The ability of anterior thalamic signals to predict seizures in temporal lobe epilepsy in kainate-treated rats.

OBJECTIVE: To analyze the local field potential (LFP) of the anterior nucleus of the thalamus (ANT) of epileptic rats using the Generic Osorio-Frei algorithm (GOFA), and to determine the ability of the ANT LFP to predict clinical seizures in temporal lobe epilepsy. METHODS: GOFA is an advanced real-time technique used to detect and predict seizures. In this article, GOFA was utilized to process the electrical signals of ANT and the motor cortex recorded in 12 rat models of temporal lobe epilepsy (TLE) induced via the injection of kainic acid into the unilateral hippocampus. The electroencephalography (EEG) data included (1) 161 clinical seizures (each contained a 10-min segment) involving the ANT and cortical regions and (2) one hundred three 10-min segments of randomly selected interictal (no seizure) data. RESULTS: Minimal false-positives (0.51 ± 0.36/h) and no false-negatives were detected based on the ANT LFP data processed using GOFA. In ANT LFP, the delay from electrographic onset (EO) to automated onset (AO) was 1.24 ± 0.47 s, and the delay from AO to clinical onset (CO) was 7.73 ± 3.23 s. The AO time occurred significantly earlier in the ANT than in the cortex (p = 0.001). In 75.2% of the clinical onsets predicted by ANT LFP, it was 1.37 ± 0.82 s ahead of the prediction of cortical potentials (CPs), and the remainder were 0.84 ± 0.31 s slower than the prediction of CPs. SIGNIFICANCE: ANT LFP appears to be an optimal option for the prediction of seizures in temporal lobe epilepsy. It was possible to upgrade the responsive neurostimulation system to emit electrical stimulation in response to the prediction of epileptic seizures based on the changes in the ANT LFP.

Preoperative Heart Rate Variability During Sleep Predicts Vagus Nerve Stimulation Outcome Better in Patients With Drug-Resistant Epilepsy.

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices. Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model. Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1). Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

[Current research and development trend of implantable neuro-stimulator].

This paper introduces the current application of various kinds of implantable neuro-stimulator at home and abroad, and also analyzes its development trend.

A Unified Framework for Tracking Based Text Detection and Recognition from Web Videos.

Video text extraction plays an important role for multimedia understanding and retrieval. Most previous research efforts are conducted within individual frames. A few of recent methods, which pay attention to text tracking using multiple frames, however, do not effectively mine the relations among text detection, tracking and recognition. In this paper, we propose a generic Bayesian-based framework of Tracking based Text Detection And Recognition (T DAR) from web videos for embedded captions, which is composed of three major components, i.e., text tracking, tracking based text detection, and tracking based text recognition. In this unified framework, text tracking is first conducted by tracking-by-detection. Tracking trajectories are then revised and refined with detection or recognition results. Text detection or recognition is finally improved with multi-frame integration. Moreover, a challenging video text (embedded caption text) database (USTB-VidTEXT) is constructed and publicly available. A variety of experiments on this dataset verify that our proposed approach largely improves the performance of text detection and recognition from web videos.

Lactobacillus reuteri F-9-35 Prevents DSS-Induced Colitis by Inhibiting Proinflammatory Gene Expression and Restoring the Gut Microbiota in Mice.

Probiotics are considered to be a potential treatment for ulcerative colitis (UC). The aim of this study was to compare the preventive effect of a space flight-induced mutant L. reuteri F-9-35 and its wild type on UC in vivo. Female mice were randomly assigned to five groups: one normal and four colitic. Mice from colitis groups were daily gavaged with 0.2 mL 12% (w/v) skim milk containing the mutant or wild type (1 × 1011 CFU/mL), skim milk alone or distilled water for the whole experiment period, starting 7 days before colitis induction. UC was induced by administrating mice with 3.5% (w/v) dextran sulfate sodium (DSS) in drinking water for 7 days, after which DSS was removed and maintained for 3 days as a recovery phase. The results showed that the mice fed with L. reuteri F-9-35 had less inflammatory phenotype according to macroscopic and histological analysis, reduced myeloperoxidase activity, and lower expression of proinflammatory genes (Tumor necrosis factor-α, cyclooxygenase-2 and interleukin-6) in colonic tissue compared with control. Furthermore, L. reuteri F-9-35 protected the mice from gut microbiota dysbiosis from DDS induced colitis. Neither wild type nor the milk alone had such beneficial effects. From above we conclude that L. reuteri F-9-35 has great potential in the prevention of UC as a dietary supplement. PRACTICAL APPLICATION: Ulcerative colitis (UC) is the most common inflammatory bowel diseases and there is still a lack of safe and effective treatments. Consumption of L. reuteri F-9-35 may effective in preventing human UC.

Pretreatment with Lactobacillus reuteri F-9-35 attenuates ethanol-induced gastric injury in rats.

BACKGROUND: Previous studies suggested that probiotics intervention may be one of the methods for preventing and/or treating gastric ulcer. OBJECTIVE: The aim of the study was to compare the preventive effects of a spaceflight mutant Lactobacillus reuteri F-9-35 and its wild type on ethanol-induced gastric injury in rats. DESIGN: Forty rats were randomly allocated into five groups: a normal group (NOR), ethanol group (EtOH), skim milk group (MILK), L. reuteri F-9-35 group (F935) and wild-type group (WT). The NOR and EtOH groups received 1 ml of distilled water by daily gavage for 14 days. The MILK group received 1 ml of skim milk alone, while the F935 and WT groups were administered 1 ml of skim milk containing the mutant and wild type (1 × 1010 colony-forming unit/ml) by daily gavage for 14 days, respectively. Acute gastric injury was induced by absolute alcohol 1 h after the final administration of different treatments, except for the NOR group. RESULTS: Pretreatment with L. reuteri F-9-35, but not milk alone or milk with the L. reuteri wild type, showed significant reduction of ethanol-induced gastric injury, as evidenced by lowering of ulcer index, ulcer area (%), and histological lesion. F-9-35 decreased the levels of lipid peroxidation and myeloperoxidase and increased mucus, glutathione, and nitric oxide levels in gastric tissue. Moreover, F-9-35 inhibited the expression of proinflammatory genes including gastric tumor necrosis factor-α, interleukin-1ß, and cyclooxygenase-2 and decreased the activity of nuclear factor kappa B (NF-κB). CONCLUSION: These findings indicated that L. reuteri F-9-35 pretreatment can attenuate ethanol-induced gastric injury in rats by inhibiting oxidative stress and inflammatory response. Together, L. reuteri F-9-35 has potential preventive efficacy on gastric ulcer.

Preoperative Heart Rate Variability as Predictors of Vagus Nerve Stimulation Outcome in Patients with Drug-resistant Epilepsy.

Vagus nerve stimulation (VNS) is an adjunctive treatment for drug-resistant epilepsy (DRE). However, it is still difficult to predict which patients will respond to VNS treatment and to what extent. We aim to explore the relationship between preoperative heart rate variability (HRV) and VNS outcome. 50 healthy control subjects and 63 DRE patients who had received VNS implants and had at least one year of follow up were included. The preoperative HRV were analyzed by traditional linear methods and heart rhythm complexity analyses with multiscale entropy (MSE). DRE patients had significantly lower complexity indices (CI) as well as traditional linear HRV measurements than healthy controls. We also found that non-responders0 had significantly lower preoperative CI including Area 1-5, Area 6-15 and Area 6-20 than those in the responders0 while those of the non-responders50 had significantly lower RMSSD, pNN50, VLF, LF, HF, TP and LF/HF than the responders50. In receiver operating characteristic (ROC) curve analysis, Area 6-20 and RMSSD had the greatest discriminatory power for the responders0 and non-responders0, responders50 and non-responders50, respectively. Our results suggest that preoperative assessment of HRV by linear and MSE analysis can help in predicting VNS outcomes in patients with DRE.

ISART: A Generic Framework for Searching Books with Social Information.

Effective book search has been discussed for decades and is still future-proof in areas as diverse as computer science, informatics, e-commerce and even culture and arts. A variety of social information contents (e.g, ratings, tags and reviews) emerge with the huge number of books on the Web, but how they are utilized for searching and finding books is seldom investigated. Here we develop an Integrated Search And Recommendation Technology (IsArt), which breaks new ground by providing a generic framework for searching books with rich social information. IsArt comprises a search engine to rank books with book contents and professional metadata, a Generalized Content-based Filtering model to thereafter rerank books with user-generated social contents, and a learning-to-rank technique to finally combine a wide range of diverse reranking results. Experiments show that this technology permits embedding social information to promote book search effectiveness, and IsArt, by making use of it, has the best performance on CLEF/INEX Social Book Search Evaluation datasets of all 4 years (from 2011 to 2014), compared with some other state-of-the-art methods.

Multi-Orientation Scene Text Detection with Adaptive Clustering.

Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks, while most current research efforts only focus on horizontal or near horizontal scene text. In this paper, first we present a unified distance metric learning framework for adaptive hierarchical clustering, which can simultaneously learn similarity weights (to adaptively combine different feature similarities) and the clustering threshold (to automatically determine the number of clusters). Then, we propose an effective multi-orientation scene text detection system, which constructs text candidates by grouping characters based on this adaptive clustering. Our text candidates construction method consists of several sequential coarse-to-fine grouping steps: morphology-based grouping via single-link clustering, orientation-based grouping via divisive hierarchical clustering, and projection-based grouping also via divisive clustering. The effectiveness of our proposed system is evaluated on several public scene text databases, e.g., ICDAR Robust Reading Competition data sets (2011 and 2013), MSRA-TD500 and NEOCR. Specifically, on the multi-orientation text data set MSRA-TD500, the f measure of our system is 71 percent, much better than the state-of-the-art performance. We also construct and release a practical challenging multi-orientation scene text data set (USTB-SV1K), which is available at http://prir.ustb.edu.cn/TexStar/MOMV-text-detection/.

A VidEo-Based Intelligent Recognition and Decision System for the Phacoemulsification Cataract Surgery.

The phacoemulsification surgery is one of the most advanced surgeries to treat cataract. However, the conventional surgeries are always with low automatic level of operation and over reliance on the ability of surgeons. Alternatively, one imaginative scene is to use video processing and pattern recognition technologies to automatically detect the cataract grade and intelligently control the release of the ultrasonic energy while operating. Unlike cataract grading in the diagnosis system with static images, complicated background, unexpected noise, and varied information are always introduced in dynamic videos of the surgery. Here we develop a Video-Based Intelligent Recognitionand Decision (VeBIRD) system, which breaks new ground by providing a generic framework for automatically tracking the operation process and classifying the cataract grade in microscope videos of the phacoemulsification cataract surgery. VeBIRD comprises a robust eye (iris) detector with randomized Hough transform to precisely locate the eye in the noise background, an effective probe tracker with Tracking-Learning-Detection to thereafter track the operation probe in the dynamic process, and an intelligent decider with discriminative learning to finally recognize the cataract grade in the complicated video. Experiments with a variety of real microscope videos of phacoemulsification verify VeBIRD's effectiveness.

Learning document semantic representation with hybrid deep belief network.

High-level abstraction, for example, semantic representation, is vital for document classification and retrieval. However, how to learn document semantic representation is still a topic open for discussion in information retrieval and natural language processing. In this paper, we propose a new Hybrid Deep Belief Network (HDBN) which uses Deep Boltzmann Machine (DBM) on the lower layers together with Deep Belief Network (DBN) on the upper layers. The advantage of DBM is that it employs undirected connection when training weight parameters which can be used to sample the states of nodes on each layer more successfully and it is also an effective way to remove noise from the different document representation type; the DBN can enhance extract abstract of the document in depth, making the model learn sufficient semantic representation. At the same time, we explore different input strategies for semantic distributed representation. Experimental results show that our model using the word embedding instead of single word has better performance.

Effect of 1.7 MHz ultrasound on a gas-vacuolate cyanobacterium and a gas-vacuole negative cyanobacterium.

Ultrasonic signals propagated through medium were directly applied to unicellular cyanobacterium cell surfaces to investigate the biological effects induced by ultrasound. The gas-vacuolate cyanobacterium Microcystis aeruginosa and the gas-vacuole negative cyanobacterium Synechococcus PCC 7942 responded differently to ultrasound. When M. aeruginosa was irradiated by 1.7 MHz ultrasound at 0.6 W cm(-2) every day, it showed a decrease of nearly 65% in biomass increment, and this group's generation time increased twice as much as the control. While Synechococcus culture irradiated every day still grew as fast as the control, and its final biomass was as much as the control. The value of the electric conductivity change (Deltasigma) sharply increased in Microcystis suspension during the exposure process, which revealed more ultrasonic cavitation yield in liquid related to the gas-vacuolate cyanobacteria. The relative malondialdehyde (MDA) content, a quantitative indicator of lipid peroxidation, increased by 65% in Microcystis cells and 9% in Synechoccus cells after ultrasonic irradiation. Moreover, the membrane permeability, quantified by measuring the relative amount of electrolyte leaking out of cells, increased to more than 60% in the Microcystis cells. The results indicated that Microcystis cells were susceptible to ultrasonic stress. According to Rayleigh-Plesset's bubble activation theory, 1.7 MHz ultrasound approached the eigenfrequency of gas-vacuolate cells. The present investigation suggested the importance of the cavitational effect relative to intracellular gas-vacuoles in the loss of cell viability. In summary, 1.7 MHz ultrasonic irradiation was effective in preventing water-bloom forming cyanobacteria from growing rapidly due to changes in the functioning and integrity of cellular and subcellular structures.

Robust Text Detection in Natural Scene Images.

Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations. Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and clustering threshold are learned automatically by a novel self-training distance metric learning algorithm. The posterior probabilities of text candidates corresponding to non-text are estimated with a character classifier; text candidates with high non-text probabilities are eliminated and texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition database; the f-measure is over 76%, much better than the state-of-the-art performance of 71%. Experiments on multilingual, street view, multi-orientation and even born-digital databases also demonstrate the effectiveness of the proposed method.

Free radical-scavenging activities of oligomeric proanthocyanidin from Rhodiola rosea L. and its antioxidant effects in vivo.

This study aimed to determine the antioxidant activity of oligomeric proanthocyanidin from Rhodiola rosea L. (OPCRR). The free radical-scavenging activities exhibited by OPCRR, as determined by using 1,1-diphenyl-2-picrylhydrazyl, hydroxyl radical and superoxide anion (√[Formula: see text]) scavenging assays, were greater than that of vitamin C. The effects of OPCRR on the antioxidant enzymes activity and lipid peroxide content in vivo were evaluated through three observation biomarkers, including superoxide dismutase (SOD), glutathione peroxidase (GSH-Px) and malondialdehyde (MDA) in serum, heart, liver and brain tissues in mice. The OPCRR significantly enhanced the SOD and GSH-Px activities, and reduced the MDA content in mice. These results indicated that the OPCRR has a great potential to be a natural antioxidant due to its considerable antioxidant activities in vitro and in vivo.

Dorsal root ganglion electrical stimulation promoted intertransverse process spinal fusion without decortications and bone grafting: a proof-of-concept study.

BACKGROUND CONTEXT: Periosteum, endosteum, and bone are innervated by sensory nerves expressing calcitonin gene-related peptide (CGRP), which is a known osteoanabolic peptide and plays an important role in fracture healing and spinal fusion. Synthesis and release of CGRP are found in sensory neurons located in the dorsal root ganglions (DRGs) and can be upregulated by electrical stimulation (ES) at DRG. PURPOSE: To prove our study hypothesis on the potential of precise ES at DRG through implantable microelectrical stimulation system (IMESS) for its effect on promoting spinal fusion in a rat model without decortications and bone grafting. STUDY DESIGN: An experimental animal study. METHODS: A novel IMESS was developed for stimulating L4-L6 DRG in rats. Sixteen rats were used and divided equally into the control group without ES and the ES group, with a daily 20 minutes ES to DRG for 6 weeks. At the end of 6 weeks, radiography and microcomputed tomography were conducted to evaluate new bone formation and spinal fusion. Bilateral L4-L6 DRGs were harvested for immunohistochemistry and quantification of neurons with upregulated CGRP expression. RESULTS: In the ES group, rate of radiographic fusion with complete and uninterrupted bony bridging was 100% (8/8) at the right L4/L5 transverse processes and 75% (6/8) at the right L5/L6 transverse processes. Bony callus formation was absent at the left L4-L6 transverse processes in the ES group and in bilateral L4-L6 transverse processes in the control group. CONCLUSIONS: We proved for the first time that precise ES at DRG through IMESS effectively promoted intertransverse process fusion in rat model without decortications and bone grafting. Electrical stimulation at DRG might be an attractive minimal invasive bioengineering approach and an alternative therapy for intertransverse process fusion that is increasingly being used for the treatment of degenerative spine disorders.

[Effects of electroacupuncture of unilateral and bilateral "zusanli" (ST 36) on serum TNF-alpha, IL-1 and IL-4 levels in rats with chronic inflammatory pain].

OBJECTIVE: To observe the effect of electroacupuncture (EA) on serum tumor necrosis factor-alpha (TNF-alpha), IL-1 and IL-4 levels in rats with chronic inflammatory pain, so as to investigate its underlying mechanism in relieving pain. Methods Forty-eight SD rats were randomized into control, model, EA-unilateral ST 36, and EA-bilateral ST 36 groups, with 12 rats in each. Chronic inflammatory pain model was established by intradermal injection of Freund's complete adjuvant (0.1 mL) into the right paw. EA (2 Hz/100 Hz, 1-2-3 mA) was applied to the unilateral "Zusanli" (ST 36) and bilateral "Zusanli" (ST 36) for 30 min, once every 3 days and continuously for 4 weeks. Serum TNF-alpha, IL-1 and IL-4 contents were detected by enzyme linked immunosorbent assay. Results Compared with the control group, serum TNF-alpha, IL-1 and IL-4 contents of the model group increased significantly (P < 0.01), while in comparison with the model group, the levels of the 3 indexes were decreased considerably in both unilateral and bilateral ST 36 groups (P < 0.01). No significant differences were found between the unilateral ST 36 and bilateral ST 36 groups in serum TNF-alpha, IL-1 and IL-4 contents (P > 0.05). CONCLUSION: EA can effectively lower serum TNF-alpha, IL-1 and IL-4 levels in inflammatory pain rats, which may contribute to its effect in relieving inflammatory pain.