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To deeply explore the typical damage characteristics and vulnerability characteristics of workshop building (WB) in actual earthquakes, the empirical field reconnaissance and observation of the WB damaged to varying degrees in the Mw 8.0 earthquake in Wenchuan County, Sichuan Province, China on May 12, 2008 were conducted. The investigation results indicated that the typical seismic damage forms of industrial buildings in multiple intensity regions were: local and overall collapse, column cracking and crane beam displacement, collapse and cracking of wall, and damage of supporting and connecting members, the field investigation pictures of typical seismic damage were provided, respectively. According to different typical failure characteristics, the seismic damage mechanism and seismic capacity were analyzed, and the measures and suggestions to improve the seismic capacity of industrial buildings with different material categories were conducted. The conclusions can provide necessary reference for the revision of the seismic code of industrial plants and the seismic intensity scale of China.

One of several challenges in design of clinical chemoprevention trials is the selection of the dose, formulation, and dose schedule of the intervention agent. Therefore, a cross-over clinical trial was undertaken to compare the bioavailability and tolerability of sulforaphane from two of broccoli sprout–derived beverages: one glucoraphanin-rich (GRR) and the other sulforaphane-rich (SFR). Sulforaphane was generated from glucoraphanin contained in GRR by gut microflora or formed by treatment of GRR with myrosinase from daikon (Raphanus sativus) sprouts to provide SFR. Fifty healthy, eligible participants were requested to refrain from crucifer consumption and randomized into two treatment arms. The study design was as follows: 5-day run-in period, 7-day administration of beverages, 5-day washout period, and 7-day administration of the opposite intervention. Isotope dilution mass spectrometry was used to measure levels of glucoraphanin, sulforaphane, and sulforaphane thiol conjugates in urine samples collected daily throughout the study. Bioavailability, as measured by urinary excretion of sulforaphane and its metabolites (in approximately 12-hour collections after dosing), was substantially greater with the SFR (mean = 70%) than with GRR (mean = 5%) beverages. Interindividual variability in excretion was considerably lower with SFR than with GRR beverage. Elimination rates were considerably slower with GRR, allowing for achievement of steady-state dosing as opposed to bolus dosing with SFR. Optimal dosing formulations in future studies should consider blends of sulforaphane and glucoraphanin as SFR and GRR mixtures to achieve peak concentrations for activation of some targets and prolonged inhibition of others implicated in the protective actions of sulforaphane. Cancer Prev Res; 4(3); 384–95. ©2011 AACR.

Supplementary Tables from Bioavailability of Sulforaphane from Two Broccoli Sprout Beverages: Results of a Short-term, Cross-over Clinical Trial in Qidong, China

Supplementary Figure from Bioavailability of Sulforaphane from Two Broccoli Sprout Beverages: Results of a Short-term, Cross-over Clinical Trial in Qidong, China

To establish a method to identify unknown sample based on the combined use of Fourier transform infrared spectroscopy (FTIR), gas chromatography-quadrupole time-of-flight mass spectrometry (GC-QTOF-MS), ultra-high performance liquid chromatography-linear ion trap quadrupole-orbitrap mass spectrometry (UPLC-LTQ-Orbitrap MS) andThe unknown sample was directly analyzed by FTIR. The unknown sample was dissolved in methanol solution containing internal standard SKFThe characteristic absorption peaks of FTIR spectra obtained from unknown sample were 1 682 (C=O bond), 1 503, 1 488, 1 436, 1 363, 1 256, 1 092, 1 035, 935, 840 and 800 cmThe method established in this study can be used for structural confirmation of Eutylone.

Ocean currents play a significant role in driving the long-distance dispersal (LDD), spatial distribution and phylogeographic patterns of many organisms. Integrating phylogeographic analyses and mechanistic ocean current modelling can provide novel insights into the evolutionary history of terrestrial littoral species but has been rarely applied in this context. We focused on a group of Cycas that have buoyant seeds and occupy coastal habitats. By integrating evidence from mechanistic simulations and whole plastomic data, we examined the role of ocean circulation in shaping the phylogeography of these Cycas species. Plastomes of the studied Cycas species showed extreme conservatism, following a post-Pleistocene divergence. Phylogenies revealed three subclades, corresponding to the Pacific Ocean, Sunda Shelf and Indian Ocean. The ocean modelling results indicate that hotspots of seed stranding coincide well with the contemporary distribution of the Cycas species and that drifting trajectories from the three subclades are largely confined to separate regions. These findings suggest that ocean current systems, by driving long-distance dispersal, have shaped the distribution and phylogeography for Cycas with buoyant seeds. This study highlights how the combination of genomic data and ocean drift modelling can help explain phylogeographic patterns and diversity in terrestrial littoral ecosystems.

Jian-Guo Chen,1,2 Jian Zhu,1 Yong-Hui Zhang,1 Yong-Sheng Chen,1 Lu-Lu Ding,1 Hai-Zhen Chen,2 Ai-Guo Shen,2 Gao-Ren Wang2 1Department of Epidemiology, Qidong Liver Cancer Institute, Qidong People’s Hospital, Affiliated Qidong Hospital of Nantong University, Qidong, People’s Republic of China; 2Cancer Institute, Nantong Tumor Hospital, Affiliated Tumor Hospital of Nantong University, Nantong, People’s Republic of ChinaCorrespondence: Jian-Guo Chen Qidong Liver Cancer Institute, Qidong People’s Hospital, Affiliated Qidong Hospital of Nantong University; Cancer Institute, Affiliated Tumor Hospital of Nantong University, Nantong, People’s Republic of China Email chenjg@ntu.edu.cnBackground and Aims: To explore the long-term trend of liver cancer survival, based on the real-world data (RWD) in the past 45 years from a population-based cancer registry, in Qidong, China.Methods: A number of 32,556 patients with liver cancer were registered during the period of 1972 to 2016. Mixed methods by active and passive follow-up were performed. Life table method was employed for survival analysis by SPSS22 software. Wilcoxon (Gehan) statistics was considered as a significant test. Relative survival was calculated by using SURV software, and its annual percent change (APC) was estimated by the Joinpoint Regression Program.Results: The overall observed survival (OS) rates of 1-, 5-, 10-, and 20-year rates from the data series were 18.51%, 6.28%, 4.03%, and 2.84%, and their relative survival (RS) rates were 18.88%, 6.95%, 4.96%, and 4.49%, respectively. For 24,338 male cases, the 5-year OS and RS rates were 5.93% and 6.54%, and for 8218 female cases, 7.34% and 8.15%, respectively, with P values less than 0.01. Survival rates of liver cancer from three 15-year periods of 1972– 1986, 1987– 2001, and 2002– 2016 have increased significantly, with 5-year OS rates of 2.02%, 4.40%, and 10.76%, 5-year RS rates of 2.18%, 4.83%, and 12.18%; 10-year OS and RS rates of 0.95%, 3.00%, and 7.02%, vs 1.13%, 3.65%, and 8.96%, respectively, showing a very significant upward trend (P< 0.01). There are significant differences among age groups (P< 0.01): those aged 55– 64 demonstrated the best OS and RS rates of 5-year, being 8.44% and 9.09%, respectively.Conclusion: There are significant gender and age differences in the survival rate of liver cancer in Qidong. RWD indicates the relative lower survival rate of liver cancer in this area, but great improvement has been achieved over the past decades.Keywords: liver cancer, survival rate, long-term trend, real-world data, Qidong

Under the pressure of rapid urbanization, the spatiotemporal dynamics of urban green spaces (UGS) have enormous impacts on the local ecological system and environment at different scales. In this study, UGS in Hefei City, which has experienced rapid urbanization from 1995 to 2015, were extracted based on time-series Landsat-5 TM and Landsat-8 OLI images, and different types of parks were mapped based on GF-2 images combined with multi-source metadata. Dynamic patterns of green space were examined by drawing the spatial variations of green space at the city, inner city, and park scales. Results revealed that: (1) At the city level, UGS decreased with the rapid urbanization, especially farmland sharply lost in areas around existing urban areas and along the transportation corridors. (2) At the inner-city level, concentric analyses showed that UGS changes in different rings had unique trends, and the dynamic changes were the most significant in Rings 2 and 3. Comparative analysis of old and new districts showed that the common characteristics of UGS changes were the transfer-out of farmland and the transfer-in of built-up land, and the newer the region, the more intense the changes. (3) At the park level, the number and area of urban parks were increasing from the center to the periphery, and the type of park gradually changed from single to rich. Significant spatial heterogeneity was identified in the landscape patterns of urban parks based on fishnet cell analysis. It is fundamental to assign urban construction land for socio-economic development, while planning UGS at different scales; moreover, integrating different green space-related policies could protect the UGS and maintain its stability. Only in this way can UGS combine economic, social, and ecological benefits under the background of rapid urbanization.

Neuroscience studies have revealed that the brain encodes visual content and embeds information in neural activity. Recently, deep learning techniques have facilitated attempts to address visual reconstructions by mapping brain activity to image stimuli using generative adversarial networks (GANs). However, none of these studies have considered the semantic meaning of latent code in image space. Omitting semantic information could potentially limit the performance. In this study, we propose a new framework to reconstruct facial images from functional Magnetic Resonance Imaging (fMRI) data. With this framework, the GAN inversion is first applied to train an image encoder to extract latent codes in image space, which are then bridged to fMRI data using linear transformation. Following the attributes identified from fMRI data using an attribute classifier, the direction in which to manipulate attributes is decided and the attribute manipulator adjusts the latent code to improve the consistency between the seen image and the reconstructed image. Our experimental results suggest that the proposed framework accomplishes two goals: (1) reconstructing clear facial images from fMRI data and (2) maintaining the consistency of semantic characteristics., Comment: Accepted at the 2022 International Joint Conference on Neural Networks (IJCNN 2022)

Euphorbiaceae, a family of plants mainly grown in the tropics and subtropics, is also widely distributed all over the world and is well known for being rich in rubber, oil, medicinal materials, starch, wood and other economically important plant products. Glutathione S-transferases (GSTs) constitute a family of proteins encoded by a large supergene family and are widely expressed in animals, bacteria, fungi and plants, but with few reports of them in Euphorbiaceae plants. These proteins participate in and regulate the detoxification and oxidative stress response of heterogeneous organisms, resistance to stress, growth and development, signal transduction and other related processes. In this study, we identified and analyzed the whole genomes of four species of Euphorbiaceae, namely Ricinus communis, Jatropha curcas, Hevea brasiliensis, and Manihot esculenta, which have high economic and practical value. A total of 244 GST genes were identified. Based on their sequence characteristics and conserved domain types, the GST supergene family in Euphorbiaceae was classified into 10 subfamilies. The GST supergene families of Euphorbiaceae and Arabidopsis have been found to be highly conserved in evolution, and tandem repeats and translocations in these genes have made the greatest contributions to gene amplification here and have experienced strong purification selection. An evolutionary analysis showed that Euphorbiaceae GST genes have also evolved into new subtribes (GSTO, EF1BG, MAPEG), which may play a specific role in Euphorbiaceae. An analysis of expression patterns of the GST supergene family in Euphorbiaceae revealed the functions of these GSTs in different tissues, including resistance to stress and participation in herbicide detoxification. In addition, an interaction analysis was performed to determine the GST gene regulatory mechanism. The results of this study have laid a foundation for further analysis of the functions of the GST supergene family in Euphorbiaceae, especially in stress and herbicide detoxification. The results have also provided new ideas for the study of the regulatory mechanism of the GST supergene family, and have provided a reference for follow-up genetics and breeding work.

Both structural and contextual information is essential and widely used in image analysis. However, current multi-view stereo (MVS) approaches usually use a single common pre-trained model as pixel descriptor to extract features, which mix structural and contextual information together and thus increase the difficulty of matching correspondence. In this paper, we propose FADE (feature aggregation for depth estimation), which treats spatial and context information separately and focuses on aggregating features for efficient learning of the MVS problem. Spatial information includes image details such as edges and corners, whereas context information comprises object features such as shapes and traits. To aggregate these multi-level features, we use an attention mechanism to select important features for matching. We then build a plane sweep volume by using a homography backward warping method to generate match candidates. Furthermore, we propose a novel cost volume regularization network aims to minimize the noise in the matching candidates. Finally, we take advantage of 3D stacked hourglass and regression to produces high-quality depth maps. With these well-aggregated features, FADE can efficiently perform dense depth reconstruction, achieving state-of-the-art performance in terms of accuracy and requiring the least amount of model parameters.

The goal of Multi-View Stereo (MVS) is to reconstruct 3D point-cloud model from multiple views. On the basis of the considerable progress of deep learning, an increasing amount of research has moved from traditional MVS methods to learning-based ones. However, two issues remain unsolved in the existing state-of-the-art methods: (1) only high-level information is considered for depth estimation. This may reduce the localization accuracy of 3D points as the learned model lacks spatial information; and (2) most of the methods require additional post-processing or network refinement to generate a smooth 3D model. This significantly increases the number of model parameters or the computational complexity. To this end, we propose MVSNet++, an end-to-end trainable network for dense depth estimation. Such an estimated depth map can further be applied to 3D model reconstruction. Different from previous methods, in the proposed method, we first adopt feature pyramid structures for both feature extraction and cost volume regularization. This can lead to accurate 3D point localization by fusing multi-level information. To generate smooth depth map, we then carefully integrate instance normalization into MVSNet++ without increasing model parameters and computational burden. Furthermore, we additionally design three loss functions and integrate Curriculum Learning framework into the training process, which can lead to an accurate reconstruction of 3D model. MVSNet++ is evaluated on DTU and Tanks & Temples benchmarks with comprehensive ablation studies. Experimental results demonstrate that our proposed method performs favorably against previous state-of-the-art methods, showing the accuracy and effectiveness of the proposed MVSNet++.

Vocal expression is essential for conveying the emotion during social interaction. Although vocal emotion has been explored in previous studies, little is known about how perception of different vocal emotional expressions modulates the functional brain network topology. In this study, we aimed to investigate the functional brain networks under different attributes of vocal emotion by graph-theoretical network analysis. Functional magnetic resonance imaging (fMRI) experiments were performed on 36 healthy participants. We utilized the Power-264 functional brain atlas to calculate the interregional functional connectivity (FC) from fMRI data under resting state and vocal stimuli at different arousal and valence levels. The orthogonal minimal spanning trees method was used for topological filtering. The paired-sample t-test with Bonferroni correction across all regions and arousal–valence levels were used for statistical comparisons. Our results show that brain network exhibits significantly altered network attributes at FC, nodal and global levels, especially under high-arousal or negative-valence vocal emotional stimuli. The alterations within/between well-known large-scale functional networks were also investigated. Through the present study, we have gained more insights into how comprehending emotional speech modulates brain networks. These findings may shed light on how the human brain processes emotional speech and how it distinguishes different emotional conditions.

In this study, we proposed a new end-to-end convolutional neural network, called MS-SincResNet, for music genre classification. MS-SincResNet appends 1D multi-scale SincNet (MS-SincNet) to 2D ResNet as the first convolutional layer in an attempt to jointly learn 1D kernels and 2D kernels during the training stage. First, an input music signal is divided into a number of fixed-duration (3 seconds in this study) music clips, and the raw waveform of each music clip is fed into 1D MS-SincNet filter learning module to obtain three-channel 2D representations. The learned representations carry rich timbral, harmonic, and percussive characteristics comparing with spectrograms, harmonic spectrograms, percussive spectrograms and Mel-spectrograms. ResNet is then used to extract discriminative embeddings from these 2D representations. The spatial pyramid pooling (SPP) module is further used to enhance the feature discriminability, in terms of both time and frequency aspects, to obtain the classification label of each music clip. Finally, the voting strategy is applied to summarize the classification results from all 3-second music clips. In our experimental results, we demonstrate that the proposed MS-SincResNet outperforms the baseline SincNet and many well-known hand-crafted features. Considering individual 2D representation, MS-SincResNet also yields competitive results with the state-of-the-art methods on the GTZAN dataset and the ISMIR2004 dataset. The code is available at https://github.com/PeiChunChang/MS-SincResNet.

(1) Background: The Alberta Stroke Program Early CT Score (ASPECTS) is a standardized scoring tool used to evaluate the severity of acute ischemic stroke (AIS) on non-contrast CT (NCCT). Our aim in this study was to automate ASPECTS. (2) Methods: We utilized a total of 258 patient images with suspected AIS symptoms. Expert ASPECTS readings on NCCT were used as ground truths. A deep learning-based automatic detection (DLAD) algorithm was developed for automated ASPECTS scoring based on 168 training patient images using a convolutional neural network (CNN) architecture. An additional 90 testing patient images were used to evaluate the performance of the DLAD algorithm, which was then compared with ASPECTS readings on NCCT as performed by physicians. (3) Results: The sensitivity, specificity, and accuracy of DLAD for the prediction of ASPECTS were 65%, 82%, and 80%, respectively. These results demonstrate that the DLAD algorithm was not inferior to radiologist-read ASPECTS on NCCT. With the assistance of DLAD, the individual sensitivity of the ER physician, neurologist, and radiologist improved. (4) Conclusion: The proposed DLAD algorithm exhibits a reasonable ability for ASPECTS scoring on NCCT images in patients presenting with AIS symptoms. The DLAD algorithm could be a valuable tool to improve and accelerate the decision-making process of front-line physicians.

Background and aims: Liver cancer is one of the most dominant malignant tumors in the world. The trends of liver cancer mortality over the past six decades have been tracked in the epidemic region of Qidong, China. Using epidemiological tools, we explore the dynamic changes in age-standardized rates to characterize important aspects of liver cancer etiology and prevention. Methods Mortality data of liver cancer in Qidong from 1958 to 1971 (death retrospective survey) and from 1972 to 2017 (cancer registration) were tabulated for the crude rate (CR), and age-standardized rate and age-birth cohorts. The average annual percentage change was calculated by the Joinpoint Regression Program. Results The natural death rate during 1958–2017 decreased from 9‰ to 5.4‰ and then increased to 8‰ as the population aged; cancer mortality rates rose continuously from 57/105 to 240/105. Liver cancer mortality increased from 20/105 to 80/105, and then dropped to less than 52/105 in 2017. Liver cancer deaths in 1972–2017 accounted for 30.53% of all cancers, with a CR of 60.48/105, age-standardized rate China (ASRC) of 34.78/105, and ASRW (world) of 45.71/105. Other key features were the CR for males and females of 91.86/105 and 29.92/105, respectively, with a sex ratio of 3.07:1. Period analysis showed that the ASRs for mortality of the age groups under 54 years old had a significant decreasing trend. Importantly, birth cohort analysis showed that the mortality rate of liver cancer in 40–44, 35–39, 30–34, 25–29, 20–24, 15–19 years cohort decreased considerably, but the rates in 70–74, and 75+ increased. Conclusions The crude mortality rate of liver cancer in Qidong has experienced trends from lower to higher levels, and from continued increase at a high plateau to most recently a gradual decline, and a change greatest in younger people. Many years of comprehensive prevention and intervention measures have influenced the decline of the liver cancer epidemic in this area. The reduction of intake levels of aflatoxin might be one of the most significant factors as evidenced by the dramatic decline of exposure biomarkers in this population during the past three decades.

Faces manifest large variations in many aspects, such as identity, expression, pose, and face styling. Therefore, it is a great challenge to disentangle and extract these characteristics from facial images, especially in an unsupervised manner. In this work, we introduce cycle-consistency in facial characteristics as free supervisory signal to learn facial representations from unlabeled facial images. The learning is realized by superimposing the facial motion cycle-consistency and identity cycle-consistency constraints. The main idea of the facial motion cycle-consistency is that, given a face with expression, we can perform de-expression to a neutral face via the removal of facial motion and further perform re-expression to reconstruct back to the original face. The main idea of the identity cycle-consistency is to exploit both de-identity into mean face by depriving the given neutral face of its identity via feature re-normalization and re-identity into neutral face by adding the personal attributes to the mean face. At training time, our model learns to disentangle two distinct facial representations to be useful for performing cycle-consistent face reconstruction. At test time, we use the linear protocol scheme for evaluating facial representations on various tasks, including facial expression recognition and head pose regression. We also can directly apply the learnt facial representations to person recognition, frontalization and image-to-image translation. Our experiments show that the results of our approach is competitive with those of existing methods, demonstrating the rich and unique information embedded in the disentangled representations. Code is available at https://github.com/JiaRenChang/FaceCycle ., Comment: ICCV 2021

This paper proposes the Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN) for fast and accurate single-shot object detection. Feature Pyramid (FP) is widely used in recent visual detection, however the top-down pathway of FP cannot preserve accurate localization due to pooling shifting. The advantage of FP is weakened as deeper backbones with more layers are used. In addition, it cannot keep up accurate detection of both small and large objects at the same time. To address these issues, we propose a new parallel FP structure with bi-directional (top-down and bottom-up) fusion and associated improvements to retain high-quality features for accurate localization. We provide the following design improvements: (1) A parallel bifusion FP structure with a bottom-up fusion module (BFM) to detect both small and large objects at once with high accuracy. (2) A concatenation and re-organization (CORE) module provides a bottom-up pathway for feature fusion, which leads to the bi-directional fusion FP that can recover lost information from lower-layer feature maps. (3) The CORE feature is further purified to retain richer contextual information. Such CORE purification in both top-down and bottom-up pathways can be finished in only a few iterations. (4) The adding of a residual design to CORE leads to a new Re-CORE module that enables easy training and integration with a wide range of deeper or lighter backbones. The proposed network achieves state-of-the-art performance on the UAVDT17 and MS COCO datasets. Code is available at https://github.com/pingyang1117/PRBNet_PyTorch., Comment: accepted by IEEE transactions on Image Processing

Ranking among the most effective anticancer drugs, anthracyclines represent an important family of aromatic polyketides generated by type II polyketide synthases (PKSs). After formation of polyketide cores, the post-PKS tailoring modifications endow the scaffold with various structural diversities and biological activities. Here we demonstrate an unprecedented four-enzyme-participated hydroxyl regioisomerization process involved in the biosynthesis of kosinostatin. First, KstA15 and KstA16 function together to catalyze a cryptic hydroxylation of the 4-hydroxyl-anthraquinone core, yielding a 1,4-dihydroxyl product, which undergoes a chemically challenging asymmetric reduction-dearomatization subsequently acted by KstA11; then, KstA10 catalyzes a region-specific reduction concomitant with dehydration to afford the 1-hydroxyl anthraquinone. Remarkably, the shunt product identifications of both hydroxylation and reduction-dehydration reactions, the crystal structure of KstA11 with bound substrate and cofactor, and isotope incorporation experiments reveal mechanistic insights into the redox dearomatization and rearomatization steps. These findings provide a distinguished tailoring paradigm for type II PKS engineering.

Decoding the neural representations of pain is essential to obtaining an objective assessment as well as an understanding of its underlying mechanisms. The complexities involved in the subjective experience of pain make it difficult to obtain a quantitative assessment from the induced spatiotemporal patterns of brain activity of high dimensionality. Most previous studies have investigated the perception of pain by analyzing the amplitude or spatial patterns in the response of the brain to external stimulation. This study investigated the decoding of endogenous pain perceptions according to resting-state magnetoencephalographic (MEG) recordings. In our experiments, we applied a beamforming method to calculate the brain activity for every brain region and examined temporal and spectral features of brain activity for predicting the intensity of perceived pain in patients with primary dysmenorrhea undergoing menstrual pain. Our results show that the asymmetric index of sample entropy in the precuneus and the sample entropy in the left posterior cingulate gyrus were the most informative characteristics associated with the perception of menstrual pain. The correlation coefficient (ρ=0.64, p

An efficient plant regeneration protocol was established for castor bean ( Ricinus communis L.), in which 0.3 mg L −1 thidiazuron (TDZ) induced shoot clusters and increased the number of adventitious shoots from hypocotyl tissue. Our results showed that treatment under dark conditions significantly promoted the average number of shoots per explant to 37.36±4.54 (with a 6-d treatment). Modified 1/2 Murashige and Skoog (MS) basal medium supplemented with 440 mg L −1 Ca 2+ , 0.2 mg L −1 gibberellic acid and 0.1 mg L −1 TDZ significantly increased shoot elongation rates and lowered vitrification rates. Furthermore, 1/2 MS media supplemented with 0.2 mg L −1 1-naphthaleneacetic acid induced a higher rooting rate compared with other culture conditions.

The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.

Alzheimer's disease (AD), an irreversible neurodegenerative disease, is the most common type of dementia in elderly people. This present study incorporated multiple structural and functional connectivity metrics into a graph theoretical analysis framework and investigated alterations in brain network topology in patients with mild cognitive impairment (MCI) and AD. By using this multiparametric analysis, we expected different connectivity metrics may reflect additional or complementary information regarding the topological changes in brain networks in MCI or AD. In our study, a total of 73 subjects participated in this study and underwent the magnetic resonance imaging scans. For the structural network, we compared commonly used connectivity metrics, including fractional anisotropy and normalized streamline count, with multiple diffusivity-based metrics. We compared Pearson correlation and covariance by investigating their sensitivities to functional network topology. Significant disruption of structural network topology in MCI and AD was found predominantly in regions within the limbic system, prefrontal and occipital regions, in addition to widespread alterations of local efficiency. At a global scale, our results showed that the disruption of the structural network was consistent across different edge definitions and global network metrics from the MCI to AD stages. Significant changes in connectivity and tract-specific diffusivity were also found in several limbic connections. Our findings suggest that tract-specific metrics (e.g., fractional anisotropy and diffusivity) provide more sensitive and interpretable measurements than does metrics based on streamline count. Besides, the use of inversed radial diffusivity provided additional information for understanding alterations in network topology caused by AD progression and its possible origins. Use of this proposed multiparametric network analysis framework may facilitate early MCI diagnosis and AD prevention., Highlights • Alzheimer's disease alters structural network mainly in limbic and superior frontal regions. • Altered global network topology may occur in earlier stage during mild cognitive impairment. • The capability of proposed multiparametric network analysis has been demonstrated. • The potential of inversed radial diffusivity on network analysis has been shown.

Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs). However, current architectures rely on patch-based Siamese networks, lacking the means to exploit context information for finding correspondence in illposed regions. To tackle this problem, we propose PSMNet, a pyramid stereo matching network consisting of two main modules: spatial pyramid pooling and 3D CNN. The spatial pyramid pooling module takes advantage of the capacity of global context information by aggregating context in different scales and locations to form a cost volume. The 3D CNN learns to regularize cost volume using stacked multiple hourglass networks in conjunction with intermediate supervision. The proposed approach was evaluated on several benchmark datasets. Our method ranked first in the KITTI 2012 and 2015 leaderboards before March 18, 2018. The codes of PSMNet are available at: https://github.com/JiaRenChang/PSMNet., Comment: CVPR 2018

How chronic pain affects brain functions remains unclear. As a potential indicator, brain complexity estimated by entropy-based methods may be helpful for revealing the underlying neurophysiological mechanism of chronic pain. In this study, complexity features with multiple time scales and spectral features were extracted from resting-state magnetoencephalographic signals of 156 female participants with/without primary dysmenorrhea (PDM) during pain-free state. Revealed by multiscale sample entropy (MSE), PDM patients (PDMs) exhibited loss of brain complexity in regions associated with sensory, affective, and evaluative components of pain, including sensorimotor, limbic, and salience networks. Significant correlations between MSE values and psychological states (depression and anxiety) were found in PDMs, which may indicate specific nonlinear disturbances in limbic and default mode network circuits after long-term menstrual pain. These findings suggest that MSE is an important measure of brain complexity and is potentially applicable to future diagnosis of chronic pain.

The aqueous extract of Chinese winter jujube (Ziziphus jujuba Mill. cv. Dongzao) was used as reducing and capping agents for the synthesis of silver nanoparticles (AgNPs) for the first time. The resulting AgNPs were characterised by UV/Visible (UV–Vis) spectroscopy, atomic force microscope, transmission electron microscopy, selected area electron diffraction, X‐ray diffraction, scanning electron microscopy, energy‐dispersive X‐ray and Fourier transform infrared spectroscopy (FTIR). The colloidal solution of AgNPs gave a maximum UV–Vis absorbance at 446 nm. The synthesised nanoparticles were almost in the spherical shapes with an average size of 11.5 ± 4. 8 nm. FTIR spectra were applied to identify the functional groups which were possibly responsible for the conversion of metal ions into nanoparticles. The results showed that the prepared AgNPs were coated with the biomolecules in the extract. The biosynthesised AgNPs showed a remarkable catalytic activity at room temperature, and they also showed good antibacterial properties against Escherichia coli and Staphylococcus aureus.

Theta oscillation (4–7 Hz) is well documented for its association with neural processes of memory. Pronounced increase of theta activity is commonly observed in patients with chronic neurogenic pain. However, its association with encoding of pain experience in patients with chronic pain is still unclear. The goal of the present study is to investigate the theta encoding of sensory and emotional information of long-term menstrual pain in women with primary dysmenorrhea (PDM). Forty-six young women with PDM and 46 age-matched control subjects underwent resting-state magnetoencephalography study during menstrual and periovulatory phases. Our results revealed increased theta activity in brain regions of pain processing in women with PDM, including the right parahippocampal gyrus, right posterior insula, and left anterior/middle cingulate gyrus during the menstrual phase and the left anterior insula and the left middle/inferior temporal gyrus during the periovulatory phase. The correlations between theta activity and the psychological measures pertaining to pain experience (depression, state anxiety, and pain rating index) implicate the role of theta oscillations in emotional and sensory processing of pain. The present study provides evidence for the role of theta oscillations in encoding the immediate and sustained effects of pain experience in young women with PDM.

The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver’s attention becomes distracted, even for a short period of time. For the purpose of alerting the driver and elevating his or her safety, in this paper we propose two components for any modern vision-based Advanced Drivers Assistance System (ADAS). These two components work separately for the single purpose of alerting the driver in dangerous situations. The purpose of the first component is to ascertain that the driver would be in a sufficiently wakeful state to receive and process warnings; this is the driver drowsiness detection component. The driver drowsiness detection component uses infrared images of the driver to analyze his eyes’ movements using a MSR plus a simple heuristic. This component issues alerts to the driver when the driver’s eyes show distraction and are closed for a longer than usual duration. Experimental results show that this component can detect closed eyes with an accuracy of 94.26% on average, which is comparable to previous results using more sophisticated methods. The purpose of the second component is to alert the driver when the driver’s vehicle is moving around larger vehicles at dusk or night time. The large vehicle detection component accepts images from a regular video driving recorder as input. A bi-level system of classifiers, which included a novel MSR-enhanced KAZE-base Bag-of-Features classifier, is proposed to avoid false negatives. In both components, we propose an improved version of the Multi-Scale Retinex (MSR) algorithm to augment the contrast of the input. Several experiments were performed to test the effects of the MSR and each classifier, and the results are presented in experimental results section of this paper.

This study investigates the cortical abnormalities of early emotion perception in patients with major depressive disorder (MDD) and bipolar disorder (BD) using gamma oscillations. Twenty-three MDD patients, twenty-five BD patients, and twenty-four normal controls were enrolled and their event-related magnetoencephalographic responses were recorded during implicit emotional tasks. Our results demonstrated abnormal gamma activity within 100 ms in the emotion-related regions (amygdala, orbitofrontal (OFC) cortex, anterior insula (AI), and superior temporal pole) in the MDD patients, suggesting that these patients may have dysfunctions or negativity biases in perceptual binding of emotional features at very early stage. Decreased left superior medial frontal cortex (smFC) responses to happy faces in the MDD patients were correlated with their serious level of depression symptoms, indicating that decreased smFC activity perhaps underlies irregular positive emotion processing in depressed patients. In the BD patients, we showed abnormal activation in visual regions (inferior/middle occipital and middle temporal cortices) which responded to emotional faces within 100 ms, supporting that the BD patients may hyperactively respond to emotional features in perceptual binding. The discriminant function of gamma activation in the left smFC, right medial OFC, right AI/inferior OFC, and the right precentral cortex accurately classified 89.6% of patients as unipolar/bipolar disorders.

Objective To investigate the therapeutic effect of autologous peripheral blood stem cell transplantation (APBSCT) in combination with rituximab in treatment of CD20+ B cellulous non-Hodgkin's lymphoma (B-NHL). Methods Sixty patients with CD20+ aggressive or refractory and recurrent B-NHL and treated with APBSCT in our department from Jan. 2005 to Jan. 2011 were admitted. All the subjects were divided into 2 groups according to their own choice: 25 patients received rituximab treatment (treatment group) and 35 patients were treated without rituximab treatment (control group). All patients underwent chemotherapy and APBSCT. For patients in treatment group, rituximab was used with CHOP before collecting the stem cells and after the transplantation. After transplantation, rituximab and IL-2 were used in treatment group every 3-6 months as maintenance treatment. Results No side effect was observed during the use of rituximab either before or after transplantation. The mononuclear cell count in treatment and control group was (8.2±2.9)×108/kg and (8.4±3.9)×108/kg (P=0.822), respectively; CD34+cell count was (12.3±12.7)×106/kg and (13.2±13.9)×106/kg (P=0.799), respectively. Haemopoiesis reconstruction was successfully achieved in the patients of treatment group, while 3 patients in control group failed to have haemopoiesis reconstruction. No significant difference was found between two groups on the recovery time of neutrophilic granulocytes and platelets. All patients achieved complete remission. The average follow-up time was 22 months. The disease relapsed in two patients in treatment group and six in control group. The 3-year overall survival rate in treatment group (91.6%) was a little higher than that in control group (69.5%, P=0.060). Conclusion To patients of CD20+ B lymphoma, the use of rituximab shows no side effect before or after collection of stem cell and hemopoiesis reconstruction, and the overall survival rate may be improved.

Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.

Population-based cancer survival is an improved index for evaluating the overall efficiency of cancer health services in a given region. The current study analysed the observed survival and relative survival of leading cancer sites from a population-based cancer registry between 1972 and 2011 in Qidong, China. A total of 92,780 incident cases with cancer were registered and followed-up for survival status. The main sites of the cancer types, based on the rank order of incidence, were the liver, stomach, lung, colon and rectum, oesophagus, breast, pancreas, leukaemia, brain and central nervous system (B and CNS), bladder, blood [non-Hodgkin's lymphoma (NHL)] and cervix. For all malignancies combined, the 5-year observed survival was 13.18% and the relative survival was 15.80%. Females had higher observed survival and relative survival (19.32 and 22.71%, respectively) compared with males (9.63 and 11.68%, respectively). The cancer sites with the highest five-year relative survival rates were the female breast, bladder, cervix and colon and rectum; followed by NHL, stomach, B and CNS cancer and leukaemia. The poorest survival rates were cancers of oesophagus, lung, pancreas and liver. Higher survival rates were observed in younger patients compared with older patients. Cancers of the oesophagus, female breast and bladder were associated with higher survival in middle-aged groups. Improved survival rates in the most recent two 5-year calendar periods were identified for stomach, lung, colon and rectum, oesophagus, female breast and bladder cancer, as well as leukaemia and NHL. The observations of the current study provide the opportunity for evaluation of the survival outcomes of frequent cancer sites that reflects the changes and improvement in a rural area in China.