Application of hybrid electrocoagulation-filtration methods in the pretreatment of marine aquaculture wastewater.

The aim of this study was to provide technical means and data support for enhancing the filtration pretreatment capacity of a recirculating aquaculture system. A continuous flow electrocoagulation (EC)-filtration system was designed and its application in the pretreatment of marine aquaculture wastewater was studied. The influences of anode combination modes, hydraulic retention times (HRTs) of the EC reactor and filter pore sizes on the water treatment capacity were investigated. Results showed that EC could significantly enhance the treatment efficiency of the filtration equipment used in subsequent steps. Al-Fe electrodes used as anode led to better processing capacity of this system, and the optimum anode was 3Al + Fe. With the increase of HRT and decrease of filter pore size, the enhanced effect of the EC process on the filter was more obvious. When the current density was 19.22 A/m2, the anode was 3Al + Fe, the HRT was 4.5 min and the filter pore size was 45 µm, the removal efficiency of the system for Vibrio, chemical oxygen demand, total ammonia nitrogen, nitrite nitrogen (NO2--N), nitrate nitrogen (NO3--N) and total nitrogen was 69.55 ± 0.93%, 48.99 ± 1.42%, 57.06 ± 1.28%, 34.09 ± 2.27%, 18.47 ± 1.88% and 55.26 ± 1.42%, respectively, and the energy consumption was (26.25 ± 4.95) × 10-3kWh/m3.

Evaluation models for soil nutrient based on support vector machine and artificial neural networks.

Soil nutrient is an important aspect that contributes to the soil fertility and environmental effects. Traditional evaluation approaches of soil nutrient are quite hard to operate, making great difficulties in practical applications. In this paper, we present a series of comprehensive evaluation models for soil nutrient by using support vector machine (SVM), multiple linear regression (MLR), and artificial neural networks (ANNs), respectively. We took the content of organic matter, total nitrogen, alkali-hydrolysable nitrogen, rapidly available phosphorus, and rapidly available potassium as independent variables, while the evaluation level of soil nutrient content was taken as dependent variable. Results show that the average prediction accuracies of SVM models are 77.87% and 83.00%, respectively, while the general regression neural network (GRNN) model's average prediction accuracy is 92.86%, indicating that SVM and GRNN models can be used effectively to assess the levels of soil nutrient with suitable dependent variables. In practical applications, both SVM and GRNN models can be used for determining the levels of soil nutrient.

Suppression of long noncoding RNA NEAT1 attenuates hypoxia-induced cardiomyocytes injury by targeting miR-378a-3p.

BACKGROUND/AIMS: lncRNA NEAT1 is involved in the development of many diseases. However, the function of lncRNA NEAT1 in myocardial infarction is unclear. Therefore, this experimental design based on lncRNA NEAT1 to explore the pathogenesis of myocardial infarction. METHODS: RT-qPCR was used to detect the expression of lncRNA NEAT1 and miR-378a-3p in peripheral blood and mouse cardiomyocytes of patients with myocardial infarction. MTT assay, flow cytometry, Caspase-3 kit and transwell assay were used to detect the effects of lncRNA NEAT1 and miR-378a-3p on cardiomyocyte proliferation, apoptosis and migration. Target gene prediction and screening, luciferase reporter assays were used to verify downstream target genes for lncRNA NEAT1 and miR-378a-3p. Western blotting was used to detect the protein expression of Atg12 and related autophagy genes. RESULTS: lncRNA NEAT1 was highly expressed in peripheral blood and mouse cardiomyocytes of patients with myocardial infarction. Moreover, lncRNA NEAT1 significantly promoted cell proliferation and migration of cardiomyocytes. In addition, lncRNA NEAT1 inhibited miR-378a-3p expression, and miR-378a-3p inhibited Atg12 expression, while lncRNA NEAT1 regulated expression of Atg12 and related autophagic factors via miR-378a-3p. Knockout of microRNA-378-3p reversed the effects of NEAT1 silencing on cell damage. CONCLUSION: lncRNA NEAT1 can regulate the proliferation of cardiomyocytes by regulating miR-378-3p/Atg12 axis, thus accelerating the occurrence and development of cardiomyocytes.

Japanese encephalitis (JE) mimicking acute ischemic stroke: A case report.

INTRODUCTION: Japanese encephalitis (JE) is one of the most serious viral infectious diseases of the central nervous system in Asia. The clinical manifestations of it might be non-specific. We herein report a case of JE mimicking acute ischemic stroke. PATIENT CONCERNS: A 52-year-old man presented with acute onset of left-sided limb weakness for 2 hours and a 5-year history of hypertension but with no fever or cold before the onset. Immediate cranial computed tomography scan showed small ischemic foci. DIAGNOSIS: Initial diagnosis revealed acute cerebral infarction as the symptoms mimicked stroke at onset. Furthermore, his symptoms progressed and magnetic resonance scan after 6 days of onset appeared negative on diffusion weighted imaging. Other etiologies were also then considered. Japanese encephalitis virus immunoglobulin M in the serum supported positive diagnosis of JE. INTERVENTIONS: The patient was given Ribavirin, and then his symptoms slowly improved. OUTCOMES: Brain MRI on day 29 after the onset revealed high-intensity lesions in bilateral thalamus on diffusion weighted imaging. During the follow-up (at about 2 months after the onset), the patient's consciousness was clear but could not walk. At about 6 months after the onset, he could walk with parkinsonian features. CONCLUSION: Diagnosis of JE that mimicked acute stroke at onset and with no fever can be challenging. Recognition of disease development, MRI and Japanese encephalitis virus immunoglobulinM findings are helpful in early definitive diagnosis.

Effect of 106PEAR1 and 168PTGS1 genetic polymorphisms on recurrent ischemic stroke in Chinese patient.

The impact of genetic polymorphisms on the occurrence of recurrent ischemic stroke (RIS) is not fully understood. This study was aimed to examine the relationships among the 106PEAR1 and 168PTGS1 polymorphisms and RIS.This was a single-center, retrospective, case-control study of patients seen in consultation between March 2016 and December 2016 at the Shandong Provincial Hospital. The 106PEAR1 (G>A) and 168PTGS1 (-842A>G) polymorphisms were determined by fluorescence in situ hybridization.There were 56 patients with RIS and 137 with initial stroke. Compared with the initial group, the RIS group showed lower LDL-C levels (P = .04). 168PTGS1 (-842A>G) did not meet the Hardy-Weinberg equilibrium. The AA genotype of the 106PEAR1 (G>A) polymorphism was more frequent in the RIS group (17.9% vs 5.8%, P = .009). The A allele also showed a higher frequency than the G allele in the RIS group (P = .02). The multivariable logistic regression analysis showed that 106PEAR1 (G>A) (OR = 3.24, 95%CI: 1.04-10.14, P = .04) and lipid-lowering agents (OR = 9.18, 95%CI: 4.48-18.84, P < .001) were independently associated with RIS.The polymorphism at 106PEAR1 (G>A) was independently associated with RIS in Chinese patients. The assessment of genetic polymorphisms in the prediction of RIS warrants further investigation in order to improve patient management and prognosis after a first ischemic stroke.

User-friendly optimization approach of fed-batch fermentation conditions for the production of iturin A using artificial neural networks and support vector machine

Background In the field of microbial fermentation technology, how to optimize the fermentation conditions is of great crucial for practical applications. Here, we use artificial neural networks (ANNs) and support vector machine (SVM) to offer a series of effective optimization methods for the production of iturin A. The concentration levels of asparagine (Asn), glutamic acid (Glu) and proline (Pro) (mg/L) were set as independent variables, while the iturin A titer (U/mL) was set as dependent variable. General regression neural network (GRNN), multilayer feed-forward neural networks (MLFNs) and the SVM were developed. Comparisons were made among different ANNs and the SVM. Results The GRNN has the lowest RMS error (457.88) and the shortest training time (1 s), with a steady fluctuation during repeated experiments, whereas the MLFNs have comparatively higher RMS errors and longer training times, which have a significant fluctuation with the change of nodes. In terms of the SVM, it also has a relatively low RMS error (466.13), with a short training time (1 s). Conclusion According to the modeling results, the GRNN is considered as the most suitable ANN model for the design of the fed-batch fermentation conditions for the production of iturin A because of its high robustness and precision, and the SVM is also considered as a very suitable alternative model. Under the tolerance of 30%, the prediction accuracies of the GRNN and SVM are both 100% respectively in repeated experiments.