Thermomorphic Hydrophilicity Base-Induced Precipitation for Effective Descaling of Hypersaline Brines

This study presents a novel technology, termed thermomorphic hydrophilicity base-induced precipitation (THBIP), for the effective removal of hardness ions from hypersaline brines with high-scaling propensity. THBIP utilizes thermoresponsive amine bases for the controlled precipitation of scaling constituents in alkaline conditions and subsequently uses low-temperature heat to trigger the phase separation of amine from aqueous solution, thus enabling reuse of the base. Three amines exhibiting distinct water solubility and basicity, specifically, diisopropylamine (DIPA), N-ethylcyclohexylamine (ECHA), and N,N-dimethylisopropylamine (DMIPA), were examined to identify the key parameters affecting THBIP performance. The amine bases were added to solutions containing hardness ions, Mg2+ or Ca2+, to induce the precipitation of hydroxide minerals (i.e., Mg(OH)2(s) and Ca(OH)2(s)). All three amines are effective in increasing solution pH, leading to high Mg2+ removals of over 90%. But because Ca(OH)2(s) is relatively more soluble, only DIPA, which is both highly miscible in water and also the strongest base, obtained significant removal of Ca2+ ions (>33%). The observed precipitation and hardness removal are quantitatively consistent with the principles of aqueous chemistry. Using a simulated hypersaline feedwater (240 g/L total dissolved solids) of high-scaling propensity, THBIP with DIPA achieved ∼80% hardness removal. Subsequent elevation of the temperature from 15 to 70 °C triggered demixing of the thermoresponsive base from the aqueous solution, to enable liquid–liquid separation for amine reuse. The study demonstrates the proof-of-concept of using thermomorphic hydrophilicity bases as an alternative strategy to effectively reduce the scaling potential of hypersaline brines

Comprehensive comparative analysis of prognostic value of serum systemic inflammation biomarkers for colorectal cancer: Results from a large multicenter collaboration

BackgroundThe incidence of colorectal cancer (CRC) is common and reliable biomarkers are lacking. We aimed to systematically and comprehensively compare the ability of various combinations of serum inflammatory signatures to predict the prognosis of CRC. Moreover, particular attention has been paid to the clinical feasibility of the newly developed inflammatory burden index (IBI) as a prognostic biomarker for CRC.MethodsThe discrimination capacity of the biomarkers was compared using receiver operating characteristic curves and Harrell’s C-index. Kaplan-Meier curves and log-rank tests were used to compare survival differences between the groups. Cox proportional hazard regression analysis was used to determine the independent prognostic factors. Logistic regression analysis was used to assess the relationship between IBI, short-term outcomes, and malnutrition.ResultsIBI had the optimal prediction accuracy among the systemic inflammation biomarkers for predicting the prognosis of CRC. Taking IBI as a reference, none of the remaining systemic inflammation biomarkers showed a gain. Patients with high IBI had significantly worse overall survival than those with low IBI (56.7% vs. 80.2%; log-rank P<0.001). Multivariate Cox regression analysis showed that continuous IBI was an independent risk factor for the prognosis of CRC patients (hazard ratio = 1.165, 95% confidence interval [CI] = 1.043–1.302, P<0.001). High IBI was an independent risk factor for short-term outcomes (odds ratio [OR] = 1.537, 95% CI = 1.258–1.878, P<0.001), malnutrition (OR = 2.996, 95% CI = 1.471–6.103, P=0.003), and recurrence (OR = 1.744, 95% CI = 1.176–2.587, p = 0.006) in CRC patients.ConclusionsIBI, as a reflection of systemic inflammation, is a feasible and promising biomarker for assessing the prognosis of CRC patients

Multi-year mapping of cropping systems in regions with smallholder farms from Sentinel-2 images in Google Earth engine

ABSTRACTAccurate acquisition of spatial and temporal distribution information for cropping systems is important for agricultural production and food security. The challenges of extracting information about cropping systems in regions with smallholder farms are considerable, given the varied crops, complex cropping patterns, and the fragmentation of cropland with frequent reclamation and abandonment. This study presents a specialized workflow to solve this problem for regions with smallholder farms, which utilizes field samples and Sentinel-2 data to extract cropping system information over multiple years. The workflow involves four steps: 1) processing Sentinel-2 data to simulate crop growth curves with the Savitzky‒Golay filter and computing feature variables for classification, including phenology indices, spectral bands, and time series of vegetation indices; 2) mapping annual croplands with one-class support vector machine; 3) mapping various cropping patterns, including single cropping, intercropping, double cropping, multiple harvest, and fallow by decision tree and K-means clustering; and 4) mapping crops with random forest where Jeffries-Matusita distance was used to select appropriate vegetation indices. The workflow was applied in the Hetao irrigation district in Inner Mongolia Autonomous Region, China from 2018 to 2021. The overall accuracies were 0.98, 0.96, and 0.97 for cropland, cropping patterns, and crop type mapping, respectively. The mapping results indicated that the study area has low cropping continuity and is dominated by single cropping patterns. Furthermore, the area of wheat cultivation has decreased, and vegetable cultivation has expanded. Overall, the proposed workflow facilitated the accurate acquisition of cropping system information in regions with smallholder farms and demonstrated the effectiveness of available Sentinel-2 imagery in classifying complex cropping patterns. The workflow is available on Google Earth Engine

Multi-year mapping of flood autumn irrigation extent and timing in harvested croplands of arid irrigation district

Flood irrigation after crop harvest, e.g. autumn irrigation (AI), is a common irrigation practice in arid and semi-arid regions like Hetao Irrigation District (HID) in Northwest China to increase soil moisture and leach soil salt. Detailed information about the extent, timing, and amount of AI is imperative for modeling agro-hydrological processes and irrigation management. However, little attention is given to the identification of the above AI factors. There are basically three major difficulties in estimating the annual changes in AI, including a suitable index to identify AI, temporal instability of thresholds, and an effective validation method for irrigation timing. Therefore, this study proposes a simple and effective threshold-based method to extract the extent and timing of AI in the HID using MODIS water indices at a daily timescale. The Multi-Band Water Index (MBWI) time series is first reconstructed using an adaptive weighted Savitzky-Golay filter and then used to identify the AI extent and time. The proposed model has a stronger generalization capability both in time and space due to robust thresholds selected from the Z-score normalized feature variable. The model is validated both at pixels generated by the segmentation of Sentinel-derived MBWI using a threshold-based model and at sampling points from the field survey. Results show that the model performed well with an overall accuracy of more than 90.0% for the irrigation area. The overall accuracies of irrigation timing are 76.4% and 91.7% based on the middle-to-late and whole irrigation periods, respectively. We found a decreasing trend in the AI area and a gradual delay in the starting time of AI in the HID, mainly due to changes in cropping patterns, climate, and irrigation fees. Overall, the model is promising in identifying flood irrigation extent and timing in large irrigation districts and is helpful for irrigation scheduling

Multi-year mapping of cropping systems in regions with smallholder farms from Sentinel-2 images in Google Earth engine

Accurate acquisition of spatial and temporal distribution information for cropping systems is important for agricultural production and food security. The challenges of extracting information about cropping systems in regions with smallholder farms are considerable, given the varied crops, complex cropping patterns, and the fragmentation of cropland with frequent reclamation and abandonment. This study presents a specialized workflow to solve this problem for regions with smallholder farms, which utilizes field samples and Sentinel-2 data to extract cropping system information over multiple years. The workflow involves four steps: 1) processing Sentinel-2 data to simulate crop growth curves with the Savitzky‒Golay filter and computing feature variables for classification, including phenology indices, spectral bands, and time series of vegetation indices; 2) mapping annual croplands with one-class support vector machine; 3) mapping various cropping patterns, including single cropping, intercropping, double cropping, multiple harvest, and fallow by decision tree and K-means clustering; and 4) mapping crops with random forest where Jeffries-Matusita distance was used to select appropriate vegetation indices. The workflow was applied in the Hetao irrigation district in Inner Mongolia Autonomous Region, China from 2018 to 2021. The overall accuracies were 0.98, 0.96, and 0.97 for cropland, cropping patterns, and crop type mapping, respectively. The mapping results indicated that the study area has low cropping continuity and is dominated by single cropping patterns. Furthermore, the area of wheat cultivation has decreased, and vegetable cultivation has expanded. Overall, the proposed workflow facilitated the accurate acquisition of cropping system information in regions with smallholder farms and demonstrated the effectiveness of available Sentinel-2 imagery in classifying complex cropping patterns. The workflow is available on Google Earth Engine. We proposed an integrated method to map cropping systems into smallholder regions. Annual cropland mapping is necessary in regions with complex cropping pattern. The method requires only crop samples as input and is completed on the GEE. Sentinel-2 data can effectively classify cropland, cropping patterns, and crops. The 10-day interval performs better on phenology curves based on Sentinel-2.</p

Quality evaluation of clinical practice guidelines for placenta accreta spectrum disorders

Introduction: We evaluated the quality of the published clinical practice guidelines on placenta accreta spectrum (PAS) disorders to provide reference for the development of high-quality PAS guidelines. Methods: China National Knowledge Infrastructure (CNKI), Wan Fang, PubMed, Embase, Web of Science, and Cochrane Library were systematically searched. Quality assessments were conducted using the appraisal of guidelines for research and evaluation (AGREE) II framework and Reporting Items for practice Guidelines in Healthcare (RIGHT) checklist. Intraclass correlation coefficients (ICCs) were used to measure the agreement among reviewers. Results: In total, 13 guidelines from different countries, published between 2015 and 2021 were included. There included 9 official guidelines, 3 consensuses, and 1 standard reference and covered subjects including epidemiology, diagnosis and treatment. The mean standardized scores across 6 domains (scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence) were 53.63%, 27.35%, 33.57%, 72.01%, 19.39% and 41.02%, respectively. Of the 13 guidelines, 11 were classified as grade B, whereas 2 as grade C. According to the RIGHT checklist, the overall reporting rate of the 13 guidelines ranged from 28.57% to 54.29%. Conclusion: The current guidelines for PAS demonstrate commendable methodological and reporting qualities. However, the methodological and reporting quality of PAS CPGs still need to be further improved, particularly in stakeholder involvement, the rigor of development, applicability, and editorial independence domains

In situ observation of dynamic galvanic replacement reactions in twinned metallic nanowires by liquid cell transmission electron microscopy

Galvanic replacement is a versatile approach to prepare hollow nanostructures with controllable morphology and elemental composition. The primary issue is to identify its fundamental mechanism. In this study, in situ liquid cell transmission electron microscopy was employed to monitor the dynamic reaction process and to explore the mechanism of galvanic replacement. The detailed reaction process was revealed based on in situ experiments in which small Au particles first appeared around Ag nanowires; they coalesced, grew, and adhered to Ag nanowires. After that, small pits grew from the edge of Ag nanowires to form tubular structures, and then extended along the Ag nanowires to obtain hollowed structures. All of our experimental observations from the viewpoint of electron microscopy, combined with DFT calculations, contribute towards an in-depth understanding of the galvanic replacement reaction process and the design of new materials with hollow structures

Greenhouse gases emissions from riparian wetlands: An example from the Inner Mongolia grassland region in China

10.5194/bg-18-4855-2021Biogeosciences18174855-487

Pharmacodynamics of frigid zone plant Taxus cuspidata S. et Z. against skin melanin deposition, oxidation, inflammation and allergy

Taxus cuspidata S. et Z. is a precious species of frigid zone plant belonging to the Taxaceae family, which possesses anticancer, anti-inflammatory, hypoglycemic, and antibacterial pharmacological properties. While taxane extracted from Taxus chinensis has been reported to elicit antioxidant activities, whether Taxus cuspidata S. et Z. has skin-protective actions against injuries remained unknown. This study aims to explore the pharmacological effects of three Taxus extracts on skin melanin deposition, oxidation, inflammation, and allergy so as to provide new ideas for the prevention and treatment of various diseases related to skin damage

Remnant cholesterol is an effective biomarker for predicting survival in patients with breast cancer

Abstract Background Breast cancer is the most common malignancy in women worldwide. The relationship between remnant cholesterol (RC) and the prognosis of patients with breast cancer has not been clearly reported. This study investigated the prognostic value of RC in predicting mortality in patients with breast cancer. Methods This study prospectively analysed 709 women patients with breast cancer from the Investigation on Nutrition Status and Clinical Outcome of Common Cancers (INSCOC) project. Restricted cubic splines were used to analyse the dose-response relationship between RC and breast cancer mortality. The Kaplan–Meier method was used to evaluate the overall survival of patients with breast cancer. A Cox regression analyses was performed to assess the independent association between RC and breast cancer mortality. Inverse probability of treatment weighting (IPTW) using the propensity score was used to reduce confounding. Sensitivity analysis was performed after excluding patients with underlying diseases and survival times shorter than one year. Results A linear dose-response relationship was identified between RC and the risk of all-cause mortality in patients with breast cancer (p = 0.036). Kaplan–Meier survival analysis and log-rank test showed that patients with high RC levels had poorer survival than those with low RC levels (p = 0.007). Univariate and multivariate Cox regression analyses showed that RC was an independent risk factor for mortality in women patients with breast cancer. IPTW-adjusted analyses and sensitivity analyses showed that CR remained a prognostic factor. Conclusions RC is an independent risk factor for the prognosis of patients with breast cancer, and patients with higher RC levels have poorer survival