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Yifang Li, Yanzhen Chen, Xing Tian, Shanshan Zhang, Jian Jiao Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, People’s Republic of ChinaCorrespondence: Jian JiaoDepartment of Gastroenterology & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, People’s Republic of ChinaTel +86 13756009567Fax +86 0431-84995850Email jjian@jlu.edu.cnObjective: Non-alcoholic fatty liver disease (NAFLD), previously thought to predominantly affect obese individuals, has also been shown to occur in subjects who have a relatively normal body mass index (BMI). Due to the normal BMI, non-obese NAFLD are easily to be ignored and eventually lead to potential liver injuries.Methods: A population-based cross-sectional study was conducted on 1608 cases with normal serum alanine aminotransferase (ALT) levels who were divided into an obese group (BMI ≥ 25 kg/m2) and a non-obese group (BMI < 25 kg/m2). NAFLD was diagnosed by ultrasound and Fibro Scan examination. Non-obese populations were divided into NAFLD group (CAP ≥ 240 db/m) and non-NAFLD group (CAP < 240 db/m). The incidence of NAFLD in the obese and non-obese populations and constituent ratios of genders, age, and serum levels of triglycerides (TG), cholesterol (CHOL), and blood glucose were compared. Risk factors of NAFLD in non-obese people were analyzed by multivariate logistics regression.Results: The occurrence of NAFLD was higher in the obese group than in the non-obese group, regardless of gender (P < 0.001). In the non-obese group, the occurrence of NAFLD in female patients was lower than that in male (P=0.001). The occurrence of NAFLD increased with age, with 50– 59 years being the peak age of incidence in both male and female. The peak age of NAFLD occurrence in non-obese male patients was more delayed than that in obese male patients. BMI (OR=1.311, P=0.000) and TG (OR=2.545, P=0.000) were risk factors for NAFLD in the non-obese population.Conclusion: Compared with obese population, the incidence of NAFLD in non-obese population was relatively low and more frequently in male than in female, the peak age of NAFLD occurrence in non-obese male patients was also delayed. BMI and TG should still be controlled to avoid the occurrence of NAFLD although the BMI of such patients is normal.Keywords: obese, non-obese, non-alcoholic liver disease, gender, characteristic

Cognitive impairments are core aspects of schizophrenia and are highly related to poor outcomes. However, the effect of therapy on cognitive impairments remains unsatisfactory as its biological mechanisms are not fully understood. The purpose of this study was to investigate the disrupted intrinsic neural activity of the frontal areas and to further examine the functional connectivity of frontal areas related to cognitive impairments in schizophrenia. We collected brain imaging data using a 3T Siemens Prisma MRI system in 32 patients with schizophrenia and 34 age- and sex-matched healthy controls. The mean fractional amplitude of low-frequency fluctuation (mfALFF) in the frontal regions was calculated and analyzed to evaluate regional neural activity alterations in schizophrenia. Seed regions were generated from clusters showing significant changes in mfALFF in schizophrenia, and its resting-state functional connectivity (rs-FC) with other brain regions were estimated to detect possible aberrant rs-FC indicating cognitive impairments in schizophrenia. We found that mfALFF in the bilateral frontal cortices was increased in schizophrenia. mfALFF-based rs-FC revealed that decreased rs-FC between left middle frontal gyrus (MFG) and left medial superior frontal gyrus (MFSG) was associated with poor delayed memory (r = 0.566, Bonferroni-corrected p = 0.012). These findings demonstrate increased neural activity in the frontal cortices in schizophrenia. FC analysis revealed a diminished rs-FC pattern between the left MFG and left MSFG that was associated with cognitive impairments. These findings have provided deeper insight into the alterations in brain function related to specific domains of cognitive impairment and may provide evidence for precise interventions for cognitive deficits in schizophrenia.

Image hashing based on deep convolutional neural networks (CNN), deep hashing, has acquired breakthrough in image retrieval. Although deep features from various CNN layers have various levels of information, most of the existing deep hashing methods extract the feature vector only from the output of the penultimate fully-connected layer, focusing primarily on semantic information whilst ignoring detailed structure information. This calls for research on multi-level hashing, utilizing multi-level features to exploit different levels of CNN characteristics. To fill this gap, a novel image hashing method, Multi-Level Supervised Hashing with deep feature (MLSH), is proposed in this paper to further exploit multiple levels of deep image features. It uses a multiple-hash-table mechanism to integrate multi-level features extracted from an individual deep convolutional neural network. It takes advantage of the complementarity among multi-level features from various layers of a single deep network. High-level features reveal the semantic content of the image, while low-level features provide the structural information that is missing in high-level features. Instead of simple concatenation, several hash tables are trained individually using different levels of features from different layers, which are then integrated for efficient image retrieval. The method has been systematically evaluated through experiments on three image databases, including CIFAR-10, MNIST and NUSWIDE, and has thus been demonstrated to set a new state of the art in image hashing, outperforming several state-of-the-art hashing methods. Furthermore, the recall and precision can be balanced and improved simultaneously.

With the rapid growth of multimedia data on the Internet, content-based image retrieval becomes a key technique for the Internet development. Hashing methods are efficient and effective for image retrieval. Dual Complementary Hashing (DCH) is one such method, which uses multiple hash tables and has good performance. However, DCH utilizes wrongly hashed image pairs to train the following hash table and discards correctly hashed image pairs. Therefore, the number of image pairs utilized for training the following hash tables will decrease rapidly. Moreover, each hash function in a hash table of DCH is trained by correcting the errors caused by its preceding one instead of holistically considering errors made by all previous hash functions. These restrictions significantly reduce the training efficiency and the overall performance of DCH. In this paper, we propose a new hashing method for image retrieval, Bootstrap Dual Complementary Hashing with semi-supervised Re-ranking (BDCHR). It is a semi-supervised multi-hashing method consisting of two parts: bootstrap DCH and semi-supervised re-ranking. The first part relieves the restrictions of DCH while the second part further enhances the image retrieval performance. Experimental results show that BDCHR yields better performance than other state-of-the-art multi-hashing methods.

Deep hashing has proven to be efficient and effective for large-scale image retrieval due to the strong representation capability of deep networks. Existing deep hashing methods only utilize a single deep hash table. In order to achieve both higher retrieval recall and precision, longer hash codes can be used but at the expense of higher space usage. To address this issue, a novel deep hashing method is proposed in this paper, weighted multi-deep ranking supervised hashing (WMDRH), which employs multiple weighted deep hash tables to improve precision/recall without increasing space usage. The hash table is constructed as an additional layer in a deep network. Hash codes are generated by minimizing the loss function that contains two terms: (1) the ranking pairwise loss and (2) the classification loss. The ranking pairwise loss ensures to generate discriminative hash codes by penalizing more for the (dis)similar image pairs with (small)large Hamming distances. The classification loss guarantees the hash codes to be effective for category prediction. Different hash bits in each individual hash table are treated differently by assigning corresponding weights based on information preservation and bit diversity. Moreover, multiple hash tables are integrated by assigning the appropriate weight to each table according to its mean average precision (MAP) score for image retrieval. Experiments on three widely-used image databases show the proposed method outperforms state-of-the-art hashing methods.

Background Anorectal melanoma (AM) is an extremely rare malignant tumor originating from anorectal melanocytes with a poor prognosis. AM has been reported to have a much lower incidence than cutaneous or choroid melanoma, accounting for 0.4%-1.6% of all melanomas. Case summary We report a 76-year-old female patient diagnosed with anorectal malignant melanoma by colonoscopy and biopsy. Intraoperative examination revealed two distinct anorectal tumors, one melanotic and another amelanotic, as well as two pigmented mucosal zones at the dentate line level. Abdominal perineal resection was performed. A pathological report confirmed all four lesions to be melanomas. Postoperatively, we followed an immunotherapy protocol targeting PD-1 (nivolumab). The patient had 24 mo of disease-free follow-up upon completion of nivolumab treatment. Conclusion This is the first reported case presenting coexistence of pigmented and unpigmented AMs in the same patient.

Current hashing-based image retrieval methods mostly assume that the database of images is static. However, this assumption is not true in cases where the databases are constantly updated (e.g., on the Internet) and there exists the problem of concept drift. The online (also known as incremental) hashing methods have been proposed recently for image retrieval where the database is not static. However, they have not considered the concept drift problem. Moreover, they update hash functions dynamically by generating new hash codes for all accumulated data over time which is clearly uneconomical. In order to solve these two problems, concept preserving hashing (CPH) is proposed. In contrast to the existing methods, CPH preserves the original concept, that is, the set of hash codes representing a concept is preserved over time, by learning a new set of hash functions to yield the same set of hash codes for images (old and new) of a concept. The objective function of CPH learning consists of three components: 1) isomorphic similarity; 2) hash codes partition balancing; and 3) heterogeneous similarity fitness. The experimental results on 11 concept drift scenarios show that CPH yields better retrieval precisions than the existing methods and does not need to update hash codes of previously stored images.

Clinical trials have reported that a four-oil intravenous lipid emulsion (SMOFlipid) play a positive role in immune function, but showed inconsistent outcomes compared to other lipid emulsions. A systematic review and meta-analysis was conducted to evaluate the effect of SMOFlipid on liver function, triglycerides (TG), inflammatory markers, and clinical outcomes in hospitalized adults after short-term use compared to others. A search of the PubMed, Medline, Embase, China National Knowledge Infrastructure, and Wanfang databases was performed to identify the included randomized controlled trials. Trials with adults who were administrated a short-term course of SMOFlipid were included. A meta-analysis on liver function markers, TG, inflammatory markers, and clinical outcomes was conducted. A total of 18 randomized controlled trials with 1188 patients were included. Compared to other lipid emulsions, SMOFlipid was associated with a significant reduction in ALT, AST, γ-glutamyltransferase, total bilirubin, TG, C-reactive protein and length of hospital stay. No effect on serum interleukin-6 levels or adverse events were observed. For adult patients, our meta-analysis indicated that SMOFlipid may be beneficial to the liver and prone to prevent hyperlipidemia. The SMOFlipid also shortened length of hospital stay.

Coinfection of hepatitis B virus (HBV) and hepatitis C virus (HCV) may result in severe liver disease and frequent progression to cirrhosis and hepatocellular carcinoma. Clinical evidence suggests that HBV replication is suppressed by replicating HCV and often rebounds after treatment with drugs against HCV. Thus, a highly efficient cell culture system permissive for HBV/HCV would facilitate investigation on the interaction and pathogenesis after coinfection. Here we reported a robust HBV/HCV coinfection cell culture model by overexpressing human sodium-taurocholate cotransporting polypeptide (NTCP), CD81 and Mir122 into HepG2 cells and investigated interactions between HBV and HCV. In this system, HepG2-NTCP/CD81/Mir122 cells not only supported robust infection and replication of HBV and HCV, but also allowed HBV/HCV coinfection in the single cell level. Our result showed cells with replicating HBV still supported HCV infection. However, HBV replication was suppressed by HCV through the inhibition of HBV core promoter and S promoter II activity, and this inhibition was attenuated by the interferon alpha (IFNα) treatment, suggesting HCV influence on HBV at transcriptional level. Coinfection of HBV/HCV in this system did not block IFN stimulated genes expression. Inhibition of HCV by direct-acting antiviral drugs restored HBV replication and expression of viral genes. Conclusions: HepG2-NTCP/CD81/Mir122 fully supports HBV/HCV coinfection, replication and interaction. This novel cell model offers a platform to advance our understanding of the molecular details of the interaction, pathogenesis and outcomes of HBV/HCV coinfection.

Undersampling is a popular method to solve imbalanced classification problems. However, sometimes it may remove too many majority samples which may lead to loss of informative samples. In this article, the hashing-based undersampling ensemble (HUE) is proposed to deal with this problem by constructing diversified training subspaces for undersampling. Samples in the majority class are divided into many subspaces by a hashing method. Each subspace corresponds to a training subset which consists of most of the samples from this subspace and a few samples from surrounding subspaces. These training subsets are used to train an ensemble of classification and regression tree classifiers with all minority class samples. The proposed method is tested on 25 UCI datasets against state-of-the-art methods. Experimental results show that the HUE outperforms other methods and yields good results on highly imbalanced datasets.

Stocking density is one of the major factors affecting production and welfare of fish in aquaculture. This study investigated the effect of different stocking densities on growth, feed efficiency, blood biochemistry, histometric response, muscular composition, and flesh quality of channel catfish Ictalurus punctatus. Fish were cultured at low, medium, and high stocking density of 50, 150, and 300 fish m−3, respectively, for 60 days in the recirculating aquaculture system. Fish growth and feed efficiency significantly decreased with increasing stocking density. Serum concentrations of glucose, triglyceride, and total cholesterol, as well as the activities of both aspartate aminotransferase and alanine aminotransferase, significantly elevated as stocking density increased. High stocking density led to the decline in serum levels of thyroid hormones. The villi length and its number, as well as the size of goblet cells in the intestine, were negatively affected by high stocking density. There was a significant density-dependent decrease in muscular fat content, diameter of dorsal muscle fiber, intensity of muscular bundles, and percentage of muscular bundles area in dorsal muscle. However, the percentage of the intramuscular connective tissue showed a significant increase in fish reared at high stocking density. Fish stocked in high density exhibited the decrease in muscle pH, drip loss, and frozen leakage rate. Overall, the increased stocking density could cause the unfavorable impacts on growth, feed efficiency, histological change, and flesh quality of I. punctatus.

// Yantao Bao 1, * , Jia Liu 1, * , Jia You 1 , Di Wu 1 , Yang Yu 1, 2 , Chang Liu 1 , Lei Wang 1, 3 , Fei Wang 1 , Lu Xu 1 , Liqun Wang 1 , Nan Wang 1 , Xing Tian 1 , Falin Wang 1 , Hongbin Liang 1 , Yating Gao 1 , Xiaobo Cui 1 , Guohua Ji 1 , Jing Bai 1 , Jingcui Yu 4 , Xiangning Meng 1 , Yan Jin 1 , Wenjing Sun 1 , Xin-yuan Guan 5, 6 , Chunyu Zhang 1 , Songbin Fu 1, 7 1 Laboratory of Medical Genetics, Harbin Medical University, Harbin, China 2 Department of Genetics and Eugenics, Maternity and Child Care Center of Qinghuangdao, Qinghuangdao, China 3 Genetic Diagnosis Center, First People's Hospital of Yunnan Province, Yunnan, China 4 Scientific Research Centre, Second Affiliated Hospital, Harbin Medical University, Harbin, China 5 Department of Clinical Oncology, Faculty of Medicine, The University of Hong Kong, Hong Kong, China 6 State Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China 7 Key Laboratory of Medical Genetics, Harbin Medical University, Heilongjiang Higher Education Institutions, Harbin, China * These authors contributed equally to this work Correspondence to: Songbin Fu, email: fusb@ems.hrbmu.edu.cn Chunyu Zhang, email: zhangcy@ems.hrbmu.edu.cn Keywords: Sei-1, in vivo passage, DMs, amplification, met Received: March 30, 2016 Accepted: July 19, 2016 Published: August 01, 2016 ABSTRACT Background: Sei-1 is an oncogene capable of inducing double minute chromosomes (DMs) formation. DMs are hallmarks of amplification and contribute to oncogenesis. However, the mechanism of Sei-1 inducing DMs formation remains unelucidated. Results: DMs formation significantly increased during serial passage in vivo and gradually decreased following culture in vitro . micro nuclei (MN) was found to be responsible for the reduction. Of the DMs-carrying genes, Met was found to be markedly amplified, overexpressed and highly correlated with DMs formation. Inhibition of Met signaling decreased the number of DMs and reduced the amplification of the DMs-carrying genes. We identified a 3.57Mb DMs representing the majority population, which consists of the 1.21 Mb AMP1 from locus 6qA2 and the 2.36 Mb AMP2 from locus 6qA2-3. Materials and Methods: We employed NIH-3T3 cell line with Sei-1 overexpression to monitor and characterize DMs in vivo and in vitro . Array comparative genome hybridization (aCGH) and fluorescence in situ hybridization (FISH) were performed to reveal amplification regions and DMs-carrying genes. Metaphase spread was prepared to count the DMs. Western blot and Met inhibition rescue experiments were performed to examine for involvement of altered Met signaling in Sei-1 induced DMs. Genomic walking and PCR were adopted to reveal DMs structure. Conclusions: Met is an important promotor of DMs formation.

Hashing-based methods have been widely applied in large scale image retrieval problem due to its high efficiency. In real world applications, it is difficult to require all images in a large database being labeled while unsupervised methods waste information from labeled images. Therefore, semi-supervised hashing methods are proposed to use partially labeled database to train hash functions using both the semantic and the unsupervised information. Multi-hashing methods achieve better precision-recall in comparison to single hashing method. However, current boosting-based multi-hashing methods do not improve performance after a small number of hash tables are created. Therefore, a bagging–boosting-based semi-supervised multi-hashing with query-adaptive re-ranking (BBSHR) is proposed in this paper. In the proposed method, an individual hash table of multi-hashing is trained using the boosting-based BSPLH, such that each hash bit corrects errors made by previous bits. Moreover, we propose a new semi-supervised weighting scheme for the query-adaptive re-ranking. Experimental results show that the proposed method yields better precision and recall rates for given numbers of hash tables and bits.

Parsing continuous acoustic streams into perceptual units is fundamental to auditory perception. Previous studies have uncovered a cortical entrainment mechanism in the delta and theta bands (~1-8 Hz) that correlates with formation of perceptual units in speech, music, and other quasi-rhythmic stimuli. Whether cortical oscillations in the delta-theta bands are passively entrained by regular acoustic patterns or play an active role in parsing the acoustic stream is debated. Here we investigate cortical oscillations using novel stimuli with 1/f modulation spectra. These 1/f signals have no rhythmic structure but contain information over many timescales because of their broadband modulation characteristics. We chose 1/f modulation spectra with varying exponents of f, which simulate the dynamics of environmental noise, speech, vocalizations, and music. While undergoing magnetoencephalography (MEG) recording, participants listened to 1/f stimuli and detected embedded target tones. Tone detection performance varied across stimuli of different exponents and can be explained by local signal to noise ratio computed using a temporal window around 200 ms. Furthermore, theta band oscillations, surprisingly, were observed for all stimuli, but robust phase coherence was preferentially displayed by stimuli with exponents 1 and 1.5. We constructed an auditory processing model to quantify acoustic information on various timescales and correlated the model outputs with the neural results. We show that cortical oscillations reflect a chunking of segments, >200 ms. These results suggest an active auditory segmentation mechanism, complementary to entrainment, operating on a timescale of ~200 ms to organize acoustic information. This article is protected by copyright. All rights reserved.

Complex mechanical and electrical equipment contains a large amount of data with the implicit information. According to the development of PHM (Prognostics and Health Management) technology at home and abroad, and the wide application prospects of data driving methods, the overall framework of data driven PHM system for complex electromechanical equipment was designed. The data driven PHM implementation process of the complex mechanical and electrical equipment was described step by step, which provides important theoretical significance and application value for the PHM research of the complex mechanical and electrical equipment. Finally, the development trend and research challenges of data driven PHM method were analyzed.

In this paper, a kind of data driven PHM system architecture based on data driven is came up; then a field data acquisition scheme which is very important in PHM system is presented; with a case, the block diagram of the scheme realization, which includes OPC server software configurations, and client software development process is shown. In this case, the data acquisition software has been developed by using the C #language and the local database and the data center database has been created to save data. The data acquisition software can read data from the PLC and save data to the database, which provides a stable and accurate data source for PHM system and has good stability and compatibility.

Semantic representation has been studied independently in neuroscience and computer science. A deep understanding of human neural computations and the revolution to strong artificial intelligence appeal for a joint force in the language domain. We investigated comparable representational formats of lexical semantics between these two complex systems with fine temporal resolution neural recordings. We found semantic representations generated from computational models significantly correlated with EEG responses at an early stage of a typical semantic processing time window in a two-word semantic priming paradigm. Moreover, three representative computational models differentially predicted EEG responses along the dynamics of word processing. Our study provided a finer-grained understanding of the neural dynamics underlying semantic processing and developed an objective biomarker for assessing human-like computation in computational models. Our novel framework trailblazed a promising way to bridge across disciplines in the investigation of higher-order cognitive functions in human and artificial intelligence.

The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems. However, the PCVM is currently only applicable to binary cases. Extending the PCVM to multiclass cases via heuristic voting strategies such as one-vs-rest or one-vs-one often results in a dilemma where classifiers make contradictory predictions, and those strategies might lose the benefits of probabilistic outputs. To overcome this problem, we extend the PCVM and propose a multiclass PCVM (mPCVM). Two learning algorithms, i.e., one top-down algorithm and one bottom-up algorithm, have been implemented in the mPCVM. The top-down algorithm obtains the maximum a posteriori (MAP) point estimates of the parameters based on an expectation–maximization algorithm, and the bottom-up algorithm is an incremental paradigm by maximizing the marginal likelihood. The superior performance of the mPCVMs, especially when the investigated problem has a large number of classes, is extensively evaluated on the synthetic and benchmark data sets.

The fine temporal resolution of electroencephalography (EEG) makes it one of the most widely used non-invasive electrophysiological recording methods in cognitive neuroscience research. One of the common ways to explore the neural dynamics is to create event-related potentials (ERPs) by averaging trials, followed by the examination of the response magnitude at peak latencies. However, a complete profile of neural dynamics, including temporal indices of onset time, offset time, duration, and processing speed, is needed to investigate cognitive neural mechanisms. Based on the multivariate topographic analysis, we developed an analytical framework that included two methods to explore neural dynamics in ERPs. The first method separates continuous ERP waveforms into distinct components based on their topographic patterns. Crucial temporal indices such as the peak latency, onset and offset times can be automatically identified and indices about processing speed such as duration, rise, and fall speed can be derived. The second method scrutinizes the temporal dynamics of identified components by reducing the temporal variance among trials. The response peaks of signal trials are identified based on a target topographic template, and temporal-variance-free ERPs are obtained after aligning individual trials. This method quantifies the temporal variance as a new measure of cognitive noise, as well as increases both the accuracy of temporal dynamics estimation and the signal-to-noise ratio (SNR) of the ERP responses. The validity and reliability of these methods were tested with simulation as well as empirical datasets from an attention study and a semantic priming (N400) study. Together, we offer an analytical framework in a data-driven, bias-free manner to investigate neural dynamics in non-invasive scalp recordings. These methods are implemented in the Python-based open-source package TTT (Topography-based Temporal-analysis Toolbox).

In this work, a generalized and accurate calculation model of the multi-pass cell is proposed. This novel calculation model without any errors can describe the ray’s propagation、reflection and refraction in the multi-pass cell, which will be suitable for kinds of multi-pass cells, such as the plane mirror multi-pass cell, ring multi-pass cell and the traditional multi-pass cell consisted by different optical elements including spherical mirror, lens or cylindrical mirror. By the numerical calculation, it is found that the non-paraxial ray can create a set of intricate spot patterns with respect to a standard Herriot circle spot pattern from the traditional ABCD matrix. The detailed numerical calculations of the different multi-pass cells are carried out, thus the improvement of the utilization efficiency of the mirror surface and production of a longer total optical path length by non-paraxial ray in a compact volume can be verified.

A novel augmented 4*4 matrix model for the slightly misaligned optical multi-pass cell whose spherical mirrors are not coaxial due to the presence of small misaligned angle and linear displacement of the spherical mirrors is reported. The augmented 4*4 matrix model is proposed to describe the behavior of light rays in the misaligned optical multi-pass cell. By augmented 4*4 matrix model, a series of numerical calculation are performed to validate that the incident light ray remains unchanged, and then retrace the same spot pattern. It is found that the set of spot patterns from numerical analysis displays a displacement on the surface of the mirror and the misaligned optical elements increase the loss and reduce pass counts. More importantly, the augmented 4*4 matrix model can be used to analyze the misalignment sensitivity of the optical multi-pass cells.

Objective Neurodegenerative diseases, such as ischemia, traumatic injury, Alzheimer's disease, and Parkinson's disease are characterized by neuronal loss and dysfunction. It is known that glutamate-induced toxicity plays an important role in neurodegenerative diseases. Glutamate toxicity seems to be mediated by excessive influx of Ca2+ into neuronal cells through activation of N-methyl-D-aspartate (NMDA) receptor. To search for potential NMDA receptor inhibitors in traditional Chinese medicine. Methods A series of computer methods including drug-likeness evaluation, ADMET tests as well as molecular docking have been used. Results 1,5-O-dicaffeoyl-quinic acid was identified as NMDA receptor inhibitor by virtual screening. Its neuroprotective activity was further confirmed by in vitro test. 1,5-O-dicaffeoyl-quinic acid showed strong neuroprotection against NMDA-induced cell injury. Conclusion 1,5-O-Dicaffeoylquinic acid may be regarded as a potential NMDA receptor inhibitor for the prevention and treatment of neurodegenerative disorders.

To flexibly convey meaning, the human language faculty iteratively combines smaller units such as words into larger structures such as phrases based on grammatical principles. During comprehension, however, it remains unclear how the brain encodes the relationship between words and combines them into phrases. One hypothesis is that internal grammatical principles governing language generation are also used to parse the hierarchical syntactic structure of spoken language during comprehension. An alternative hypothesis suggests, in contrast, that decoding language during comprehension solely relies on statistical relationships between words or strings of words, i.e., the N-gram statistics, while grammatical rules are not used and no hierarchical linguistic structures are constructed. Here, we briefly review distinctions between rule-based hierarchical models and statistics-based linear string models for comprehension, and how the neurolinguistic approach can shed light on this debate. Recent neurolinguistic studies show that tracking of probabilistic relationships between words is not sufficient to explain cortical encoding of linguistic constituent structure and support the involvement of rule-based processing during language comprehension.

A ring multi-pass optical cell which consists of three spherical mirrors to form a ring cell has been proposed to achieve a long path length in compact volumes. As with the astigmatic multi-pass optical cell, the dense beam spot pattern from the ring multi-pass optical cell more evenly fills the interior surface of the spherical mirror. The disadvantage of the astigmatic cell is that it requires extremely high-precision mirrors to ensure that the pattern will close. However, with the ring multi-pass optical cell, adjusting the inclination angle of spherical mirrors can make up for errors of the mirrors, and the self-consistent condition always can be established. The desirable patterns are based on the given base length, the inclination angle of spherical mirrors, the coupling window from the optical axis and the incident angle of the beam. In consideration of the astigmatism of the off-axis mirror, the matrix optics is employed in describing and analyzing the behaviors of the ring multi-pass optical cell. The new ring multi-pass cell presented in the paper achieves a 120 m optical path length in a volume of 717 cm3. The optical simulation based on the ABCD matrix and optical software shows the validity and the usefulness of the techniques proposed.

IntroductionNonalcoholic fatty liver disease (NAFLD) is seen commonly in patients with obesity, but there are many non-obese people who also have NAFLD, which we call lean NAFLD. The objective of this study is to compare the characters of lean and obese NAFLD individuals aimed to better understand the pathogenesis of NAFLD.Material and methodsA total of 496 NAFLD cases (history of alcohol consumption, medication used within the last 3 months, hepatitis virus infection, autoimmune or hereditary liver disease were excluded), divided into an obese (body mass index (BMI) > 25 kg/m2) NAFLD group (n = 395) and lean (BMI < 25 kg/m2) NAFLD group (n = 101), were investigated. Gender, age, alanine aminotransferase, triglycerides, cholesterol, and the blood glucose were compared between the two groups.ResultsA male-dominated discrepancy of gender distribution was more evident in obese NAFLD group. Elevated alanine amonotransferase was more common in the obese NAFLD group. 50–59 years was the peak age of both lean and obese NAFLD groups. Normal triglycerides (TG) patients were more common in the lean NAFLD group, while the proportion of elevated TG patients was higher in the obese NAFLD group. No statistically significant difference in the proportion of elevated cholesterol patients was found between the two groups. In the lean NAFLD group, the proportion of females was higher in the normal fasting glucose group.ConclusionsSexual dimorphism exists in lean NAFLD patients, but this trend was most pronounced during the age range 40–49 years and disappeared after entering the 50–59-year-old age range. In lean NAFLD patients, normal levels of TG and blood glucose were more common, and occurrence of non-alcoholic steatohepatitis was less common than among obese NAFLD.

A sensitive dual-gas sensor for the detection of CH4 and C2H6 is demonstrated. Two tunable semiconductor lasers operating at 1.653 &mu, m (for CH4 monitoring) and 1.684 &mu, m (for C2H6) were used as the light source for spectroscopic measurements of CH4 and C2H6. Long-path absorption in a home-made compact dense-pattern multipass cell (Leff = 29.37 m) was employed, combined with wavelength modulation and second harmonic detection. Simultaneous detection of CH4 and C2H6 was achieved by separated wavelength modulations of the two lasers. Modulation frequencies and amplitudes were optimized for sensitivity detection of CH4 and C2H6 simultaneously. The dual-gas sensor exhibits 1&sigma, detection limits of 1.5 ppbv for CH4 in 140 s averaging time and 100 ppbv for C2H6 in 200 s.

Chunking in language comprehension is a process that segments continuous linguistic input into smaller chunks that are in reader’s mental lexicon. Effective chunking during reading facilitates disambiguation and enhances efficiency for comprehension. However, the mechanisms of chunking remain elusive, especially in reading given that information arrives simultaneously yet the written systems may not have explicit cues for labeling boundaries such as Chinese. What are the mechanisms of chunking operation that mediates the reading of the text that normally contains hierarchical information? We investigated this question by manipulating the lexical status of the chunks at distinct levels of grain-size in four-character Chinese strings, including the two-character local chunk and four-character global chunk. Participants were asked to make lexical decision on these strings in a behavioral experiment, followed by a passive reading task when their electroencephalography (EEG) were recorded. The behavioral results showed that the lexical decision time of lexicalized two-character local chunks was influenced by the lexical status of four-character global chunk, but not vice versa, which indicated that the processing of global chunks possessed priority over the local chunks. The EEG results revealed that familiar lexical chunks were detected simultaneously at both levels and further processed in a different temporal order -- the onset of lexical access for the global chunks was earlier than that of local chunks. These consistent behavioral and EEG results suggest that chunking in reading occurs at multiple levels via a two-stage operation -- simultaneous detection and global-first recognition.Significance StatementThe learners of a new language often read word by word. But why can proficient readers read multiple words at a time? The current study investigates how we efficiently segment a complicate text into smaller pieces and how we process these pieces. Participants read Chinese strings with different structures while their key-press responses and brain EEG signals were recorded. We found that texts were quickly (about 100 ms from their occurrences) segmented to varied sizes of pieces, and larger pieces were then processed earlier than small pieces. Our results suggest that readers can use existing knowledge to efficiently segment and process written information.

Product color plays a vital role in shaping brand style and affecting users’ purchase decision. However, users’ preferences about product color design schemes may vary due to their cognition differences. Although considering users’ perception of product color has been widely performed by industrial designers, it is not effective to support this activity. In order to provide users with plentiful product color solutions as well as embody users’ preference into product design process, involving users in interactive genetic algorithms (IGAs) is an effectual way to find optimum solutions. Nevertheless, cognition difference and uncertainty among users may lead to various understanding in line with IGA progressing. To address this issue, this study presents an advanced IGA by combining users’ cognition noise which includes cognition phase, intermediate phase, and fatigue phase. Trapezoidal fuzzy numbers are employed to represent uncertainty of users’ evaluations. An algorithm is designed to find key parameters through similarity calculation between RGB value and their area proportion of two individuals and users’ judgment. The interactive product color design process is put forward with an instance by comparing with an ordinary IGA. Results show that (1) knowledge background will significantly affect users’ cognition about product colors and (2) the proposed method is helpful to improve convergence speed and evolution efficiency with convergence increasing from 67.5% to 82.5% and overall average evolutionary generations decreasing from 18.15 to 15.825. It is promising that the proposed method can help reduce users’ cognition noise, promote convergence, and improve evolution efficiency of interactive product color design.

Background The relationship between phospholipase C ε‐1 (PLCE1) rs2274223 variant and digestive tract cancer remains inconclusive despite extensive investigations. Therefore, we performed this meta‐analysis to obtain a more credible conclusion. Methods PubMed, Medline, and Embase were systematic searched. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Results A total of 27 studies were finally included. Pooled analyses suggested that PLCE1 rs2274223 variant was significantly correlated with the likelihood of esophageal cancer (dominant model: p

Auditory hallucinations are the most common psychiatric symptoms of schizophrenia with high recurrence and refractoriness. Transcranial direct current stimulation (tDCS), a novel, non-invasion and affordable brain stimulation technique, has been recently applying on the schizophrenia patients to treat the auditory hallucinations. To analyze the efficacy of tDCS treatment on such symptoms and to reveal its potential working mechanisms, we carried out a structured literature search in PubMed, Embase and Cochrane Library database up to May 12, 2018. Five studies that met inclusion criteria with a total of 137 patients were included in this meta-analysis. After pooling all the data, we found that there was no significant effect between active group and sham group of tDCS (p = 0.18). When we removed one study that did not collaboratively stimulate the frontal-temporal sites, the active tDCS group marks a significant improvement of therapeutic effect compared with sham group (p = 0.007). Our findings suggested that tDCS could be a promising tool to alleviate auditory hallucinations, provided that the simulation sites and protocols are targeting at the sensorimotor frontal-parietal network.

With the acceleration of the opening process of the distribution side, the power supply modes of various types of industrial parks have undergone great changes. Therefore, it is significant that we study the evolution mechanism of the power supply mode which plays an important role in forecasting the electricity distribution patterns of park and formulating strategies of the relevant principals' market participation. This paper firstly proposes a method of the research on the evolution of power supply mode of park that based on Apriori algorithm. Then through mining the association rules of the typical features of the regional power supply and the participating ways of the main users, we have analyzed the evolution mechanism of the power supply mode. At last, the effectiveness of the method is verified by the actual data of a certain province in Northwest China.

Electroencephalography (EEG) provides high temporal resolution cognitive information from non-invasive recordings. However, one of the common practices-using a subset of sensors in ERP analysis is hard to provide a holistic and precise dynamic results. Selecting or grouping subsets of sensors may also be subject to selection bias, multiple comparison, and further complicated by individual differences in the group-level analysis. More importantly, changes in neural generators and variations in response magnitude from the same neural sources are difficult to separate, which limit the capacity of testing different aspects of cognitive hypotheses. We introduce EasyEEG, a toolbox that includes several multivariate analysis methods to directly test cognitive hypotheses based on topographic responses that include data from all sensors. These multivariate methods can investigate effects in the dimensions of response magnitude and topographic patterns separately using data in the sensor space, therefore enable assessing neural response dynamics. The concise workflow and the modular design provide user-friendly and programmer-friendly features. Users of all levels can benefit from the open-sourced, free EasyEEG to obtain a straightforward solution for efficient processing of EEG data and a complete pipeline from raw data to final results for publication.

Background/Aims: Traditional cardiovascular (CV) risk factors (RFs) and their management targets may not be applicable to specific medical subpopulations, particularly dialysis patients. This study aimed to evaluate the dose-response association between measurements of RFs, cardiovascular mortality, and potential metabolic targets among Chinese patients initializing peritoneal dialysis (PD). Methods: Risk-set sampling was applied to two population based 1: 10 case-control studies of incident PD patients, matched by age, sex and the year of initialisation of PD: a main sample (204 cases and 2,040 controls) and a replication sample (81 cases and 810 controls). The dose-response association between continuous measurements of CV RFs (blood pressure, fasting glucose, body mass index, total cholesterol, phosphate and ejection fraction) at baseline and the 2-year CV mortality were analyzed using conditional Logistic regression. The final threshold was chosen based upon a significant break in the regression coefficients and achievement of the minimum Bayesian information criterion (BIC). Results: A linear relationship was identified between fasting glucose and CV mortality. Non-linear associations between other measurements and CV mortality suggested potential metabolic treatment intensification thresholds as < 145/92mmHg for blood pressure, < 1.70mmol/L for phosphate, 24 kg/m2 for body mass index, 4.6mmol/L for total cholesterol, and > 60% for ejection fraction respectively. Conclusion: Our findings highlight the potential importance of more intensive glucose management, anti-hypertensive treatment and dietary management among PD patients. We recommend that the clinical relevance of these epidemiological associations be tested using randomized controlled trials of multifaceted interventions.

Background/Aims: Cerebrovascular disease (CeVD) is one of the leading causes of death in patients initialising peritoneal dialysis (PD). Currently there is no risk score to predict the future risk of CeVD on entry into PD. This study aimed to derive and validate a risk prediction model for CeVD mortality in 2 years after the initialisation of PD. Methods: All patients registered with the Henan Peritoneal Dialysis Registry (HPDR) between 2007 and 2014 were included. Multivariable logistic regression modelling was applied to derive the risk score. All accessible clinical measurements were screened as potential predictors. Internal validation through bootstrapping was applied to test the model performance. Results: The absolute risk of CeVD mortality was 2.9%. Systolic and diastolic blood pressure, total cholesterol, phosphate, and sodium concentrations were the strongest predictors of CeVD mortality in the final risk score. Good model discrimination with C statistics above 0.70 and calibration of agreed observed and predicted risks were identified in the model. Conclusion: The new risk score, developed and validated using clinical measurements that are accessible on entry into PD, could be used clinically to screen for patients at high risk of CeVD mortality. Such patients might benefit from therapies reducing the incidence of CeVD related events.

A very large volume of images is uploaded to the Internet daily. However, current hashing methods for image retrieval are designed for static databases only. They fail to consider the fact that the distribution of images can change when new images are added to the database over time. The changes in the distribution of images include both discovery of a new class and a distribution of images within a class owing to concept drift. Retraining of hash tables using all images in the database requires a large computation effort. This is also biased to old data owing to the huge volume of old images which leads to a poor retrieval performance over time. In this paper, we propose the incremental hashing (ICH) method to deal with the two aforementioned types of changes in the data distribution. The ICH uses a multihashing to retain knowledge coming from images arriving over time and a weight-based ranking to make the retrieval results adaptive to the new data environment. Experimental results show that the proposed method is effective in dealing with changes in the database.

Under traditional unified power quality conditioner (UPQC) control, a UPQC series converter (SC) is mainly used to handle grid-side power quality problems while its parallel converter (PC) is mainly used to handle load-side power quality problems. The SC and PC are relatively independent. The SC is usually in standby mode and it only runs when the grid voltage abruptly changes. In this paper, novel UPQC coordinated control strategies are proposed which use the SC to share the reactive power compensation function of the PC especially without grid-side power quality problems. However, in some cases, there will be a circulating current between the SC and the PC, which will probably influence the compensation fashion, the compensation capacity, or the normal work of the UPQC. Through an active power circulation analysis, strategies with and without a circulating current are presented which fuses the reactive power allocation strategy of the SC and the PC, the composite control strategy of the SC and the compensation strategy of the DC storage unit. Both of the strategies effectively solve the SC long term idle problem, limit the influence of the circulating current, optimize all of the UPQC units and reduce the production cost. An analysis, along with simulation and experimental results, is presented to verify the feasibility and effectiveness of the proposed control strategies.

Background/Aims: During peritoneal equilibrium test (PET), intermediate measures of Dt/P (dialysate/plasma creatinine ratio), D0/P, D2/P and D4/P ratios might provide additional information regarding a patient’s cardiovascular mortality. We aimed to characterise heterogeneity of dialysate creatinine response patterns based on three ratios and compare cardiovascular mortality risks by response patterns. Methods: 3,477 patients initialising peritoneal dialysis (PD) between 2007-2015 were measured D0/P, D2/P and D4/P at baseline and 2-year cardiovascular mortality was defined as the primary outcome. Latent class mixed-effect models were fitted to identify distinct patterns of dialysate creatinine response. Multivariable Logistic regression model with adjustment of cardiometabolic factors were used to compare cardiovascular mortality by latent classes. Results: The 1st latent class including 36% of participants was characterised by consistently increasing and high Dt/P ratios both at 2-hour and 4-hour. The 2nd class including 61% of participants characterised by sharply increased ratio at 2-hour and slightly increased ratio at 4-hour. The 3rd class included 3% of participants with decreased ratio at 2-hour and increased ratio at 4-hour. Comparing the 1st class, the lower cardiovascular mortality risk was identified in the 2nd and 3rd class with adjusted odds ratio 0.65 (95% confidence interval: 0.62-0.69) and 0.48 (0.41-0.57), respectively. Conclusion: Patients with decreased Dt/P ratio between 0-hour and 2-hour and low ratios at 2-hour and 4-hour tend to have low cardiovascular mortality. Latent class analysis seems to be a promising method to reveal unidentified subgroups that do not fit into the risk category defined by Dt/P ratio at 4-hour.

At present, when 3D printers print large angle surfaces, hollow and cantilever structures, printing materials are affected by gravity factors, photosensitive resins or special materials need to be added as support. To save material, increase stability, solve the problem of filling several supporting structure and so on, we optimize the structure of 3D printer. We design a platform flip 3D printer and introduces its structure and working principle to establish the simulation model of the system. The new structure is analyzed and the feasibility of the new 3D printer structure is proved finally.

A critical subroutine of self-monitoring during speech production is to detect any deviance between expected and actual auditory feedback. Here we investigated the associated neural dynamics using MEG recording in mental-imagery-of-speech paradigms. Participants covertly articulated the vowel /a/; their own (individually recorded) speech was played back, with parametric manipulation using four levels of pitch shift, crossed with four levels of onset delay. A nonmonotonic function was observed in early auditory responses when the onset delay was shorter than 100 msec: Suppression was observed for normal playback, but enhancement for pitch-shifted playback; however, the magnitude of enhancement decreased at the largest level of pitch shift that was out of pitch range for normal conversion, as suggested in two behavioral experiments. No difference was observed among different types of playback when the onset delay was longer than 100 msec. These results suggest that the prediction suppresses the response to normal feedback, which mediates source monitoring. When auditory feedback does not match the prediction, an “error term” is generated, which underlies deviance detection. We argue that, based on the observed nonmonotonic function, a frequency window (addressing spectral difference) and a time window (constraining temporal difference) jointly regulate the comparison between prediction and feedback in speech.

We report a multipass cell (MPC) that consists of two inexpensive silver (or gold)-coated plane mirrors and a biconvex lens instead of expensive concave spherical mirrors. The silver (or gold)-coated two plane mirrors are separated by a biconvex lens with an inner hole for the detection of the gas flow. The two mirrors (diameter of 50.8 mm) and the convex lens (diameter of 50.8 mm) are mounted on an optical board with a dimension of 75 mm * 75 mm * 30 mm and 75 mm * 75 mm * 21 mm, and the inner diameter of the two quartz glass tubes is 48 mm with a hole for the detection of the gas in flow and out flow. For example, the plane MPC, which is ∼236 cm3 with 130 times reflections, is much smaller than a traditional concave spherical mirror based on a Herriott or a White MPC, which is ∼700 cm3 with 80 times reflections, and offers an effective optical path-length of about 31.32 m. This low cost, compact MPC is very suitable for various applications, such as climate change, environmental monitoring, and agriculture.

Purpose: To investigate the clinical value in lung cancer of a combination of four serum tumor markers, haptoglobin (Hp), carcinoembryonic antigen (CEA), neuron specific enolase (NSE) as well as the cytokeratin 19 fragment (CYFRA21-1). Materials and Methods: Serum Hp (with immune-turbidimetric method), CEA, NSE, CYFRA21-1 (with chemiluminescence method) level were assessed in 193 patients with lung cancer, 87 patients with benign lung disease and 150 healthy controls. Differences of expression were compared among groups, and joint effects of these tumor markers for the diagnosis of lung cancer were analyzed. Results: Serum tumor marker levels in patients with lung cancer were obviously higher than those with benign lung disease and normal controls (p

This paper presents a novel method for optimal location of multiple-type power quality improving devices in distribution network through lots of simulations in IEEE 30 bus system. Three type devices have been considered, active power filter (APF), dynamic voltage restorer (DVR) and static synchronous compensator (D-STATCOM). Using proposed method, configuration is fulfilled according to a certain order and their own law. Results show this strategy is an effective method to find optimal locations for power quality improving devices with lower cost.

Reversible data hiding enables the cover image to be restored from the stego image without any loss after the secret message is extracted. In this paper, we proposed a novel reversible data-hiding scheme for high image quality based on the histogram-shifting method. In our scheme, we proposed two mechanisms for choosing reference pixels according to the high correlation of adjacent pixels. The differences between reference pixels and their corresponding, neighboring, non-reference pixels are exploited to hide secret messages according to the difference shifting mechanism. To achieve larger embedding capacity, the reference pixels are designed to further carry secret messages based on the interpolation prediction method. Experimental results showed that our proposed scheme achieved higher embedding payload while maintaining better visual quality of the images than did other schemes. Copyright © 2014 John Wiley & Sons, Ltd.

Injecting radio frequency (RF) white noise to the current driving of the laser can broaden the laser emission linewidth and efficiently suppress cavity-mode noise in off-axis integrated cavity output spectroscopy (OA-ICOS). The effect of the injected RF noise level on the cavity-mode noise and the deformation of the absorption line shape in off-axis integrated cavity output spectroscopy (OA-ICOS) with a distributed feedback laser (DFB) at 1.65 µm were investigated. We measured methane at different concentrations between 0.1 ppmv and 2 ppmv associated with a -20 dBm RF noise injection. A linear spectral response of the intensity of the cavity output spectra with the CH4 concentration was observed. A threefold improvement in the detection limit was achieved compared to unperturbed OA-ICOS. The response time of the improved OA-ICOS system is about 30 s and the minimum detectable concentration (MDC) of CH4 is 7.6 ppbv, which corresponds to a minimum detectable fractional absorption scaled to the path length of 7.3 × 10-10 cm-1. The noise equivalent absorption sensitivity of the system is 5.51 × 10-9 cm-1Hz-1/2.

In this Letter, a new method is proposed to further improve the detection sensitivity of the gas sensor based on off-axis integrated cavity spectroscopy (OA-ICOS) using a distributed feedback laser operating in the near-infrared at 1653 nm that relies on implementation of wavelength modulation and radio frequency (RF) white noise perturbation to diode laser current. More than sixfold improvement in detection limit is achieved for an RF noised wavelength-modulated OA-ICOS compared to the unperturbed conventional OA-ICOS approach.

Sub-ppmv level detection of hydrogen sulphide (H2S) using a 1.578- μ m distributed feedback tunable diode laser combining with wavelength modulation spectroscopy and second harmonic detection scheme is reported. A home-developed novel compact dense-pattern multipass gas cell with an effective optical path length of 29.37 m is used to improve sensitivity and reduce sample volume. Detection parameters are optimized, including modulation frequency and amplitude. The analysis of Allan variance shows that a minimum detectable concentration 60 ppbv is obtained with a lock-in time constant of 10 ms, and a detection limit of 13 ppbv can be achieved by average in 300 s. The demonstrated H2S sensor has a strong penitential application in natural gas process for regulating and controlling H2S concentration.

In this article, the LS-SVM model was established against the working stability control for the vacuum feeding device. Based on the experimental sample data, the learning and training was performed. The actual operating results of the control system proved its efficiency and reliance.

The recursive compensatory fuzzy neural network model was established against the characteristics of temperature control for film laminating machine. The neural network can be used to construct the fuzzy system, and the self-adaptive and self-learning capability of neural networks was used to automatically adjust fuzzy system parameters, BP network could be learned and trained by the gradient descent algorithm. Based on the test data for the study and testing of network, system error is less than the national standard error requirements, the results proved the effectiveness and feasibility of the algorithm.