harmonic vibration and to solve analytical solution of the mechanism when it is changing configuration and suffering internal impact. And numerical solution of the nonlinear dynamic model is solved by newmark method. Finally, dynamic simulation analysis is completed through example study. Time-domain and frequency-domain dynamic responses under the influence of super harmonic resonance when the mechanism is changing configuration and suffering internal impact are obtained. The research achievement of this paper provides fundamental references for nonlinear vibration study of controllable metamorphic mechanism.

Many herbaceous lignocelluloses suitable for ethanol production have high extractives contents, such as some straws and corn stover. The high extractives content might affect pretreatment or enzymatic hydrolysis. In this study, extractives were removed from corn stover, and then extractives-free corn stover and ordinary corn stover were respectively pretreated using a liquid hot water (LHW) method and hydrolyzed to evaluate the effect of extractives on cellulose digestibility. Extractives-free corn stover presented higher cellulose digestibility than ordinary corn stover after the same pretreatment conditions. A total of 87.3% of cellulose was digested in extractives-free corn stover, compared to 71.0% in ordinary corn stover, after pretreatment at 200 °C for 20 min. It is speculated that some water-soluble extractives could buffer H+ ions from water and acetic acid during LHW pretreatment process, reducing xylan removal. Another reason for these results might be that some extractives could condense on corn stover after LHW pretreatment, which hinders cellulose hydrolysis.

The rapidly spread coronavirus disease (COVID-19) has caused substantial reductions in anthropogenic emissions around the world. However, the air quality in some megacities has not been improved as expected due to the complex responses of aerosol chemistry to the changes in precursors and meteorology. Here we demonstrate the responses of primary and secondary aerosol species to the changes in anthropogenic emissions during the COVID-19 outbreak in Beijing, China along with the Chinese New Year (CNY) holiday effects on air pollution by using 6-year aerosol particle composition measurements. Our results showed large reductions in primary aerosol species associated with traffic, cooking and coal combustion emissions by 30 – 50% on average during the CNY, while the decreases in secondary aerosol species were much small (5 – 12%). These results point towards a future challenge in mitigating secondary air pollution because the reduced gaseous precursors may not suppress secondary aerosol formation efficiently under stagnant meteorological conditions. By analyzing the long-term measurements from 2012 to 2020, we found considerable increases in the ratios of nitrate to sulfate, secondary to primary OA, and sulfur and nitrogen oxidation capacity despite the overall decreasing trends in mass concentrations of most aerosol species, suggesting that the decreases in anthropogenic emissions have facilitated secondary formation processes during the last decade. Therefore, a better understanding of the mechanisms driving the chemical responses of secondary aerosol to the changes in anthropogenic emissions under complex meteorological environment is essential for future mitigation of air pollution in China.