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Glacier changes are regarded as the conspicuous icon of climate change in High Mountain Asia (HMA) alpine environments. Multi-temporal glacier coverage is essential for mass balance estimations and understanding glacial changes in response to climate variability. However, consistent multi-temporal glacier area datasets across the HMA are limited due to challenges posed by seasonal snow and cloud cover in remote sensing satellite images. In this study, a new method is proposed to estimate glacier and nonseasonal snow (GNS) areas by combining the normalized difference snow index (NDSI) and bright temperature bands from Landsat remote sensing images in 1990s, 2000s, 2010s and 2020s. Meanwhile, the integrated ice dynamic Open Global Glacier Model (OGGM) is applied to simulate nine typical glaciers and project their changes using 15 general circulation models (GCMs) under four shared socioeconomic pathways (SSPs). The results showed that total GNS areas in HMA were estimated at 71.26 × 10 3  km 2 in the 1990s, and decreased to 53.17 × 10 3  km 2 in the 2020s. Massive proportions of GNS were mainly distributed in the southeast and northwest region. Tipping points of GNS indicating a transition from mass balance gain to loss during four periods were detected in the Upper Tarim basin, Inner Tibetan Plateau, Upper Yangtze river basin, Upper Brahmaputra river basin, and Upper Salween river basin. The reduction of total GNS area was attributed to significantly rising temperature and decreasing solid precipitation. The projections indicate that glacier shrinkages will be the dominant trend in the future, and most typical glaciers will experience a mass loss of 60% under SSP245 (SSP2-RCP4.5) and over 90% under SSP585 (SSP5-RCP8.5) by the end of the century. The response of glaciers to climate change is also found to be influenced by factors including local topography and elevation ranges. The findings of this study provides a better understanding of glacial dynamics in response to climate change and highlights the importance of making water resources management strategies to mitigate the influence of glacier retreat., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Attributing soil erosion to land management and climatic drivers is important for global policy development to protect soils. The Chinese Loess Plateau is one of the most eroded areas in the world. However, there has been limited assessment of historic spatial changes in erosion rates on the Loess Plateau and the major contributors driving these spatial changes. In this study, the Revised Universal Soil Loss Equation was empirically validated and employed to assess spatially distributed historical erosion rates on the Loess Plateau from 1901 to 2016. A double mass curve attribution technique was then used to investigate the impact of land management and climatic drivers on the Loess Plateau. Decadal average erosion rates and the total area with intensive erosion (>5000 t km -2 yr -1 ) experienced a sharp increase from the 1930s to 1970s, followed by a decline to an historic low between the 1980s and 2000s. Mean erosion rates for the 2000s were 54.3% less than those of the 1970s. However, a recent increase in erosion rates was observed between 2010 and 2016. Land management change was the dominant driver of historical erosion rate changes before 2010. Extensive deforestation and farming, driven by population increase, were responsible for intensifying erosion between the 1930s and 1970s, while policy-driven conservation schemes and revegetation led to reduction thereafter. However, the recent increase in erosion between 2010 and 2016 was mainly driven by extreme rainfall events, a major concern given climate change projections. Advanced erosion control strategies are therefore required as part of integrated catchment management that both maintain water supplies for human use during dry periods while reducing erosion during storm events., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2022. Published by Elsevier B.V. All rights reserved.)

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named 'Global Applications of Soil Erosion Modelling Tracker (GASEMT)', includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: We determined whether plasma concentrations of ZO-1 proteins may be used a predictor of sepsis severity and 30-day mortality., Methods: A total of 143 patients with sepsis and 30 healthy controls were enrolled. Plasma ZO-1 proteins concentrations were measured. Various methods, including area under the curves (AUCs), Kaplan-Meier curve, Cox regression, net reclassification improvement (NRI) and integrated discrimination improvement (IDI), were carried out to determine the value of ZO-1 in predicting 30-day mortality., Results: Plasma ZO-1 concentrations in patients with sepsis and septic shock were significantly higher than those in healthy controls and were associated with the number of organ failures. ZO-1 concentrations also correlated with APACHE II or SOFA score and predicted 30-day mortality in sepsis patients with an AUC of 0.754. Multivariable regression analyses showed that a ZO-1 concentration ≥2.60 ng/ml remained a significant predictor of 30-day mortality in sepsis patients. Kaplan-Meier survival plots showed that patients with ZO-1 concentrations <2.60 ng/ml had a clear survival benefit. Adding ZO-1 to the SOFA score significantly improved its prognostic accuracy., Conclusion: Plasma ZO-1 proteins appear to be a valuable prognostic biomarker for the severity of sepsis and a predictor of 30-day mortality for patients with sepsis., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Understanding hydrological alteration of rivers and the potential driving factors are crucial for water resources management in the watershed. This study analyzed the daily runoff time series at six gauging stations during 1960-2016 in Wuding River basin, northwestern China. The Mann-Kendall test and Lee-Heghinian method were employed to detect the temporal trends and abrupt changes in annual streamflow. The flow duration curve (FDC) and the index of hydrologic alteration (IHA)/Range of Variability Approach (RVA) were applied to assess the daily streamflow and degree of hydrologic alteration (DHA). In addition, we analyzed the changes of index of hydrological connectivity (IC) and reservoirs/dams (RI) in 1990, 1995, 2000 and 2015 in the basin. The relationship between IC, RI and DHA were assessed to investigate the potential influences of land use changes and constructions of reservoirs/dams on hydrological alteration. The results indicated that annual streamflow at five stations showed significant downward trends (p < 0.01) from 1960 to 2016, and an abrupt changing point appeared in the beginning of 1970s in Wuding River basin. Exception is Qingyangcha station without significant changes, and Hanjiamao station with changing point in 1967. FDC analysis indicated that both high and low flow indices reduced greatly. The integral DHA were higher than 70% at all the stations in the Wuding River basin, suggesting great variation in the magnitude, duration, frequency, timing and rate of change of daily streamflow. Both IC value and RI had close relationship with DHA, implying that DHA was highly affected by land use changes and dams/reservoirs constructions, and was more sensitive to the land use change (p < 0.01). This study provides good insight to understand the effects of soil and water conservation measures on hydrological regime., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The sediment load on the Chinese Loess Plateau has sharply decreased in recent years. However, the contribution of terrace construction and vegetation restoration projects to sediment discharge reduction remains uncertain. In this paper, eight catchments located in the Loess Plateau were chosen to explore the effects of different driving factors on sediment discharge changes during the period from the 1960s to 2012. Attribution approaches were applied to evaluate the effects of climate, terrace, and vegetation coverage changes on sediment discharge. The results showed that the annual sediment discharge decreased significantly in all catchments ranging from -0.007 to -0.039 Gt·yr -1 . Sediment discharge in most tributaries has shown abrupt changes since 1996, and the total sediment discharge was reduced by 60.1% during 1997-2012. We determined that increasing vegetation coverage was the primary factor driving the reductions in sediment loads since 1996 and accounted for 47.7% of the total reduction. Climate variability and terrace construction accounted for 9.1% and 18.6% of sediment discharge reductions, respectively., (Copyright © 2018. Published by Elsevier B.V.)

Understanding the relative contributions of climate change and human activities to variations in sediment load is of great importance for regional soil, and river basin management. Considerable studies have investigated spatial-temporal variation of sediment load within the Loess Plateau; however, contradictory findings exist among methods used. This study systematically reviewed six quantitative methods: simple linear regression, double mass curve, sediment identity factor analysis, dam-sedimentation based method, the Sediment Delivery Distributed (SEDD) model, and the Soil Water Assessment Tool (SWAT) model. The calculation procedures and merits for each method were systematically explained. A case study in the Huangfuchuan watershed on the northern Loess Plateau has been undertaken. The results showed that sediment load had been reduced by 70.5% during the changing period from 1990 to 2012 compared to that of the baseline period from 1955 to 1989. Human activities accounted for an average of 93.6 ± 4.1% of the total decline in sediment load, whereas climate change contributed 6.4 ± 4.1%. Five methods produced similar estimates, but the linear regression yielded relatively different results. The results of this study provide a good reference for assessing the effects of climate change and human activities on sediment load variation by using different methods., (Copyright © 2018. Published by Elsevier B.V.)

The middle reaches of the Yellow River Basin transport the vast majority of sediment (>85% of the basin's total available sediment load), which has had profound effects on the characteristics of the middle and lower reaches of the Yellow River. Since the late 1950s, soil and water conservation measures have been extensively implemented in the Loess Plateau, China, especially since the 1970s. This has resulted in sediment discharge changing significantly. In this study, data from 22 catchments in the region of the Loess Plateau from Hekou to Longmen in the middle reaches of the Yellow River were analyzed to investigate the responses of the sediment regime to climate change and human activities. The non-parametric Mann-Kendall test and the Pettitt test were used to identify trends and shifts in sediment discharge. All 22 catchments had a significantly decreasing trend (P<0.01) in annual sediment discharge. Change point years were detected between 1971 and 1994, and were concentrated between 1978 and 1984 in 17 catchments. Moreover, erosive rainfall exhibited a tendency to decrease, but this was not a significant trend. Compared to rainfall, human activities, primarily soil and water conservation and environmental rehabilitation campaigns, have played a more prominent role in the changes in sediment regimes. In order to reduce soil erosion and sediment yield, more attention should be paid to proper and rational soil and water conservation and eco-restoration in this region., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Paraquat (PQ) has caused deaths of numerous people around the world. In order to assess the lethal plasma concentration, the patients who acquired acute PQ intoxication were analyzed by plasma concentration monitoring. The plasma PQ concentrations were determined by high performance liquid chromatography (HPLC) which used 5-bromopyrimidine as internal standard and trichloroacetic acid-methanol (1:9) as protein precipitant. The liver, kidney and coagulation function were determined by automatic biochemical analyzer. According to plasma PQ concentration, 90 patients were divided into four groups: trace PQ group (<50ng/mL), low PQ group (<1000ng/mL), medium PQ group (1000-5000ng/mL) and high PQ group (>5000ng/mL). The clinical data from the four groups was statistically analyzed. The results showed the developed HPLC methods exhibited a high degree of accuracy and good linearity within 50-25000ng/mL (R=0.9998). The Spearman's correlation analysis showed PQ concentration had a strong relationship to total bilirubin, direct bilirubin, aspartic transaminase, urea nitrogen, prothrombin time, prothrombin activity, and international normalized ratio (P<0.01). The cured or survival PQ poisoned patients among the trace PQ group, the low PQ group, the medium PQ group, and the high PQ group were 19/19 (100%), 19/21 (90.47%), 11/25 (44.0%), and 0/25 (0%) respectively. The mean hospital days were (10.37±8.04), (18.76±12.06), (16.76±14.44), and (4.04±5.41) days respectively. The Cox regression analysis indicated that plasma PQ concentration was highly related to prognosis (P<0.05). In conclusion, no patient presenting with a PQ concentration over 5000ng/mL survived. The plasma PQ level is related to liver, kidney and coagulation function, which can be used as an important clinical index to judge the prognosis of PQ poisoned patients., Chemical Compounds: Paraquat (PubChem CID: 15938), 5-bromopyrimidine (PubChem CID: 78344), acetonitrile (PubChem CID: 6342), sodium dihydrogen phosphate (PubChem CID: 23672064), sodium heptanesulfonate (PubChem CID: 23672332), methylprednisolone (PubChem CID: 6741), cyclophosphamide (PubChem CID: 2907)., (Copyright © 2016. Published by Elsevier Inc.)