Jian Liu

Both SKP2 and transforming growth factor-β1 play important roles in cancer metastasis through different mechanisms: TGF-β1 via induction of epithelial-mesenchymal transition and SKP2 via downregulating p27kip1. Recent studies indicated that c-Myc and Akt1 were active players in metastasis. In this study we demonstrated a crosstalk between these pathways. Specifically, we found that TGF-β1 treatment increased SKP2 expression accompanied with increased phosphorylation of Akt1 and c-Myc protein accumulation during EMT. We demonstrated that Akt1 was required for TGF-β1-mediated SKP2 upregulation and that c-Myc transcription factor specifically bound to the promoter of SKP2 for its enhanced transcription. Analysis of 25 samples of normal human skin, nevi, and melanomas revealed a positive correlation between c-Myc and SKP2 accumulation. Furthermore, accumulation of SKP2 and c-Myc proteins was significantly higher in metastatic melanoma samples as compared with that in primary melanomas, which again was higher than that in normal skin or nevi. In summary, our results integrated TGF-β1 signals to SKP2 via Akt1 and c-Myc during EMT, and provided, to our knowledge, a previously unreported mechanistic molecular event for TGF-β1-induced metastasis in human melanoma. © 2014 The Society for Investigative Dermatology.

OBJECTIVE: To evaluate the association of nicotinic acetylcholine receptor single nucleotide polymorphism with 7-day point prevalence abstinence in randomized clinical trials of smoking cessation therapies in individuals grouped by pharmacotherapy randomization to inform the development of personalized smoking cessation therapy. MATERIALS AND METHODS: We quantified association of four SNPs at three nAChRs with abstinence in eight randomized clinical trials. Participants were 2633 outpatient treatment-seeking, self-identified European ancestry individuals smoking at least 10 cigarettes/day, recruited through advertisement, prescribed pharmacotherapy, and provided with behavioral therapy. Interventions included nicotine replacement therapy, bupropion, varenicline, placebo, or combined NRT and bupropion, and five modes of group and individual behavioral therapy. Outcome measures tested in multivariate logistic regression were end of treatment and 6 month abstinence, with demographic, behavioral, and genetic covariates. RESULTS: 'Risk' alleles previously associated with smoking heaviness were significantly associated with reduced abstinence in the PLA pharmacotherapy group at 6MO [for rs588765, odds ratio 0.41 ], and at end of treatment and at 6MO [for rs1051730, 0.42 and 0.31 ], and with increased abstinence in the NRT PG at 6MO [for rs588765, 2.07 and for rs1051730, 2.54 ]. We observed significant heterogeneity in rs1051730 effects between PGs. CONCLUSION: chr15q25.1 nAChR SNP risk alleles for smoking heaviness significantly increase relapse with PLA treatment and significantly increase abstinence with NRT. These SNP-PG associations require replication in independent samples for validation, and testing in larger sample sizes to evaluate whether similar effects occur in other PGs. © 2013 Wolters Kluwer Health | Lippincott Williams &Wilkins.

We utilized a cohort of 828 treatment-seeking self-identified white cigarette smokers to rank candidate gene single nucleotide polymorphisms associated with the Fagerström Test for Nicotine Dependence, a measure of nicotine dependence which assesses quantity of cigarettes smoked and time- and place-dependent characteristics of the respondent's smoking behavior. A total of 1123 SNPs at 55 autosomal candidate genes, nicotinic acetylcholine receptors and genes involved in dopaminergic function, were tested for association to baseline FTND scores adjusted for age, depression, education, sex, and study site. SNP P-values were adjusted for the number of transmission models, the number of SNPs tested per candidate gene, and their intragenic correlation. DRD2, SLC6A3, and NR4A2 SNPs with adjusted P-values <0.10 were considered sufficiently noteworthy to justify further genetic, bioinformatic, and literature analyses. Each independent signal among the top-ranked SNPs accounted for ∼1% of the FTND variance in this sample. The DRD2 SNP appears to represent a novel association with nicotine dependence. The SLC6A3 SNPs have previously been shown to be associated with SLC6A3 transcription or dopamine transporter density in vitro, in vivo, and ex vivo. Analysis of SLC6A3 and NR4A2 SNPs identified a statistically significant gene-gene interaction, consistent with in vitro evidence that the NR4A2 protein product regulates SLC6A3 transcription. A community cohort of N=175 multiplex ever-smoking pedigrees provided nominal evidence for association with the FTND at these top ranked SNPs, uncorrected for multiple comparisons. © 2009 Nature Publishing Group All rights reserved.

Although the efficacy of pharmacotherapy for tobacco dependence has been previously demonstrated, there is substantial variability among individuals in treatment response. We performed a systems-based candidate gene study of 1295 single nucleotide polymorphisms in 58 genes within the neuronal nicotinic receptor and dopamine systems to investigate their role in smoking cessation in a bupropion placebo-controlled randomized clinical trial. Putative functional variants were supplemented with tagSNPs within each gene. We used global tests of main effects and treatment interactions, adjusting the P-values for multiple correlated tests. An SNP in the 3′ UTR region of the β2 nicotinic acetylcholine receptor subunit has an impact on abstinence rates at the end of treatment and after a 6-month follow-up period. This latter P-value is also significant with adjustment for the number of genes tested. Independent of treatment at 6-month follow-up, individuals carrying the minor allele have substantially decreased the odds of quitting. Effect of estimates indicate that the treatment is more effective for individuals with the wild-type compared with individuals carrying the minor allele, although this difference is only suggestive. Furthermore, this SNP demonstrated a role in the time to relapse and an impact on withdrawal symptoms at target quit date. Overall, while our results indicate strong evidence for CHRNB2 in ability to quit smoking, these results require replication in an independent sample. © 2008 The Author.

Contactin-Associated protein-like 2 is a large multidomain neuronal adhesion molecule implicated in a number of neurological disorders, including epilepsy, schizophrenia, autism spectrum disorder, intellectual disability, and language delay. We reveal here by electron microscopy that the architecture of CNTNAP2 is composed of a large, medium, and small lobe that flex with respect to each other. Using epitope labeling and fragments, we assign the F58C, L1, and L2 domains to the large lobe, the FBG and L3 domains to the middle lobe, and the L4 domain to the small lobe of the CNTNAP2 molecular envelope. Our data reveal that CNTNAP2 has a very different architecture compared with neurexin 1α, a fellow member of the neurexin superfamily and a prototype, suggesting that CNTNAP2 uses a different strategy to integrate into the synaptic protein network. We show that the ectodomains of CNTNAP2 and contactin 2 bind directly and specifically, with low nanomolar affinity. We show further that mutations in CNTNAP2 implicated in autism spectrum disorder arenot segregated but are distributed over the whole ectodomain. The molecular shape and dimensions of CNTNAP2 place constraints on how CNTNAP2 integrates in the cleft of axo-glial and neuronal contact sites and how it functions as an organizing and adhesive molecule.

Knowledge of three-dimensional structures of each individual particles of asymmetric and flexible proteins is essential in understanding those proteins' functions; but their structures are difficult to determine. Electron tomography provides a tool for imaging a single and unique biological object from a series of tilted angles, but it is challenging to image a single protein for three-dimensional reconstruction due to the imperfect mechanical control capability of the specimen goniometer under both a medium to high magnification and an optimized beam coherence condition. Here, we report a fully mechanical control method for automating ET data acquisition without using beam tilt/shift processes. This method could reduce the accumulation of beam tilt/shift that used to compensate the error from the mechanical control, but downgraded the beam coherence. Our method was developed by minimizing the error of the target object center during the tilting process through a closed-loop proportional-integral control algorithm. The validations by both negative staining and cryo-electron microscopy suggest that this method has a comparable capability to other ET methods in tracking target proteins while maintaining optimized beam coherence conditions for imaging.

DNA base pairing has been used for many years to direct the arrangement of inorganic nanocrystals into small groupings and arrays with tailored optical and electrical properties. The control of DNA-mediated assembly depends crucially on a better understanding of three-dimensional structure of DNA-nanocrystal-hybridized building blocks. Existing techniques do not allow for structural determination of these flexible and heterogeneous samples. Here we report cryo-electron microscopy and negative-staining electron tomography approaches to image, and three-dimensionally reconstruct a single DNA-nanogold conjugate, an 84-bp double-stranded DNA with two 5-nm nanogold particles for potential substrates in plasmon-coupling experiments. By individual-particle electron tomography reconstruction, we obtain 14 density maps at ~ 2-nm resolution. Using these maps as constraints, we derive 14 conformations of dsDNA by molecular dynamics simulations. The conformational variation is consistent with that from liquid solution, suggesting that individual-particle electron tomography could be an expected approach to study DNA-assembling and flexible protein structure and dynamics.

Cerebral ischemia, a focal or global insufficiency of blood flow to the brain, can arise through multiple mechanisms, including thrombosis and arterial hemorrhage. Ischemia is a major driver of stroke, one of the leading causes of morbidity and mortality worldwide. While the general etiology of cerebral ischemia and stroke has been known for some time, the conditions have only recently been considered treatable. This report describes current research in this field seeking to fully understand the pathomechanisms underlying stroke; to characterize the brain's intrinsic injury, survival, and repair mechanisms; to identify putative drug targets as well as cell-based therapies; and to optimize the delivery of therapeutic agents to the damaged cerebral tissue. © 2013 New York Academy of Sciences.

Group-level variance estimates of zero often arise when fitting multilevel or hierarchical linear models, especially when the number of groups is small. For situations where zero variances are implausible a priori, we propose a maximum penalized likelihood approach to avoid such boundary estimates. This approach is equivalent to estimating variance parameters by their posterior mode, given a weakly informative prior distribution. By choosing the penalty from the log-gamma family with shape parameter greater than 1, we ensure that the estimated variance will be positive. We suggest a default log-gamma penalty with λ→0, which ensures that the maximum penalized likelihood estimate is approximately one standard error from zero when the maximum likelihood estimate is zero, thus remaining consistent with the data while being nondegenerate. We also show that the maximum penalized likelihood estimator with this default penalty is a good approximation to the posterior median obtained under a noninformative prior. Our default method provides better estimates of model parameters and standard errors than the maximum likelihood or the restricted maximum likelihood estimators. The log-gamma family can also be used to convey substantive prior information. In either case-pure penalization or prior information-our recommended procedure gives nondegenerate estimates and in the limit coincides with maximum likelihood as the number of groups increases. © 2013 The Psychometric Society.

Traditional spatial enabled grammars lack flexibility in specifying the spatial semantics of graphs. This paper describes a new graph grammar formalism called the multigranularity Coordinate Graph Grammar (mgCGG) for spatial graphs. Based on the Coordinate Graph Grammar (CGG), the mgCGG divides coordinates into two categories, physical coordinates and grammatical coordinates, where physical coordinates are the common coordinates in the real world, and grammatical coordinates describe the restrictions on the spatial semantics. In the derivation and reduction of the mgCGG, the spatial matching conditions between nodes are evaluated on the basis of the grammatical coordinates, which can be obtained by the specified granularities. By adjusting the granularities, both qualitative and quantitative analyses for spatial semantics can be obtained, including the transition states between them. In addition, a new redex searching algorithm with polynomial time complexity is designed for the mgCGG. A running example of drawing a flowchart is given to illustrate a practical application of the mgCGG. Through a detailed comparison with related spatial-enabled grammars, it is found that the mgCGG has good performance in four critical aspects—the semantics processing mechanism, fault-tolerance, redex searching algorithm, and practical application—providing a flexible yet uniform way to specify spatial graphs and building a bridge between nonspatial and spatial grammars.

The digital transformation of the innovation ecosystem is not only an inevitable direction of innovation activities in the era of digital economy but also a highly complex and uncertain process. The way to facilitate transformation with policies has become a topic of common concern of academia and policymakers. This paper builds a multiagent model and studies the impacts of supply-side policies, demand-side policies, and environmental policies on enterprises’ transformation willingness, digital level, and income level as well as the proportion of enterprises that carry out transformation in the whole innovation ecosystem and innovation network structure by numerical experiments. According to research findings, supply-side policies play the biggest role in the facilitation of transformation, demand-side policies are second important to them, and environmental policies have comparatively weak impacts.

Understanding the neural basis of schizophrenia (SZ) is important for shedding light on the neurobiological mechanisms underlying this mental disorder. Structural and functional alterations in the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), hippocampus, and medial prefrontal cortex (MPFC) have been implicated in the neurobiology of SZ. However, the effective connectivity among them in SZ remains unclear. The current study investigated how neuronal pathways involving these regions were affected in first-episode SZ using functional magnetic resonance imaging (fMRI). Forty-nine patients with a first-episode of psychosis and diagnosis of SZ—according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision—were studied. Fifty healthy controls (HCs) were included for comparison. All subjects underwent resting state fMRI. We used spectral dynamic causal modeling (DCM) to estimate directed connections among the bilateral ACC, DLPFC, hippocampus, and MPFC. We characterized the differences using Bayesian parameter averaging (BPA) in addition to classical inference (t-test). In addition to common effective connectivity in these two groups, HCs displayed widespread significant connections predominantly involved in ACC not detected in SZ patients, but SZ showed few connections. Based on BPA results, SZ patients exhibited anterior cingulate cortico-prefrontal-hippocampal hyperconnectivity, as well as ACC-related and hippocampal-dorsolateral prefrontal-medial prefrontal hypoconnectivity. In summary, spectral DCM revealed the pattern of effective connectivity involving ACC in patients with first-episode SZ. This study provides a potential link between SZ and dysfunction of ACC, creating an ideal situation to associate mechanisms behind SZ with aberrant connectivity among these cognition and emotion-related regions.

ObjectiveTo explore the effect of independently developed transcranial pulsed current stimulation on alleviating athlete’s mental fatigue.MethodsA total of 60 college athletes were randomly divided into the active stimulation group and the sham stimulation group. Subjective questionnaires, behavior test, and functional near-infrared spectroscopy test were conducted before and after the experiment. Two-way ANOVA with repeated measures was used to compare the differences in mental fatigue indexes before and after the two experimental conditions.ResultsAfter 7 days of exercise training, there was a significant difference in the main effect of the time factor in all indexes of the two groups. The scores of rated perceived exertion scale, positive and negative affect schedule, critical flicker frequency, and reaction time, in the tPCS treatment group, were better than those in the sham stimulation group. After 7 days of exercise training, all the subjects had different degrees of athlete’s mental fatigue; the subjects in the active stimulation group have a good evaluation of the tPCS developed by the research group without adverse actions.ConclusiontPCS intervention can improve emotional state, reduce the subjective evaluation of fatigue, improve behavioral levels such as attention and reaction time and increase cerebral prefrontal blood flow and oxygen supply.

Teacher self-efficacy is one of the most critical factors influencing Students’ learning outcomes. Studies have shown that teacher-perceived principal leadership, teacher collaboration, and teaching experience are the critical factor that affects teacher self-efficacy. However, little is known about the mechanisms behind this relationship. This study examined whether teacher collaboration would mediate the relationship between teacher-perceived principal leadership and teacher self-efficacy, and the moderating role of teaching experience in the mediating process. With an analysis of a dataset from 14,121 middle school teachers in China, this study first testified to the positive role that teacher-perceived principal leadership played in teacher self-efficacy. Furthermore, it revealed that teacher collaboration mediates this relationship and the mediated path was moderated by teaching experience. Finally, it also indicated that the threshold of teaching experience linking the teacher-perceived leadership with teacher self-efficacy was approximately in the third year, and their relationship was stronger when teaching experience was below the threshold. This study highlighted the mediating and moderating mechanisms linking the teacher-perceived principal leadership and teacher self-efficacy, which has important theoretical and practical implications for intervention and enhancement of teacher self-efficacy.

Rheumatoid arthritis is a progressive systemic autoimmune disease characterized by inflammation of the joints and surrounding tissues, which seriously affects the life of patients. The Sharp/van der Heijde method has been widely used in clinical evaluation for the RA disease. However, this manual method is time-consuming and laborious. Even if two radiologists evaluate a specific location, their subjective evaluation may lead to low inter-rater reliability. Here, we developed an efficient model powered by deep convolutional neural networks to solve these problems and automated the overall scoring on hand X-rays. The depthwise separable convolution technique is used based on ResNet-50 due to the high resolution of hand X-rays. An inverted residual block is introduced to devise a ResNet-Dwise50 model to enhance the efficiency of the model. The model was trained and tested using bilateral posteroanterior images of 3818 patients. The experiment results show the ResNet-Dwise50 model achieved an MAE of 14.90 and RMSE of 22.01 while ensuring high efficiency. There was no statistical difference between the average scores given by two experienced radiologists and predicted scores from our model.

At present, there are more and more frauds in the financial field. The detection and prevention of financial frauds are of great significance for regulating and maintaining a reasonable financial order. Deep learning algorithms are widely used because of their high recognition rate, good robustness, and strong implementation. Therefore, in the context of e-commerce big data, this paper proposes a quantitative detection algorithm for financial fraud based on deep learning. First, the encoders are used to extract the features of the behaviour. At the same time, in order to reduce the computational complexity, the feature extraction is restricted to the space-time volume of the dense trajectory. Second, the neural network model is used to transform features into behavioural visual word representations, and feature fusion is performed using weighted correlation methods to improve feature classification capabilities. Finally, sparse reconstruction errors are used to judge and detect financial fraud. This method builds a deep neural network model with multiple hidden layers, learns the characteristic expression of the data, and fully depicts the rich internal information of the data, thereby improving the accuracy of financial fraud detection. Experimental results show that this method can effectively learn the essential characteristics of the data, and significantly improve the detection rate of fraud detection algorithms.

The remodeling of biological membranes is crucial for a vast number of cellular activities and is an inherently multiscale process in both time and space. Seminal work has provided important insights into nanometer‐scale membrane deformations, and highlighted the remarkable variation and complexity in the underlying molecular machineries and mechanisms. However, how membranes are remodeled at the micron‐scale, particularly in vivo, remains poorly understood. Here, we discuss how using regulated exocytosis of large (1.5–2.0 μm) membrane‐bound secretory granules in the salivary gland of live mice as a model system, has provided evidence for the importance of the actomyosin cytoskeleton in micron‐scale membrane remodeling in physiological conditions. We highlight some of these advances, and present mechanistic hypotheses for how the various biochemical and biophysical properties of distinct actomyosin networks may drive this process.