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A multiscale clustering-based topology optimization for thermo-elastic lattice structures is studied based on the extended multiscale finite element method (EMsFEM). The strain energy of thermo-elastic lattice structures is chosen as the objective function. The microstructural configuration and the macrostructural distribution of the thermo-elastic lattice material are designed through topology optimization concurrently. The K-means clustering-based method is proposed to group the microstructures of the lattice materials. The effects of the number of clusters (groups), magnitude of the thermal loads, size factor of the microstructure, and material volume fraction on the optimization results are discussed. The results show that the clustering-based multiscale design optimization is superior to the classical multiscale design optimization of lattice structures.

In the current electrical load profile analysis, considering the shortage of traditional methods on the typical load profile extraction of single consumers and the load profile feature extraction, this paper proposes an approach based on time series data mining. Firstly, this method reduces the dimension of the load profile of a single consumer based on the Piecewise Aggregate Approximation(PAA), and re-expresses the load profile of the consumer over a period based on the Symbolic Aggregate approXimation(SAX), representing the consumer’s load profile with a symbolic string to extract the typical load profile. Then, combined with the load characteristic indices and time series-based features, the typical load profiles of different consumers are clustered based on the K-means algorithm to analyze the power consumption behaviors. Finally, this paper performs a case analysis with a UCI test data set, and the results show that the proposed approach can excavate typical power consumption behaviors of consumers and improve the electrical load profile analysis efficiency and the clustering quality.

With the dramatic increase of energy demand and the continuous increase of power system operation pressure, higher requirements are put forward for the development of power grid planning and optimization operation. It is important for the refinement of distribution network planning to deeply extract the characteristics of user load. First, the process of load characteristic analysis method from the user level to the industry level is proposed, which achieves the division of electricity consumption patterns of various industries, thus building a panoramic portrait of industry electricity consumption behavior. Then, by expanding the information filled in by traditional customers, the feature vector of each user is extracted, and the users' industry electricity consumption patterns are used as the label. Therefore, a method for identifying the electricity consumption pattern of the customer based on the BB-stacking model fusion framework is proposed, which yields the preliminary forecast results of customer load based on the actual load accounting results of the customers. Finally, comparative simulations with different methods verify the effectiveness of the proposed algorithm, which can provide prominent guidance for the actual distribution network planning work.

Predicting the passenger flow of public transport in a newly developed area of a city is very urgent for designing a precise and efficient public transport network. This paper proposes a new prediction model by exploring the relationship between the passenger flow of a station and its surrounding area's factors. First, in order to obtain more accurate factors affecting the passenger flow, the city is divided into multiple regions with similar internal traffic properties and moderate spatial size using the data of urban road network and buildings. Second, to effectively solve the problem of fuzziness of the station's attraction scope, the concept of the membership degree and fuzzy processing method is proposed. Finally, the station's passenger flow prediction model is launched based on Xgboost. The experimental results on three districts in Beijing show that our method outperforms all baselines significantly, which improves the accuracy by more than 20%.

This study aims to investigate the topology characteristic indices which have a close relationship with reliability of distribution networks via mathematical tools. First, a practical network is extracted into a topology and presented by weighted adjacency matrix associated with a failure degree matrix, which contains more significant information than that of original adjacency matrix. Second, singular value sequence and vertex/edge sharing are proposed as two similarity indices. Finally, similarity functions are applied on two cases to establish the relationship with reliability by curve fitting and clustering. optimisation strategy based on modified contingency-load-loss index is elaborated to improve the reliability and guide to distribution network planning.

For the effective monitoring of batch processes with uneven multiphases, phase partitioning and discriminant analysis are two critical problems. To fully solve these two problems, a systematic strategy including fuzzy phase partitioning and hybrid discriminant analysis is proposed. First, using a new unsupervised, multiscale, sequential partition (UMSP), each batch is divided into phases with transitions using different clustering scales. On this basis, two multiple-support-vector-data-description (SVDD) models are built for online phase partitioning and monitoring, and a hybrid-discriminant-analysis method is then developed for online fault detection. The effectiveness and advantages of the proposed method are illustrated with a 2D, handwritten example and a fed-batch penicillin-fermentation process.

Background The neurophysiologic mechanisms of propofol-induced loss of consciousness have been studied in detail at the macro (scalp electroencephalogram) and micro (spiking or local field potential) scales. However, the changes in information integration and cortical connectivity during propofol anesthesia at the mesoscopic level (the cortical scale) are less clear. Methods The authors analyzed electrocorticogram data recorded from surgical patients during propofol-induced unconsciousness (n = 9). A new information measure, genuine permutation cross mutual information, was used to analyze how electrocorticogram cross-electrode coupling changed with electrode-distances in different brain areas (within the frontal, parietal, and temporal regions, as well as between the temporal and parietal regions). The changes in cortical networks during anesthesia—at nodal and global levels—were investigated using clustering coefficient, path length, and nodal efficiency measures. Results In all cortical regions, and in both wakeful and unconscious states (early and late), the genuine permutation cross mutual information and the percentage of genuine connections decreased with increasing distance, especially up to about 3 cm. The nodal cortical network metrics (the nodal clustering coefficients and nodal efficiency) decreased from wakefulness to unconscious state in the cortical regions we analyzed. In contrast, the global cortical network metrics slightly increased in the early unconscious state (the time span from loss of consciousness to 200 s after loss of consciousness), as compared with wakefulness (normalized average clustering coefficient: 1.05 ± 0.01 vs. 1.06 ± 0.03, P = 0.037; normalized average path length: 1.02 ± 0.01 vs. 1.04 ± 0.01, P = 0.021). Conclusions The genuine permutation cross mutual information reflected propofol-induced coupling changes measured at a cortical scale. Loss of consciousness was associated with a redistribution of the pattern of information integration; losing efficient global information transmission capacity but increasing local functional segregation in the cortical network. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Microblog is a new forum to publish short text. Because of its huge user base and its convenience, it promises to be an excellent instrumentation for feeding and sustaining web-based communities. Therefore, the analysis of microblog topics is introduced and proposed as reference for correct decision making in online communities. This paper briefly introduces the collection method of microblog data and the clustering algorithm of topic classification. Then the clustering algorithm is improved, and the Storm system was introduced. The K-means algorithm before and after the improvement was simulated and analysed in the single-machine environment and Storm framework. The results show that the improved clustering algorithm has a higher classification accuracy and a lower false alarm rate both in the single-machine environment and Storm framework. Under the same clustering algorithm, the algorithm in the Storm framework has a higher classification accuracy, a lower false alarm rate and a shorter recognition time.

Autophagy is considered as a double-edged sword. It can prolong the survival of cancer cells and enhance its resistance to apoptosis, and paradoxically, defective autophagy has been linked to increased tumorigenesis, but the mechanism behind this phenomenon is unclear. In this study, we demonstrated that decreased phosphorylation of signal transducer and activator of transcription 3 (p-STAT3) was correlated with increased autophagy through the Akt/mTOR and Erk signaling pathways in human head and neck squamous cell carcinoma (HNSCC). We also showed that blockage of STAT3 by NSC74859 could markedly induce apoptotic cell death and autophagy. Meanwhile, increased autophagy inhibited apoptosis. The pharmacological or genetic inhibition of autophagy and STAT3 further sensitized HNSCC cells to apoptosis. Furthermore, evidence from xenograft model proved that suppressed STAT3 activity combined with inhibition of autophagy promoted tumor regression better than either treatment alone. Taken together, this present study demonstrated that autophagy alleviates apoptotic cell death in HNSCC, and combination of inhibition of STAT3 by NSC74859 and autophagy might be a promising new therapeutic strategy for HNSCC.

Background: microRNAs can repress the expression of target genes by destabilizing their mRNAs or by inhibiting their translation. Our previous findings suggested that miR-193a-3p inhibited the progression of NSCLC both in vitro and in vivo. However, the biological processes and molecular pathways through which this miRNA exerts its positive effects are unknown. Methods: To explore the molecular mechanisms by which miR-193a-3p inhibited NSCLC metastasis, we investigated the changes in the protein profile of SPC-A-1sci (highly metastatic) cells in response to the up-regulation of miR-193a-3p expression using a proteomics approach (iTRAQ combined with NanoLC-MS/MS). Changes in the profiles of the expressed proteins were verified using western blotting and were analyzed using the DAVID and STRING programs. Results: In the two replicated experiments, 4962/4946 proteins were identified, and the levels of expression of 4923/4902 proteins were quantified. In total, 112 of these proteins were differentially expressed. Among them, the up-regulated levels of expression of two of the 62 proteins with up-regulated expression (PPP2R2A and GSN) and the down-regulated levels of expression four of the 50 proteins with down-regulated expression (LMNB2, UHRF1, G3BP1, and HNRNPU) were verified using western blotting. The bioinformatics analysis revealed the interactions and signaling networks of these differentially expressed proteins. Conclusion: miR-193a-3p inhibited the metastasis of lung cancer cells by deregulating the expression of tumor-related proteins. These findings may improve the understanding of the molecular mechanisms underlying the metastatic-inhibitory effect of miR-193a-3p on lung cancer cells.

Background Grain development in maize is an essential process in the plant’s life cycle and is vital for use of the plant as a crop for animals and humans. However, little is known regarding the protein regulatory networks that control grain development. Here, isobaric tag for relative and absolute quantification (iTRAQ) technology was used to analyze temporal changes in protein expression during maize grain development. Results Maize grain proteins and changes in protein expression at eight developmental stages from 3 to 50 d after pollination (DAP) were performed using iTRAQ-based proteomics. Overall, 4751 proteins were identified; 2639 of these were quantified and 1235 showed at least 1.5-fold changes in expression levels at different developmental stages and were identified as differentially expressed proteins (DEPs). The DEPs were involved in different cellular and metabolic processes with a preferential distribution to protein synthesis/destination and metabolism categories. A K-means clustering analysis revealed coordinated protein expression associated with different functional categories/subcategories at different development stages. Conclusions Our results revealed developing maize grain display different proteomic characteristics at distinct stages, such as numerous DEPs for cell growth/division were highly expressed during early stages, whereas those for starch biosynthesis and defense/stress accumulated in middle and late stages, respectively. We also observed coordinated expression of multiple proteins of the antioxidant system, which are essential for the maintenance of reactive oxygen species (ROS) homeostasis during grain development. Particularly, some DEPs, such as zinc metallothionein class II, pyruvate orthophosphate dikinase (PPDK) and 14-3-3 proteins, undergo major changes in expression at specific developmental stages, suggesting their roles in maize grain development. These results provide a valuable resource for analyzing protein function on a global scale and also provide new insights into the potential protein regulatory networks that control grain yield and quality. Electronic supplementary material The online version of this article (doi:10.1186/s12870-016-0878-1) contains supplementary material, which is available to authorized users.

The mechanism of self-assembly of 140 peptide amphiphiles (PAs) to give nanofiber structures was investigated using a coarse-grained method to quantitatively determine whether the assembly process involves discrete intermediates or is a continuous process. Two novel concepts are introduced for this analysis, a cluster analysis of the time dependence of PA assembly and use of the fraction of native contacts as reaction coordinates for characterizing thermodynamic functions during assembly. The cluster analysis of the assembly kinetics demonstrates that a pillar-like intermediate state is formed before the final cylindrical semifiber structure. We also find that head group assembly occurs on a much shorter time scale than tail group assembly. A 2D free-energy landscape with respect to the fraction of native contacts was calculated, and the pillar-like intermediate structure was also found, with free energies about 1.2 kcal/mol higher than the final state. Although this intermediate state exists for only hundreds of nanoseconds, the PA self-assembly process can be recognized as involving two steps, (a) transition from the disordered state to the noncylindrical pillar-like intermediate and (b) pillar-like to final semifiber transition. These results are important to the further design of PAs as functional nanostructures.

CXC chemokine receptor 4 (CXCR4) is involved in many human malignant tumors and plays an important role in tumor growth and metastasis. To explore the effects of CXCR4 expression on the malignant cells of oral squamous cell carcinoma (OSCC), Tca8113 and SCC-9 cell lines, as well as their xenograft models, of nude mice were used to detect cancer cell proliferation alteration. This study also examined the corresponding molecular mechanism after CXCR4 knockdown using a recombinant lentiviral vector expressing small interference RNA (siRNA) for CXCR4. RNA interference-mediated knockdown of CXCR4 in highly aggressive (Tca8113 and SCC-9) tumor cells significantly inhibited the proliferation of the two cell lines in vitro and in vivo. The expression levels of >1,500 genes involved in cell cycle, apoptosis, and multiple signaling pathways were also altered. These results provide new evidence of CXCR4 as a promising tumor gene therapeutic target.

The endosperm at the storage phase undergoes a series of coordinated cellular and metabolic events, including starchy endosperm cell death, starch synthesis, and starch granule packaging, which leads to efficient accumulation of starch. However, the mechanism underlying the interconnections remains unknown. We used integrated proteomic and cytological approaches to probe the interconnections in rice (Oryza sativa) endosperm at the storage phase from 12 to 18 days after flowering (DAF). Starch granule packaging was completed first in the inner part of endosperm at 15 DAF and spread to almost the entire endosperm at 18 DAF. Programmed starchy endosperm cell death occurred after the starch granule packaging. Endogenous H(2)O(2) was detectable in the inner part of endosperm at 12 DAF and the region beyond the inner part at 15 DAF, with an H(2)O(2) burst at 15 DAF. Proteomics analysis with 2-D fluorescent difference gel electrophoresis and matrix-assisted laser-desorption ionization time-of-flight/time-of-flight mass spectrometry revealed 317 proteins, including almost all known antioxidants, differentially expressed throughout the 3 stages of the developmental phase. More than two-thirds of the 317 proteins were potential thioredoxin targets, with a preferential skew toward central carbon metabolism, alcoholic fermentation, starch metabolism, amino acid metabolism, and protein synthesis or folding. These proteins implicated in starch synthesis and gluconeogenesis were upregulated, whereas those involved in anabolism of biomacromolecules such as proteins, lipids, and cell wall components were downregulated, with upregulated expression of proteins involved in catabolism of these biomacromolecules, which suggests remobilization of nutrients for starch synthesis. These data suggested important roles of the H(2)O(2)-antioxidant interface in coordinating starch accumulation, programmed cell death of starchy endosperm, and remobilization of nutrients during the cell death.

We characterized the bacterial populations in surface water receiving effluent from an oxytetracycline (OTC) production plant. Additional sampling sites included the receiving river water 5 km upstream and 20 km downstream from the discharge point. High levels of OTC were found in the wastewater (WW), and the antibiotic was still detectable in river water downstream (RWD), with undetectable levels in river water upstream (RWU). A total of 341 bacterial strains were isolated using nonselective media, with the majority being identified as Gammaproteobacteria . The MICs were determined for 10 antibiotics representing seven different classes of antibiotics, and the corresponding values were significantly higher for the WW and RWD isolates than for the RWU isolates. Almost all bacteria (97%) from the WW and RWD samples demonstrated multidrug-resistant (MDR) phenotypes, while in RWU samples, these were less frequent (28%). The WW and RWD isolates were analyzed for the presence of 23 tetracycline ( tet ) resistance genes. The majority of isolates (94.2% and 95.4% in WW and RWD, respectively) harbored the corresponding genes, with tet (A) being the most common (67.0%), followed by tet (W), tet (C), tet (J), tet (L), tet (D), tet (Y), and tet (K) (in the range between 21.0% and 40.6%). Class I integrons were detected in the majority of WW and RWD isolates (97.4% and 86.2%, respectively) but were not associated with the tet genes. We hypothesize that the strong selective pressure imposed by a high concentration of OTC contributes to the wide dissemination of tetracycline resistance genes and other antibiotic resistance genes, possibly through mobile genetic elements.

The development and starch accumulation of cereal endosperms rely on the sugar supply of leaves, which is subject to diurnal cycles, and the endosperm itself also experiences a light/dark switch. However, revealing how the cereal endosperm responds to diurnal input remains a major challenge. We used comparative proteomic approaches to probe diurnally affected processes in rice endosperm (Oryza sativa) 10 days after flowering under 12-h light/12-h dark. Starch granules in rice endosperm showed a growth ring structure under a normal light/dark cycle but not under constant light. Sucrose showed a high level in light and low level in dark. Two-dimensional (2-D) differential in-gel electrophoresis-based proteomic analysis revealed 101 protein spots diurnally changed and 91 identities, which were involved in diverse processes with preferred distribution in stress response, protein synthesis/destination and metabolism. Proteins involved in cell division showed high expression in light and those in cell enlargement and cell wall synthesis high in dark, while starch synthesis proteins were light-downregulated and dark-upregulated. Redox homeostasis-associated proteins showed in-phase peaks under light and dark. These data demonstrate diurnal input-regulated diverse cellular and metabolic processes in rice endosperm, and coordination among these processes is essential for development and starch accumulation with diurnal input.

Classification algorithm is an important technique that has been studied for many years in hyperspectral image processing along with the development of hyperspectral remote sensing. Traditional classification algorithms mostly focus on the differences of spectral dimension but neglecting spatial structures of geography objects. By improving the ECHO algorithm, we put forward a classification algorithm based on pixel's spatial association. This algorithm could possibly be used as an important step in hyperspectral data classification and identification. It may also be a good approach to get rid of redundancy information in endmember extracting, which means that pure spectrum can be extracted from less pixels in the hyperspectral image. In this way, endmember extracting and spectral unmixing will take less time and become more efficient.

In order to overcome the problem that the fax server was inadequate on the spam fax prevention, this paper provided a spam fax detection and classification method based on clustering. The method took advantage of behavioral characteristics of spam fax. And it separated spam faxes from normal faxes with clustering method on the basis of the skew detection. Then use OCR keywords recognition technology to classify spam faxes once again. This method is simple to implement with high accuracy, and it has applied successfully.

Background The Saccharopolyspora erythraea genome sequence was released in 2007. In order to look at the gene regulations at whole transcriptome level, an expression microarray was specifically designed on the S. erythraea strain NRRL 2338 genome sequence. Based on these data, we set out to investigate the potential transcriptional regulatory networks and their organization. Methodology/Principal Findings In view of the hierarchical structure of bacterial transcriptional regulation, we constructed a hierarchical coexpression network at whole transcriptome level. A total of 27 modules were identified from 1255 differentially expressed transcript units (TUs) across time course, which were further classified in to four groups. Functional enrichment analysis indicated the biological significance of our hierarchical network. It was indicated that primary metabolism is activated in the first rapid growth phase (phase A), and secondary metabolism is induced when the growth is slowed down (phase B). Among the 27 modules, two are highly correlated to erythromycin production. One contains all genes in the erythromycin-biosynthetic (ery) gene cluster and the other seems to be associated with erythromycin production by sharing common intermediate metabolites. Non-concomitant correlation between production and expression regulation was observed. Especially, by calculating the partial correlation coefficients and building the network based on Gaussian graphical model, intrinsic associations between modules were found, and the association between those two erythromycin production-correlated modules was included as expected. Conclusions This work created a hierarchical model clustering transcriptome data into coordinated modules, and modules into groups across the time course, giving insight into the concerted transcriptional regulations especially the regulation corresponding to erythromycin production of S. erythraea. This strategy may be extendable to studies on other prokaryotic microorganisms.