Silencing MicroRNA-134 Alleviates Hippocampal Damage and Occurrence of Spontaneous Seizures After Intraventricular Kainic Acid-Induced Status Epilepticus in Rats

Epilepsy is a disorder of abnormal brain activity typified by spontaneous and recurrent seizures. MicroRNAs (miRNAs) are short non-coding RNAs, critical for the post-transcriptional regulation of gene expression. MiRNA dysregulation has previously been implicated in the induction of epilepsy. In this study, we examined the effect of silencing miR-134 against status epilepticus (SE). Our results showed that level of miR-134 was significantly up-regulated in rat brain after Kainic acid (KA)-induced SE. TUNEL staining showed that silencing miR-134 alleviated seizure-induced neuronal apoptosis in the CA3 subfield of the hippocampus. Western blot showed that a miR-134 antagonist suppressed lesion-induced endoplasmic reticulum (ER) stress and apoptosis related expression of CHOP, Bim and Cytochrome C, while facilitated the expression of CREB at 24 h post KA-induced lesion in the hippocampus. Consistently, silencing miR-134 significantly diminished loss of CA3 pyramidal neurons using Nissl staining as well as reducing aberrant mossy fiber sprouting (MFS) in a rat epileptic model. In addition, the results of EEG and behavior analyses showed seizures were alleviated by miR-134 antagonist in our experimental models. These results suggest that silencing miR-134 modulates the epileptic phenotype by upregulating its target gene, CREB. This in turn attenuates oxidative and ER stress, inhibits apoptosis, and decreases MFS long term. This indicates that silencing miR-134 might be a promising intervention for the treatment of epilepsy

Secondary Production of Gaseous Nitrated Phenols in Polluted Urban Environments

Nitrated phenols (NPs) are important atmospheric pollutants that affect air quality, radiation, and health. The recent development of the time-of-flight chemical ionization mass spectrometer (ToF-CIMS) allows quantitative online measurements of NPs for a better understanding of their sources and environmental impacts. Herein, we deployed nitrate ions as reagent ions in the ToF-CIMS and quantified six classes of gaseous NPs in Beijing. The concentrations of NPs are in the range of 1 to 520 ng m(-3). Nitrophenol (NPh) has the greatest mean concentration. Dinitrophenol (DNP) shows the greatest haze-to-clean concentration ratio, which may be associated with aqueous production. The high concentrations and distinct diurnal profiles of NPs indicate a strong secondary formation to overweigh losses, driven by high emissions of precursors, strong oxidative capacity, and high NOx levels. The budget analysis on the basis of our measurements and box-model calculations suggest a minor role of the photolysis of NPs (Peer reviewe

FTO gene polymorphisms and obesity risk: a meta-analysis

<p>Abstract</p> <p>Background</p> <p>The pathogenesis of obesity is reportedly related to variations in the fat mass and an obesity-associated gene (<it>FTO</it>); however, as the number of reports increases, particularly with respect to varying ethnicities, there is a need to determine more precisely the effect sizes in each ethnic group. In addition, some reports have claimed ethnic-specific associations with alternative SNPs, and to that end there has been a degree of confusion.</p> <p>Methods</p> <p>We searched PubMed, MEDLINE, Web of Science, EMBASE, and BIOSIS Preview to identify studies investigating the associations between the five polymorphisms and obesity risk. Individual study odds ratios (OR) and their 95% confidence intervals (CI) were estimated using per-allele comparison. Summary ORs were estimated using a random effects model.</p> <p>Results</p> <p>We identified 59 eligible case-control studies in 27 articles, investigating 41,734 obesity cases and 69,837 healthy controls. Significant associations were detected between obesity risk and the five polymorphisms: rs9939609 (OR: 1.31, 95% CI: 1.26 to 1.36), rs1421085 (OR: 1.43, 95% CI: 1.33 to 1.53), rs8050136 (OR: 1.25, 95% CI: 1.13 to 1.38), rs17817449 (OR: 1.54, 95% CI: 1.41 to 1.68), and rs1121980 (OR: 1.34, 95% CI: 1.10 to 1.62). Begg's and Egger's tests provided no evidence of publication bias for the polymorphisms except rs1121980. There is evidence of higher heterogeneity, with <it>I</it>²test values ranging from 38.1% to 84.5%.</p> <p>Conclusions</p> <p>This meta-analysis suggests that <it>FTO </it>may represent a low-penetrance susceptible gene for obesity risk. Individual studies with large sample size are needed to further evaluate the associations between the polymorphisms and obesity risk in various ethnic populations.</p

Astatic balance debugging method for inner ring assembly of frame type angle measuring device

The seeker position calibration system is a frame type angle measuring device, and the static balance of its inner ring components directly determines the stability of the product performance. This paper makes full use of the advantages of the pressure sensor, carries out analog-to-digital conversion and data acquisition through HX711, displays the collected results on the LCD screen, and analyzes and processes the data. A device that automatically measures the static balance of the frame in the seeker position calibration system is designed. The device uses four embedded pressure sensors to realize that if the product is unbalanced when placing the inner frame of the product on the balancing tool rest, one end of the inner frame will press on the balancing device due to gravity. By displaying the size of the pressure value and fitting with the least square method, the relationship between the pressure value and the thickness of the increased or decreased shims of the product is realized, and the universality of the relationship is verified. One calculation can realize the accurate adjustment of the static balance of the frame in the seeker position calibration system

A Data Mining Method Using Deep Learning for Anomaly Detection in Cloud Computing Environment

Aiming at problems such as slow training speed, poor prediction effect, and unstable detection results of traditional anomaly detection algorithms, a data mining method for anomaly detection based on the deep variational dimensionality reduction model and MapReduce (DMAD-DVDMR) in cloud computing environment is proposed. First of all, the data are preprocessed by a dimensionality reduction model based on deep variational learning and based on ensuring complete data information as much as possible, the dimensionality of the data is reduced, and the computational pressure is reduced. Secondly, the data set stored on the Hadoop Distributed File System (HDFS) is logically divided into several data blocks, and the data blocks are processed in parallel through the principle of MapReduce, so the k-distance and LOF value of each data point can only be calculated in each block. Thirdly, based on stochastic gradient descent, the concept of k-neighboring distance is redefined, thus avoiding the situation where there are greater than or equal to k-repeated points and infinite local density in the data set. Finally, compared with CNN, DeepAnt, and SVM-IDS algorithms, the accuracy of the scheme is increased by 10.3%, 18.0%, and 17.2%, respectively. The experimental data set verifies the effectiveness and scalability of the proposed DMAD-DVDMR algorithm

Luteolin and triptolide: Potential therapeutic compounds for post-stroke depression via protein STAT

Post stroke depression (PSD) is a common neuropsychiatric complication following stroke closely associated with the immune system. The development of medications for PSD remains to be a considerable challenge due to the unclear mechanism of PSD. Multiple researches agree that the functions of gene ontology (GO) are efficient for the investigation of disease mechanisms, and DeepPurpose (DP) is extremely valuable for the mining of new drugs. However, GO terms and DP have not yet been applied to explore the pathogenesis and drug treatment of PSD. This study aimed to interpret the mechanism of PSD and discover important drug candidates targeting risk proteins, based on immune-related risk GO functions and informatics algorithms. According to the risk genes of PSD, we identified 335 immune-related risk GO functions and 37 compounds. Based on the construction of the GO function network, we found that STAT protein may be a pivot protein in underlying the mechanism of PSD. Additionally, we also established networks of Protein-Protein Interaction as well as Gene-GO function to facilitate the evaluation of key genes. Based on DP, a total of 37 candidate compounds targeting 7 key proteins were identified with a potential for the therapy of PSD. Furthermore, we noted that the mechanisms by which luteolin and triptolide acting on STAT-related GO function might involve three crucial pathways, including specifically hsa04010 (MAPK signaling pathway), hsa04151 (PI3K-Akt signaling pathway) and hsa04060 (Cytokine-cytokine receptor interaction). Thus, this study provided fresh and powerful information for the mechanism and therapeutic strategies of PSD