Dehydroabietyl polyethylene glycol glycidyl ether-grafted hydroxyethyl cellulose (HEC) polymer surfactant (DA (EO) 5 GE-g-HEC) was prepared using ring-opening polymerization with biobased rosin and hydroxyethyl cellulose as feedstocks.Dehydroabietyl polyethylene glycol glycidyl ether (DA(EO) 5 GE) was formed by condensation of dehydroabietyl alcohol polyoxyethylene ether (Rosin derivative: DA(EO) 5 H) and epichlorohydrin.The grafting degree of DA(EO) 5 GE-g-HEC was manipulated by adjusting the mass ratio of HEC and DA(EO) 5 GE and confirmed by EA.According to the formula, when m (HEC) /m (DA(EO)2GE) was 1:1~1:5, the grafting rate of DA(EO) 5 GE in DA(EO) 5 GE-g-HEC varied from 34.43% to 38.33%.The surface activity and foam properties of DA(EO) 5 GE-g-HEC aqueous solution were studied.The results showed that with the increase in grafting rate, the critical micellar concentration (CMC) in aqueous solution changed from 1.28 to 0.96 g/L.The results of the thermogravimetric analysis showed that the temperature range of the main stage of mass loss of DA(EO) 5 GE-g-HEC was 310°C~410°C, and the thermal decomposition processes of the samples with five mass ratios were similar.An oil in water emulsion was prepared by choosing cyclohexane as the oil phase and DA(EO) 5 GE-g-HEC as the emulsifier.The effect of DA(EO) 5 GE-g-HEC mass fraction on emulsion particle size and stability was analyzed.The results suggested that when the oil-water ratio was 8:2 with 0.4% emulsifier, the emulsion droplets were the smallest in terms of particle size and were the most stable.The rheological test results showed that the apparent viscosity decreased with the increase in shear rate and showed a typical elastic gel phenomenon.

Uncertain changes in data streams present challenges for machine learning models to dynamically adapt and uphold performance in real-time. Particularly, classification boundary change, also known as real concept drift, is the major cause of classification performance deterioration. However, accurately detecting real concept drift remains challenging because the theoretical foundations of existing drift detection methods - two-sample distribution tests and monitoring classification error rate, both suffer from inherent limitations such as the inability to distinguish virtual drift (changes not affecting the classification boundary, will introduce unnecessary model maintenance), limited statistical power, or high computational cost. Furthermore, no existing detection method can provide information on the trend of the drift, which could be invaluable for model maintenance. This work presents a novel real concept drift detection method based on Neighbor-Searching Discrepancy, a new statistic that measures the classification boundary difference between two samples. The proposed method is able to detect real concept drift with high accuracy while ignoring virtual drift. It can also indicate the direction of the classification boundary change by identifying the invasion or retreat of a certain class, which is also an indicator of separability change between classes. A comprehensive evaluation of 11 experiments is conducted, including empirical verification of the proposed theory using artificial datasets, and experimental comparisons with commonly used drift handling methods on real-world datasets. The results show that the proposed theory is robust against a range of distributions and dimensions, and the drift detection method outperforms state-of-the-art alternative methods.

Accurate recognition of patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) is important for the subsequent treatment and rehabilitation. Recently, with the fast development of artificial intelligence (AI), AI-assisted diagnosis has been widely used. Feature selection as a key component is very important in AI-assisted diagnosis. So far, many feature selection methods have been developed. However, few studies consider the stability of a feature selection method. Therefore, in this study, we introduce a frequency-based criterion to evaluate the stability of feature selection and design a pipeline to select feature selection methods considering both stability and discriminability. There are two main contributions of this study: (1) It designs a bootstrap sampling-based workflow to simulate real-world scenario of feature selection. (2) It develops a decision graph to determine the optimal combination of supervised and unsupervised feature selection both considering feature stability and discriminability. Experimental results on the ADNI dataset have demonstrated the feasibility of our method.

Cloud manufacturing adopts contemporary information cutting-edge technologies including advanced concepts such as cloud computing to support the manufacturing industry. Under a wide range of network resource manufacturing environments, it can add high value-added products, reduce costs, and meet globalization. The optimization of cloud manufacturing services is based on the attributes of cloud manufacturing services as evaluation indicators, and corresponding optimization strategies are formulated to ensure the selection process of manufacturing services. Taking into account the uncertainty of cloud service, rough set is used to assign the weight to each evaluation index and furthermore solve the cloud manufacturing service selection. Finally, an example is used to verify the effectiveness of the proposed method.

Background Reprogrammed glucose metabolism of enhanced Warburg effect (or aerobic glycolysis) is considered as a hallmark of cancer. Long non-coding RNAs (lncRNAs) have been certified to play a crucial role in tumor progression. The current study aims to inquire into the potential regulatory mechanism of long intergenic non-protein coding RNA 242 (LINC00242) on aerobic glycolysis in gastric cancer. Method LINC00242, miR-1-3p and G6PD expression levels in gastric cancer tissues and cells were determined by qRT-PCR. Cell apoptosis or viability were examined by Flow cytometry or MTT assay. Western blot was utilized to investigate G6PD protein expression levels. Immunohistochemical (IHC) and hematoxylin and eosin (H&E) staining were used for histopathological detection. The targeted relationship between LINC00242 or G6PD and miR-1-3p was verified by luciferase reporter gene assay. Nude mouse xenograft was utilized to detect tumor formation in vivo. Result LINC00242 and G6PD was high-expressed in gastric cancer tissues and cells, and LINC00242 is positively correlated with G6PD . Silencing of LINC00242 or G6PD within gastric cancer cells prominently inhibited cell proliferation and aerobic glycolysis in vitro and relieved the tumorigenesis of gastric cancer in vivo. miR-1-3p was predicted to directly target both LINC00242 and G6PD . Overexpression of miR-1-3p suppressed gastric cancer cells proliferation and aerobic glycolysis. LINC00242 competitively combined miR-1-3p, therefore relieving miR-1-3p-mediated suppression on G6PD . Conclusion LINC00242 plays a stimulative role in gastric cancer aerobic glycolysis via regulation of miR-1-3p/ G6PD axis, therefore affecting gastric cancer cell proliferation.

BACKGROUND Gastric cancer (GC) is a prevalent malignancy, leading to a high incidence of cancer-associated death. Cisplatin (DDP)-based chemotherapy is the principal therapy for clinical GC treatment, but DDP resistance is a severe clinical challenge and the mechanism remains poorly understood. Circular RNAs (circRNAs) have been identified to play crucial roles in modulating the chemoresistance of gastric cancer cells. AIM To explore the effect of circVAPA on chemotherapy resistance during GC progression. METHODS The effect of circVAPA on GC progression and chemotherapy resistance was analyzed by MTT assay, colony formation assay, Transwell assay, wound healing assay, and flow cytometry analysis in GC cells and DDP resistant GC cell lines, and tumorigenicity analysis in nude mice in vivo. The mechanism was investigated by luciferase reporter assay, quantitative real-time PCR, and Western blot analysis. RESULTS CircVAPA expression was up-regulated in clinical GC tissues compared with normal samples. CircVAPA depletion inhibited proliferation, migration, and invasion and increased apoptosis of GC cells. The expression of circVAPA, STAT3, and STAT3 downstream genes was elevated in DDP resistant SGC7901/DDP cell lines. CircVAPA knockdown attenuated the DDP resistance of GC cells. Mechanically, circVAPA was able to sponge miR-125b-5p, and miR-125b-5p could target STAT3 in the GC cells. MiR-125b-5p inhibitor reversed circVAPA depletion-enhanced inhibitory effect of DDP on GC cells, and STAT3 knockdown blocked circVAPA overexpression-induced proliferation of DDP-treated SGC7901/DDP cells. The depletion of STAT3 and miR-125b-5p inhibitor reversed circVAPA depletion-induced GC cell apoptosis. Functionally, circVAPA contributed to the tumor growth of SGC7901/DDP cells in vivo. CONCLUSION CircVAPA promotes chemotherapy resistance and malignant progression in GC by miR-125b-5p/STAT3 signaling. Our findings present novel insights into the mechanism by which circVAPA regulates chemotherapy resistance of GC cells. CircVAPA and miR-125b-5p may be considered as the potential targets for GC therapy.

Machine learning-based models are widely used for neuroimage-based dementia recognition and achieve great success. However, most models omit the interpretability that is a very important factor regarding the confidence of a model. Takagi–Sugeno–Kang (TSK) fuzzy classifiers as the high interpretability and promising classification performance have widely used in many scenarios. TSK fuzzy classifier can generate interpretable fuzzy rules showing the reasoning process. However, when facing high-dimensional data, the antecedent become complex which may reduce the interpretability. In this study, to keep the antecedent of fuzzy rule concise, we introduce the subspace clustering technique and use it for antecedent learning. Experimental results show that the used model can generate promising recognition performance as well as concise fuzzy rules.

Reading, an important source of language input, is of critical importance to English teaching. However, the traditional teaching model for English reading rarely considers the linguistic psychology of students. To improve the traditional model, this paper thoroughly analyzes the process of English reading based on the theories on linguistic psychology, and evaluates the traditional teaching model for English reading through tests on a group of students. On this basis, the factors of linguistic psychology were integrated with the traditional model, creating an improved teaching model for English reading. Through contrastive simulation, the author demonstrated that the students educated by the improved model achieved 35% higher reading ability and efficiency than those educated by the traditional model, and witnessed improvements in terms of linguistic knowledge and cognitive reaction. The research results provide a good reference for the development of innovative teaching models for college English education in China.

芳醛类化合物是有机合成化学研究和相关应用中不可替代的基本化学品,其中具有联芳基骨架的联芳醛类化合物不仅具有常规芳醛的性质,更在很多前沿研究领域,如手性荧光探针合成、手性试剂制备以及多环芳基稠环化合物构建等方面展示了重要而独特的应用.已有的联芳醛合成主要策略是官能团转化,即联芳基底物结构上的其它官能团向甲酰基的转化[1].此外,通过单芳基醛和芳基偶联试剂之间的交叉偶联反应也是构建联芳醛的常用方法[2].特定情况下,多步目标导向合成的方法也可以实现某些联芳醛的合成[3].

The advent of mobile Internet era has brought opportunities and challenges to tourism development. The Fourth Plenary Session of the 19th CPC Central Committee once again proposed to promote modernization of the national governance system and governance capabilities, and clarified the position of tourism industry in the national economy in attempt to develop the tourism industry into a modern service industry more satisfactory to the people. The essential difference between location-based social networking services and traditional networking services lies in the acquisition and application of location information. Application of this technology can help us better adapt to the dynamics of tourism activities and enhance intelligence degree of tourism. This paper sorts out the research on the construction of smart tourism cities and location-based social networking services at home and abroad, and combines the importance of location based services to tourism activities to analyze the needs of location-based social networking services in tourism city from the perspective of different interests of tourism cities, and finally proposes specific ways to meet specific needs from the four perspectives of infrastructure construction, new technology application, platform development, and marketing.

Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.

With the rapid development of interconnections of regional power network, wind power and photovoltaic power etc. new energy connected to power grid, it poses a huge challenge to power grid control systems. The frequency stability and main ways of dealing with the instantaneous power gap in the power grid mainly depends on primary frequency compensation(PFC) based on thermal power units. By analyzing the control characteristic and operations performance of coordinated control system of supercritical units, combined with the insufficient of actual PFC and boiler master strategy response for power grid accidents, the new boiler control strategy were offered. The practical application results show that the proposed control strategy can make full use of the supercritical unit's potential to effectively reduce the frequency fluctuation of power grid.

In mobile Internet,the intelligent mobile terminal and mobile applications are widely used in all fields.At the same time,the diversity and heterogeneity of the mobile terminal hardware and platform have caused redundant work in mobile application development and testing.Cross platform issues arise and it becomes a hot area for study and practice in academic and industry.This paper proposed model driven testing for mobile applieaitons.Firstly,the method uses UML state machine to describe the behaviour of the application.Secondly,based on the behaviour model,the method generates platform independent test cases automatically.Lastly,the method maps the test cases which are unrelated to platform to multiple platforms and generates executable test cases.This paper chose a power application as an example and realized the automation test in both IOS and Android.The validity of the model driven testing method was verified in solving the cross platform problem.

Monoamine oxidases (MAOs) catalyze the metabolism of monoamine neurotransmitters, such as serotonin, dopamine, and norepinephrine, and are key regulators for brain function. In this study, we analyzed the activities of MAO-A and MAO-B in the cerebellum and frontal cortex from subjects with autism and age-matched control subjects. In the cerebellum, MAO-A activity in subjects with autism (aged 4-38 years) was significantly lower by 20.6% than in controls. When the subjects were divided into children (aged 4-12 years) and young adults (aged 13-38 years) subgroups, a significant decrease by 27.8% in the MAO-A activity was observed only in children with autism compared with controls. When the 95% confidence interval of the control group was taken as a reference range, reduced activity of MAO-A was observed in 70% of children with autism. In the frontal cortex, MAO-A activity in children with autism was also lower by 30% than in the control group, and impaired activity of MAO-A was observed in 55.6% of children with autism, although the difference between the autism and control groups was not significant when all subjects were considered. On the contrary, there was no significant difference in MAO-B activity in both the cerebellum and frontal cortex between children with autism and the control group as well as in adults. These results suggest impaired MAO-A activity in the brain of subjects with autism, especially in children with autism. Decreased activity of MAOs may lead to increased levels of monoaminergic neurotransmitters, such as serotonin, which have been suggested to have a critical role in autism. © 2017 Wiley Periodicals, Inc.

An important problem that remains in online data mining systems is how to accurately and efficiently detect changes in the underlying distribution of large data streams. The challenge for change detection methods is to maximise the accumulative effect of changing regions with unknown distribution, while at the same time providing sufficient information to describe the nature of the changes. In this paper, we propose a novel change detection method based on the estimation of equal density regions, with the aim of overcoming the issues of instability and inefficiency that underlie methods of predefined space partitioning schemes. Our method is general, nonparametric and requires no prior knowledge of the data distribution. A series of experiments demonstrate that our method effectively detects concept drift in single dimension as well as high dimension data, and is also able to explain the change by locating the data points that contribute most to the change. The detection result is guaranteed by statistical tests.

Advance reservation services are being used by a range of applications to schedule connection bandwidth resources at future time intervals. To date many different algorithms have been developed to support various point-to-point reservation models. However, with expanding data distribution needs there is a need to schedule more complex service types to provide connectivity between multiple sites/locations. In particular, these offerings can help improve network resource utilization and help expand carrier service portfolios. Along these lines, this paper presents a novel, scalable optimization solution to schedule (virtual) overlay networks with fixed end-point nodes. An improved re-routing heuristic scheme is also proposed and analyzed for comparison purposes.

This paper studies progressive recovery in optical cloud substrates supporting virtualized infrastructure services. Several resource placement/scheduling schemes are presented to improve post-fault recovery and also evaluated against a baseline scheme.

Event Abstract Back to Event Impaired activity of monoamine oxidase A in the brain of children with autism Ved Chauhan1*, Feng Gu1 and Abha Chauhan1 1 NYS Institute for Basic Research in Developmental Disabilities, Department of Neurochemistry, United States Autism is a neurodevelopmental disorder characterized by abnormal social and behavioral abnormalities. Extensive evidence from our and other groups has suggested oxidative stress and mitochondrial dysfunction in autism. Monoamine oxidase A (MAOA), a mitochondrial-bound enzyme, catalyzes the oxidation of endogenous amine-containing neurotransmitters such as serotonin and norepinephrine. The role of MAOA in autism is of particular interest because this enzyme affects the levels of serotonin, which are known to be abnormal in some individuals with autism. In comparison to other alleles of MAOA, the 3-repeat allele is associated with reduced transcription and therefore, reduced activity of MAOA. A few studies have reported an association of the low-activity, 3-repeat MAOA-uVNTR allele with autism. In this study, we analyzed the MAOA activity in the cerebellum and frontal cortex from autistic subjects and age-matched control subjects. In the cerebellum, the activity of MAOA was significantly lower in autism than in control subjects. When the subjects were divided into two subgroups according to their ages: children (ages 4-12 years) and adults (ages 13-38 years), a significant decrease in the activity of MAOA in cerebellum was observed in only children with autism but not in adult autistic group. In the frontal cortex, the MAOA activity in autistic children group was also reduced by 30% than in controls, but there was no significant difference. These results suggest that the brain MAOA activity is lower in autistic children than in control subjects. Lower MAOA activity will cause increase in the levels of related neurotransmitters, such as serotonin, which is one of the most important neurotransmitters influencing behavior and has been reported to have critical relationship with autism. Keywords: Cerebellum, autism, enzyme, frontal cortex, Monoamine oxidase A Conference: 14th Meeting of the Asian-Pacific Society for Neurochemistry, Kuala Lumpur, Malaysia, 27 Aug - 30 Aug, 2016. Presentation Type: Poster Presentation Session Topic: 14th Meeting of the Asian-Pacific Society for Neurochemistry Citation: Chauhan V, Gu F and Chauhan A (2016). Impaired activity of monoamine oxidase A in the brain of children with autism. Conference Abstract: 14th Meeting of the Asian-Pacific Society for Neurochemistry. doi: 10.3389/conf.fncel.2016.36.00168 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 Aug 2016; Published Online: 11 Aug 2016. * Correspondence: Prof. Ved Chauhan, NYS Institute for Basic Research in Developmental Disabilities, Department of Neurochemistry, Staten Island, New York, United States, ved.chauhan@opwdd.ny.gov Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Ved Chauhan Feng Gu Abha Chauhan Google Ved Chauhan Feng Gu Abha Chauhan Google Scholar Ved Chauhan Feng Gu Abha Chauhan PubMed Ved Chauhan Feng Gu Abha Chauhan Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.