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

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

Cholesterol-modified prognostic nutritional index (CPNI) as an effective tool for assessing the nutrition status and predicting survival in patients with breast cancer

Abstract Background Malnutrition is associated with poor overall survival (OS) in breast cancer patients; however, the most predictive nutritional indicators for the prognosis of patients with breast cancer are not well-established. This study aimed to compare the predictive effects of common nutritional indicators on OS and to refine existing nutritional indicators, thereby identifying a more effective nutritional evaluation indicator for predicting the prognosis in breast cancer patients. Methods This prospective study analyzed data from 776 breast cancer patients enrolled in the “Investigation on Nutritional Status and its Clinical Outcome of Common Cancers” (INSCOC) project, which was conducted in 40 hospitals in China. We used the time-dependent receiver operating characteristic curve (ROC), Kaplan–Meier survival curve, and Cox regression analysis to evaluate the predictive effects of several nutritional assessments. These assessments included the patient-generated subjective nutrition assessment (PGSGA), the global leadership initiative on malnutrition (GLIM), the controlling nutritional status (CONUT), the nutritional risk index (NRI), and the prognostic nutritional index (PNI). Utilizing machine learning, these nutritional indicators were screened through single-factor analysis, and relatively important variables were selected to modify the PNI. The modified PNI, termed the cholesterol-modified prognostic nutritional index (CPNI), was evaluated for its predictive effect on the prognosis of patients. Results Among the nutritional assessments (including PGSGA, GLIM, CONUT, NRI, and PNI), PNI showed the highest predictive ability for patient prognosis (time-dependent ROC = 0.58). CPNI, which evolved from PNI, emerged as the superior nutritional index for OS in breast cancer patients, with the time-dependent ROC of 0.65. It also acted as an independent risk factor for mortality (p < 0.05). Moreover, the risk of malnutrition and mortality was observed to increase gradually among both premenopausal and postmenopausal age women, as well as among women categorized as non-overweight, overweight, and obese. Conclusions The CPNI proves to be an effective nutritional assessment tool for predicting the prognosis of patients with breast cancer

Supplemental Material, Table_S1_Information_of_104_ESCC_patients - High TSTA3 Expression as a Candidate Biomarker for Poor Prognosis of Patients With ESCC

<p>Supplemental Material, Table_S1_Information_of_104_ESCC_patients for High TSTA3 Expression as a Candidate Biomarker for Poor Prognosis of Patients With ESCC by Jie Yang, Pengzhou Kong, Jian Yang, Zhiwu Jia, Xiaoling Hu, Zianyi Wang, Heyang Cui, Yanghui Bi, Yu Qian, Hongyi Li, Fang Wang, Bin Yang, Ting Yan, Yanchun Ma, Ling Zhang, Caixia Cheng, Bin Song, Yaoping Li, Enwei Xu, Haiyan Liu, Wei Gao, Juan Wang, Yiqian Liu, Yuanfang Zhai, Lu Chang, Yi Wang, Yingchun Zhang, Ruyi Shi, Jing Liu, Qi Wang, Xiaolong Cheng, and Yongping Cui in Technology in Cancer Research & Treatment</p