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Tumour samples are heterogeneous and are comprised of multiple cell types in addition to cancer cells. Here, the authors devised a method to estimate the relative levels of haematopoietic cells in breast cancer samples and demonstrate that this correlates with prognosis.

The whole big query tree is divided into several branches,and the work of tag recognition is executed in each branch,thus reducing the probability of tag collision.The proposed algorithm can be divided into prefix determination and branch query two phases.In the prefix determination phase,the prefix of every tag is identified,and each prefix denotes a branch.Traverse each branch by turns to recognize tags; in the branch query phase,backward search strategy and dynamic search method are used.Theoretical analysis and simulation experiment show that when the improved algorithm can improved the overall performance of RFID system from the aspects of the number of queries,time delay,and throughput.

Wnt/β-catenin signal transduction directs intestinal stem cell (ISC) proliferation during homeostasis. Hyperactivation of Wnt signaling initiates colorectal cancer, which most frequently results from truncation of the tumor suppressor Adenomatous polyposis coli (APC). The β-catenin-TCF transcription complex activates both the physiological expression of Wnt target genes in the normal intestinal epithelium and their aberrantly increased expression in colorectal tumors. Whether mechanistic differences in the Wnt transcription machinery drive these distinct levels of target gene activation in physiological versus pathological states remains uncertain, but is relevant for the design of new therapeutic strategies. Here, using a Drosophila model, we demonstrate that two evolutionarily conserved transcription cofactors, Earthbound (Ebd) and Erect wing (Ewg), are essential for all major consequences of Apc1 inactivation in the intestine: the hyperactivation of Wnt target gene expression, excess number of ISCs, and hyperplasia of the epithelium. In contrast, only Ebd, but not Ewg, mediates the Wnt-dependent regulation of ISC proliferation during homeostasis. Therefore, in the adult intestine, Ebd acts independently of Ewg in physiological Wnt signaling, but cooperates with Ewg to induce the hyperactivation of Wnt target gene expression following Apc1 loss. These findings have relevance for human tumorigenesis, as Jerky (JRK/JH8), the human Ebd homolog, promotes Wnt pathway hyperactivation and is overexpressed in colorectal, breast, and ovarian cancers. Together, our findings reveal distinct requirements for Ebd and Ewg in physiological Wnt pathway activation versus oncogenic Wnt pathway hyperactivation following Apc1 loss. Such differentially utilized transcription cofactors may offer new opportunities for the selective targeting of Wnt-driven cancers.

Aspergillus fumigatus is responsible for a disproportionate number of invasive mycosis cases relative to other common filamentous fungi. While many fungal factors critical for infection establishment are known, genes essential for disease persistence and progression are ill defined. We propose that fungal factors that promote navigation of the rapidly changing nutrient and structural landscape characteristic of disease progression represent untapped clinically relevant therapeutic targets. To this end, we find that A. fumigatus requires a carbon catabolite repression (CCR) mediated genetic network to support in vivo fungal fitness and disease progression. While CCR as mediated by the transcriptional repressor CreA is not required for pulmonary infection establishment, loss of CCR inhibits fungal metabolic plasticity and the ability to thrive in the dynamic infection microenvironment. Our results suggest a model whereby CCR in an environmental filamentous fungus is dispensable for initiation of pulmonary infection but essential for infection maintenance and disease progression. Conceptually, we argue these data provide a foundation for additional studies on fungal factors required to support fungal fitness and disease progression and term such genes and factors, DPFs (disease progression factors).

As one of the critical components of high-speed trains, the running gears system directly affects the operation performance of the train. This paper proposes a state-degradation-oriented method for fault diagnosis of an actual running gears system based on the Wiener state degradation process and multi-sensor filtering. First of all, for the given measurements of the high-speed train, this paper considers the information acquisition and transfer characteristics of composite sensors, which establish a distributed topology for axle box bearing. Secondly, a distributed filtering is built based on the bilinear system model, and the gain parameters of the filter are designed to minimize the mean square error. For a better presentation of the degradation characteristics in actual operation, this paper constructs an improved nonlinear model. Finally, threshold is determined based on the Chebyshev’s inequality for a reliable fault diagnosis. Open datasets of rotating machinery bearings and the real measurements are utilized in the case studies to demonstrate the effectiveness of the proposed method. Results obtained in this paper are consistent with the actual situation, which validate the proposed methods.

In this study, Fe–xMn–14Al–8Ni–C alloy (x = 10, 15, 20, 25 wt.%) was prepared by vacuum arc melting method. The microstructure of this series of alloys and the oxidation behavior at 600 °C were studied. The conclusions are as follows: Fe–xMn–14Al–8Ni–C alloy mainly contains austenite phase, NiAl intermetallic compound phase and k-carbide phase. As the content of Mn increases, the amount of austenite increases while the amount of NiAl compound decreases. At the same time, the content of k-carbide phase precipitated at the interface between austenite and NiAl compound and inside austenite increases. The oxidation resistance results show that as the Mn content increases, the oxidation resistance of the alloy is improved. After oxidation, due to the difference in thermal stress and thermal expansion coefficient, the oxide film is mainly divided into two layers (when the Mn content is 10% and 15%, respectively), the outer oxides are Fe _2 O _3 and a small amount of Mn _2 O _3 , the inner oxides are mainly the mixture of Al _2 O _3 , Mn _2 O _3 and NiO. When the Mn content increases to 20%, the oxide film is a three-layer, and a uniform dense oxide film mainly composed of Al _2 O _3 appears at the junction with the substrate, which better prevents the further diffusion of oxygen in the air to the inside and protects the substrate. The surface oxide film is dense and stable, so it can prevent further oxidation. Therefore, the alloy with 25% Mn content exhibits the most excellent oxidation resistance.

Despite correlations between histone methyltransferase (HMT) activity and gene regulation, direct evidence that HMT activity is responsible for gene activation is sparse. We address the role of the HMT activity for MLL1, a histone H3 lysine 4 (H3K4) methyltransferase critical for maintaining hematopoietic stem cells (HSCs). Here, we show that the SET domain, and thus HMT activity of MLL1, is dispensable for maintaining HSCs and supporting leukemogenesis driven by the MLL-AF9 fusion oncoprotein. Upon Mll1 deletion, histone H4 lysine 16 (H4K16) acetylation is selectively depleted at MLL1 target genes in conjunction with reduced transcription. Surprisingly, inhibition of SIRT1 is sufficient to prevent the loss of H4K16 acetylation and the reduction in MLL1 target gene expression. Thus, recruited MOF activity, and not the intrinsic HMT activity of MLL1, is central for the maintenance of HSC target genes. In addition, this work reveals a role for SIRT1 in opposing MLL1 function.

Regarding the relatively big errors with running the DV-hop localization algorithm in a network topology scenario,with which nodes randomly distributes,a particle swarm optimization localization algorithm for WSN nodes based on modifying average hop distance was proposed.changing the structure of data packets sent by anchor nodes with broadcasting,weighting the average hop distance error of reference anchor nodes to modify the average hop distance,and using an improved particle swarm algorithm to optimize iteration process for localization,thus,WPDV-Hop localization algorithm improvements were carried out.The simulation results indicate that the localization accuracy and the stability of the WPDV-Hop localization algorithm are significantly improved compared with the original algorithm.

BACKGROUND:We analyzed and integrated transcriptome data from two large studies of lung adenocarcinomas on distinct populations. Our goal was to investigate the variable gene expression alterations between paired tumor-normal tissues and prospectively identify those alterations that can reliably predict lung disease related outcomes across populations. METHODS:We developed a mixed model that combined the paired tumor-normal RNA-seq from two populations. Alterations in gene expression common to both populations were detected and validated in two independent DNA microarray datasets. A 10-gene prognosis signature was developed through a l1 penalized regression approach and its prognostic value was evaluated in a third independent microarray cohort. RESULTS:Deregulation of apoptosis pathways and increased expression of cell cycle pathways were identified in tumors of both Caucasian and Asian lung adenocarcinoma patients. We demonstrate that a 10-gene biomarker panel can predict prognosis of lung adenocarcinoma in both Caucasians and Asians. Compared to low risk groups, high risk groups showed significantly shorter overall survival time (Caucasian patients data: HR = 3.63, p-value = 0.007; Asian patients data: HR = 3.25, p-value = 0.001). CONCLUSIONS:This study uses a statistical framework to detect DEGs between paired tumor and normal tissues that considers variances among patients and ethnicities, which will aid in understanding the common genes and signalling pathways with the largest effect sizes in ethnically diverse cohorts. We propose multifunctional markers for distinguishing tumor from normal tissue and prognosis for both populations studied.

Gene expression regulators, such as transcription factors (TFs) and microRNAs (miRNAs), have varying regulatory targets based on the tissue and physiological state (context) within which they are expressed. While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts, methods for transferring regulator target genes across contexts are lacking. Further, regulator target gene lists frequently are not curated or have permissive inclusion criteria, impairing their use. Here, we present a method called iterative Contextual Transcriptional Activity Inference of Regulators (icTAIR) to resolve these issues. icTAIR takes a regulator's previously-identified target gene list and combines it with gene expression data from a context, quantifying that regulator's activity for that context. It then calculates the correlation between each listed target gene's expression and the quantitative score of regulatory activity, removes the uncorrelated genes from the list, and iterates the process until it derives a stable list of refined target genes. To validate and demonstrate icTAIR's power, we use it to refine the MSigDB c3 database of TF, miRNA and unclassified motif target gene lists for breast cancer. We then use its output for survival analysis with clinicopathological multivariable adjustment in 7 independent breast cancer datasets covering 3,430 patients. We uncover many novel prognostic regulators that were obscured prior to refinement, in particular NFY, and offer a detailed look at the composition and relationships among the breast cancer prognostic regulome. We anticipate icTAIR will be of general use in contextually refining regulator target genes for discoveries across many contexts. The icTAIR algorithm can be downloaded from https://github.com/icTAIR.

In breast cancer, nuclear receptors (NRs) play a prominent role in governing gene expression, have prognostic utility, and are therapeutic targets. We built a regulatory map for 24 NRs, six chromatin state markers, and 14 breast-cancer-associated transcription factors (TFs) that are expressed in the breast cancer cell line MCF-7. The resulting network reveals a highly interconnected regulatory matrix where extensive crosstalk occurs among NRs and other breast -cancer-associated TFs. We show that large numbers of factors are coordinately bound to highly occupied target regions throughout the genome, and these regions are associated with active chromatin state and hormone-responsive gene expression. This network also provides a framework for stratifying and predicting patient outcomes, and we use it to show that the peroxisome proliferator-activated receptor delta binds to a set of genes also regulated by the retinoic acid receptors and whose expression is associated with poor prognosis in breast cancer.

According to original dynamic resource allocation algorithms,a new dynamic resource allocation algorithm for multiuser OFDM-UWB system based on users'rates was proposed to minimize the total transmitting power whil sa-tisfy requests for QoS and data rate of all users.Qual ty of the system was improved running the subcarrier allocation and bit allocation algorithms based on fairness of users.In subcarrier allocation process,rate impact factor was used to com-pare influence of different subcarriers to a user.In allocation process,bits were allocated to each user's subcarriers first,then bits on each subcarriers were adjusted according to rate request of each user.In or to reduce the complexity system of the furthermore,a subcarrier and bit allocation scheme is employed through subcarrier grouping.Simulation results indicate that the algorithm can lower power consume,BER and operation complexity.

PURPOSE:To evaluate the accuracy of shear wave elastography (SWE) in the quantitative diagnosis of liver fibrosis severity. METHODS:The published literatures were systematically retrieved from PubMed, Embase, Web of science and Scopus up to May 13th, 2016. Included studies reported the pooled sensitivity, specificity, positive and negative predictive values, as well as the diagnostic odds ratio of SWE in populations with liver fibrosis. A bivariate mixed-effects regression model was used, which was estimated by the I2 statistics. The quality of articles was evaluated by quality assessment of diagnostic accuracy studies (QUADAS). RESULTS:Thirteen articles including 2303 patients were qualified for the study. The pooled sensitivity and specificity of SWE for the diagnosis of liver fibrosis are as follows: ≥F1 0.76 (p

The regulatory architecture of breast cancer is extraordinarily complex and gene misregulation can occur at many levels, with transcriptional malfunction being a major cause. This dysfunctional process typically involves additional regulatory modulators including DNA methylation. Thus, the interplay between transcription factor (TF) binding and DNA methylation are two components of a cancer regulatory interactome presumed to display correlated signals. As proof of concept, we performed a systematic motif-based in silico analysis to infer all potential TFs that are involved in breast cancer prognosis through an association with DNA methylation changes. Using breast cancer DNA methylation and clinical data derived from The Cancer Genome Atlas (TCGA), we carried out a systematic inference of TFs whose misregulation underlie different clinical subtypes of breast cancer. Our analysis identified TFs known to be associated with clinical outcomes of p53 and ER (estrogen receptor) subtypes of breast cancer, while also predicting new TFs that may also be involved. Furthermore, our results suggest that misregulation in breast cancer can be caused by the binding of alternative factors to the binding sites of TFs whose activity has been ablated. Overall, this study provides a comprehensive analysis that links DNA methylation to TF binding to patient prognosis.

The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet's observed gene expression pattern across many conditions. We make Loregic available as a general-purpose tool (github.com/gersteinlab/loregic). We validate it with known yeast transcription-factor knockout experiments. Next, using human ENCODE ChIP-Seq and TCGA RNA-Seq data, we are able to demonstrate how Loregic characterizes complex circuits involving both proximally and distally regulating transcription factors (TFs) and also miRNAs. Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs. Finally, we inter-relate Loregic's gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy.

The global gene regulator Special AT-rich sequence-binding protein-1 (SATB1) has been reported to induce EMT-like changes and be associated with poor clinical outcome in several cancers. This study aims to evaluate whether SATB1 affects the biological behaviors of bladder transitional cell carcinoma (BTCC) and further elucidate if this effect works through an epithelial-mesenchymal transition (EMT) pathway. The expression of SATB1, E-cadherin (epithelial markers), vimentin (mesenchymal markers) in BTCC tissues and adjacent noncancerous tissues, as well as in two cell lines of bladder cancer were investigated. Whether the SATB1 expression is associated with clinicopathological factors or not was statistically analyzed. Cell invasion and migration, cell cycle, cell proliferation and apoptosis were evaluated in SATB1 knockdown and overexpressed cell lines. Our results showed that the expression of SATB1 was remarkably up-regulated both in BTCC tissues and in bladder cancer cell lines with high potential of metastasis. The results were also associated with EMT markers and poor prognosis of BTCC patients. Moreover, SATB1 induced EMT processes through downregulation of E-cadherin, upregulation of E-cadherin repressors (Snail, Slug and vimentin). SATB1 also promoted cell cycle progression, cell proliferation, cell invasion and cell migration, but did not alter cell survival. In conclusion, our results suggest that SATB1 plays a crucial role in the progression of bladder cancer by regulating genes controlling EMT processes. Further, it may be a novel therapeutic target for aggressive bladder cancers.

The overexpression of gelatinases, that is, matrix metalloproteinase MMP2 and MMP9, has been associated with tumor progression, invasion, and metastasis. To image MMP2 in tumors, we developed a novel ligand termed [18F]AlF-NOTA-C6, with consideration that: c(KAHWGFTLD)NH2 (herein, C6) is a selective gelatinase inhibitor; Cy5.5-C6 has been visualized in many in vivo tumor models; positron emission tomography (PET) has a higher detection sensitivity and a wider field of view than optical imaging; fluorine-18 (18F) is the optimal PET radioisotope, and the creation of a [18F]AlF-peptide complex is a simple procedure.C6 was conjugated to the bifunctional chelator NOTA (1, 4, 7-triazacyclononanetriacetic acid) for radiolabeling [18F]AlF conjugation. The MMP2-binding characteristics and tumor-targeting efficacy of [18F]AlF-NOTA-C6 were tested in vitro and in vivo.The non-decay corrected yield of [18F]AlF-NOTA-C6 was 46.2-64.2%, and the radiochemical purity exceeded 95%. [18F]AlF-NOTA-C6 was favorably retained in SKOV3 and PC3 cells, determined by cell uptake. Using NOTA-C6 as a competitive ligand, the uptake of [18F]AlF-NOTA-C6 in SKOV3 cells decreased in a dose-dependent manner. In biodistribution and PET imaging studies, higher radioactivity concentrations were observed in tumors. Pre-injection of C6 caused a marked reduction in tumor tissue uptake. Immunohistochemistry showed MMP2 in tumor tissues.[18F]AlF-NOTA-C6 was easy to synthesize and has substantial potential as an imaging agent that targets MMP2 in tumors.

The Aspergillus fumigatus sterol regulatory element binding protein (SREBP) SrbA belongs to the basic Helix-Loop-Helix (bHLH) family of transcription factors and is crucial for antifungal drug resistance and virulence. The latter phenotype is especially striking, as loss of SrbA results in complete loss of virulence in murine models of invasive pulmonary aspergillosis (IPA). How fungal SREBPs mediate fungal virulence is unknown, though it has been suggested that lack of growth in hypoxic conditions accounts for the attenuated virulence. To further understand the role of SrbA in fungal infection site pathobiology, chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) was used to identify genes under direct SrbA transcriptional regulation in hypoxia. These results confirmed the direct regulation of ergosterol biosynthesis and iron uptake by SrbA in hypoxia and revealed new roles for SrbA in nitrate assimilation and heme biosynthesis. Moreover, functional characterization of an SrbA target gene with sequence similarity to SrbA identified a new transcriptional regulator of the fungal hypoxia response and virulence, SrbB. SrbB co-regulates genes involved in heme biosynthesis and demethylation of C4-sterols with SrbA in hypoxic conditions. However, SrbB also has regulatory functions independent of SrbA including regulation of carbohydrate metabolism. Loss of SrbB markedly attenuates A. fumigatus virulence, and loss of both SREBPs further reduces in vivo fungal growth. These data suggest that both A. fumigatus SREBPs are critical for hypoxia adaptation and virulence and reveal new insights into SREBPs' complex role in infection site adaptation and fungal virulence.

In a previous study, activation of the peroxisome proliferator-activated receptor γ (PPARγ) inhibited chronic cardiac rejection. However, because of the complexity of chronic rejection and the fact that PPARγ is widely expressed in immune cells, the mechanism of the PPARγ-induced protective effect was unclear.A chronic rejection model was established using B6.C-H-2bm12KhEg (H-2bm12) mice as donors, and MHC II-mismatched T-cell-specific PPARγ knockout mice or wild type (WT) littermates as recipients. The allograft lesion was assessed by histology and immunohistochemistry. T cells infiltrates in the allograft were isolated, and cytokines and subpopulations were detected using cytokine arrays and flow cytometry. Transcription levels in the allograft were measured by RT-PCR. In vitro, the T cell subset differentiation was investigated after culture in various polarizing conditions. PPARγ-deficient regulatory T cells (Treg) were cocultured with monocytes to test their ability to induce alternatively activated macrophages (AAM).T cell-specific PPARγ knockout recipients displayed reduced cardiac allograft survival and an increased degree of pathology compared with WT littermates. T cell-specific PPARγ knockout resulted in more CD4+ T cells infiltrating into the allograft and altered the Th1/Th2 and Th17/Treg ratios. The polarization of AAM was also reduced by PPARγ deficiency in T cells through the action of Th2 and Treg. PPARγ-deficient T cells eliminated the pioglitazone-induced polarization of AAM and reduced allograft survival.PPARγ-deficient T cells influenced the T cell subset and AAM polarization in chronic allograft rejection. The mechanism of PPARγ activation in transplantation tolerance could yield a novel treatment without side effects.

BackgroundRecent studies have demonstrated that acute myocardial infarction induces a distinctive miRNA signature, suggesting that miRNAs may serve as diagnostic markers. Although many studies have investigated the use of miRNAs in the detection of cardiac injury, some had small sample sizes (Methods and resultsMedline, SCI, Embase, and Cochrane databases were searched up to January 2013 for studies that evaluated associations between miRNAs and myocardial infarction. Relevant publications were identified by searching for combinations of "myocardial infarction," "miRNAs," and their synonyms. Methodological quality was scored using a standardized list of criteria, and diagnostic performance was assessed using estimates of test sensitivity and specificity. These values were summarized using summary receiver-operating characteristic curves. Nineteen studies met the inclusion criteria: 15 studies reported sensitivity, specificity, and AUC, but 4 studies did not. Total miRNAs: sensitivity: 0.78 (95%CI: 0.77-0.80; P = 0.0000); specificity: 0.82 (95%CI: 0.80-0.83; P = 0.0000). miR-499: sensitivity: 0.88 (95%CI:0.86-0.90; P = 0.0000); specificity: 0.87 (95%CI:0.84-0.90; P = 0.0000). miR-1: sensitivity: 0.63 (95%CI:0.59-0.66; P = 0.0000); specificity: 0.76 (95%CI:0.71-0.80; P = 0.0000). miR-133a: sensitivity: 0.89 (95%CI:0.83-0.94; P = 0.0047); specificity: 0.87 (95%CI:0.79-0.92; P = 0.0262). miR-208b: sensitivity: 0.78 (95%CI:0.76-0.81; P = 0.0581); specificity: 0.88 (95%CI:0.84-0.91; P = 0.0000). The correlation between miRNAs and other diagnostic biomarkers of myocardial infarction was obvious.ConclusionMiRNAs, especially miR-499 and miR-133a, may be suitable for use as diagnostic biomarkers of myocardial infarction.

Special AT-rich sequence-binding protein-1 (SATB1) has been reported to be aberrantly expressed in various cancers and correlated with the malignant behavior of cancer cells. However, the function of SATB1 in RCC remains unclear. With the combination of immunohistochemistry, western blotting, immunofluorescence, qRT-PCR, and cell proliferation, migration and invasion assays, we found that levels of SATB1 mRNA and protein were dramatically increased in human ccRCC tissues (P

The interaction between λ—DNA and cationic surfactants with varying alkyl chain lengths was investigated. By dynamic light scattering method, the trimethyl-ammonium bromides-DNA complex formation was shown to be dependent on the length of the surfactant’s alkyl chain. For surfactants with sufficient long alkyl chain (CTAB, TTAB, DTAB), the compacted particles exist with a size of ~60–110 nm at low surfactant concentrations. In contrast, high concentration of surfactants leads to aggregates with increased sizes. Atomic force microscope scanning also supports the above observation. Zeta potential measurements show that the potential of the particles decreases with the increase of surfactant concentration (CTAB, TTAB, DTAB), which contributes much to the coagulation of the particles. For OTAB, the surfactant with the shortest chain in this study, it cannot fully neutralize the charges of DNA molecules; consequently, the complex is looser than other surfactant-DNA structures.

Cell cycle is a complex and highly supervised process that must proceed with regulatory precision to achieve successful cellular division. Despite the wide application, microarray time course experiments have several limitations in identifying cell cycle genes. We thus propose a computational model to predict human cell cycle genes based on transcription factor (TF) binding and regulatory motif information in their promoters. We utilize ENCODE ChIP-seq data and motif information as predictors to discriminate cell cycle against non-cell cycle genes. Our results show that both the trans- TF features and the cis- motif features are predictive of cell cycle genes, and a combination of the two types of features can further improve prediction accuracy. We apply our model to a complete list of GENCODE promoters to predict novel cell cycle driving promoters for both protein-coding genes and non-coding RNAs such as lincRNAs. We find that a similar percentage of lincRNAs are cell cycle regulated as protein-coding genes, suggesting the importance of non-coding RNAs in cell cycle division. The model we propose here provides not only a practical tool for identifying novel cell cycle genes with high accuracy, but also new insights on cell cycle regulation by TFs and cis-regulatory elements.

With the ease of using information technology tools, the explosive growth of smartphone applications (apps), and the rise of learning communities on social media, the acceptance of learning communities has become one of the most significant challenges for higher education institutions in Taiwan. In order to better understand teachers' collaborative performance in learning communities, this study employs the cognitive dimension (opportunism) and internal tension dimension (e.g. rising expectation, relationship burden) as restrictive factors; on the other hand, it uses emotional support, sense of belonging, and interpersonal altruism as facilitating factors; and community interaction, relationship performance, and collaborative performance as endrogenous factors. With a cross-sectional survey method and a quantitative approach, this study further dives into the collaborative performance of professional learning communities. A total of 157 teachers (87 male and 70 female) were surveyed, and a structural equation modeling approach was used. It was found that social media learning communities have done better than previous courses of field learning in unrestraining learning styles and increasing the breadth of knowledge. Facilitating and restrictive factors led to the rearrangement of the entire knowledge contribution process, enabling new configurations of individuals, members, and community. Moreover, community interactions are important drivers of relationship and collaboration performance supported by empirical data. The findings offer guidelines for policymakers and educators who evaluate teachers' collaborative performance and relationship performance to promote teaching efficiency and effectiveness by incorporating cyberethics in educational activities.

We present a network framework for analyzing multi-level regulation in higher eukaryotes based on systematic integration of various high-throughput datasets. The network, namely the integrated regulatory network, consists of three major types of regulation: TF→gene, TF→miRNA and miRNA→gene. We identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles, the predicted targets of miRNAs using annotated 3'UTR sequences and conservation information. Making use of the system-wide RNA-Seq profiles, we classified transcription factors into positive and negative regulators and assigned a sign for each regulatory interaction. Other types of edges such as protein-protein interactions and potential intra-regulations between miRNAs based on the embedding of miRNAs in their host genes were further incorporated. We examined the topological structures of the network, including its hierarchical organization and motif enrichment. We found that transcription factors downstream of the hierarchy distinguish themselves by expressing more uniformly at various tissues, have more interacting partners, and are more likely to be essential. We found an over-representation of notable network motifs, including a FFL in which a miRNA cost-effectively shuts down a transcription factor and its target. We used data of C. elegans from the modENCODE project as a primary model to illustrate our framework, but further verified the results using other two data sets. As more and more genome-wide ChIP-Seq and RNA-Seq data becomes available in the near future, our methods of data integration have various potential applications.

BACKGROUND: Deregulation of biological pathways has been shown to be involved in the turmorigenesis of a variety of cancers. The co-regulation of pathways in tumor and normal tissues has not been studied in a systematic manner. RESULTS: In this study we propose a novel statistic named AR-score (average rank based score) to measure pathway activities based on microarray gene expression profiles. We calculate and compare the AR-scores of pathways in microarray datasets containing expression profiles for a wide range of cancer types as well as the corresponding normal tissues. We find that many pathways undergo significant activity changes in tumors with respect to normal tissues. AR-scores for a small subset of pathways are capable of distinguishing tumor from normal tissues or classifying tumor subtypes. In normal tissues many pathways are highly correlated in their activities, whereas their correlations reduce significantly in tumors and cancer cell lines. The co-expression of genes in the same pathways was also significantly perturbed in tumors. CONCLUSIONS: The co-regulation of genes in the same pathways and co-regulation of different pathways are significantly perturbed in tumors versus normal tissues. Our method provides a useful tool for better understanding the mechanistic changes in tumors, which can also be used for exploring other biological problems.

Due to the large number of putative microRNA gene targets predicted by sequence-alignment databases and the relative low accuracy of such predictions which are conducted independently of biological context by design, systematic experimental identification and validation of every functional microRNA target is currently challenging. Consequently, biological studies have yet to identify, on a genome scale, key regulatory networks perturbed by altered microRNA functions in the context of cancer. In this report, we demonstrate for the first time how phenotypic knowledge of inheritable cancer traits and of risk factor loci can be utilized jointly with gene expression analysis to efficiently prioritize deregulated microRNAs for biological characterization. Using this approach we characterize miR-204 as a tumor suppressor microRNA and uncover previously unknown connections between microRNA regulation, network topology, and expression dynamics. Specifically, we validate 18 gene targets of miR-204 that show elevated mRNA expression and are enriched in biological processes associated with tumor progression in squamous cell carcinoma of the head and neck (HNSCC). We further demonstrate the enrichment of bottleneckness, a key molecular network topology, among miR-204 gene targets. Restoration of miR-204 function in HNSCC cell lines inhibits the expression of its functionally related gene targets, leads to the reduced adhesion, migration and invasion in vitro and attenuates experimental lung metastasis in vivo. As importantly, our investigation also provides experimental evidence linking the function of microRNAs that are located in the cancer-associated genomic regions (CAGRs) to the observed predisposition to human cancers. Specifically, we show miR-204 may serve as a tumor suppressor gene at the 9q21.1-22.3 CAGR locus, a well established risk factor locus in head and neck cancers for which tumor suppressor genes have not been identified. This new strategy that integrates expression profiling, genetics and novel computational biology approaches provides for improved efficiency in characterization and modeling of microRNA functions in cancer as compared to the state of art and is applicable to the investigation of microRNA functions in other biological processes and diseases.

MicroRNAs (miRNAs) play crucial roles in a variety of biological processes via regulating expression of their target genes at the mRNA level. A number of computational approaches regarding miRNAs have been proposed, but most of them focus on miRNA gene finding or target predictions. Little computational work has been done to investigate the effective regulation of miRNAs.We propose a method to infer the effective regulatory activities of miRNAs by integrating microarray expression data with miRNA target predictions. The method is based on the idea that regulatory activity changes of miRNAs could be reflected by the expression changes of their target transcripts measured by microarray. To validate this method, we apply it to the microarray data sets that measure gene expression changes in cell lines after transfection or inhibition of several specific miRNAs. The results indicate that our method can detect activity enhancement of the transfected miRNAs as well as activity reduction of the inhibited miRNAs with high sensitivity and specificity. Furthermore, we show that our inference is robust with respect to false positives of target prediction.A huge amount of gene expression data sets are available in the literature, but miRNA regulation underlying these data sets is largely unknown. The method is easy to be implemented and can be used to investigate the miRNA effective regulation underlying the expression change profiles obtained from microarray experiments.

Calorie restriction (CR), the only non-genetic intervention known to slow aging and extend life span in organisms ranging from yeast to mice, has been linked to the down-regulation of Tor, Akt, and Ras signaling. In this study, we demonstrate that the serine/threonine kinase Rim15 is required for yeast chronological life span extension caused by deficiencies in Ras2, Tor1, and Sch9, and by calorie restriction. Deletion of stress resistance transcription factors Gis1 and Msn2/4, which are positively regulated by Rim15, also caused a major although not complete reversion of the effect of calorie restriction on life span. The deletion of both RAS2 and the Akt and S6 kinase homolog SCH9 in combination with calorie restriction caused a remarkable 10-fold life span extension, which, surprisingly, was only partially reversed by the lack of Rim15. These results indicate that the Ras/cAMP/PKA/Rim15/Msn2/4 and the Tor/Sch9/Rim15/Gis1 pathways are major mediators of the calorie restriction-dependent stress resistance and life span extension, although additional mediators are involved. Notably, the anti-aging effect caused by the inactivation of both pathways is much more potent than that caused by CR.

Recent studies suggest that the regulation of longevity may be partially conserved in many eukaryotes ranging from yeast to mammals. The three yeast mutants sch9Delta, ras2Delta, tor1Delta show extended chronological life span up to three folds. Our aim is to dissect the mechanisms that lead to the yeast life span extension.We obtain gene expression profiles of sch9Delta, ras2Delta, tor1Delta as well as that for a wild type at day 2.5 in SDC medium using Affymetrix Yeast2.0 arrays. To accurately estimate the expression differentiation between the wild type and the long-lived mutants, we use sub-array normalization followed by a variant of the median-polishing summarization. The results are validated by the probe sets of S. pombe on the same chips. To translate the differentiation into changes of biological activities, we make statistical inference by integrating the expression profiles with biological gene subsets defined by Gene Ontology, KEGG pathways, and cellular localization of proteins. Other than subset-versus-other comparisons, we also make local comparisons between two directly-related gene subsets such as cytosolic and mitochondrial ribosomes. Our consensus is obtained by cross-examination of these inferences. The significant and systematic differentiation in the three long-lived strains includes: lower transcriptional activities; down-regulation of TCA cycle and oxidative phosphorylation versus up-regulation of the KEGG pathway Glycolysis/Gluconeogenesis; the overall reduction of mitochondrial activities. We also report some different expression patterns such as reduction of the activities relating to mitosis in ras2Delta.The modification of energy pathways and modification of compartment activities such as down-regulation of mitochondrial ribosome proteins versus up-regulation of cytosolic ribosome proteins are directly associated with the life span extension in yeast. The results provide a new and systematic S. cerevisiae version of the free radical theory from the perspective of functional genomics.

Purpose: The purpose of this study is to develop and empirically test a model that can predict factors affecting student recycling behavior. The theoretical model was based on motivation, place attachment, environmental concern and interpersonal altruism. Design/methodology/approach: A cross-sectional study was conducted with college students in Taiwan using self-report questionnaires. Of the 800 distributed questionnaires, 523 were completed (response rate of 65.4%) and were analyzed using structural equation modeling. Partial least squares (PLS) were used to test the models and hypotheses. Findings: The results showed that environmental concern, motivation, interpersonal altruism and place attachment have significant positive effects on recycling behavior and motivation and place attachment have significant positive effects on interpersonal altruism. This research contributes to the existing literature by discriminating between two sorts of motivation: intrinsic and extrinsic. Based on these findings, suggestions for future research and practical implications are presented. Originality/value: Few studies have linked motivation, interpersonal altruism, environmental concern and place attachment to recycling behavior. Therefore, this study aimed to explore these relationships, specifically as they affect college students' behavior. This paper anticipates that increased knowledge about recycling behavior could be used to support the wider adoption of recycling practices.