Sunflower Crop Irrigation in Autonomous Regions: Case of Hetao District; Inner Mongolia, China

Irrigation is a main factor for agricultural development, given the place occupied by agriculture and the weight of the food bill. Suitable researches on irrigation are necessary for this operation and for better water resources managements. . Hetao Irrigation District has uneven climate distribution and irregular basis water during the agricultural year. It faces major water shortage problem, low and irregular rainfall.  The Yellow River is the second largest River in China. The River basin is an area with severe water scarcity, with less than 500 m3 per capita per year, accounting for 81 % of the total water use, the head stream of the Yellow river basin becoming drier due to climate change.. The increased water abstraction for industrial, domestic and hydropower uses exacerbate water scarcity in the basin. Due to this water scarcity conditions the middle and low reaches of the river dried up 21 times during 1972-2008, with 226 days of no flow in 1997. The current research aims at providing an optimal irrigation scheduling to sunflower crop, by supplying the right amount of water at the right time, in order to increase crop yield and achieve 70% field efficiency. The results shows that, The water deficit vary between 2.8 mm/day to 3.09 mm/day from 2001 to 2008; crop water requirements between 247.4 mm and 392.7 mm and net irrigation water requirements between 300.8 to 459.3 mm Keywords: Reference evapotranspiration; ArcGIS, Cropwat; Optimal Irrigation Scheduling; Sunflower

Optimal Irrigation Scheduling for Summer Maize Crop: Based on GIS and CROPWAT Application in Hetao District; Inner Mongolia Autonomous Region, China

Net Irrigation Water Requirement was estimated using GIS and CROPWAT software. The study aims to develop an optimal irrigation scheduling in summer, to increase crop yield under water scarcity conditions. The proportion of rainwater evaporated over year 2008 “868.6 mm/dec” was used to compare with the water requirements of the maize crop “734.1 mm/dec” The results showed deficits ranging between 30.7 mm/month and 200.8 mm/month, for the period between April and September. In addition, there was uneven distribution of precipitation and an irregular basis during the agricultural year (2000-2008). The ET0 was between 0.47mm and 3.08mm, and net irrigation water requirement was 833.4 mm for the maize crop. On refilling soil to field capacity with irrigation at critical depletion, 70% field efficiency was achieved which correspond to optimal condition, while adapting fixed interval per stage gave a yield reduction of about 2.5 %. Keywords: Evapotranspiration; ArcGIS; CROPWAT; Optimal Irrigation Scheduling; Cor

Simulation and Prediction of Ion Transport in the Reclamation of Sodic Soils with Gypsum Based on the Support Vector Machine

The effect of gypsum on the physical and chemical characteristics of sodic soils is nonlinear and controlled by multiple factors. The support vector machine (SVM) is able to solve practical problems such as small samples, nonlinearity, high dimensions, and local minima points. This paper reports the use of the SVM regression method to predict changes in the chemical properties of sodic soils under different gypsum application rates in a soil column experiment and to evaluate the effect of gypsum reclamation on sodic soils. The research results show that (1) the SVM soil solute transport model using the Matlab toolbox represents the change in Ca2+ and Na+ in the soil solution and leachate well, with a high prediction accuracy. (2) Using the SVM model to predict the spatial and temporal variations in the soil solute content is feasible and does not require a specific mathematical model. The SVM model can take full advantage of the distribution characteristics of the training sample. (3) The workload of the soil solute transport prediction model based on the SVM is greatly reduced by not having to determine the hydrodynamic dispersion coefficient and retardation coefficient, and the model is thus highly practical

Quantitative Fractal Evaluation of Herbicide Effects on the Water-Absorbing Capacity of Superabsorbent Polymers

The water absorption capacity of superabsorbent polymers (SAPs) is important for agricultural drought resistance. However, herbicides may leach into the soil and affect water absorption by damaging the SAP three-dimensional membrane structures. We used 100-mesh sieves, electron microscopy, and fractal theory to study swelling and water absorption in SAPs in the presence of three common herbicides (atrazine, alachlor, and tribenuron-methyl) at concentrations of 0.5, 1.0, and 2.0 mg/L. In the sieve experiments it was found that 2.0 mg/L atrazine reduces the capacity by 9.64–23.3% at different swelling points; no significant diminution was observed for the other herbicides or for lower atrazine concentrations. We found that the hydrogel membrane pore distributions have fractal characteristics in both deionized water and atrazine solution. The 2.0 mg/L atrazine destroyed the water-retaining polymer membrane pores and reduced the water-absorbing mass by modifying its three-dimensional membrane structure. A linear correlation was observed between the fractal analysis and the water-absorbing mass. Multifractal analysis characterized the membrane pore distribution by using the range of singularity indexes Δα (relative distinguishing range of 16.54–23.44%), which is superior to single-fractal analysis that uses the fractal dimension D (relative distinguishing range of 2.5–4.0%)

Detection of KRAS mutation using plasma samples in non-small-cell lung cancer: a systematic review and meta-analysis

BackgroundThe aim of this study was to investigate the diagnostic accuracy of KRAS mutation detection using plasma sample of patients with non-small cell lung cancer (NSCLC).MethodsDatabases of Pubmed, Embase, Cochrane Library, and Web of Science were searched for studies detecting KRAS mutation in paired tissue and plasma samples of patients with NSCLC. Data were extracted from each eligible study and analyzed using MetaDiSc and STATA.ResultsAfter database searching and screening of the studies with pre-defined criteria, 43 eligible studies were identified and relevant data were extracted. After pooling the accuracy data from 3341 patients, the pooled sensitivity, specificity and diagnostic odds ratio were 71%, 94%, and 59.28, respectively. Area under curve of summary receiver operating characteristic curve was 0.8883. Subgroup analysis revealed that next-generation sequencing outperformed PCR-based techniques in detecting KRAS mutation using plasma sample of patients with NSCLC, with sensitivity, specificity, and diagnostic odds ratio of 73%, 94%, and 82.60, respectively.ConclusionCompared to paired tumor tissue sample, plasma sample showed overall good performance in detecting KRAS mutation in patients with NSCLC, which could serve as good surrogate when tissue samples are not available

Effects of Superabsorbent Polymers on the Hydraulic Parameters and Water Retention Properties of Soil

Superabsorbent polymers (SAPs) are widely applied in dryland agriculture. However, their functional property of repeated absorption and release of soil water exerts periodic effects on the hydraulic parameters and water-retention properties of soil, and as this property gradually diminishes with time, its effects tend to be unstable. During the 120-day continuous soil cultivation experiment described in this paper, horizontal soil column infiltration and high-speed centrifugation tests were conducted on SAP-treated soil to measure unsaturated diffusivity D and soil water characteristic curves. The experimental results suggest that the SAP increased the water retaining capacity of soil sections where the suction pressure was between 0 and 3,000 cm. The SAP significantly obstructed water diffusion in the soil in the early days of the experiment, but the effect gradually decreased in the later period. The average decrease in water diffusivity in the treatment groups fell from 76.6% at 0 days to 1.2% at 120 days. This research also provided parameters of time-varying functions that describe the unsaturated diffusivity D and unsaturated hydraulic conductivity K of soils under the effects of SAPs; in future research, these functions can be used to construct water movement models applicable to SAP-treated soil

Efficacy and Safety of Tribendimidine Against Clonorchis sinensis

In this randomized open-label trial, tribendimidine was shown to have an efficacy comparable to praziquantel for the treatment of Clonorchis sinensis infection. Patients treated with praziquantel experienced significantly more adverse events than tribendimidine recipient

Phase evolution and superconductivity enhancement in Se-substituted MoTe2_2 thin films

The strong spin$-$orbit coupling (SOC) and numerous crystal phases in few$-$layer transition metal dichalcogenides (TMDCs) MX$_2$ (M$=$W, Mo, and X$=$Te, Se, S) has led to a variety of novel physics, such as Ising superconductivity and quantum spin Hall effect realized in monolayer 2H$-$ and Td$-$MX$_2$, respectively. Consecutive tailoring of the MX$_2$ structure from 2H to Td phase may realize the long$-$sought topological superconductivity in one material system by incorporating superconductivity and quantum spin Hall effect together. In this work, by combing Raman spectrum, X-ray photoelectron spectrum (XPS), scanning transmission electron microscopy imaging (STEM) as well as electrical transport measurements, we demonstrate that a consecutively structural phase transitions from Td to 1T$'$ to 2H polytype can be realized as the Se-substitution concentration increases. More importantly, the Se$-$substitution has been found to notably enhance the superconductivity of the MoTe$_2$ thin film, which is interpreted as the introduction of the two$-$band superconductivity. The chemical constituent induced phase transition offers a new strategy to study the s$_{+-}$ superconductivity and the possible topological superconductivity as well as to develop phase$-$sensitive devices based on MX$_2$ materials.Comment: 27 pages, 5 figure

Benefits and risks of drug combination therapy for diabetes mellitus and its complications: a comprehensive review

Diabetes is a chronic metabolic disease, and its therapeutic goals focus on the effective management of blood glucose and various complications. Drug combination therapy has emerged as a comprehensive treatment approach for diabetes. An increasing number of studies have shown that, compared with monotherapy, combination therapy can bring significant clinical benefits while controlling blood glucose, weight, and blood pressure, as well as mitigating damage from certain complications and delaying their progression in diabetes, including both type 1 diabetes (T1D), type 2 diabetes (T2D) and related complications. This evidence provides strong support for the recommendation of combination therapy for diabetes and highlights the importance of combined treatment. In this review, we first provided a brief overview of the phenotype and pathogenesis of diabetes and discussed several conventional anti-diabetic medications currently used for the treatment of diabetes. We then reviewed several clinical trials and pre-clinical animal experiments on T1D, T2D, and their common complications to evaluate the efficacy and safety of different classes of drug combinations. In general, combination therapy plays a pivotal role in the management of diabetes. Integrating the effectiveness of multiple drugs enables more comprehensive and effective control of blood glucose without increasing the risk of hypoglycemia or other serious adverse events. However, specific treatment regimens should be tailored to individual patients and implemented under the guidance of healthcare professionals

Accurately identifying hemagglutinin using sequence information and machine learning methods

IntroductionHemagglutinin (HA) is responsible for facilitating viral entry and infection by promoting the fusion between the host membrane and the virus. Given its significance in the process of influenza virus infestation, HA has garnered attention as a target for influenza drug and vaccine development. Thus, accurately identifying HA is crucial for the development of targeted vaccine drugs. However, the identification of HA using in-silico methods is still lacking. This study aims to design a computational model to identify HA.MethodsIn this study, a benchmark dataset comprising 106 HA and 106 non-HA sequences were obtained from UniProt. Various sequence-based features were used to formulate samples. By perform feature optimization and inputting them four kinds of machine learning methods, we constructed an integrated classifier model using the stacking algorithm.Results and discussionThe model achieved an accuracy of 95.85% and with an area under the receiver operating characteristic (ROC) curve of 0.9863 in the 5-fold cross-validation. In the independent test, the model exhibited an accuracy of 93.18% and with an area under the ROC curve of 0.9793. The code can be found from https://github.com/Zouxidan/HA_predict.git. The proposed model has excellent prediction performance. The model will provide convenience for biochemical scholars for the study of HA