CRF07_BC Strain Dominates the HIV-1 Epidemic in Injection Drug Users in Liangshan Prefecture of Sichuan, China

The Liangshan prefecture in Sichuan province is an area in China severely affected by the HIV epidemic, with intravenous drug use (IDU) as the main risk factor. No reports on HIV subtypes prevalent in IDUs in Liangshan prefecture could be found. In this study, we have characterized the genotypes of HIV-1 in the IDU population in Liangshan prefecture and further determined the phylogenetic relationship of the CRF07_BC strains to HIV-1 sequences from the other regions of China, including Xinjiang and Yunnan provinces, to explore the pattern and possible diffusion pathway of HIV-1 in these regions. HIV-1-seropositive drug-naive IDUs identified in Liangshan prefecture, Sichuan province were enrolled in 2009. Full-length gag and pol genes were amplified by reverse transcription and nested PCR and then sequenced. All of the sequences were subtyped. Phylogenetic trees were constructed using the neighbor-joining and maximum likelihood methods. Divergence times were estimated using a Bayesian molecular clock approach. CRF07_BC was found to be the predominant strain in IDUs in Liangshan prefecture (95.5%). The CRF07_BC strains from Liangshan prefecture were found to be intermixed with those from Yunnan province in phylogenetic trees. The CRF07_BC sequences from Xinjiang province can be grouped into several clusters, suggesting that the expansion of the CRF07_BC epidemic in Xinjiang province was the result of a local epidemic driven by multiple independent introductions in the late 1990s. Only low-level drug-resistant viruses were found in the IDU population. CRF07_BC strains from Liangshan prefecture were more similar to those from Yunnan province than those from Xinjiang province. This finding will contribute to our understanding of the distribution, the evolution, and the potential source of CRF07_BC founder strains, and will also provide useful information for the development of strategies to prevent transmission

Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots

Our aim was to assess the utility of a novel machine learning software (Orbit Image Analysis) in the histological quantification of acute ischemic stroke (AIS) clots. We analyzed 50 AIS blood clots retrieved using mechanical thrombectomy procedures. Following HandE staining, quantification of clot components was performed by two different methods: a pathologist using a reference standard method (Adobe Photoshop CC) and an experienced researcher using Orbit Image Analysis. Following quantification, the clots were categorized into 3 types: RBC dominant (\u3e/=60% RBCs), Mixed and Fibrin dominant ( \u3e /=60% Fibrin). Correlations between clot composition and Hounsfield Units density on Computed Tomography (CT) were assessed. There was a significant correlation between the components of clots as quantified by the Orbit Image Analysis algorithm and the reference standard approach (rho = 0.944**, p \u3c 0.001, n = 150). A significant relationship was found between clot composition (RBC-Rich, Mixed, Fibrin-Rich) and the presence of a Hyperdense artery sign using the algorithmic method (X2(2) = 6.712, p = 0.035*) but not using the reference standard method (X2(2) = 3.924, p = 0.141). Orbit Image Analysis machine learning software can be used for the histological quantification of AIS clots, reproducibly generating composition analyses similar to current reference standard methods

Application of Single-Handed Trans-Umbilical Laparo-Endoscopic Single-Site Surgery Using the Suspension Line Method for Salpingectomy in Ectopic Pregnancy

Background: To evaluate the safety and feasibility of single-handed trans-umbilical single-site laparo-endoscopic surgery using the suspension-line method for salpingectomy in ectopic pregnancy. Methods: This study reviewed 54 patients with ectopic pregnancy who underwent salpingectomy in the First Affiliated Hospital of Xiamen University from June 2018 to June 2019. The control group (n = 29) was treated with routine two-handed trans-umbilical laparoendoscopic single-site surgery (TU-LESS), while the study group (n = 25) was treated with single-handed TU-LESS using the suspension line method. Results: There were no significant differences in clinical indicators such as operation time, postoperative recovery ventilation time, recovery time of β-human chorionic gonadotropin (β-HCG) to normal level, and postoperative complications (p > 0.05) between two groups, whereas the patients in the study group suffered less pain and were more satisfied with the incision (p < 0.05). Conclusions: Single-handed LESS using the suspension method can be safely and effectively applied to salpingectomy, with less trauma, more beautiful incision, and more comfortable operation

LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion

The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low signal-to-noise ratio, and inspired by the symmetry theory, we propose a new approach for the LPI radar signal recognition method based on a dual-channel convolutional neural network (CNN) and feature fusion. Our new approach contains three main modules: the preprocessing module that converts the LPI radar waveforms into two-dimensional time-frequency images using the Choi–Williams distribution (CWD) transformation and performs image binarization, the feature extraction module that extracts different features obtained from the images, and the recognition module that utilizes a multi-layer perceptron (MLP) network to fuse these features and distinguish the type of LPI radar signals. In the feature extraction module, a two-channel CNN model is proposed that extracts Histogram of Oriented Gradients (HOG) features and deep features from time-frequency images, respectively. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Its overall recognition rate reaches 97% when the signal-to-noise ratio is −6 dB

LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion

The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low signal-to-noise ratio, and inspired by the symmetry theory, we propose a new approach for the LPI radar signal recognition method based on a dual-channel convolutional neural network (CNN) and feature fusion. Our new approach contains three main modules: the preprocessing module that converts the LPI radar waveforms into two-dimensional time-frequency images using the Choi&ndash;Williams distribution (CWD) transformation and performs image binarization, the feature extraction module that extracts different features obtained from the images, and the recognition module that utilizes a multi-layer perceptron (MLP) network to fuse these features and distinguish the type of LPI radar signals. In the feature extraction module, a two-channel CNN model is proposed that extracts Histogram of Oriented Gradients (HOG) features and deep features from time-frequency images, respectively. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Its overall recognition rate reaches 97% when the signal-to-noise ratio is &minus;6 dB

mTORC2 phosphorylation of Akt1: a possible mechanism for hydrogen sulfide-induced cardioprotection.

Hydrogen sulfide (H2S) is known to have cardiac protective effects through Akt activation. Akt acts as a 'central sensor' for myocyte survival or death; its activity is regulated by multiple kinases including PI3K, mTORC2, PDK1 and phosphatases including PTEN, PP2A and PHLPPL. Based on the previous finding that PI3K inhibitor LY294002 abolishes H2S-induced Akt phosphorylation and cardioprotection, it is accepted that PI3K is the mediator of H2S-induced Akt phosphorylation. However, LY294002 inhibits both PI3K and mTOR, and PI3K only recruits Akt to the membrane where Akt is phosphorylated by Akt kinases. We undertook a series of experiments to further evaluate the role of mTORC2, PDK1, PTEN, PP2A and PHLPPL in H2S-induced Akt phosphorylation and cardioprotection, which, we believe, has not been investigated before. Hearts from adult Sprague-Dawley rats were isolated and subjected to (i) normoxia, (ii) global ischemia and (iii) ischemia/reperfusion in the presence or absence of 50 µM of H2S donor NaHS. Cardiac mechanical function and lactate dehydrogenase (LDH) release were assessed. All hearts also were Western analyzed at the end of perfusion for Akt and a panel of appropriate Akt regulators and targets. Hearts pretreated with 50 µM NaHS had improved function at the end of reperfusion (Rate pressure product; 19±4×10(3) vs. 10±3×10(3) mmHg/min, p<0.05) and reduced cell injury (LDH release 19±10 vs. 170±87 mU/ml p<0.05) compared to untreated hearts. NaHS significantly increased phospho-Akt, phospho-mTOR, phospho-Bim and Bcl-2 in reperfused hearts (P<0.05). Furthermore using H9c2 cells we demonstrate that NaHS pretreatment reduces apoptosis following hypoxia/re-oxygenation. Importantly, PP242, a specific mTOR inhibitor, abolished both cardioprotection and protein phosphorylation in isolated heart and reduced apoptotic effects in H9c2 cells. Treating hearts with NaHS only during reperfusion produced less cardioprotection through a similar mechanism. These data suggest mTORC2 phosphorylation of Akt is a key mediator of H2S-induced cardioprotection in I/R

NNCD-IQA: A new neural networks based compressed database for image quality assessment

International audienceObjective and subjective quality assessment is still a challenging problem in various image processing tasks. For instance, in the context of image compression, most of the conducted studies have focused on image datasets encoded using standard algorithms such as JPEG and JPEG2000. In this paper, we propose to further investigate the quality assessment issue in the presence of neural networks-based compressed images. More precisely, a new database of compressed images has been firstly built using JPEG2000 standard as well as four recent neural networks based coding schemes. Then, subjective experiments are performed to obtain the mean opinion scores of the generated distorted images. Finally, an extensive evaluation and analysis of objective image quality assessment metrics is achieved. For instance, in addition to conventional and machine learning metrics, we have considered different deep learning based models, which have been trained on our database. The new subjective database with its associated mean opinion scores as well as the learned models are publicly available at https://github.com/zakopz/NNCD-IQA-Database. The obtained results show the interest of deep learning based metrics in the context of neural networks-based compressed images. Keywords Image compression • Neural networks • Quality assessment • Subjective scores • Learning based metrics Zohaib Amjad Khan and Tassnim Dardouri contributed equally

A Novel Double Ensemble Algorithm for the Classification of Multi-Class Imbalanced Hyperspectral Data

International audienceThe class imbalance problem has been reported to exist in remote sensing and hinders the classification performance of many machine learning algorithms. Several technologies, such as data sampling methods, feature selection-based methods, and ensemble-based methods, have been proposed to solve the class imbalance problem. However, these methods suffer from the loss of useful information or from artificial noise, or result in overfitting. A novel double ensemble algorithm is proposed to deal with the multi-class imbalance problem of the hyperspectral image in this paper. This method first computes the feature importance values of the hyperspectral data via an ensemble model, then produces several balanced data sets based on oversampling and builds a number of classifiers. Finally, the classification results of these diversity classifiers are combined according to a specific ensemble rule. In the experiment, different data-handling methods and classification methods including random undersampling (RUS), random oversampling (ROS), Adaboost, Bagging, and random forest are compared with the proposed double random forest method. The experimental results on three imbalanced hyperspectral data sets demonstrate the effectiveness of the proposed algorithm

MiR-199-3p Suppressed Inflammatory Response by Targeting MECP2 to Alleviate RTX-Induced PHN in Mice

Varicella zoster virus–induced postherpetic neuralgia (PHN) can be alleviated by limited medications with serious side effects. This study aims to investigate the underlying molecular mechanism of miR-199-3p in mediating PHN in mice. 293T cells were transfected with miR-199-3p vectors (mimic/inhibitor). The target relationship between miR-199-3p and MECP2 was confirmed using luciferase reporter assay. PHN mouse model was established by TRX injection. Animal behaviors were evaluated using Hargreaves test and Von Frey test. Western blot was used for protein analysis, and quantitative reverse transcription polymerase chain reaction was performed for messenger RNA quantification. Serum levels of inflammatory mediators were determined using ELISA. Increased thermal withdrawal latency (TWL) and decreased mechanical withdrawal threshold (MWT) were observed in resiniferatoxin-induced PHN mice. Downregulated miR-199-3p and upregulated MECP2 were found in PHN mice. Upregulated miR-199-3p increased PWL and MWT, but inhibited MECP2 in PHN mice. Besides, increased miR-199-3p suppressed proinflammatory indicators and activated anti-inflammatory mediators. It also found that MECP2 was the target of miR-199-3p. Further study showed miR-199-3p enhanced PWL and MWT, and supported inflammatory response via targeting MECP2. miR-199-3p regulated inflammation by targeting MECP2 to alleviate TRX-induced PHN in mice

CRF07_BC Strain Dominates the HIV-1 Epidemic in Injection Drug Users in Liangshan Prefecture of Sichuan, China

The Liangshan prefecture in Sichuan province is an area in China severely affected by the HIV epidemic, with intravenous drug use (IDU) as the main risk factor. No reports on HIV subtypes prevalent in IDUs in Liangshan prefecture could be found. In this study, we have characterized the genotypes of HIV-1 in the IDU population in Liangshan prefecture and further determined the phylogenetic relationship of the CRF07_BC strains to HIV-1 sequences from the other regions of China, including Xinjiang and Yunnan provinces, to explore the pattern and possible diffusion pathway of HIV-1 in these regions. HIV-1-seropositive drug-naive IDUs identified in Liangshan prefecture, Sichuan province were enrolled in 2009. Full-length gag and pol genes were amplified by reverse transcription and nested PCR and then sequenced. All of the sequences were subtyped. Phylogenetic trees were constructed using the neighbor-joining and maximum likelihood methods. Divergence times were estimated using a Bayesian molecular clock approach. CRF07_BC was found to be the predominant strain in IDUs in Liangshan prefecture (95.5%). The CRF07_BC strains from Liangshan prefecture were found to be intermixed with those from Yunnan province in phylogenetic trees. The CRF07_BC sequences from Xinjiang province can be grouped into several clusters, suggesting that the expansion of the CRF07_BC epidemic in Xinjiang province was the result of a local epidemic driven by multiple independent introductions in the late 1990s. Only low-level drug-resistant viruses were found in the IDU population. CRF07_BC strains from Liangshan prefecture were more similar to those from Yunnan province than those from Xinjiang province. This finding will contribute to our understanding of the distribution, the evolution, and the potential source of CRF07_BC founder strains, and will also provide useful information for the development of strategies to prevent transmission