Search

MoO3 gas-sensing materials with different Ni2+ doping concentration were prepared by a simple one-step solvothermal method. The phase structure, micro-structure and morphology of the as-prepared samples were characterized by X-ray powder diffraction (XRD), field emission scanning electron microscope (FESEM) and the transmission electron microscope (TEM) etc., respectively. The morphology can be changed by adjusting the doping concentration of Ni2+. Among them, the 5 mol% Ni-doped MoO3 sample with a pompon-like morphology improved gas accessibility significantly. Prominently, the response of the sensors on xylene was increased 18 times from 3.48 to 62.6, the response time was about 1 s and the best selectivity was obtained at the optimum temperature of 250 °C. These breakthroughs are attributed to the increase of surface-active sites and the improvement of micro-morphology caused by Ni2+ doping.

Recently, caption generation with an encoder-decoder framework has been extensively studied and applied in different domains, such as image captioning, code captioning, and so on. In this paper, we propose a novel architecture, namely Auto-Reconstructor Network (ARNet), which, coupling with the conventional encoder-decoder framework, works in an end-to-end fashion to generate captions. ARNet aims at reconstructing the previous hidden state with the present one, besides behaving as the input-dependent transition operator. Therefore, ARNet encourages the current hidden state to embed more information from the previous one, which can help regularize the transition dynamics of recurrent neural networks (RNNs). Extensive experimental results show that our proposed ARNet boosts the performance over the existing encoder-decoder models on both image captioning and source code captioning tasks. Additionally, ARNet remarkably reduces the discrepancy between training and inference processes for caption generation. Furthermore, the performance on permuted sequential MNIST demonstrates that ARNet can effectively regularize RNN, especially on modeling long-term dependencies. Our code is available at: https://github.com/chenxinpeng/ARNet, Accepted by CVPR 2018

Recently, much advance has been made in image captioning, and an encoder-decoder framework has been adopted by all the state-of-the-art models. Under this framework, an input image is encoded by a convolutional neural network (CNN) and then translated into natural language with a recurrent neural network (RNN). The existing models counting on this framework employ only one kind of CNNs, e.g., ResNet or Inception-X, which describes the image contents from only one specific view point. Thus, the semantic meaning of the input image cannot be comprehensively understood, which restricts improving the performance. In this paper, to exploit the complementary information from multiple encoders, we propose a novel recurrent fusion network (RFNet) for the image captioning task. The fusion process in our model can exploit the interactions among the outputs of the image encoders and generate new compact and informative representations for the decoder. Experiments on the MSCOCO dataset demonstrate the effectiveness of our proposed RFNet, which sets a new state-of-the-art for image captioning.

In the present study, the presence of extracellular matrix components, including collagenous and elastic fibers, and the expression of their key regulating enzymes, were investigated in different stages of hamster tongue carcinoma development. Immunohistochemical and computer-assisted morphological analyses were performed to quantify the staining intensity and area (integral optical density) of matrix metalloproteinase (MMP)-1 and -2, tissue inhibitors of metalloproteinase (TIMP)-1 and -2, and the extent of elastic and collagenous fibers in histological sections. MMP-1, MMP-2, TIMP-1 and TIMP-2 expression levels gradually increased with tongue cancer progression, and were associated with disease pathology staging (r=0.705, 0.633, 0.759 and 0.751, respectively). By contrast, elastic fiber levels gradually decreased with cancer progression and were negatively correlated with disease staging (r=-0.881). The levels of collagenous fibers gradually increased with cancer progression and showed a positive correlation (r=0.619). In summary, the study demonstrated that MMP1/2 and TIMP1/2 expression levels, and collagenous and elastic fiber levels were significantly correlated with disease progression in a hamster model of tongue cancer.

VGF, a non-acronymic neuropeptide, is important in the pathogenesis of major depressive disorder (MDD) and in the functioning and efficacy of some antidepressant drugs. In this study we assessed whether serum VGF levels change in MDD patients and if antidepressant treatments can restore these changes.We measured serum VGF concentrations using sandwich ELISA in drug-free MDD patients before treatment began (n = 26) and at 8 weeks after antidepressant treatment (n = 26) with escitalopram and duloxetine, two common antidepressants. The severity of depression was assessed with the 17-item Hamilton Depression Rating Scale (HDRS).VGF serum levels were significantly lower in MDD patients compared to controls (P = .002), even after controlling for the effects of age and education (P = .037), and they were reversed by 8 weeks of drug treatment (P.0001). Both escitalopram and duloxetine restored the decreased serum VGF levels (P .05). We observed no correlation between VGF levels and HDRS scores in pre-treatment MDD patients (P = .879).The results suggest that VGF may be implicated in the pathophysiology of MDD and in the mechanisms underlying the action of antidepressants, and serum VGF may be regarded as a trait parameter for MDD.

Yingying Yue,1,2 Rui Liu,3 Yin Cao,4 Yanfeng Wu,5 Shining Zhang,6 Huajie Li,7 Jijun Zhu,8 Wenhao Jiang,1,2 Aiqin Wu,9 Yonggui Yuan1,2 1Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 2Institute of Psychosomatics, Medical school of Southeast university, Nanjing, 3School of Information Science and Engineering, Southeast University, Nanjing, 4Department of Neurology, Changzhou Second People’s Hospital Affiliated to NanjingMedical University, Changzhou, 5Department of Neurology, The Second Affiliated Hospital of Nanjing Medical University, 6Department of Psychiatry, Nanjing Brain Hospital of Nanjing Medical University, Nanjing, 7Department of Neurology, Changzhou First People’s Hospital, Changzhou, 8Department of Neurology, Yancheng Third People’s Hospital, Yancheng, 9Department of Psychosomatics, The Affiliated First Hospital of Suzhou University, Suzhou, People’s Republic of China Aim: Poststroke depression (PSD) is the most common complication of stroke. However, some stroke survivors with depression cannot meet the diagnostic criteria of PSD. The aim of this study was to propose the new conception of stroke patients with depression and then make them to receive reasonable diagnosis and treatment. Methods: We first put forward the opinion that the general PSD should consist of PSD disorder (PSDD) and PSD symptoms (PSDS) according to the Diagnostic and Statistical Manual of Mental Disorder – Fifth Edition (DSM-5) and ZhongDa diagnostic criteria – first edition (ZD-1), respectively. The ZD-1 was established based on the suggestions of 65Chinese chief doctors considering that the symptoms of PSDS might be different from those of PSDD and the duration of DSM-5 was too strict. Then, 166 stroke inpatients were recruited, and the study was conducted using the diagnosis and classification of PSD to verify the new concept. Results: A total of 24 (14.46%) and 80 (48.19%) stroke patients were diagnosed with PSDD and PSDS, respectively, according to individual diagnosis criteria. Moreover, patients meeting the diagnostic criteria of PSDD should satisfy the criteria of PSDS first. The distribution frequencies of depressive symptoms were different, which suggested that there might be discrepant depressive symptoms between PSDS and PSDD. Conclusion: The present study proposes new opinion about the classification and diagnosis of depression in stroke survivors. The definition and criteria of PSDS are beneficial to explore phenomenological consistency and provide useful information for early recognition and appropriate interventions. Keywords: poststroke depression, subtypes, diagnostic criteria

Most dimensionality reduction techniques are based on one metric or one kernel, hence it is necessary to select an appropriate kernel for kernel-based dimensionality reduction. Multiple kernel learning for dimensionality reduction (MKL-DR) has been recently proposed to learn a kernel from a set of base kernels which are seen as different descriptions of data. As MKL-DR does not involve regularization, it might be ill-posed under some conditions and consequently its applications are hindered. This paper proposes a multiple kernel learning framework for dimensionality reduction based on regularized trace ratio, termed as MKL-TR. Our method aims at learning a transformation into a space of lower dimension and a corresponding kernel from the given base kernels among which some may not be suitable for the given data. The solutions for the proposed framework can be found based on trace ratio maximization. The experimental results demonstrate its effectiveness in benchmark datasets, which include text, image and sound datasets, for supervised, unsupervised as well as semi-supervised settings.

Identifying a robust pretreatment neuroimaging marker would be helpful for the selection of an optimal therapy for major depressive disorder (MDD). We recruited 82 MDD patients [n = 42 treatment-responsive depression (RD) and n = 40 non-responding depression (NRD)] and 50 healthy controls (HC) for this study. Based on the thresholded partial correlation matrices of 58 specific brain regions, a graph theory approach was applied to analyse the topological properties. When compared to HC, both RD and NRD patients exhibited a lower nodal degree (Dnodal) in the left anterior cingulate gyrus; as for RD, the Dnodal of the left superior medial orbitofrontal gyrus was significantly reduced, but the right inferior orbitofrontal gyrus was increased (all P

To identify the association between the functional and structural changes of default mode network (DMN) underlying the cognitive impairment in Late-onset depression (LOD), 32 LOD patients and 39 normal controls were recruited and underwent resting-state fMRI, DTI scans, and cognitive assessments. Seed-based correlation analysis was conducted to explore the functional connectivity (FC) of the DMN. Deterministic tractography between FC-impaired regions was performed to examine the structural connectivity (SC). Partial correlation analyses were employed to evaluate the cognitive association of those altered FC and SC. Compared with controls, LOD patients showed decreased FC between DMN and the cingulo-opercular network (CON), as well as the thalamus. Decreased FA and increased RD of these fiber tracts connecting DMN with CON were found in LOD patient. The DMN-CON FC and the FA, RD of the fiber tracts were both significantly correlated with the cognitive performance. Therefore, the cognitive impairment in LOD might be associated with the decreased FC between the DMN and the CON, which probably resulted from the demyelination of the white matter.

Accurate assessment of the left ventricular ejection fraction (LVEF) is crucial for making therapeutic decisions. The echocardiographic method for quantification of LVEF recommended by the American Society of Echocardiography is the biplane Simpson's method. However, this method requires manual

In this paper, we propose a novel multimodal matching model for the image and sentence based on their multiple representations. Each representation of the image or sentence undergoes an independent neural network, consisting of multiple layers of nonlinear mappings to yield the corresponding embedding. Besides exploiting the image and sentence relationship based on their embeddings, we propose one novel loss to further exploit the relationship within each single modality, namely, image and sentence based on the yielded multiple embeddings, which is used to train the neural networks simultaneously. The experimental results demonstrate that multiple representations can help to capture the image contents and the sentence semantic meaning more precisely, thus making comprehensive exploitations of the complicated image and sentence matching relationship. More concretely, the proposed matching model significantly outperforms the state-of-the-art approaches in bidirectional image-sentence retrieval on the Flickr8K, Flickr30K, and Microsoft COCO datasets.

Background Current diagnoses of Schizophrenia (SZ) and related psychiatric disorders are catalogued by phenomenological principles and clinical descriptions. Complicated gene and environmental factor interactions likely play an important role in the onset of SZ and induce great heterogeneity. This unclear etiology and heterogeneity prevent the establishing of accurate subgroups within SZ through clinical observation alone; simultaneously, the boundaries among psychiatric disorders previously drawn by phenomenological findings are merging because there is great overlap, especially in genetic variations and brain imaging findings. By characterizing and quantifying brain regions affected in psychiatric disorders, imaging genetics is a valuable research method to fulfill a new diagnostic system. We review contributions of imaging genetics to SZ diagnosis, limitations, and possible future directions. Methods Among numerous imaging genetic studies, we reviewed recent studies with relatively clear and consistent findings. They were either done in task-phase imaging to indicate certain cognitive impairments of SZ, or the genes included were highlighted in SZ itself or an important cognitive domain related to SZ. Results Some pioneering studies contribute to subtypes and disorder boundaries. The clustering study from Arnedo et al. formed correlations between disjoined networks of symptom groups and Single Nucleotide Polymorphism (SNP) sets, and it may be promising for future SZ subtypes. Bipolar-SZ Network on Intermediate Phenotypes studies combining various clinical biomarkers and brain imaging divided SZ, bipolar disorder and schizoaffective disorder into three different “biotypes” regardless original diagnoses, and it provided meaningful insight into the disorder boundaries. For imaging genetics research, large numbers of associations between functional imaging intermediate phenotypes and genes have been found in working memory, episodic memory, emotion, attention, cognitive control, and theory of mind in SZ or healthy risk allele carriers. Such associations were difficult to identify while using structural brains measures. Discussion Imaging genetics will continue to shape the diagnosis of SZ and related disorders. However, there are still problems to solve. Most importantly, the logic behind imaging genetics has been questioned. Franke et al. reminds us the structural brain deficits believed to be important pathological alterations of SZ may reflect prenatal and later development environmental effects not specific to SZ, or the diagnostic category of SZ may not be uniformly organized. The following aspects need future effort. Firstly, more brain measures need to be introduced, and the criteria of intermediate phenotypes should be carefully checked. The specificity of brain alterations will need careful examination in clinical settings. Secondly, more genetic factors need to be collected. Polygenic risk scores, parallel independent component analysis and other clustering method may be promising. Factors other than SNPs, such as copy number variations and regulatory factors will need to be included. Finally, imaging genetics will need compatible phenomenological assessment scales which need not distinguish current diagnoses but provide a comprehensive scan of clinical features.

In power plants of steel mills, the performance of pulverized coal and blast-furnace gas co-fired boilers was tested according to GB/T10184-2015 "Performance test code of utility boiler". However, the calculation and analysis models in this code are mainly based on conventional fuels. Besides, the special properties of blast furnace gas were not taken into account. Therefore, the code GB/T10184-2015 is not suitable to be directly used for pulverized coal and blast furnace gas co-fired boilers. According to the differences between co-fired boilers and conventional boilers, the key points for calculating the performance of co-fired boilers were analyzed on the basis of GB/T10184-2015, and the corresponding improved methods were proposed, in view of fuel composition conversion, combustion calculation, coal feed rate, exhaust gas temperature value, and pollutant emission concentration conversion. The results can provide reference for the performance test of such boilers, with certain practical significance.

Recently, deep learning methods that employ stacked denoising autoencoders (SDAs) have been successfully applied in domain adaptation. Remarkable performance in multi-domain sentiment analysis datasets has been reported, making deep learning a promising approach to domain adaptation problems. SDAs are distinguished by learning robust data representations for recovering the original features that have been artificially corrupted with noise. The idea has been further exploited to marginalize out the random corruptions by a state-of-the-art method called mSDA. In this paper, a deep learning method for domain adaptation called l2,1-norm stacked robust autoencoders (l2,1-SRA) is proposed to learn useful representations for domain adaptation tasks. Each layer of l2,1-SRA contains two steps: a robust linear reconstruction step which is based on l2,1 robust regression and a non-linear squashing transformation step. The experimental results demonstrate that the proposed method is very effective in multiple cross domain classification datasets which include Amazon review dataset, spam dataset from ECML/PKDD discovery challenge 2006 and 20 newsgroups dataset.

Depression is the most disabling disorder worldwide that accounts for the highest proportion of global burden attributable to mental disorders. Major depressive disorder (MDD) is characterized by deep sadness, reduced energy, vegetative nervous system dysregulation, cognitive dysfunction, and even a high suicidal tendency. Although other treatment choices are available, antidepressant medication is the front-line treatment option for MDD. Regarding clinical efficacy, only ~50% of patients respond to frontline antidepressants, and

No effective targeted therapies exist for cancers with somatic KRAS mutations. Here we develop a synthetic lethal chemical screen in isogenic KRAS-mutant and wild-type cells to identify clinical drug pairs. Our results show that dual inhibition of polo-like kinase 1 and RhoA/Rho kinase (ROCK) leads to the synergistic effects in KRAS-mutant cancers. Microarray analysis reveals that this combinatory inhibition significantly increases transcription and activity of cyclin-dependent kinase inhibitor p21WAF1/CIP1, leading to specific G2/M phase blockade in KRAS-mutant cells. Overexpression of p21WAF1/CIP1, either by cDNA transfection or clinical drugs, preferentially impairs the growth of KRAS-mutant cells, suggesting a druggable synthetic lethal interaction between KRAS and p21WAF1/CIP1. Co-administration of BI-2536 and fasudil either in the LSL-KRASG12D mouse model or in a patient tumour explant mouse model of KRAS-mutant lung cancer suppresses tumour growth and significantly prolongs mouse survival, suggesting a strong synergy in vivo and a potential avenue for therapeutic treatment of KRAS-mutant cancers., KRas is frequently mutated in multiple cancer types; identifying drugs to treat such cancers is a good therapeutic strategy. Here, the authors perform a synthetic lethal screen in mice and show that inhibiting Plk1 and ROCK results in the inhibition of tumour growth by increasing expression of the tumour suppressor p21.

Boiler thermal efficiency is a key index of unit performance assessment. The thermal efficiency of a pulverized coal and blast furnace gas co-firing boiler can be calculated based on either the mixed fuel combustion calculation or the separate combustion calculation. Based on the Chinese Standard Specification GB/T10184-2015 "Performance test code for utility boiler", this paper proposes a calculating model for the thermal efficiency of the co-firing boilers based on the calculation of the itemized fuel combustion, and takes a 220t/h boiler burning pulverized coal and blast furnace gas in a steel plant as the research object. The comparison between the proposed model and the traditional model based on mixed fuel combustion calculation shows that the boiler thermal efficiency calculated results obtained by the two methods are consistent. The new model proposed in this paper is reasonable and feasible. It can be used to calculate the thermal efficiency of the pulverized coal and blast furnace gas co-fired boilers.

Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions. The primary difficulty in designing effective learning models to solve such problems lies in how to bridge the gap between the source and target distributions. In this paper, we provide comprehensive analysis of feature learning algorithms used in conjunction with linear classifiers for domain adaptation. Our analysis shows that in order to achieve good adaptation performance, the second moments of the source domain distribution and target domain distribution should be similar. Based on our new analysis, a novel extremely easy feature learning algorithm for domain adaptation is proposed. Furthermore, our algorithm is extended by leveraging multiple layers, leading to a deep linear model. We evaluate the effectiveness of the proposed algorithms in terms of domain adaptation tasks on the Amazon review dataset and the spam dataset from the ECML/PKDD 2006 discovery challenge., ijcai

Background Anhedonia is a prominent symptom of major depressive disorder (MDD) and schizophrenia. At present, it is believed that hedonic processing rather consists of the anticipatory and consummatory phase. The aim of this research is to explore the different anhedonia components in MDD and schizophrenia in Chinese populations. Methods A Chinese version of the Temporal Experience of Pleasure Scale (TEPS) was used to evaluate 176 MDD patients, 346 schizophrenia patients, and 268 healthy controls. Additionally, the 17-item Hamilton Depression Rating Scale (HAMD-17) was used for MDD patients, while the Positive and Negative Syndrome Scale (PANSS) was applied for schizophrenia. Results The scores of consummatory (TEPS-CON) and anticipatory pleasure (TEPS-ANT) in MDD and schizophrenia were both significantly lower than healthy controls (both P 0.05). There was no significant correlation between TEPS-ANT and any clinical variables (P > 0.05). Conclusions The consummatory and anticipatory pleasures were both impaired in MDD and schizophrenia. Consummatory and anticipatory anhedonia can be considered as a “state” in MDD, but as a “trait” in schizophrenia.

In the high-speed system of space optical communication, NRZ modulation on differential phase shift keying, 66% duty cycle RZ modulation and 33% duty cycle RZ modulation. By using the Optisystem optical communication software design of DPSK optical communication system based on communication rate in 40Gb/s, the signal power is -42dBm, at a distance of 100 km for atmospheric channel 0.5dB/km transmission attenuation and then through the light of the noise coefficient of 3.2 dB preamplifier makes light signal gain after reaching 40 dB, compares different modulation modes respectively discussed.

ObjectiveThe potential pattern of regional cerebral blood flow (rCBF) in major depressive disorder (MDD) underlies different response to antidepressants medication remain unclear. This study aimed to investigate the differences of rCBF between patients with different treatment response.MethodsEighty MDD patients [(44 treatment-responsive depression (RD) and 36 non-responding depression (NRD)] and 42 healthy controls (HC) underwent pulsed arterial spin labeling (PASL) scans in magnetic resonance imaging and clinical estimates. The exact rCBF values of each groups were obtained via quantification evaluation.ResultsCompared to NRD, the RD patients showed decreased rCBF values in frontal sensorimotor network (i.e. left paracentral lobule, left medial frontal gyrus, right superior frontal gyrus and right middle frontal gyrus), and further receiver operating curve (ROC) analyses demonstrated that the altered rCBF in these four regions exhibited outstanding performance on distinguishing NRD from RD. The NRD also exhibited reduced rCBF in bilateral cerebellum posterior lobe and right middle occipital gyrus and elevated rCBF in right postcentral gyrus and right middle frontal gyrus as compared to HC.ConclusionsThe decreased rCBF in frontal sensorimotor network appeared to be distinct characteristics for NRD, and might be severed as promising neuroimaging markers to differentiate depressed patients with weak early response to antidepressant medication. These findings expand our understanding of neural substrate underlying the antidepressant efficacy.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Summary Aims A longitudinal study investigated the remitted geriatric depression (RGD) patients' persistent cognitive impairment and potential correlation with their PCC functional connectivity network. Methods A total of 14 RGD patients and 18 matched controls were recruited. All subjects finished the neuropsychological tests and functional magnetic resonance imaging scan at baseline and follow-up. A spherical region of interest was placed in PCC to calculate the functional connectivity, and further analysis was employed to detect correlations between longitudinal changes in the brain regions and neuropsychological data. Results There were significant cognitive declines in RGD patients at baseline and follow-up. Altered patterns of functional connectivity were detected within the RGD group showing correlations with neuropsychological tests. The longitudinal change in functional connectivity between PCC and cerebellum posterior lobe was correlated with longitudinal changes in auditory verbal memory test-recall (r = 0.550, P = 0.042). The longitudinal change in functional connectivity between PCC and right parahippocampal gyrus was correlated with Trail Making Test-A (r = 0.631, P = 0.015). The longitudinal change in functional connectivity between PCC and supramarginal_R was correlated with Mini-Mental State Examination (r = −0.630, P = 0.016). Conclusions RGD patients performed worse cognitive function, and altered PCC functional connectivity network might have a role in these cognitive declines.

BackgroundThe intuitive association between cognitive dysfunction in late onset depression (LOD) and the aberrant functional activity in the brain's default-mode network (DMN) has prompted interest in exploring the role of the DMN in LOD. The altered pattern of resting state voxel-mirrored homotopic connectivity (VMHC) in cognitive processes is not yet well understood in LOD.MethodsThe study was designed to examine the implicit coupling between the alteration of interhemispheric functional coordination and cognitive impairment in LOD. Thirty-one LOD patients and 37 matched healthy controls (HC) underwent neuropsychological tests and functional magnetic resonance imaging (fMRI) in this study.ResultsCompared to HC group, attenuated VMHC in superior frontal gyrus, superior temporal gyrus, posterior cerebellar lobe, postcentral and precentral gyrus was observed in LOD. Neuro-behavioral relevancy approach revealed that the imbalanced interhemispheric functional coordination in bilateral cerebellum was positively correlated with the performance of trail making test in LOD (r = 0.367, P = 0.040).ConclusionAltered linkage pattern of intrinsic homotopic connectivity and cognition was firstly investigated in LOD, and it would provide a novel clue to reveal the neural substrates underlying the cognitive dysfunction in LOD.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Aiming at completing search task under disaster condition problems, an optimizing strategy based on multi-sensor information fusion is proposed in this paper. Firstly, search and rescue robot control system hardware circuit is designed; secondly, embedded system software design is realized; and then, a polymerization Kalman filtering model is proposed, it uses local Kalman filter weights scheduling principle to improve system fault-tolerant ability and overall fusion performance. What’s more, Adaboost algorithm realizes the multi-sensor information optimal fusion. Through simulation test experiment, the robot search traversal ability is verified under unstructured environment.

Transferring knowledge from auxiliary datasets has been proved useful in machine learning tasks. Its adoption in clustering however is still limited. Despite of its superior performance, spectral clustering has not yet been incorporated with knowledge transfer or transfer learning. In this paper, we make such an attempt and propose a new algorithm called transfer spectral clustering (TSC). It involves not only the data manifold information of the clustering task but also the feature manifold information shared between related clustering tasks. Furthermore, it makes use of co-clustering to achieve and control the knowledge transfer among tasks. As demonstrated by the experimental results, TSC can greatly improve the clustering performance by effectively using auxiliary unlabeled data when compared with other state-of-the-art clustering algorithms.

Triptolide (TP) has been used in the treatment of rheumatoid arthritis (RA), but its mechanism of action is not understood. T-cell activation and associated release of cytokines appear to be major factors in the pathogenesis of RA. The overexpression of T-cell receptor (TCR) variable gene (V gene) fragments can cause the activation and infiltration of autoreactive T cells. This study examines the effects of TP on rats with collagen-induced arthritis (CIA). The levels of interleukin-10 (IL-10) in the serum were examined with ELISA. Compared to the CIA group, the levels of IL-10 were greater in the TP treatment group. Real-time quantitative polymerase chain reaction confirmed that the expression of TCR V beta (BV) 15 and TCR BV19 was increased in the CIA group, whereas in the TP treatment group, the expression was decreased. In this study, TP was found to enhance IL-10 levels and decrease the expression levels of TCR BV15 and TCR BV19. These changes might help explain the effectiveness of TP in the treatment of RA.

The change of appearance characteristic is caused by changing viewpoint in the maneuvering target tracking. The traditional Camshift tracking algorithm based on a single color histogram model is not robust to this variation. In this paper, image moment invariant is proposed to describe maneuvering target to enhance model description, through its recursive algorithm to optimize the target model and refresh the weight value. The experiment result demonstrates that, comparing with the single fixed model and the self-adaption single model, the new algorithm can adapt for the appearance variation of the target rapidly and with small calculated amount.

Aiming at puncture image characteristic of robot auxiliary radio frequency ablation, a modified tradition camera calibration method is proposed in this paper. Based on the comprehensive analysis for puncture image distortion characteristic, there are three geometric distortions in the puncture image system: radial distortion, centrifugal distortion and thin lens distortion, we research the distortions and establish the correction matrixes one by one, and then establish the camera mathematical model under the puncture condition, complete the coordinate systems’ conversion calculation to obtain the camera internal and external parameters, finally to get the puncture system calibration polynomial. Through the comparative experiment that takes the area as parameter, we get that the puncture system calibration method after image correction can meet the accuracy requirement in actually.

Covert channels are widely used for secret information transmission on networks. Although packet length based covert channels have greatly enhanced tamper resistance, they are still vulnerable to detections due to abnormal network traffic. Thus, in this paper, we are motivated to design a novel protocol-independent covert channel that could deliver normal network traffic. We simulated our covert channel on HTTP protocol and analyzed its security and bandwidth.

To study the pattern extrahepatic arteriy supply to hepatocellular carcinoma and catheterization technique for interventional therapy.Routine celiac and superior mesenteric artery angiography was done before transcatheter arterial chemoembolization (TACE) for 78 collateral arterial pathways of 62 hepatocellular carcinoma patients. Super selective catheterization and transcatheter dual arterial chemoembolization (TDACE) to extrahepatic arterial and hepatic arteries were performed.Extrahepatic blood supply was found in 43.1% of hepatocellular carcinoma patients. There was close correlation between extrahepatic arterial blood supply and location of tumor. Success rate of super selective extrahepatic artery catheterization was 71.8% by the combined use of RH, Cobra and SP catheters. Follow-up studies revealed reduction of tumor and complete dense deposition of lipiodol.Ample extrahepatic arterial blood supply is found in hepatocellular carcinoma. Transcatherdual arterial chemoembolization is effective, necessary and feasible for hepatocellular carcinoma with extrahepatic arterial blood supply.

Background Major depressive disorder (MDD) is associated with decreased function of cortico-limbic circuits, which play important roles in the pathogenesis of MDD. Abnormal functional connectivity (FC) with the amygdala, which is involved in cortico-limbic circuits, has also been observed in MDD. However, little is known about connectivity alterations in late-onset depression (LOD) or whether disrupted connectivity is correlated with cognitive impairment in LOD. Methods and Results A total of twenty-two LOD patients and twenty-two matched healthy controls (HC) underwent neuropsychological tests and resting state functional magnetic resonance imaging (rs-fMRI). Regional homogeneity (ReHo) and FC with bilateral amygdala seeds were used to analyze blood oxygen level-dependent fMRI data between two groups. Compared with HC, LOD patients showed decreased ReHo in the right middle frontal gyrus and left superior frontal gyrus. In the LOD group, the left amygdala had decreased FC with the right middle frontal gyrus and the left superior frontal gyrus in the amygdala positive network, and it had increased FC with the right post-central gyrus in the amygdala negative network. However, significantly reduced FC with the right amygdala was observed in the right middle occipital gyrus in the amygdala negative network. Further correlative analyses revealed that decreased FC between the amygdala and the right middle occipital gyrus was negatively correlated with the verbal fluency test (VFT, r = −0.485, P = 0.022) and the digit span test (DST, r = −0.561, P = 0.007). Conclusions Our findings of reduced activity of the prefrontal gyrus and abnormal FC with the bilateral amygdala may be key markers of cognitive dysfunction in LOD patients.

Background Major depressive disorder (MDD) is associated with abnormal functional connectivity (FC) of amygdala and decreased function of cortico-limibic circuit, which play important roles in the pathogenesis of MDD. However, little is known about the connectivity alterations in late-onset depression (LOD), and whether such disrupted function is correlated with cognitive impairment is unclear. Methods A total of twenty-three LOD patients and thirty-seven controls underwent neuropsychological tests and resting-state functional magnetic resonance imaging (RS-fMRI). Regional homogeneity (ReHo) and FC of bilateral amygdala seed were used to analyze blood oxygen level-dependent fMRI data between groups. Results Compared to controls, LOD groups showed weaker functional activity in bilateral middle frontal gyrus and left medial orbitofrontal gyrus, moreover, the decreased ReHo was positively correlated with Trail making test-B score (TMTB, r= 0.462,P= 0.04). In aspects of FC, left amygdala has reduced FC with right fusiform gyrus, right superior temporal gyrus and right putamen, while right amygdala has reduced FC with left cerebellum. Further correlative analysis found that the decreased FC between amygdala and right putamen was positively correlated with Verbal fluency test-verb score (VFT-verb, r= 0.513,P= 0.021) and the decreased FC between amygdala and superior temporal gyrus was positively correlated with Auditory Verbal Memory Test-delayed recall score (AVLT-delayed recall, r= 0.446,P= 0.049). Conclusions Our finding of reduced activation of prefrontal gyrus as well as decreased connection of bilateral amygdala may be key factors of impaired cognitive function in LOD patients and these changes could be early indicator for cognitive deficits.

IntroductionEmerging evidences indicate that the alteration of interhemispheric functional coordination may be involved in the pathogenesis of major depressive disorder (MDD). In present study, we aim to explore the potential marker by using the voxel-mirrored homotopic connectivity (VMHC) approach, which may be contributing to predict the clinical prognosis in MDD.MethodsEighty-two MDD patients and 50 normal control (NC) subjects participated in this study. We divided the MDD group into unremitted and remitted group according to the reduction rate of Hamilton Rating Scale for Depression (HAMD) within 2 weeks.ResultsThe study detected significantly decreased VMHC in bilateral precuneus (pCu), inferior temporal gyrus (ITG) and increased VMHC in middle frontal gyrus (MFG) and caudate nucleus when compared remitted depression (RD) group to unremitted depression (URD) group. Meanwhile, when compared with NC group, the URD group presented reduced VMHC in bilateral cerebellum anterior lobe, thalamus and postcentral gyrus. Furthermore, the VHMC in media frontal gyrus, postcentral gyrus and precentral gyrus were significantly decreased in RD group. Correlation analysis suggested that reduced VMHC in bilateral pCu was negatively correlated with the baseline HAMD score of URD (r = −0.325, P = 0.041). Receiver operating characteristic (ROC) curve indicated that three regional VMHC changes could identify depressed patient with poorer treatment response: ITG [area under curve (AUC) = 0.699, P = 0.002, 95% CI = 0.586–0.812], MFG (AUC = 0.692, P = 0.003, 95% CI = 0.580–0.805), pCu (AUC = 0.714, P = 0.001, 95% CI = 0.603–0.825).ConclusionThe current study combined with previous evidence indicates that the subdued intrinsic interhemispheric functional connectivity might represents a novel neural trait involved in the pathophysiology of MDD.Disclosure of interestThe authors have not supplied their declaration of competing interest.