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Flexible barriers are a type of environmentally friendly and easily constructed retaining structure. Owing to the deformability and limited strength of the flexible barrier material, these barriers can only be used in small- or medium-sized debris-flow gullies. In this study, we proposed an improved layout, namely the multiple stepped flexible barriers, to address the limitations of traditional flexible barriers in the application of debris-flow control. We used experimental and numerical methods to investigate the separation effect of the multiple stepped flexible barriers and their stress and strain distributions in solid aggradation after reaching its maximum storage capacity. The results demonstrated that the ability of the multiple stepped flexible barriers to separate large particles gradually decreases with increasing debris-flow density, which implies that the debris-flow regulation ability of the multiple stepped flexible barriers decreases with increasing debris-flow density. When the debris-flow density exceeds 2200 kg/m3, the multiple stepped flexible barriers will completely lose their ability to reduce the density of debris flows. In addition, the thickness of the solid aggradation initially increases and then subsequently decreases upstream; the thickness of the solid aggradation increases with increasing debris-flow density. The results obtained from numerical simulation demonstrated that there would be relatively large shear stress and shear strain near the bottom of each flexible barrier. When the longitudinal spacing of the flexible barriers increases to 1.6 times their height, the internal stress of the upper aggradation layer that was transferred to the lower layer gradually weakens or disappears. Under this condition, the soil pressure of the flexible barrier is relatively small. The results of this study provide guidance for the design and construction of multiple stepped flexible barriers in wide debris-flow gullies.

Population growth and agricultural intensification lead to stress on landscapes that are highly sensitive to land-use changes. An increase in irrigation-triggered landslides (ITL) in dry climates has negative impacts on local communities. However, evolution and global impacts of ITL are little-known. Here, we use Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR), vectorization, and differential method to study surface deformation, ground displacement, and changes in headscarp morphology and topography in regions prone to ITL, aiming to uncover the evolution and spatiotemporal distribution of ITL. Findings show that the most severe surface deformation of ITL occurs on the landslide body. Meanwhile, the ITL displacement curve indicates the ITL will maintain continuous movement for at least 7 years, while ancient ITL also poses a threat. Moreover, the headscarp of ITL shows lateral expansion and longitudinal retrogression on the horizontal ground, whereby the scale of expansion is greater than that of retrogression, which transforms landslides into landslide clusters. Finally, the topographic changes further reveal that the main development pattern of ITL is lateral expansion. We suggest that the frequency and disaster-causing ability of ITL will increase greatly with further population growth and related intensification in the agricultural sector.

Study region: Shaanxi Province, including the Loess Plateau in northern Shaanxi, the Guanzhong Plain, and the Qinba Mountains in southern Shaanxi. Study focus: Frequent extreme rainfall exacerbates the risk of rainfall-induced landslides, highlighting the importance of rainfall thresholds quantification for accurate hazard prevention. New hydrological insights for the region: The rainfall on the day and 7–28 cm soil moisture have a strong impact on landslide occurrences. The effective duration threshold for rainfall-induced landslides is largest in the Qinba Mountains area (high vegetation cover), and the 50 % threshold curve has a better early warning effect. Under high soil moisture and rainfall conditions, the probability for rainfall-induced landslides increases. In contrast, the probability threshold for landslides in areas with high vegetation coverage is also higher. This study quantified the probability thresholds for rainfall-induced landslides based on the coupled effective duration threshold and soil moisture. The results provide a theoretical reference for the precise prevention of landslides.

Climate change has recently increased the frequency of landslides in alpine areas. Susceptibility mapping is crucial for anticipating and assessing landslide risk. However, traditional methods focus on static environmental variables to emphasize the spatial distribution of landslides, ignoring temporal dynamics in landslide development in the context of climate change. In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. The assessment and prediction were based on a catalog of climate change-related landslides over the past 20 years, the causative factors, and predicted climatic variables for the Shared Socioeconomic Pathways (SSP1-2.6) scenario. The results indicated that elevation, slope, groundwater, slope length gradient (LS) factor, Topographic Wetness Index (TWI), valley depth, and maximum precipitation were the key causes of slides below the snow line. The key factors causing debris flow above the snow line were elevation, slope, topographic relief, aspect, LS factor, distance to the river, and maximum temperature. The accuracy of slide and debris flow susceptibility was 0.92 and 0.89, respectively. The area of slides with medium, high, and very high susceptibility is 25.5% of the Taxkorgan. In addition, 82.6% of the slides happened in this region, and 49.5% of the entire area is covered by debris flows with medium, high, and very high susceptibility. Moreover, this area accounts for 91.8% of all debris flows. Until 2060, the region’s climate is anticipated to become warmer and wetter. Slides below the snow line will gradually decrease and shift eastward, and debris flows above the snow line will expand. Our findings will contribute to the management of landslide risks at the regional scale.

The reliability of data-driven approaches in generating landslide susceptibility maps depends on data quality, analytical method selection, and sampling techniques. Selecting optimal datasets and determining the most effective analytical methods pose significant challenges. This study assesses the performance of seven machine learning classifiers in the Himalayan region of the China–Pakistan Economic Corridor, utilizing statistical techniques and validation metrics. Thirteen geo-environmental variables were analyzed, including topographic (8), land cover (1), hydrological (1), geological (2), and meteorological (1) factors. These variables were evaluated for multicollinearity, feature importance, and their influence on landslide incidences. Our findings indicate that Support Vector Machines and Logistic Regression were highly effective, particularly near fault zones and roads, due to their effectiveness in handling complex, non-linear terrain interactions. Conversely, Random Forest and Logistic Regression demonstrated variability in their results. Each model distinctly identified landslide susceptibility zones ranging from very low to very high risk. Significant conditioning variables such as elevation, rainfall, lithology, slope, and land use were identified, reflecting the unique geomorphological conditions of the Himalayas. Further analysis using the Variance Inflation Factor and Pearson correlation coefficient showed minimal multicollinearity among the variables. Moreover, evaluations of Area Under the Receiver Operating Characteristic Curve (AUC-ROC) values confirmed the strong predictive capabilities of the models, with the Random Forest Classifier performing exceptionally well, achieving an AUC of 0.96 and an F-Score of 0.86. This study shows the importance of model selection based on dataset characteristics to enhance decision-making and strategy effectiveness.

A rational landslide susceptibility mapping (LSM) can minimize the losses caused by landslides and enhance the efficiency of disaster prevention and reduction. At present, frequency ratio (FR), information value (IV), and certainty factor (CF) are widely used to quantify the relationships between landslides and their causative factors; however, it remains unclear which method is the most effective. Moreover, existing landslide susceptibility zoning methods lack full automation; thus, the results are full of uncertainties. To address this, the FR, IV, and CF were used to analyze the relationship between landslides and causative factors. Subsequently, three distinct sets of models were developed, namely random forest models (RF_FR, RF_IV, and RF_CF), support vector machine models (SVM_FR, SVM_IV, and SVM_CF), and logistic regression models (LR_FR, LR_IV, and LR_CF) using the analysis results as inputs. A Gaussian mixture model (GMM) was introduced as a new method for landslide susceptibility zoning, classifying the LSM into five distinct levels. An accuracy evaluation of the models and a rationality analysis of the LSM indicated that the FR is superior to the IV and CF in quantifying the relationship between landslides and causative factors. Additionally, the quantile method was employed as a comparative approach to the GMM, further validating the effectiveness of the GMM. This research contributes to more effective and efficient LSM, ultimately enhancing landslide prevention measures.

Abstract Children with birth defects (BD) express distinct clinical features that often have various medical consequences, one of which is predisposition to the development of cancers. Identification of the underlying genetic mechanisms related to the development of cancer in BD patients would allow for preventive measures. We performed a whole genome sequencing (WGS) study on blood-derived DNA samples from 1566 individuals without chromosomal anomalies, including 454 BD probands with at least one type of malignant tumors, 767 cancer-free BD probands, and 345 healthy individuals. Exclusive recurrent variants were identified in BD-cancer and BD-only patients and mapped to their corresponding genomic regions. We observed statistically significant overlaps for protein-coding/ncRNA with exclusive variants in exons, introns, ncRNAs, and 3’UTR regions. Exclusive exonic variants, especially synonymous variants, tend to occur in prior exons locus in BD-cancer children. Intronic variants close to splicing site (

Machine learning (ML) is increasingly utilized in Landslide Susceptibility Mapping (LSM), though challenges remain in interpreting the predictions of ML models. To reveal the response relationship between landslide susceptibility and evaluation factors, an interpretability model was constructed to analyze how the results of the ML model are realized. This study focuses on Zhenba County in Shaanxi Province, China, employing both Random Forest (RF) and Support Vector Machine (SVM) to develop LSM models optimized through Random Search (RS). To enhance interpretability, the study incorporates techniques such as Partial Dependence Plot (PDP), Local Interpretable Model-Agnostic Explanations (LIMEs), and Shapley Additive Explanations (SHAP). The RS-optimized RF model demonstrated superior performance, achieving an Area Under the Curve (AUC) of 0.965. The interpretability model identified the NDVI and distance from road as important factors influencing landslides occurrence. NDVI plays a positive role in the occurrence of landslides in this region, and the landslide-prone areas are within 500 m from the road. These analyses indicate the importance of improved hyperparameter selection in enhancing model accuracy and performance. The interpretability model provides valuable insights into LSM, facilitating a deeper understanding of landslide formation mechanisms and guiding the formulation of effective prevention and control strategies.

Using the physical deterministic model to analyze landslide stability has become a hotspot of landslide disasters research all over the world. The Digital Elevation Model (DEM) resolution has a great influence on the simulation effect of 3 D physical models. However, few researchers have studied the prediction performance of the 3 D models under different DEM resolutions. Therefore, based on the 3 D model Scoops3D, the spatial distribution of landslides was simulated and predicted under five different DEMs resolutions (2.5 m, 5 m, 10 m, 20 m, and 30 m). The optimal parameters of the model were obtained through field investigations and laboratory experiments, and then, the simulation results were compared with the actual landslides distribution. Receiver operating characteristic (ROC) analysis and %LRclass index were used to quantitatively evaluate the prediction performance of the 3 D model under five different DEMs resolutions. The results show that Scoops3D has good performance in landslide spatial distribution prediction. In addition, we also found that the simulation results of high-resolution DEM were not ideal, while the prediction results of medium resolution DEMs (i.e., 5 m and 10 m) were more accurate. Therefore, this study provide a reference to select the most suitable DEM resolution for landslide stability analysis.

Abstract Background Children with birth defects (BD) are more likely to develop cancer and the increased risk of cancer persists into adulthood. Prior population-based assessments have demonstrated that even non-chromosomal BDs are associated with at least two-fold increase of cancer risk. Identification of variants that are associated with malignant tumor in BD patients without chromosomal anomalies may improve our understanding of the underlying molecular mechanisms and provide clues for early cancer detection in children with BD. Methods In this study, whole genome sequencing (WGS) data of blood-derived DNA for 1653 individuals without chromosomal anomalies were acquired from the Kids First Data Resource Center (DRC), including 541 BD probands with at least one type of malignant tumors, 767 BD probands without malignant tumor, and 345 healthy family members who are the parents or siblings of the probands. Recurrent variants exclusively seen in cancer patients were selected and mapped to their corresponding genomic regions. The targeted genes/non-coding RNAs were further reduced using random forest and forward feature selection (ffs) models. Results The filtered genes/non-coding RNAs, including variants in non-coding areas, showed enrichment in cancer-related pathways. To further support the validity of these variants, blood WGS data of additional 40 independent BD probands, including 25 patients with at least one type of cancers from unrelated projects, were acquired. The counts of variants of interest identified in the Kid First data showed clear deviation in the validation dataset between BD patients with cancer and without cancer. Furthermore, a deep learning model was built to assess the predictive abilities in the 40 patients using variants of interest identified in the Kids First cohort as feature vectors. The accuracies are ~ 75%, with the noteworthy observation that variants mapped to non-coding regions provided the highest accuracy (31 out of 40 patients were labeled correctly). Conclusion We present for the first time a panorama of genetic variants that are associated with cancers in non-chromosomal BD patients, implying that our approach may potentially serve for the early detection of malignant tumors in patients with BD.

In this study, optimization of the methane purification method by gas chromatography (GC) is described for the determination of methane clumped isotope compositions of natural gas. Methane was purified under the carrying of helium on the assembly line composed of a vacuum apparatus and two-column gas chromatography. Once methane was detected by the passive thermal conductivity detector (TCD) on the GC, it was collected through a silica trap immersed in liquid nitrogen. Optimum conditions of chromatographic column diameter (1/4 inch outside diameter), temperature (25 °C), and flow rate (50.8 ml min-1) have been determined. When N2/CH4 (by volume) is

Paleohydrological investigations were carried out in the Yellow River source area on the northeast (NE) Tibetan Plateau. During our fieldwork investigations, two units of overbank flood deposits (OFD) were found in the Maqu-Kesheng reach. These OFD units were studied using a multi-index approach, including magnetic susceptibility, hygroscopic water, grain size distribution and micro-morphological features. It can be inferred that they have recorded two episodes of overbank flooding of the Yellow River. Using the optically stimulated luminescence (OSL) and AMS 14C dating techniques, the first episode was dated to 34,680 ± 1880-29000 ± 1790 a and correlated with the late Marine Isotope Stage 3a (MIS 3a) with a warm and wet climate. The second episode occurred at 16,500 ± 1,250-14000 ± 1,280 a, which is coincided with the Bølling-Allerød warm stage during the last deglaciation, a period of coexisting global warming and rapidly shrinking ice sheets. At these two periods, the water was mainly derived from the accelerated melting of mountain glaciers surrounding the basin and/or the large-scale precipitation, which led to the overbank flooding events. At the same time, the related overbank flood deposits were interbedded within glacial outwash/flashflood deposits in the valley bottom of the Yellow River, which intensified the valley aggradation. These results are of great significance in enriching the paleoflood records in the Yellow River source area, recognizing the effect of river system on the development of valley landform, as well as understanding the hydro-climatic response of the Tibetan Plateau to global climate warming occurring presently.

Over 90% of the global hydrate resources are stored in very-low-permeability clayey silt reservoirs. The low permeability significantly restricts the efficiency of gas and water flow into the production well. To enhance gas production efficiency in low-permeability hydrate reservoirs, the high-pressure rotating water jets (HPRWJ) technology is proposed to construct near wellbore artificial fractures (NWAFs) in hydrate reservoirs. The HPRWJ avoid the risks of hydraulic fracturing as well as large-scale reservoir damage, which makes it more suitable for constructing fractures in hydrate-bearing sediments (HBS). In this article, the site SH7 in the South China Sea is studied to evaluate the feasibility of this technology for enhancing gas production of low-permeability hydrate reservoirs by numerical simulation. The results show that the gas productivity is increased by approximately three times by using the HPRWJ technology to construct NWAFs with a depth of 3 m. It is suggested that the proposed technology is a promising method for improving gas production from the low-permeability hydrate reservoirs. Furthermore, the gas production performance is closely related to NWAF depth, NWAF permeability, and NWAF spacing. For the site SH7 in the South China Sea, the NWAF depth, permeability, and spacing are recommended as 3 m, 3D, and 3 m, respectively.

Associations of copy number variations (CNVs) with complex traits are challenging to study because of their low frequency. Here, the authors analyse SNP array and array comparative genomic hybridization data of 100,028 individuals and report their associations with immune-related, cardiometabolic and neuropsychiatric diseases as well as cancer.

The deformation characteristics and instability patterns of rotational landslides are complicated. Such landslides are large and occur continuously, seriously threatening people’s lives. We used interferometry synthetic aperture radar (InSAR), digital elevation models of difference (DODs), numerical simulations, and other techniques for analyzing the topographic changes, surface deformation and movement process before, during and after a landslide. Based on the high-resolution terrain data before and after the landslide, the topographic changes were analyzed, and the active zone of the landslide was identified. The areas of the topographic changes were mainly located on the main scarp, toe and secondary landslides. The topographic changes were influenced by rainfall and rill erosion. The geomorphologically-guided InSAR interpretation method was applied to explore the displacement pattern. The deformation area in the middle of the landslide coincided with the secondary landslides. A time-series InSAR analysis revealed the dynamic evolution of the deformation before and after the landslide. Based on its evolution, the simulated landslide process included the main landslide and three secondary landslides. Based on the displacement of the longitudinal ground surface profiles, the displacement characteristics and kinematic behavior were summarized and compared with those of a single rotational landslide and multiple rotational landslides. The single rotational landslide had obvious secondary and progressive characteristics, developing into multiple rotational landslides triggered by conditions such as rainfall.

Gas hydrates have been considered as a new energy that could replace conventional fossil resources in the future because of their high energy density, environmental friendliness, and enormous reserves. To further analyze the potential distribution of gas hydrate stability zones (GHSZ) and the formation of a gas hydrate system in the Shenhu area of the South China Sea (SCS), a 3D petroleum simulation model (PSM) was built from 3D seismic interpretations and all available geological data. Based on the thermal calibration of the 3D model, the evolution of the GHSZ, hydrocarbon generation and migration, and the formation and accumulation of gas hydrates were simulated for the first time in the area. Thermal simulation shows that the methane source of gas hydrate originated from shallow biogenic gas and deep thermogenic gas. Most areas are dominated by shallow biogenic gas, while, only about 3% of the deep thermogenic gas derived from Enping Formation source rock and contributed to the gas hydrate formation within a few areas in the southeast. The thermogenic gas migrated vertically into the GHSZ through connecting faults, mud diapir, and/or gas chimney to form gas hydrate. The source rocks of the Wenchang Formation, a deep thermogenic gas source, began to enter the main hydrocarbon generation window at 28.4 Ma. The Enping source rock began to generate oil from 25 Ma on and gas from 16 Ma on. Since 5.3 Ma, most areas of the source rocks have generated a gas window, and only the shallower parts in the east still in the oil window, which had lasted until now. The shallow biogenic gas source rocks from the Hanjiang, Yuehai, and Wanshan formations generated gas in different periods, respectively. The Qionghai Formation began to generate hydrocarbon from 0.3 Ma and until now. Other results show that the GHSZ developed mainly during the Quaternary and Neogene (Wanshan Formation) and the GHSZ is thicker in the southern area and thinner in the northern part with a positive correlation with water depth. Starting at 11.6 Ma, the GHSZ developed in the Hanjiang Formation in the south of the Shenhu area and gradually expanded to the north to cover most of the study area at 5.3 Ma during the Yuehai Formation. From 1.8 Ma on, the GHSZ covered the entire study area. At the same time, the GHSZ in the Hanjiang Formation disappeared because of the change in temperature and pressure. At present, the GHSZ in the Yuehai Formation has disappeared, while the Quaternary and Wanshan are the two main formations for GHSZ development. The formation and distribution of gas hydrates are fundamentally controlled by the space-time coupling between the hydrocarbon generation and expulsion time and distribution of the GHSZ. The simulation results of gas hydrate accumulation and distribution were verified by drilling results and the matching rate is 84%. This is the first time that 3D simulation was successfully conducted with PSM technology in the Shenhu area and it provides important guidance for gas hydrate study in other areas of the SCS.

Rainfall-induced shallow landslides are widespread throughout the world, and vegetation is frequently utilized to control them. However, in recent years, shallow landslides have continued to frequently occur during the rainy season on the vegetated slopes of the Loess Plateau in China. To better probe this phenomenon, we considered vegetation cover in the sensitivity analysis of landslide hazards and used the transient rainfall infiltration and grid-based regional slope stability (TRIGRS) model to quantitatively describe the impacts of different types of vegetation cover on slope stability. Based on the rainfall information for landslide events, the spatiotemporal distributions of the pore water pressure and the factor of safety of the vegetated slopes were inverted under the driving changes in the soil properties under different vegetation types, and the average prediction accuracy reached 79.88%. It was found that there was a strong positive correlation between the cumulative precipitation and the proportion of landslide-prone areas in woodland covered by tall trees, grassland covered by shrubs and grasses, and cultivated land. The highest landslide susceptibility, which has the greatest potential to hasten the occurrence of rainfall-induced landslides, is found in woodland with tall trees. Therefore, this paper proposes the promoting relationship between vegetation and landslide erosion, which provides a new scientific perspective on watershed management to prevent shallow landslide disasters and manage and develop watershed vegetation.

This paper quantitatively examines the effects of slope height and slope gradient on landslide size distributions. We developed a loess slide inventory by using field survey data. Statistical analysis shows that most landslides are concentrated in areas with slope heights less than 60 m, and approximately 30% of the loess slides occurred on slopes with a gradient between 30° and 40°. However, high and steep slopes are rare in nature. We calculated the relative density of landslides, and the results showed that the relative density of landslides is greater on higher slopes with steeper slope gradients. Moreover, landslide size is correlated with slope height and slope gradient. The results demonstrate that landslide size increases as slope height increases and decreases as slope gradient increases. Furthermore, we determined the probability density of landslide area using kernel density estimation. The results showed that the landslide size distribution exhibits power law scaling above a certain size threshold, and the size threshold differs for different slope heights and slope gradients. The exponential scalings are influenced by slope height and slope gradient. Our results indicate that the exponential scaling decreases with increasing slope height and increases with increasing slope gradient. Large landslides are more frequent with a higher slope height and gentler slope gradient.

The spatial and temporal distributions of landslides can be used to assess the potential future impacts of landslides over large scales. However, quantitatively characterizing the spatial and temporal distributions of landslides and their causes remains a critical challenge. In this work, a typical landslide-prone region (the Qinba Mountains) is selected to identify this spatial and temporal trend. Information on 295 landslides spanning ten years from 2005 to 2014 was collected. The results revealed that landslide occurrences were clustered in time and space. Approximately 81% of the total landslides occurred from July to October. Moreover, a power law relationship between the cumulative frequency and number of landslides per day was discovered. Notably, the probability density of the time interval decreased as the time interval between landslide events increased, and this relationship was well described by a negative power-law correlation. Furthermore, the spatial and temporal distribution pattern of most landslides were influenced by rainfall events and earthquakes. There were several clustered centers in the study area, and the mean centers of the landslide distribution varied among years.

Abstract The appraisal of tectonic‐geomorphic features is the basis for the development and management of land, the selection of road routes, and the site selection and construction process of hydropower projects. However, properly evaluating tectonic‐geomorphic features and revealing the relationships among geomorphic evolution, landslides, and tectonic activity remain major challenges in geography and geomorphology. We take the core area of the planned Diamer‐Bhasha Dam as a study area. On the strength of digital elevation models (DEMs), geomorphic indices are extracted to evaluate the tectonic activity and geomorphic evolution. Landslide cataloging by field investigation is used to reveal the relationships between geomorphic evolution, landslides, and tectonic activity. We found that the tectonic activity and geomorphic evolution of the sub‐basin where the Diamer‐Bhasha Dam is planned and the sub‐basin along the Indus are in the stable and old stage, respectively. In contrast, the tectonic‐geomorphic features in the marginal sub‐basins far from the Indus are still active. The correlations among lithology, AF, Vf, HI, SL, and landslide indicate that tectonic activity can influence geomorphic evolution and induce landslides, whereas changes in lithology do not. In addition, landslides can exacerbate geomorphic evolution.

Investigating landslide deformation patterns in different evolution stages is important for understanding landslide movement. Translational landslides generally slide along a relatively straight surface of rupture. Whether the post-failure spatiotemporal deformation for certain translational landslides follows the pre-failure pattern remains untested. Here, the pre- and post-failure spatiotemporal deformations of the Simencun landslide along the Yellow River in 2018 were analyzed through multi-temporal remote sensing image analysis, Interferometric Synthetic Aperture Radar (InSAR) deformation monitoring and intensive field investigations. The results show that the pre- and post-failure spatial deformations both follow a retrogressive failure pattern. The long time series of the displacement before and after failure is characterized by obvious seasonal and periodic stage acceleration movements. Effective rainfall played an important role in the increase of the displacement acceleration, and the change in temperature might have accelerated the displacement. Finally, there is a possibility that the post-failure spatiotemporal deformation pattern of translational landslides does follow the pre-failure pattern when certain conditions are satisfied. The results are of great significance to improving our understanding of the spatiotemporal deformation pattern of landslides and to post-failure risk prevention and control.

Identification and monitoring of unstable slopes across wide regions using Synthetic Aperture Radar Interferometry (InSAR) can further help to prevent and mitigate geological hazards. However, the low spatial density of measurement points (MPs) extracted using the traditional time-series InSAR method in topographically complex mountains and vegetation-covered slopes makes the final result unreliable. In this study, a method of time-series InSAR analysis using single- and multi-look phases were adopted to solve this problem, which exploited single- and multi-look phases to increase the number of MPs in the natural environment. Archived ascending and descending Sentinel-1 datasets covering Zhouqu County were processed. The results revealed that nine landslides could be quickly identified from the average phase rate maps using the Stacking method. Then, the time-series InSAR analysis with single- and multi-look phases could be used to effectively monitor the deformation of these landslides and to quantitatively analyze the magnitude and dynamic evolution of the deformation in various parts of the landslides. The reliability of the InSAR results was further verified by field investigations and Unmanned Aerial Vehicle (UAV) surveys. In addition, the precursory movements and causative factors of the recent Yahuokou landslide were analyzed in detail, and the application of the time-series InSAR method in landslide investigations was discussed and summarized. Therefore, this study has practical significance for early warning of landslides and risk mitigation.

Landslide processes are a consequence of the interactions between their triggers and the surrounding environment. Understanding the differences in landslide movement processes and characteristics can provide new insights for landslide prevention and mitigation. Three adjacent landslides characterized by different movement processes were triggered from August to September in 2018 in Hualong County, China. A combination of surface and subsurface characteristics illustrated that Xiongwa (XW) landslides 1 and 2 have deformed several times and exhibit significant heterogeneity, whereas the Xiashitang (XST) landslide is a typical retrogressive landslide, and its material has moved downslope along a shear surface. Time-series Interferometric Synthetic Aperture Radar (InSAR) and Differential InSAR (DInSAR) techniques were used to detect the displacement processes of these three landslides. The pre-failure displacement signals of a slow-moving landslide (the XST landslide) can be clearly revealed by using time-series InSAR. However, these sudden landslides, which are a typical catastrophic natural hazard across the globe, are easily ignored by time-series InSAR. We confirmed that effective antecedent precipitation played an important role in the three landslides’ occurrence. The deformation of an existing landslide itself can also trigger new adjacent landslides in this study. These findings indicate that landslide early warnings are still a challenge since landslide processes and mechanisms are complicated. We need to learn to live with natural disasters, and more relevant detection and field investigations should be conducted for landslide risk mitigation.

Information about the long-term spatiotemporal evolution of landslides can improve the understanding of landslides. However, since landslide deformation characteristics differ it is difficult to monitor the entire movement of a landslide using a single method. The Interferometric Synthetic Aperture Radar (InSAR) and pixel offset tracking (POT) method can complement each other when monitoring deformation at different landslide stages. Therefore, the InSAR and improved POT method were adapted to study the pre- and post-failure surface deformation characteristics of the Gaojiawan landslide to deepen understanding of the long-term spatiotemporal evolution characteristics of landslides. The results show that the deformation displacement gradient of the Gaojiawan landslide exhibited rapid movement that exceeded the measurable limit of InSAR during the first disaster. Moreover, the Gaojiawan landslide has experienced long-term creep, and while studying the post-second landslide’s failure stability, the acceleration trend was identified via time series analysis, which can be used as a precursor signal for landslide disaster warning. Our study aims to provide scientific reference for local governments to help prevent and mitigate geological disasters in this region.

Chromosomal abnormalities such as 11q deletion are associated with poor prognosis in neuroblastoma. Here, the authors perform a genome-wide association study and identify an association between a variant within a Matrix metalloproteinase (MMP) gene member, MMP20, and 11q-deletion subtype neuroblastoma.

Abstract A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

Abstract We conducted a genome-wide association study (GWAS) of anorexia nervosa (AN) using a stringently defined phenotype. Analysis of phenotypic variability led to the identification of a specific genetic risk factor that approached genome-wide significance (rs929626 in EBF1 (Early B-Cell Factor 1); P = 2.04 × 10−7; OR = 0.7; 95% confidence interval (CI) = 0.61–0.8) with independent replication (P = 0.04), suggesting a variant-mediated dysregulation of leptin signaling may play a role in AN. Multiple SNPs in LD with the variant support the nominal association. This demonstrates that although the clinical and etiologic heterogeneity of AN is universally recognized, further careful sub-typing of cases may provide more precise genomic signals. In this study, through a refinement of the phenotype spectrum of AN, we present a replicable GWAS signal that is nominally associated with AN, highlighting a potentially important candidate locus for further investigation.

Infiltration plays an important role in influencing slope stability. However, the influences of slope failure on infiltration and the evolution of infiltration over time and space remain unclear. We studied and compared the infiltration rates in undisturbed loess and disturbed loess in different years and at different sites on loess landslide bodies. The results showed that the average initial infiltration rate in a new landslide body (triggered on 11 October 2017) were dramatically higher than those in a previous landslide body (triggered on 17 September 2011) and that the infiltration rates of both landslide types were higher than the rate of undisturbed loess. The initial infiltration rate in the new landslide body sharply decreased over the 4−5 months following the landslide because of the appearance of physical crusts. Our observations indicated that the infiltration rate of the disturbed soil in a landslide evolved over time and that the infiltration rate gradually approached that of undisturbed loess. Furthermore, in the undisturbed loess, both the initial and quasi-steady infiltration rates were slightly higher in the loess than in the paleosol, and in the previous landslide body, the infiltration rate was highest in the upper part, intermediate in the middle part, and lowest in the lower part. This study can help us to better understand the evolution process of infiltration in undisturbed loess, previous landslides, and new landslides.

At present, the increase in proved reserves and production of coalbed methane (CBM) in China depends mainly on several CBM bases in the southern Qinshui Basin and in the eastern margin of the Ordos Basin. Therefore, it is urgent to find new batches of exploration and development strategic replacement areas. For this purpose, we investigated the resources, reservoir properties, preservation conditions, and development conditions of CBM in China, and put forward eight general factors and four critical factors that can be used in establishing selection criteria of strategic replacement areas for affecting the CBM exploration and development in China. The eight general factors are resource abundance, coalbed thickness, gas content, original permeability, burial depth, hydrogeological conditions, coalbed depositional environment, and landforms, and the four critical factors include genetic type, stability type, later reservoir reformation, and damage degree of coal structure. Comparison shows that the low-rank coal area in the northwestern area, the northeastern medium-low rank coal area, and the southwestern medium-high rank and structurally-complex coal area are the major replacement areas for CBM following the northern area of China. Key factors affecting CBM enrichment in each of the three key areas, respectively, are: a) genetic type and stability of coal seams, b) genetic type and reconstruction of the reservoir by volcanic intrusion, and c) stability of coal seams and damage degree of coal structure. Based on these factors, a system for selection and evaluation of strategic replacement areas for CBM development was established. Fifteen blocks in the above three areas were evaluated by using multi-layered fuzzy mathematics, selecting eight favorable areas and seven relatively favorable areas that contain 1.8 trillion m3 of predicted CBM geological resources. The eight favorable areas include the Wucaiwan-Dajing coal exploration area in the Zhundong coalfield (eastern Junggar Basin), the Hami-Dananhu mining area in the Turpan-Hami Basin, the Longdong coalfield (eastern Gansu Province), the Yilan and Hegang coalfields in eastern Heilongjiang Province, the Hunchun coalfield in Jilin Province, the southern Sichuan coalfield, and the Shuicheng coalfield in Guizhou Province. These favorable areas were recommended to be the CBM exploration and pilot development target areas in the near future.

The spatiotemporal distribution of landslides provides valuable insight for the understanding of disastrous processes and landslide risk assessment. In this work, we compiled a catalog of landslides from 1996 to 2017 based on existing records, yearbooks, archives, and fieldwork in Shaanxi Province, China. The statistical analyses demonstrated that the cumulative frequency distribution of the annual landslide number was empirically described by a power-law regression. Most landslides occurred from July to October. The relationship between landslide time interval and their cumulative frequency could be fitted using an exponential regression. The cumulative frequency of the landslide number could be approximated using the power-law function. Moreover, many landslides caused fatalities, and the number of fatalities was related to the number of landslides each month. Moreover, the cumulative frequency was significantly correlated with the number of fatalities and exhibited a power-law relationship. Furthermore, obvious differences were observed in the type and density of landslides between the Loess Plateau and the Qinba Mountains. Most landslides were close to stream channels and faults, and were concentrated in cropland at elevations from 600−900 m and on slope gradients from 30−40°. In addition, the landslide frequency increased as the annual rainfall levels increased over a large spatial scale, and the monthly distribution of landslides presented a significant association with the precipitation level. This study provides a powerful method for understanding the spatiotemporal distribution of landslides via a rare landslide catalog, which is important for engineering design and planning and risk management.

The sediment of the hydrate reservoir in the Shenhu Area is mainly clayey silt. Its characteristic small particles and poor cementation challenge the quantification of the reservoir permeability during gas production. An accurate description of the seepage mechanism of the clayey-silt reservoir is the basis, and also a difficulty, of effective development of gas in the South China Sea. In this study, four sets of water seepage experiments under different pressure gradients are carried out using the clayey-silt reservoir sediments, in which the fourth sample was subjected to computed tomographic (CT) scans. The experimental results shows that the clayey-silt reservoir has a compression of the pore structure and decreasing permeability with the increasing pressure gradient. CT images are used to show the reduction of pore spaces for fluid flow. When the pressure gradient is less than 3 MPa per meter, the pore structure of the reservoir has minor changes. When the pressure gradient is greater than this value, the pore structure of the reservoir will be quickly compressed. This leads to a rapid decrease in permeability, and the process of permeability reduction is irreversible. The decrease of permeability can be predicted directly by establishing a power law model with the change of porosity. Our experimental results preliminarily reveal the dynamic evolution law of pore structure and permeability of clayey-silt reservoir in the process of gas hydrate exploitation via depressurization. The permeability evolution law at various pressure gradients provides a scientific and reasonable basis of a productivity control system for clayey-silt gas hydrate in depressurized gas production.

ObjectiveCohort selection is challenging for large-scale electronic health record (EHR) analyses, as International Classification of Diseases 9th edition (ICD-9) diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD) patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD.MethodsWe extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children's Hospital (BCH) (N = 150) and Cincinnati Children's Hospital and Medical Center (CCHMC) (N = 152). Two algorithms were created: (1) rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4th edition, (2) predictive classifier. The positive predictive values (PPV) achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups.ResultsThe rule-based algorithm produced the best PPV: (a) BCH: 0.885 vs. 0.273 (baseline); (b) CCHMC: 0.840 vs. 0.645 (baseline); (c) combined: 0.864 vs. 0.460 (baseline). A validation at Children's Hospital of Philadelphia yielded 0.848 (PPV). Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients) identified psychiatric, developmental, and seizure disorder clusters.ConclusionsIn a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD.

Three hundred gas samples recovered from SHSC-4 during China’s first gas hydrate production test in the South China Sea were examined for gas component and isotopic composition. According to the gas chromatography analysis, all the gas samples from SHSC-4 are predominated by CH4, with minor N2 + O2, as well as trace amounts of CO2, C2H6, and C3H8. No H2S was detected. The molecular and isotopic data of the gas samples fall into the region of “mixed origin” on the plot of C1/(C2 + C3) − δ13C1, which is close to the microbial origin. The discrimination diagram of δ13C1 − δDCH4 shows that the methane in all of the samples is of microbial origin, and is derived from the CO2 reduction.

Calcium, magnesium, potassium, sodium, chloride and phosphorus are the major dietary minerals involved in various biological functions and are commonly measured in the blood serum. Sufficient mineral intake is especially important for children due to their rapid growth. Currently, the genetic mechanisms influencing serum mineral levels are poorly understood, especially for children. We carried out a genome-wide association (GWA) study on 5,602 European-American children and 4,706 African-American children who had mineral measures available in their electronic medical records (EMR). While no locus met the criteria for genome-wide significant association, our results demonstrated a nominal association of total serum calcium levels with a missense variant in the calcium -sensing receptor (CASR) gene on 3q13 (rs1801725, P = 1.96 × 10(-3)) in the African-American pediatric cohort, a locus previously reported in Caucasians. We also confirmed the association result in our pediatric European-American cohort (P = 1.38 × 10(-4)). We further replicated two other loci associated with serum calcium levels in the European-American cohort (rs780094, GCKR, P = 4.26 × 10(-3); rs10491003, GATA3, P = 0.02). In addition, we replicated a previously reported locus on 1q21, demonstrating association of serum magnesium levels with MUC1 (rs4072037, P = 2.04 × 10(-6)). Moreover, in an extended gene-based association analysis we uncovered evidence for association of calcium levels with the previously reported gene locus DGKD in both European-American children and African-American children. Taken together, our results support a role for CASR and DGKD mediated calcium regulation in both African-American and European-American children, and corroborate the association of calcium levels with GCKR and GATA3, and the association of magnesium levels with MUC1 in the European-American children.

Diabetes impacts approximately 200 million people worldwide, of whom approximately 10% are affected by type 1 diabetes (T1D). The application of genome-wide association studies (GWAS) has robustly revealed dozens of genetic contributors to the pathogenesis of T1D, with the most recent meta-analysis identifying in excess of 40 loci. To identify additional genetic loci for T1D susceptibility, we examined associations in the largest meta-analysis to date between the disease and ∼2.54 million SNPs in a combined cohort of 9,934 cases and 16,956 controls. Targeted follow-up of 53 SNPs in 1,120 affected trios uncovered three new loci associated with T1D that reached genome-wide significance. The most significantly associated SNP (rs539514, P = 5.66×10⁻¹¹) resides in an intronic region of the LMO7 (LIM domain only 7) gene on 13q22. The second most significantly associated SNP (rs478222, P = 3.50×10⁻⁹ resides in an intronic region of the EFR3B (protein EFR3 homolog B) gene on 2p23; however, the region of linkage disequilibrium is approximately 800 kb and harbors additional multiple genes, including NCOA1, C2orf79, CENPO, ADCY3, DNAJC27, POMC, and DNMT3A. The third most significantly associated SNP (rs924043, P = 8.06×10⁻⁹ lies in an intergenic region on 6q27, where the region of association is approximately 900 kb and harbors multiple genes including WDR27, C6orf120, PHF10, TCTE3, C6orf208, LOC154449, DLL1, FAM120B, PSMB1, TBP, and PCD2. These latest associated regions add to the growing repertoire of gene networks predisposing to T1D.

Major depressive disorder (MDD) is a common psychiatric and behavioral disorder. To discover novel variants conferring risk to MDD, we conducted a whole-genome scan of copy number variation (CNV), including 1,693 MDD cases and 4,506 controls genotyped on the Perlegen 600K platform. The most significant locus was observed on 5q35.1, harboring the SLIT3 gene (P = 2×10(-3)). Extending the controls with 30,000 subjects typed on the Illumina 550 k array, we found the CNV to remain exclusive to MDD cases (P = 3.2×10(-9)). Duplication was observed in 5 unrelated MDD cases encompassing 646 kb with highly similar breakpoints. SLIT3 is integral to repulsive axon guidance based on binding to Roundabout receptors. Duplication of 5q35.1 is a highly penetrant variation accounting for 0.7% of the subset of 647 cases harboring large CNVs, using a threshold of a minimum of 10 SNPs and 100 kb. This study leverages a large dataset of MDD cases and controls for the analysis of CNVs with matched platform and ethnicity. SLIT3 duplication is a novel association which explains a definitive proportion of the largely unknown etiology of MDD.

The geological structure of the reservoir bank landslide is complex and intricate. After deformation and damage, it causes river blockage, surges, and loss of people's lives and property, posing a huge threat. At present, in academia and engineering applications, a large number of techniques such as inclinometers, rain gauges, and surface GNSS deformation monitoring are still used for monitoring landslides on reservoir banks. This type of monitoring method has the problem of "point to surface" which can easily lead to missed detection and reporting in some areas, some disaster points are close to water and steep, difficult to reach, and equipment installation is difficult. This work designs and implements a non-contact video quantitative monitoring system for surface deformation of geological disaster. By constructing a deep learning neural network, deformation area recognition and displacement quantitative calculation are achieved; By obtaining continuous images for a long time, draw the surface displacement-time curve, and output the surface deformation data and landform changes of the disaster. Meanwhile, this work explores the impact of different lighting conditions on the recognition results of target areas. This work can provide non-contact monitoring methods and dynamic warning support for large-scale monitoring of geological disasters. [ABSTRACT FROM AUTHOR]