Compound events of wet and dry extremes: Identification, variations, and risky patterns

Compound hydrometeorological extremes have been widely examined under climate change, they have significant impacts on ecological and societal well-being. This study sheds light on a new category compound of contrasting extremes, namely compounding wet and dry extremes (CWDEs). The CWDEs are characterized as devastating dry events (EDs) accompanied by wet extremes (EWs) in a given time window. Notably, we first adopt a separate system to identify coinciding events considering the different evolving processes and impacting patterns of EDs and EWs. The peak-over-threshold and standardized index methods are used in a daily and monthly window to identify EWs and EDs respectively. Furthermore, the spatial-temporal changes and risky patterns of CWDEs are revealed by using the Mann-Kendall test, the Ordinary Least Squares, and the Global and Local Moran indices. Germany is the study case. As one major finding, the results indicate a pronounced seasonal effect and spatial clustering pattern of CWDEs. The summer is the most vulnerable period for CWDEs, and the spatial hotspots are mainly located in the southern tip of Germany, as well as in the vicinity of the capital city Berlin. Besides, robust uptrends in CWDEs across all evaluation metrics have been discovered in historical periods, and the moist climate and complex geography collectively contribute to severe CWDEs. Unexpectedly, the study finds that compounding events in dry regions are mainly driven by wet extremes, whereas they show a higher dependency on dry anomalies in wet regions. The research provides new insights into compound extremes which are composed of individual hazards with distinct features. Related findings will aid decision-makers in producing effective risk mitigation plans for prioritizing vulnerable regions. Lastly, the robust framework and open access data allow for extensive exploration of various compounding hazards in different regions.

Development and application of high resolution SPEI drought dataset for Central Asia

Central Asia is a data scarce region, which makes it difficult to monitor and minimize the impacts of a drought. To address this challenge, in this study, a high-resolution (5 km) Standardized Precipitation Evaporation Index (SPEI-HR) drought dataset was developed for Central Asia with different time scales from 1981-2018, using Climate Hazards group InfraRed Precipitation with Station's (CHIRPS) precipitation and Global Land Evaporation Amsterdam Model's (GLEAM) potential evaporation (E_p) datasets. As indicated by the results, in general, over time and space, the SPEI-HR correlated well with SPEI values estimated from coarse-resolution Climate Research Unit (CRU) gridded time series dataset. The 6-month timescale SPEI-HR dataset displayed a good correlation of 0.66 with GLEAM root zone soil moisture (RSM) and a positive correlation of 0.26 with normalized difference vegetation index (NDVI) from Global Inventory Monitoring and Modelling System (GIMMS). After observing a clear agreement between SPEI-HR and drought indicators for the 2001 and 2008 drought events, an emerging hotspot analysis was conducted to identify drought prone districts and sub-basins.

Drivers of the water use efficiency changes in China during 1982-2015

This study investigates the drivers of water use efficiency (WUE), a key metric of water resources management, and its changes over eight regions across China from 1982 to 2015 based on gross primary production (GPP) and actual evapotranspiration (AET) datasets. The order of seasonal change of WUE from large to small is autumn, summer, spring and winter. The drivers include seven variables, air temperature, specific humidity, precipitation, short-wave radiation, Normalized Difference Vegetation Index (NDVI), soil moisture and CO₂. Our analysis suggests that the sensitivity of annual average NDVI to WUE changes was high nationwide, but there were some differences in seasonal scales. The annual average contribution of air temperature and CO₂ affecting WUE change was relatively high in China's largest area (SW, SE, E, NP). Other influencing factors were only relatively high in the local area. Seasonally, NDVI is the driving factor with the highest contribution rate in summer and autumn for NC and NW region. The seasonal contribution rates of driving factors in other regions are significantly different. For the study period (1982-2015), the shrubland ecosystem had the highest annual WUE followed by forest and cropland. The WUE of the farmland ecosystem was higher than that of the grassland ecosystem in most areas.

[Hepatic echinococcus granulosus: a clinicopathological analysis of thirteen cases]

Objective: To investigate the clinicopathologic characteristics of hepatic echinococcus granulosus (HEG). Methods: Thirteen cases of HEG were collected from Linzhi People's Hospital between January 2017 to October 2020, and their clinicopathologic features, ultrasound classification, immunophenotype and histochemical data were analyzed, retrospectively and the relevant literature was reviewed. Results: Thirteen patients (5 male patients, 8 female patients) were included in this cohort, and the mean age was 40 years. The most common clinical presentation was mild abdominal distention and pain (9/13). Based on WHO-IWGE ultrasound standardized classification, these cases were classified into 5 types, including type CL (1 case), type CE1 (2 cases), type CE2 (4 cases), type CE3 (3 cases) and type CE4 (3 cases). Gross examination revealed a solitary cyst localized in the liver, varying from 2.7 to 13.5 cm in diameter, and most of them(10/13)were more than 10 cm. Histopathologically, these cysts possessed a thin inner germinal layer and outer adventitial layer, and a central cavity filled with a clear"hydatid"fluid. The germinal layer was continuous and generated brood capsules and protoscoleces. The laminated membranes were clearly demonstrated by elastic fiber and Gomori's stains. Inside the"mother"cyst, there were a varying number of"daughter"vesicles of variable sizes. The inflammatory reaction around the cyst consisted of eosinophils, mononuclear cells immediately next to the cyst layer and sometimes formed granuloma and giant cells resembling the Langhan's type giant cells. The lymphoid cells were positive for CD20 and CD3. The CD68 immunohistochemistry clearly demonstrated epithelioid cells of granuloma in two cases. Moreover, immunohistochemistry revealed plasma cells were locally positive for CD38, IgG and IgG4, but not meeting the criteria for IgG4 related lesion. Conclusions: Hepatic echinococcus granulosus is a zoonotic parasitic disease prevalent in pastoral areas such as Tibet. It is important to understand its clinical features, ultrasound characteristics and histological morphology.

Determination of Electrocution Using Fourier Transform Infrared Microspectroscopy and Machine Learning Algorithm

Abstract

Objective To analyze the differences among electrical damage, burns and abrasions in pig skin using Fourier transform infrared microspectroscopy （FTIR-MSP） combined with machine learning algorithm, to construct three kinds of skin injury determination models and select characteristic markers of electric injuries, in order to provide a new method for skin electric mark identification. Methods Models of electrical damage, burns and abrasions in pig skin were established. Morphological changes of different injuries were examined using traditional HE staining. The FTIR-MSP was used to detect the epidermal cell spectrum. Principal component method and partial least squares method were used to analyze the injury classification. Linear discriminant and support vector machine were used to construct the classification model, and factor loading was used to select the characteristic markers. Results Compared with the control group, the epidermal cells of the electrical damage group, burn group and abrasion group showed polarization, which was more obvious in the electrical damage group and burn group. Different types of damage was distinguished by principal component and partial least squares method. Linear discriminant and support vector machine models could effectively diagnose different damages. The absorption peaks at 2 923 cm^-1, 2 854 cm^-1, 1 623 cm^-1, and 1 535 cm^-1 showed significant differences in different injury groups. The peak intensity of electrical injury's 2 923 cm^-1 absorption peak was the highest. Conclusion FTIR-MSP combined with machine learning algorithm provides a new technique to diagnose skin electrical damage and identification electrocution.

Fully automated snow depth measurements from time-lapse images applying a convolutional neural network

Time-lapse cameras in combination with simple measuring rods can form a highly reliable low-cost sensor network monitoring snow depth in a high spatial and temporal resolution. Depending on the number of cameras and the temporal recording resolution, such a network produces large sets of image time series. In order to extract the snow depth time series from these collections of images in acceptable time, automated processing methods have to be applied. Besides classic image processing based on edge detection methods, there are nowadays ready-to-use convolutional neural network frameworks like Mask R-CNN that facilitate instance segmentation and thus allow for fully automated snow depth measurements from images using a detectable measuring rod. This study investigates the applicability of Mask R-CNN embedded in a newly developed work flow for snow depth measurements. The new method is compared to an automated image processing method carried out utilizing functionalities provided by the OpenCV library. The quality of both methods was assessed with the inclusion of manual evaluations of the image series. As a result, the newly introduced work flow outperforms the present classic image processing method in regard to stability, accuracy and portability. By applying the Mask R-CNN framework, the overall RMSE of two considered time series is reduced to approximately 20% of the value produced by means of the classic image processing approach. Moreover, the ratio of values within five centimeter deviation from the reference value was increased from 75% to 88% on average. Since no parameters have to be adjusted, the Mask R-CNN framework is able to detect known shapes reliably in almost any environment, making the presented method highly flexible.

[Correlation between polymorphism of CYP19A1, GSTM1, GSTT1 and GSTP1 gene and endometriosis]

Objective: To study the association between polymorphisms in CYP19A1 rs2899470, GSTM1, GSTT1, and GSTP1 rs1695 and the development of endometriosis. Methods: Between October 2015 and October 2017, 262 endometriosis patients and 275 control subjects were recruited from the Affiliated Hospital of Inner Mongolia Medical University. Genotyping was conducted using polymerase chain reaction-coupled with restriction fragment length polymorphism and multiplex allele specific polymerase chain reaction. Results: Individuals carrying the TT genotype of CYP19A1 rs2899470 expressed a higher risk of endometriosis than that carrying the GG genotype (P<0.01), and the adjusted OR was 2.33 (95%CI 1.27-4.33). In addition, individuals with the CYP19A1 rs2899470 the TT genotype aggravated the condition of endometriosis (OR=2.27, 95%CI 1.05-4.90). However, GSTM1, GSTT1 and GSTP1 rs1695 polymorphisms did not affect the pathogenesis of endometriosis. Conclusion: Our results suggested that CYP19A1 rs2899470 polymorphism is associated with the risk of endometriosis and the risk of disease.

Coupling hydrological modeling and support vector regression to model hydropeaking in alpine catchments

Water management in the alpine region has an important impact on streamflow. In particular, hydropower production is known to cause hydropeaking i.e., sudden fluctuations in river stage caused by the release or storage of water in artificial reservoirs. Modeling hydropeaking with hydrological models, such as the Soil Water Assessment Tool (SWAT), requires knowledge of reservoir management rules. These data are often not available since they are sensitive information belonging to hydropower production companies. In this short communication, we propose to couple the results of a calibrated hydrological model with a machine learning method to reproduce hydropeaking without requiring the knowledge of the actual reservoir management operation. We trained a support vector machine (SVM) with SWAT model outputs, the day of the week and the energy price. We tested the model for the Upper Adige river basin in North-East Italy. A wavelet analysis showed that energy price has a significant influence on river discharge, and a wavelet coherence analysis demonstrated the improved performance of the SVM model in comparison to the SWAT model alone. The SVM model was also able to capture the fluctuations in streamflow caused by hydropeaking when both energy price and river discharge displayed a complex temporal dynamic.

[Effects of Temperature on FTIR Spectral Characteristics of Renal Tissue in Rats after Death]

OBJECTIVES

To analyse the Fourier transform infrared （FTIR） spectral data of renal tissue at different temperatures in rats after death, and to explore the effects of temperature on the FTIR spectral characteristics of renal tissue.

METHODS

The rats were sacrificed by cervical dislocation and placed at 4 ℃, 20 ℃ and 30 ℃. The FTIR spectral data of renal tissue were collected at different time points and analysed by data mining method.

RESULTS

The principal component analysis （PCA） results showed that there were significant trends of clustering in the samples of partial time point at 4 ℃, 20 ℃ and 30 ℃. Partial least square （PLS） regression models were established with the spectral data at three temperature groups. The performance of PLS regression models in 20 ℃ and 30 ℃ groups were more superior than that in 4 ℃ group, and the stability of the model in 20 ℃ group was better than that in 30 ℃ group.

CONCLUSIONS

There are differences in the FTIR spectral characteristics of renal tissue of rats after death at different temperatures. Temperature has a major impact on the performance of FTIR spectral PLS regression model. Therefore, in order to improve the accuracy of postmortem interval estimation, the effects of temperature on the model should be considered in the related study by spectral method.

[Establishment of the Mathematical Model for PMI Estimation Using FTIR Spectroscopy and Data Mining Method]

OBJECTIVES

To analyse the relationship between Fourier transform infrared （FTIR） spectrum of rat's spleen tissue and postmortem interval （PMI） for PMI estimation using FTIR spectroscopy combined with data mining method.

METHODS

Rats were sacrificed by cervical dislocation, and the cadavers were placed at 20 ℃. The FTIR spectrum data of rats' spleen tissues were taken and measured at different time points. After pretreatment, the data was analysed by data mining method.

RESULTS

The absorption peak intensity of rat's spleen tissue spectrum changed with the PMI, while the absorption peak position was unchanged. The results of principal component analysis （PCA） showed that the cumulative contribution rate of the first three principal components was 96%. There was an obvious clustering tendency for the spectrum sample at each time point. The methods of partial least squares discriminant analysis （PLS-DA） and support vector machine classification （SVMC） effectively divided the spectrum samples with different PMI into four categories （0-24 h, 48-72 h, 96-120 h and 144-168 h）. The determination coefficient （R²） of the PMI estimation model established by PLS regression analysis was 0.96, and the root mean square error of calibration （RMSEC） and root mean square error of cross validation （RMSECV） were 9.90 h and 11.39 h respectively. In prediction set, the R² was 0.97, and the root mean square error of prediction （RMSEP） was 10.49 h.

CONCLUSIONS

The FTIR spectrum of the rat's spleen tissue can be effectively analyzed qualitatively and quantitatively by the combination of FTIR spectroscopy and data mining method, and the classification and PLS regression models can be established for PMI estimation.

Uncertainty of modelled flow regime for flow-ecological assessment in Southern Europe

Sustainable water basin management requires characterization of flow regime in river networks impacted by anthropogenic pressures. Flow regime in ungauged catchments under current, future, or natural conditions can be assessed with hydrological models. Developing hydrological models is, however, resource demanding such that decision makers might revert to models that have been developed for other purposes and are made available to them ('off-the-shelf' models). In this study, the impact of epistemic uncertainty of flow regime indicators on flow-ecological assessment was assessed at selected stations with drainage areas ranging from about 400 to almost 90,000km² in four South European basins (Adige, Ebro, Evrotas and Sava). For each basin, at least two models were employed. Models differed in structure, data input, spatio-temporal resolution, and calibration strategy, reflecting the variety of conditions and purposes for which they were initially developed. The uncertainty of modelled flow regime was assessed by comparing the modelled hydrologic indicators of magnitude, timing, duration, frequency and rate of change to those obtained from observed flow. The results showed that modelled flow magnitude indicators at medium and high flows were generally reliable, whereas indicators for flow timing, duration, and rate of change were affected by large uncertainties, with correlation coefficients mostly below 0.50. These findings mirror uncertainty in flow regime indicators assessed with other methods, including from measured streamflow. The large indicator uncertainty may significantly affect assessment of ecological status in freshwater systems, particularly in ungauged catchments. Finally, flow-ecological assessments proved very sensitive to reference flow regime (i.e., without anthropogenic pressures). Model simulations could not adequately capture flow regime in the reference sites comprised in this study. The lack of reliable reference conditions may seriously hamper flow-ecological assessments. This study shows the pressing need for improving assessment of natural flow regime at pan-European scale.

Evaluation of precipitation input for SWAT modeling in Alpine catchment: A case study in the Adige river basin (Italy)

Precipitation is often the most important input data in hydrological models when simulating streamflow. The Soil and Water Assessment Tool (SWAT), a widely used hydrological model, only makes use of data from one precipitation gauge station that is nearest to the centroid of each subbasin, which is eventually corrected using the elevation band method. This leads in general to inaccurate representation of subbasin precipitation input data, particularly in catchments with complex topography. To investigate the impact of different precipitation inputs on the SWAT model simulations in Alpine catchments, 13years (1998-2010) of daily precipitation data from four datasets including OP (Observed precipitation), IDW (Inverse Distance Weighting data), CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and TRMM (Tropical Rainfall Measuring Mission) has been considered. Both model performances (comparing simulated and measured streamflow data at the catchment outlet) as well as parameter and prediction uncertainties have been quantified. For all three subbasins, the use of elevation bands is fundamental to match the water budget. Streamflow predictions obtained using IDW inputs are better than those obtained using the other datasets in terms of both model performance and prediction uncertainty. Models using the CHIRPS product as input provide satisfactory streamflow estimation, suggesting that this satellite product can be applied to this data-scarce Alpine region. Comparing the performance of SWAT models using different precipitation datasets is therefore important in data-scarce regions. This study has shown that, precipitation is the main source of uncertainty, and different precipitation datasets in SWAT models lead to different best estimate ranges for the calibrated parameters. This has important implications for the interpretation of the simulated hydrological processes.

Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales

This study provides a comprehensive evaluation of eight high spatial resolution gridded precipitation products in Adige Basin located in Italy within 45-47.1°N. The Adige Basin is characterized by a complex topography, and independent ground data are available from a network of 101 rain gauges during 2000-2010. The eight products include the Version 7 TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Analysis 3B42 product, three products from CMORPH (the Climate Prediction Center MORPHing technique), i.e., CMORPH_RAW, CMORPH_CRT and CMORPH_BLD, PCDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record), PGF (Global Meteorological Forcing Dataset for land surface modelling developed by Princeton University), CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and GSMaP_MVK (Global Satellite Mapping of Precipitation project Moving Vector with Kalman-filter product). All eight products are evaluated against interpolated rain gauge data at the common 0.25° spatial resolution, and additional evaluations at native finer spatial resolution are conducted for CHIRPS (0.05°) and GSMaP_MVK (0.10°). Evaluation is performed at multiple temporal (daily, monthly and annual) and spatial scales (grid and watershed). Evaluation results show that in terms of overall statistical metrics the CHIRPS, TRMM and CMORPH_BLD comparably rank as the top three best performing products, while the PGF performs worst. All eight products underestimate and overestimate the occurrence frequency of daily precipitation for some intensity ranges. All products tend to show higher error in the winter months (December-February) when precipitation is low. Very slight difference can be observed in the evaluation metrics and aspects between at the aggregated 0.25° spatial resolution and at the native finer resolutions (0.05°) for CHIRPS and (0.10°) for GSMaP_MVK products. This study has implications for precipitation product development and the global view of the performance of various precipitation products, and provides valuable guidance when choosing alternative precipitation data for local community.

Correlation between RNA Degradation Patterns of Rat's Brain and Early PMI at Different Temperatures

OBJECTIVES

To explore the correlation between early postmortem interval （PMI） and eight RNA markers of rat's brain at different temperatures.

METHODS

Total 222 SD rats were randomly divided into control group （PMI=0 h） and four experimental groups. And the rats in the experimental groups were sacrificed by cervical dislocation and respectively kept at 5 ℃, 15 ℃, 25 ℃ and 35 ℃ in a controlled environment chamber. The RNA was extracted from brain tissues, which was taken at 9 time points from 1 h to 24 h postmortem. The expression levels of eight markers, β-actin, GAPDH, RPS29, 18S rRNA, 5S rRNA, U6 snRNA, miRNA-9 and miRNA-125b, were detected using real-time fluorescent quantitative PCR, respectively. Proper internal reference was selected by geNorm software. Regression analysis of normalized RNA markers was performed by SPSS software. Mathematical model for PMI estimation was established using R software. Another 6 SD rats with known PMI were used to verify the mathematical model.

RESULTS

5S rRNA, miR-9 and miR-125b were suitable as internal reference markers for their stable expression. Both β-actin and GAPDH had well time-dependent degradation patterns and degraded continually with prolongation of PMI in 24 h postmortem. The mathematical model of the variation of ΔCt values with PMI and temperature was set up by R software and the model could be used for PMI estimation. The average error rates of model validation using β-actin and GAPDH were 14.1% and 22.2%, respectively.

CONCLUSIONS

The expression levels of β-actin and GAPDH are well correlated with PMI and environmental temperature. The mathematical model established in present study can provide references for estimating early PMI under various temperature conditions.

Studies on the growth metabolism of Bacillus thuringiensis and its vegetative insecticidal protein engineered strains by microcalorimetry

The metabolic power-times curves of Bacillus thuringiensis and its vegetative insecticidal protein engineered strains were determined at 30 degrees C by using a thermal activity monitor air Isothermal Microcalorimeter, ampoule method. From the power-times curves, the maximum power (Pmax) in the log phase, the growth rate constant (k), the generation times (tG), the time of the maximum power (tmax), the heat effects (Qlog) for log phase, and the total heat effect in 45 h (Qtotal) of B. thuringiensis strains can be obtained. The results indicate that their power-times curves are different. The relationship between their metabolic power-times curves and character of bacteria metabolism, and thermokinetics and gene expression were analyzed and discussed. The character of the bacteria power-times curves reflected the physiologic character of gene expression. The microcalorimetric method proved to be a reliable and sensitive tool for the assessment of the growth metabolism, the heat output in bacteria and its engineered strains. The determination of the thermokinetic character is beneficial to the control of fermentation.