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Inspired by the specific optical properties of DNA photonic nanowires (PNs) on which fluorescent dyes are to be precisely positioned and photonic energy transfer over nanometers can be achieved, he...

Background Cold chain logistics (CCL) is not only vital for maintaining the quality and safety of fresh agro-products and reducing losses but also provides important support to help increase farmer income and thereby promote the revitalization of rural industry in China. In recent years, numerous studies have focused on improving the efficiency and sustainability of CCL, and the results have important implications for promoting innovation, applying technologies, improving facilities and equipment, and optimizing management in the CCL industry. Scope and approach This review discusses active research areas, gaps in the existing state of research, and future research challenges for CCL. Furthermore, we summarize the current status of China's CCL industry and technology and compare the state of CCL development in China with that in more developed countries in terms of infrastructure, data handling, and national policies. Key findings and conclusions The future trends of CCL involve low carbon strategies and intelligent innovation, which are the key to meeting environmental concerns and the evolving needs of the market. Advances in next-generation information technology (including IoT, blockchain, AI, etc.) have significantly accelerated the modernization of CCL. Meanwhile, attaining these dual objectives of a low-carbon footprint and intelligent innovation requires cooperation between national regulators, industry, consumers, and interdisciplinary experts. A key finding of this review is that national policy and financial intervention in China are expected to be the main forces behind renovating infrastructure and upgrading standardization, which is required to narrow the CCL development gap between China and other more developed nations.

Unsupervised domain adaptation provides a variety of methods to leverage the previously gained knowledge from a labeled source domain to help complete a task from a similar unlabeled target domain. Many existing methods focus on transferring knowledge across single source and single target domains, while few studies deal with multi-source domain adaptation, which is more realistic and challengeable. Existing multi-source domain adaptation methods rarely consider the uncertainty of the transformed knowledge resulting from limited information in target domain. A fuzzy system allows imprecision and ambiguity within transfer, thus it can deal with problems with uncertainty. This work proposes a multi-source domain adaptation method with fuzzy-rule based deep neural networks (MDAFuz). The proposed method first extracts multi-view adapted features and pre-trains source classifiers. Using the learned features and classifiers, training samples are then split into multiple clusters, hence fuzzy rules can be built to learn new classifiers. At the same time, the cluster discriminator is trained to define the membership. Finally, by measuring the similarities among source and target domains using the pseudo target labels and a domain discriminator, the target task is completed by combining all source classifiers with regard to the learned weights. The experiment results on real-world visual datasets show the superiority of the proposed method.

© 2018 IEEE. Transfer learning is gaining considerable attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy transfer learning, which is based on fuzzy system (especially fuzzy rule-based models), has been developed because of its capability to deal with the uncertainty in transfer learning. However, two issues with fuzzy transfer learning have not yet been resolved: choosing an appropriate source domain and efficiently selecting labeled data for the target domain. This paper proposes an innovative method based on fuzzy rules that combines an infinite Gaussian mixture model (IGMM) with active learning to enhance the performance and generalizability of the constructed model. An IGMM is used to identify the data structures in the source and target domains providing a promising solution to the domain selection dilemma. Further, we exploit the interactive query strategy in active learning to correct imbalances in the knowledge to improve the generalizability of fuzzy learning models. Through experiments on synthetic datasets, we demonstrate the rationality of employing an IGMM and the effectiveness of applying an active learning technique. Additional experiments on real-world datasets further support the capabilities of the proposed method in practical situations.

IntroductionOsteoporotic spinal fracture, characterized by high morbidity and mortality, has become a health burden for the aging population. The inactivation of the Wnt signaling has been proved to promote osteoporotic fractures. Our study is to identify the key genes, miRNAs, and pathways that possibly lead to osteoporosis and osteoporotic spinal fracture after the aberrant activation or mutation of Wnt signaling pathway.Material and methodsImpute R package was used to screen out the differently expressed genes (DEGs) and differently expressed miRNAs in GEO datasets. STRING and Metascape were used to construct protein-protein interactions (PPI) network, gene ontology (GO) enrichment and pathway enrichment. The relative expression of ADCY2, ADCY5, and GRIA1 in bone tissues was measured by RT-qPCR.Results562 DEGs were screened out using Impute R package, and a PPI network involving the 562 DEGs was constructed using STRING and Metascape. GO enrichment and pathway enrichment showed that the 562 DEGs were associated with membrane protein-related signaling pathways. Then, 75 genes between the target genes of miR-18a-3p and 562 DEGs were overlapped using Venny 2.1.0. Finally, the cAMP signaling pathway was identified as the key pathway, whilst ADCY2, ADCY5, and GRIA1 were identified the key genes that possibly participate in osteoporotic spinal fracture after the manipulation of Wnt signaling pathway, which was further proved by their excessive downregulation in osteoporotic patients with spinal fracture.ConclusionsThe results demonstrated that ADCY2, ADCY5, and GRIA1 were the key genes to regulate the cAMP signaling pathway in osteoporotic spinal fracture after abnormal Wnt signaling.

Transfer learning becomes an attractive technology to tackle a task from a target domain by leveraging previously acquired knowledge from a similar domain (source domain). Many existing transfer learning methods focus on learning one discriminator with single-source domain. Sometimes, knowledge from single-source domain might not be enough for predicting the target task. Thus, multiple source domains carrying richer transferable information are considered to complete the target task. Although there are some previous studies dealing with multi-source domain adaptation, these methods commonly combine source predictions by averaging source performances. Different source domains contain different transferable information; they may contribute differently to a target domain compared with each other. Hence, the source contribution should be taken into account when predicting a target task. In this article, we propose a novel multi-source contribution learning method for domain adaptation (MSCLDA). As proposed, the similarities and diversities of domains are learned simultaneously by extracting multi-view features. One view represents common features (similarities) among all domains. Other views represent different characteristics (diversities) in a target domain; each characteristic is expressed by features extracted in a source domain. Then multi-level distribution matching is employed to improve the transferability of latent features, aiming to reduce misclassification of boundary samples by maximizing discrepancy between different classes and minimizing discrepancy between the same classes. Concurrently, when completing a target task by combining source predictions, instead of averaging source predictions or weighting sources using normalized similarities, the original weights learned by normalizing similarities between source and target domains are adjusted using pseudo target labels to increase the disparities of weight values, which is desired to improve the performance of the final target predictor if the predictions of sources exist significant difference. Experiments on real-world visual data sets demonstrate the superiorities of our proposed method.

Background Ramsay Hunt syndrome (RHS) is caused by a reactivation of varicella-zoster virus (VZV) infection, and it is characterized by the symptoms of facial paralysis, otalgia, auricular rash, and/or an oral lesion. Elderly patients or immunocompromised patients, deep pain at the initial visit and no prompt treatment are significant predictors of postherpetic neuralgia (PHN). When PHN occurs, especially involved cranial polyneuropathy, multiple modalities should be administered for patients with the intractable PHN. The use of thermography in the follow-up of PHN secondary to RHS with multicranial nerve involvement has not yet been described yet in the literature. Case presentation The patient was a 78-year-old man with the chief complaint of a 3-month history of PHN secondary to RHS with polycranial nerve (V, VII, VIII, and IX) involvement. Multimodality therapy with oral gabapentin, pulsed radiofrequency (PRF) application to the Gasserian ganglion for pain in the trigeminal nerve region, linear-polarized near-infrared light irradiation for pain in the facial nerve region, and 2% lidocaine spray for pain in the glossopharyngeal nerve region was used to the treat patient, and follow-up evaluations included thermography. This comprehensive treatment obviously improved the quality of life, resulting in considerable pain relief, as indicated by a decrease in the numerical rating scale (NRS) score from 9 to 3 and a decrease in thermal imaging temperature from higher to average temperature on the ipsilateral side compared with the contralateral side. Lidocaine spray on the tonsillar branches of the glossopharyngeal nerve resulted in an improvement in odynophagia, and the NRS score decreased from 9 to 0 for glossopharyngeal neuralgia after three applications. Conclusion Although the use of thermography in the follow-up of RHS with multiple cranial nerve (V, VII, VIII, and IX) involvement is very rare, in this patient, thermal imaging showed the efficacy of combination therapy (oral gabapentin, 2% lidocaine sprayed, PRF application and linear-polarized near-infrared light irradiation) and that is a good option for treatment.

Transfer learning is gaining increasing attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy transfer learning, which is based on fuzzy systems and particularly fuzzy rule-based models, was developed due to its capacity to deal with uncertainty. However, one issue with fuzzy transfer learning, even in the area of general transfer learning, has not been resolved: how to combine and then use knowledge when multiple-source domains are available. This study presents new methods for merging fuzzy rules from multiple domains for regression tasks. Two different settings are separately explored: homogeneous and heterogeneous space. In homogeneous situations, knowledge from the source domains is merged in the form of fuzzy rules. In heterogeneous situations, knowledge is merged in the form of both data and fuzzy rules. Experiments on both synthetic and real-world datasets provide insights into the scope of applications suitable for the proposed methods and validate their effectiveness through comparisons with other state-of-the-art transfer learning methods. An analysis of parameter sensitivity is also included.

© 2020 IEEE. Transfer learning is gaining increasing attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a similar domain. While many existing transfer learning methods deal with single source and single target problem without considering the fact that a target domain maybe similar to multiple source domains, this work proposes a multi-source domain adaptation method based on a deep neural network. Our method contains common feature extraction, specific predictor learning and target predictor estimation. Common feature extraction explores the relationship between source domains and target domain by distribution fusion and guarantees the strength of similar source domains during training, something which has not been well considered in existing works. Specific predictor learning trains source tasks with cross-domain distribution constraint and cross-domain predictor constraint to enhance the performance of single source. Target predictor estimation employs relationship extraction and selective strategy to improve the performance of the target task and to avoid negative transfer. Experiments on real-world visual datasets show the performance of the proposed method is superior to other deep learning baselines.

PurposeBiomechanical comparison of wedge and biconcave deformity of different height restoration after augmentation of osteoporotic vertebral compression fractures was analyzed by three-dimensional finite element analysis (FEA).MethodsThree-dimensional finite element model (FEM) of T11-L2 segment was constructed from CT scan of elderly osteoporosis patient. The von Mises stresses of vertebrae, intervertebral disc, facet joints, displacement, and range of motion (ROM) of wedge and biconcave deformity were compared at four different heights (Genant 0–3 grade) after T12 vertebral augmentation.ResultsIn wedge deformity, the stress of T12 decreased as the vertebral height in neutral position, flexion, extension, and left axial rotation, whereas increased sharply in bending at Genant 0; L1 and L2 decreased in all positions excluding flexion of L2, and T11 increased in neutral position, flexion, extension, and right axial rotation at Genant 0. No significant changes in biconcave deformity. The stress of T11-T12, T12-L1, and L1-L2 intervertebral disc gradually increased or decreased under other positions in wedge fracture, whereas L1-L2 no significant change in biconcave fracture. The utmost overall facet joint stress is at Genant 3, whereas there is no significant change under the same position in biconcave fracture. The displacement and ROM of the wedge fracture had ups and downs, while a decline in all positions excluding extension in biconcave fracture.ConclusionsThe vertebral restoration height after augmentation to Genant 0 affects the von Mises stress, displacement, and ROM in wedge deformity, which may increase the risk of fracture, whereas restored or not in biconcave deformity.

© 1993-2012 IEEE. Fuzzy rough-set-based feature selection is an important technique for big data analysis. However, the classic fuzzy rough set algorithm takes all the data correlations into account, which leads to the centralized computing mode, requiring high computing and memory space resources. With the increasing amount of data in the big data era, the centralized server cannot afford the computation of fuzzy rough set. To enable the fuzzy rough set for big data analysis, in this article, we propose the novel distributed fuzzy rough set (DFRS)-based feature selection, which separates and assigns the tasks to multiple nodes for parallel computing. The key challenge is to maintain the global information on each distributed node without conserving the entire fuzzy relation matrix. We tackle this challenge by a dynamic data decomposition algorithm and a data summarization process on each distributed node. Extensive experiments based on multiple real datasets demonstrate that DFRS significantly improves the runtime, and its feature selection accuracy is nearly the same as the traditional centralized computing.

In classical data-driven machine learning methods, massive amounts of labeled data are required to build a high-performance prediction model. However, the amount of labeled data in many real-world applications is insufficient, so establishing a prediction model is impossible. Transfer learning has recently emerged as a solution to this problem. It exploits the knowledge accumulated in auxiliary domains to help construct prediction models in a target domain with inadequate training data. Most existing transfer learning methods solve classification tasks; only a few are devoted to regression problems. In addition, the current methods ignore the inherent phenomenon of information granularity in transfer learning. In this study, granular computing techniques are applied to transfer learning. Three granular fuzzy regression domain adaptation methods to determine the estimated values for a regression target are proposed to address three challenging cases in domain adaptation. The proposed granular fuzzy regression domain adaptation methods change the input and/or output space of the source domain's model using space transformation, so that the fuzzy rules are more compatible with the target data. Experiments on synthetic and real-world datasets validate the effectiveness of the proposed methods.

Data science is a research field concerned with processes and systems that extract knowledge from massive amounts of data. In some situations, however, data shortage renders existing data-driven methods difficult or even impossible to apply. Transfer learning has recently emerged as a way of exploiting previously acquired knowledge to solve new yet similar problems much more quickly and effectively. In contrast to classical data-driven machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling in the current domain. A significant number of transfer learning methods that address classification tasks have been proposed, but studies on transfer learning in the case of regression problems are still scarce. This study focuses on using transfer learning techniques to handle regression problems in a domain that has insufficient training data. We propose an original fuzzy regression transfer learning method, based on fuzzy rules, to address the problem of estimating the value of the target for regression. A Takagi–Sugeno fuzzy regression model is developed to transfer knowledge from a source domain to a target domain. Experimental results using synthetic data and real-world datasets demonstrate that the proposed fuzzy regression transfer learning method significantly improves the performance of existing models when tackling regression problems in the target domain.

// Xiaofang Yao 1, 2 , Hao Tang 1 , Qiao Ren 1 , Xiaoyan Zhao 1 , Hua Zuo 1 and Zhubo Li 1 1 College of Pharmaceutical Sciences, Southwest University, Chongqing 400716, China 2 International Academy of Targeted Therapeutics and Innovation, Chongqing University of Arts and Sciences, Chongqing 402160, China Correspondence to: Zhubo Li, email: lizhubo2004@163.com Keywords: caffeic acid p -nitro phenethyl ester (CAPE- p NO 2 ), cervical cancer cells, endogenous apoptosis pathway, xenograft, metabolites Received: June 21, 2017 Accepted: August 27, 2017 Published: October 07, 2017 ABSTRACT The development of advanced cervical cancer therapies is a particularly urgent need due to the strong side effects and toxicities of current treatments. Caffeic acid phenethyl ester (CAPE) exhibits broad-spectrum antitumor activities and little toxicity or side effects. In our previous study, caffeic acid para-nitro phenethyl ester (CAPE- p NO 2 ) significantly improved the effect of anti-platelet aggregation and attenuated myocardial ischemia. Based on this finding, we aimed to further explore the antitumor activity of CAPE- p NO 2 in cervical cancer cells and tumor xenografts. In addition, we assessed the biotransformation of CAPE- p NO 2 in cervical cancer cells. Our study demonstrated that both CAPE and CAPE- p NO 2 can inhibit cell proliferation via the induction of G2/M cell cycle arrest. More importantly, CAPE- p NO 2 dramatically induced cell apoptosis via significant down-regulation of pro-caspase-3, pro-caspase-9, Bcl-2, Cyclin B1 and Cdc2 and up-regulation of cleaved-caspase-3, Bax, CytoC and P21 Cip1 . Moreover, CAPE and CAPE- p NO 2 significantly suppressed the growth and angiogenesis of nude mice xenografts. CAPE and CAPE- p NO 2 were found to degrade into four and six metabolites, respectively. The metabolites of CAPE and CAPE- p NO 2 were different, and the major metabolic pathway may be phase II reactions. These results suggest that CAPE- p NO 2 induced cell apoptosis and cell cycle arrest via a strong regulatory effect on relevant apoptotic proteins. Therefore, CAPE- p NO 2 should be further studied as a potent anti-cancer agent.

The present study aims at the design and making of measuring instrument of whole direct method for bed shear stress under two-dimensional water-flow co-action. The instrument combines the traditional strain gauge with a precise pressure gauge, and adopts the method directly measuring the difference between the lateral hydrodynamic pressure and different head pressures on both sides of the force plate. As a result, such an instrument solves a technical puzzle of the past strain gauge, i.e. the difficulty to set apart shear stress and lateral force. Static force test and sink test both prove that the instrument is precise, stable and applicable to the measurement of rough beds with different shear stresses.

© 2019 IEEE. Transfer learning has emerged as a solution for the cases where little or no labeled data are available in the training process. It leverages the previously acquired knowledge (a source domain with a large amount of labeled data) to facilitate solving the current tasks (a target domain with little labeled data). Many transfer learning methods have been proposed, and especially fuzzy transfer learning method, which is based on fuzzy systems, has been developed because of its capability to deal with the uncertainty in transfer learning. However, there is one issue with fuzzy transfer learning that has not yet been resolved: the domain selection problem, which is heavily depended on the knowledge transfer method and the applied prediction model. In this work, we explore the domain selection problem in TakagiSugeno fuzzy model when multiple source domains are accessible, and define the similarity between the source and target domains to provide guidance for the domain selection. The experiments on synthetic datasets are designed to simulate the situations of multiple sources in transfer learning, and demonstrate the rationality of the proposed similarity in selecting the source domain for the target domain. Further, the real-world datasets are used to validate the proposed domain adaptation method, and verify its capability in solving practical situations.

© Springer Nature Switzerland AG 2019. Transfer learning is gaining increasing attention due to its ability to leverage previously acquired knowledge (a source domain with a large amount of labeled data) to assist in completing a prediction task in a related domain (a target domain with little labeled data). Many transfer learning methods have been proposed, and especially the fuzzy transfer learning method, which is based on fuzzy systems, has been developed because of its capability to deal with the uncertainty. However, there is one issue with fuzzy transfer learning that has not yet resolved: The domain adaptation methods for regression tasks in heterogeneous space are still scarce, and the relation of features in two domains have not been explored to assist the construction of target model. In this work, we proposed a new fuzzy transfer learning method, which constructs the transformed mappings for the domain-independent and domain-dependent features, separately. The existing fuzzy rules of the source domain are transferred to the target domain through modifying the input space using the mappings, and the parameters of the mappings are optimized by the few labeled target data. The experiments on real-world datasets validate the effectiveness of the proposed method and discuss the impact of some important parameters to the performance of the constructed target model.

Tauopathy is a complex disorder associated at the junction of several other pathologies. Intrinsically disordered tau protein remains therapeutically challenging due to its undruggable nature and is a possible reason for monumental failure of several tau-based therapies. Herein, nanogold remodeled tau is reported as a pseudo-nanochaperon and shows therapeutic benefit by passive targeting in transgenic tau P301L mutant mice. Treatment with nanogold polyethylene glycol (Au-PEG) conjugate moderately improves the learning ability of the tau P301L mice that corroborates with diminished phosphorylated tau burden. Circulating total tau level that acts in a prion fashion is significantly reduced upon Au-PEG treatment. Similarly, a high level of tau is found in macaque monkey serum and Au-PEG inhibits amyloidosis of Alzheimer's patients and primate's serum samples ex vivo. Addtionally, brain MRI of an old aged macaque monkey shows the decrease of grey matter, which correlates with mutual loss of grey matter upon progressive dementia as reported. Au-PEG tunes tau and other circulating pro-dementia factors that are present in human AD serum, by remodeling the protein and repairing aberrant proteostasis. Alteration of proteotoxic tau function by nanogold as a kinetic stablizer holds translational potential to combat socially challenging dementia.

© Springer Nature Switzerland AG 2018. Transfer learning has been emerging recently and gaining more attention because of its ability to deal with “small labeled data” issue in new markets and for new products. It addresses the problem of leveraging knowledge acquired from previous domain (a source domain with a large amount of labeled data) to improve the accuracy of tasks in the current domain (a target domain with little labeled data). Fuzzy rule-based transfer learning methods are developed due to the ability to dealing with the uncertainty in domain adaptation scenarios. Although some effort is made to develop the fuzzy methods, they only apply the knowledge of the labeled data in the target domain to assist the model’s construction. This work develops a new method that explores and utilizes the information contained in the unlabeled target data to improve the performance of the new constructed model. The experiments on both synthetic datasets and real-world datasets illustrate the effectiveness of our method, and also give the application scope of applying it.

This study analyzes the safety of a seawall slope armor based on the reliability theory. By taking a typical slope-type seawall in Guangdong Province as an example to establish a performance function for the slope armor panel, we first calculate the probability of panel failure under the existing conditions and then conduct a sensitivity analysis of factors such as wave height, period, water depth, and plate thickness. The results of this study can provide a technical reference for the design, construction management, and operation and maintenance of the armor structure for similar seawalls in the future.

© 2017 IEEE. As the age of big data approaches, methods of massive scale data management are rapidly evolving. The traditional machine learning methods can no longer satisfy the exponential development of big data; there is a common assumption in these data-driving methods that the distribution of both the training data and testing data should be equivalent. A model built using today's data will not adequately address the classification tasks tomorrow if the distribution of the data item values has changed. Transfer learning is emerging as a solution to this issue, and many methods have been proposed. Few of the existing methods, however, explicitly indicate the solution to the case where the labels' distributions in two domains are different. This work proposes the fuzzy rule-based methods to deal with transfer learning problems where the discrepancy between the two domains shows in the label spaces. The presented methods are validated in both the synthetic and real-world datasets, and the experimental results verify the effectiveness of the introduced methods.

Objective To identify new genes that correlate with prognosis of clear-cell renal cell carcinoma (ccRCC) via bioinformatics analysis. Methods The gene expression profiles of 62 ccRCC and 54 normal kidney tissues were available from the Gene Expression Omnibus database: GSE12606, GSE36895 and GSE66272. The differentially expressed genes were screened with GEO2R and J Venn online tools. Functional annotation including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was applied to identify the possible function of the hub genes involved in prognosis of ccRCC. In protein protein interaction network (PPI network), the STRING online tool was used to visualize the network of the differentially expressed genes, and the core gene was selected by MCODE App in Cytoscape software. Finally, GEPIA Survival Plot was performed to assess genes associated with worse survival. Results We totally found 648 differentially expressed genes, including 222 up-regulated genes and 426 down-regulated genes. PPI network showed that in 28 up-regulated genes 7 (CCNE2, CDK1, CDC6, CCNB2, BUB1, TTK and PTTG1) enriched in cell cycle and 4 genes (CCNE2, CDK1, CCNB2 and RRM2) enriched in p53 signaling pathway. GEPIA Survival Plot assay revealed that ccRCC patients carrying CDK1, CCNB2, RRM2, BUB1, and PTTG1 had a worse survival. GEPIA Box Plot showed that BUB1, CCNB2, PTTG1, and RRM2 were over expressed in the ccRCC tissues in contrast to the normal tissues (P

The silicon-boron(Si-B) pitch was successfully synthesized by solution-thermal treatment method using coal pitch, polycarbosilane and borane pyridine as starting materials. The B-Si pitch carbonization product was attained by carbonization in the temperature 900°C under flowing argon. The Si-B pitch prepared and its carbonization product were characterized by SEM, FT-IR, TG-DSC and XRD. The results show that the carbonization product has C-Si and C-B bonds and the silicon and boron components of the product is still amorphous when it is treated at 900°C. The weight loss of the Si-B pitch carbonization product at 900°C for 12h in air atmosphere is only 3.76wt%. The weight loss of the product is less than 4 wt% when the oxidation temperature reaches 1180°C. The prepared B-Si pitch carbonization product possesses good anti-oxidation property at high temperature in air atmosphere.

In this paper, a 2D-FFT processor design on CORDIC algorithm has proposed. This design extracts the radix-4 algorithm in FFT as the foundation, uses the assembly line technology to enhance the turnover rate for the whole system, and has many characteristics with the simple hardware architecture, low component, stable running and high precision. This design has carried on the timing simulation on Altera chip EP2C35F672C6, can satisfy 50MHz system clock.

This paper designed and realized Manchester encoder and decoder based on FPGA. The M sequence generator produced input baseband signal, Manchester CODEC possessed parity check function, and the output signals of encoding and decoding were stable. This design used VHDL language programme, encoder and decoder used modular design, simulated and tested in Altera development software Quartus II 8.0, and downloaded to FPGA chip Cyclone II EP2C35F672C6 for verification. The results showed that the design scheme is good to realize Manchester CODEC, and possesses good stability and reliability.

3D CAPP has become trend. Based on recognition of manufacturing features, the design model is reconstructed. First, the features of the engine case are classified. The workflow of reconstruction is put forward. Then, the detail ways to identifying manufacturing features are illuminated, and the functions are listed. The design model is reconstructed by redefining geometric and no-geometric information, including surfaces, holes, slots, text, sizes, and so on. The model of the engine case is reconstructed in the developed system.

The clinical use of doxorubicin (DOX), a potent antineoplastic agent, is limited by its serious side-effects, which include acute and chronic cumulative dose-related cardiotoxicity. Berberine (BER), a botanical alkaloid, has been reported to possess cardioprotective and antitumor effects. The 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-tetrazolium bromide (MTT) assay was used to detect the cell viability of A549, HeLa and HepG2 cells after each cell line was treated with DOX, BER or a combination of DOX and BER for 24 h. Apoptosis was evaluated by acridine orange staining. The results showed that BER and DOX exhibited dose-dependent inhibitory effects on A549 and HeLa cells which were likely mediated by inducing apoptosis. The same result was found in the combination group. Isobologram illustration and combination index (CI) analyses revealed that the combination of DOX and BER generates synergistic effects in A549 (CI=0.61) and HeLa (CI=0.73) cells. These findings indicate that BER sensitizes cells to the anticancer effects of DOX.

© 2015 IEEE. Transfer learning provides an approach to solve target tasks more quickly and effectively by using previously acquired knowledge learned from source tasks. As one category of transfer learning approaches, feature-based transfer learning approaches aim to find a latent feature space shared between source and target domains. The issue is that the sole feature space can't exploit the relationship of source domain and target domain fully. To deal with this issue, this paper proposes a transfer learning method that uses deep learning to extract hierarchical feature spaces, so knowledge of source domain can be exploited and transferred in multiple feature spaces with different levels of abstraction. In the experiment, the effectiveness of transfer learning in multiple feature spaces is compared and this can help us find the optimal feature space for transfer learning.

This study investigated the effects of berberine, a natural alkaloid, on doxorubicin-induced cardiotoxicity in mice. Mice were injected intraperitoneally with saline 10 ml/kg ( n = 10), doxorubicin 2.5 mg/kg ( n = 10), 60 mg/kg berberine 1 h before doxorubicin 2.5 mg/kg ( n = 10), or 60 mg/kg berberine alone ( n = 10) every other day for 14 days. Body weight, general condition and mortality were recorded over the 14-day study period. Electrocardiography was performed before the start of treatment and after 14 days and plasma lactate dehydrogenase (LDH) activity was measured after 14 days. At the end of the study period the heart was excised and examined histologically. An increase in mortality, an initial decrease in body weight, increased LDH activity, prolongation of QRS duration and increased myocardial injury were seen in the doxorubicin-treated group compared with the saline control group. These changes were significantly attenuated by pretreatment with berberine. The study suggests that berberine may have a potential protective role against doxorubicin-induced cardiotoxicity in mice.

During the rolling process of big size parts, crinkle and hollows often appear in the surfaces. The reason of producing the defects is that the motions of the rolling mill tool are not consistent with the deformation of the part. On the basis of the principle of radial-axial ring rolling, a dynamics model is put forward. The relationship between part deformation and motion parameters is set up. The stress and strain of the part are simulated in the Finite Element Method by DEFORM software. A ring rolling process curve is defined. The rolling process is controlled by the curve. It is able to make the deformation of the part be consistent with the process parameters, and improve the product quality.

Background: Osteoporotic vertebral compression fractures (OVCFs) commonly afflicts most aged people resulting back pain, substantial vertebral deformity, functional disability, decreased quality of life, and increased adjacent spinal fractures and mortality. Percutaneous vertebral augmentation (PVA) included percutaneous vertebroplasty (PVP) and percutaneous kyphoplasty (PKP), nerve block (NB), and conservative treatment (CT) are used for the nonsurgery treatment strategy of OVCFs, however, current evaluation of their efficacy remains controversial. Methods and analysis: A systematic literature search was carried out in PubMed, EMBASE, Web of Knowledge, and the Cochrane Central Register of Controlled Trials up to October 31, 2017. Randomized controlled trials (RCTs) were compared PVP, PKP, NB, or CT for treating OVCFs. The risk of bias for each trial was rated according to the Cochrane Handbook. Mean differences (MDs) with 95% confidence intervals (CIs) were utilized to express VAS (visual analog scale) outcomes. The network meta-analysis (NMA) of the comparative efficacy measured by change of VAS on acute/subacute and chronic OVCFs was conducted for a short-term (

Objective To investigate the feasibility of minimal invasive repair of cartilage defect by arthroscope-aided microfracture surgery and autologous transplantation of mesenchymal stem cells. Methods Bone marrow of minipigs was taken out and the bone marrow derived mesenchymal stem cells (BMSCs) were isolated and cultured to passage 3. Then 6 minipigs were randomly divided into 2 groups with 6 knees in each group. After the articular cartilage defect was induced in each knee, the left defect received microfracture surgery and was injected with 2.5 ml BMSCs cells at a concentration of 3 × 10 7 cells/ml into the articular cavity; while right knee got single microfracture or served as blank control group. The animals were killed at 8 or 16 weeks, and the repair tissue was histologically and immunohistochemically examined for the presence of type I collagen and glycosaminoglycans (GAGs) at 8 and 16 weeks. Results Eight weeks after the surgery, the overlying articular surface of the cartilage defect showed normal color and integrated to adjacent cartilage. And 16 weeks after surgery, hyaline cartilage was observed at the repairing tissues and immunostaining indicated the diffuse presence of this type I collagen and GAGs throughout the repair cartilage in the treated defects. Single microfracture group had the repairing of fibro-cartilage, while during the treatment, the defects of blank group were covered with fewer fiber tissues, and no blood capillary growth or any immunological rejection was observed. Conclusion Microfracture technique and BMSCs transplantation to repair cartilage defect is characterized with minimal invasion and easy operation, and it will greatly promote the regeneration repair of articular cartilage defect.

© 2015 Elsevier B.V. All rights reserved. Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, computational intelligence has recently been applied in transfer learning. This paper systematically examines computational intelligence-based transfer learning techniques and clusters related technique developments into four main categories: (a) neural network-based transfer learning; (b) Bayes-based transfer learning; (c) fuzzy transfer learning, and (d) applications of computational intelligence-based transfer learning. By providing state-of-the-art knowledge, this survey will directly support researchers and practice-based professionals to understand the developments in computational intelligence-based transfer learning research and applications.

Transfer learning provides an approach to solve target tasks more quickly and effectively by using previouslyacquired knowledge learned from source tasks. Most of transfer learning approaches extract knowledge of source domain in the given feature space. The issue is that single perspective can‟t mine the relationship of source domain and target domain fully. To deal with this issue, this paper develops a method using Stacked Denoising Autoencoder (SDA) to extract new feature spaces for source domain and target domain, and define two fuzzy sets to analyse the variation of prediction accuracy of target task in new feature spaces.

In this paper, an in-depth comparison is conducted on the architecture of data services, and also the shared disc architecture of the cloud computing database is analyzed. Besides, the application of the cloud computing is implemented based on the open-source cloud platform. Furthermore, the research result improves the parallelism and extensibility of the actual system, and simultaneously increases the data capacity.

OBJECTIVE: To analyze and identify the chemical constituents in rat brain tissues after oral administration of Chuanxiong Rhizoma extracts. METHOD: The dosed and blank rat brain tissues were analyzed by UPLC-Q-TOF-MS. Different peaks were observed in total ion chromatograms and then identified according to their retention time, accurate mass weight, MS and MS/MS data. RESULT: After oral administration of Chuanxiong Rhizoma extracts, 3 compounds were absorbed into rat brain tissues through BBB. They were identified as senkyunolide I, senkyunolide A and ligustilide. CONCLUSION: The study is helpful for interpreting effective substance of Ligusticum chuanxiong.

The inertia weight is an important parameter in the Particle Swarm Optimization algorithm, which controls the degree of influence of the contemporary speed to the next generation and plays a role of balancing global search and local search. In the iteration process, the inertia weight will decrease nonlinearly at the early stage and decrease linearly at the later stage. The improved algorithm will effectively prevent premature convergence of the algorithm. The simulation results show that the improved algorithm is superior to the particle swarm optimization algorithm of the linear decreasing weight.

We investigate theoretically the polarization dynamics of vertical-cavity surface-emitting lasers (VCSELs) subjected to negative optoelectronic feedback. As we vary the feedback delay and strength, the VCSEL exhibits interesting nonlinear dynamics in the two-linear polarized directions. The output instability and negative current modulation induced by delayed feedback can also cause polarization switching (PS) to happen. In addition, we investigate the case when the free-running VCSEL emits in the modulated-elliptical state and find that the laser experiences in succession the elliptical polarization, the x-mode polarization, and the mixed-mode polarization states with an increasing feedback strength.

Fuzzy decision tree induction is one of the most popular choices for learning and reasoning from feature-based examples. A fuzzy decision tree can be constructed from a training set of cases and converted into a set of fuzzy rules. In this paper, the reasoning ability of four matching operators (or,nland) , (or,times) , (+, nland) and (+,times), which are used for applying fuzzy rules to classification, are analyzed and compared. The purpose of this study is to show some useful guidelines on how to choose an appropriate operator for classified problem.

The high quality Ge islands material with 1.55 mu m photo-response grown on Sol substrate is reported. Due to the modulation of the cavity formed by the mirrors at the surface and the buried SiO2 interface, seven sharp and strong peaks with narrow linewidth are found. And a 1.55 mu m Ge islands resonant-cavity-enhanced (RCE) detector with narrowband was fabricated by a simple method. The bottom mirror was deposited in the hole formed by anisotropically etching, in a basic solution from the backside of the sample with the buried SiO2 layer in silicon-on-insulator substrate as the etch-stop layer. Reflectivity spectrum indicates that the mirror deposited in the hole has a reflectivity as high as 99% in the range of 1.2-1.65 mu m. The peak responsivity of the RCE detector at 1543.8 nm is 0.028 mA/W and a full width at half maximum of 5 nm is obtained. Compared with the conventional p-i-n photodetector, the responsivity of RCE detector has a nearly threefold enhancement.

Screening of hollow-organ cancers has been carried out for 16 years using the occult blood test. We are currently assessing the result of serial public cancer screening in China. Human cancers can be divided into 3 types: 1) surface cancer (15%), 2) hollow-organ cancer (65%), and 3) deep-seated solid-organ cancer (20%). Through an education program, the general population was taught to self-examine the body surface and to use a self-examination kit to detect any occult blood discharged from the hollow organs. On discovering positive or doubtful results, these subjects were referred to the hospital for a complete clinical checkup. A total of 10,000 people ages 40 to 70 years chosen from the university areas in Beijing were divided in a test group (5,000) and a control group (5,000). This study, which was carried out for 4 years, showed that 79 cancers were discovered in the test group, with an annual cancer detection rate of 482.5/10,000 population and a cancer mortality rate of 36.6/10,000. The control group showed an annual cancer mortality rate of 206/10,000 population. The difference has very marked statistical significance ( p< 0.001). During the same interval, an extensive cancer screening was carried out in the high-incidence areas. The Cancer Institute in Sihui city of Guangdong province discovered, by screening 758 persons in the outpatient department during a 2-year period, 16 cancers with 15 as nasopharyngeal cancer. From 1999 to 2000, another extensive public cancer screening in the high-incidence areas in Jiangsu province discovered 480 cancers from an 80,000 population. Together with the screening test carried out by Qin's self-examination kit before the National Supported Cancer Research Project on a population of 431,000, 1,272 cancers and more than 10,000 precancerous lesions have been detected. The self-examination kit and public cancer screening are highly important in the practice of public cancer screening.

A previously described bead method used to detect blood is simple, cheap, and effective; it can be repeated periodically. In 11 provinces in China, 233,825 persons (age range, 30–70 years) were screened. A positive occult blood test result was found in 28,557 persons (12%). Of these, 16,918 underwent a gastroscopy, resulting in the detection of 581 cancers that were located in similar frequencies in the esophagus, gastric cardia, and gastric body; 70% of the lesions were in an early or moderately advanced stage. Among 119 patients with early-stage cancer, the 3-year survival rate was 98.3%. After preliminary screening of cancer, the 4-year follow-up found a mortality rate in the group with negative occult blood test results that was only 25% of that of the positive group. Preliminary yearly screening would detect more early cancers and fewer advanced cancers. The mortality rate of esophageal and gastric cancer might be reduced drastically. Cancer 1993; 71:216-8.