AtPAN: an integrated system for reconstructing transcriptional regulatory networks in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Construction of transcriptional regulatory networks (TRNs) is of priority concern in systems biology. Numerous high-throughput approaches, including microarray and next-generation sequencing, are extensively adopted to examine transcriptional expression patterns on the whole-genome scale; those data are helpful in reconstructing TRNs. Identifying transcription factor binding sites (TFBSs) in a gene promoter is the initial step in elucidating the transcriptional regulation mechanism. Since transcription factors usually co-regulate a common group of genes by forming regulatory modules with similar TFBSs. Therefore, the combinatorial interactions of transcription factors must be modeled to reconstruct the gene regulatory networks.</p> <p>Description For systems biology applications, this work develops a novel database called <it>Arabidopsis thaliana </it>Promoter Analysis Net (AtPAN), capable of detecting TFBSs and their corresponding transcription factors (TFs) in a promoter or a set of promoters in <it>Arabidopsis</it>. For further analysis, according to the microarray expression data and literature, the co-expressed TFs and their target genes can be retrieved from AtPAN. Additionally, proteins interacting with the co-expressed TFs are also incorporated to reconstruct co-expressed TRNs. Moreover, combinatorial TFs can be detected by the frequency of TFBSs co-occurrence in a group of gene promoters. In addition, TFBSs in the conserved regions between the two input sequences or homologous genes in <it>Arabidopsis </it>and rice are also provided in AtPAN. The output results also suggest conducting wet experiments in the future.</p> <p>Conclusions</p> <p>The AtPAN, which has a user-friendly input/output interface and provide graphical view of the TRNs. This novel and creative resource is freely available online at <url>http://AtPAN.itps.ncku.edu.tw/</url>.</p

Variational Monte Carlo simulations using tensor-product projected states

We propose an efficient numerical method, which combines the advantages of recently developed tensor-network based methods and standard trial wave functions, to study the ground state properties of quantum many-body systems. In this approach, we apply a projector in the form of a tensor-product operator to an input wave function, such as a Jastrow-type or Hartree-Fock wave function, and optimize the tensor elements via variational Monte Carlo. The entanglement already contained in the input wave function can considerably reduce the bond dimensions compared to the regular tensor-product state representation. In particular, this allows us to also represent states that do not obey the area law of entanglement entropy. In addition, for fermionic systems, the fermion sign structure can be encoded in the input wave function. We show that the optimized states provide good approximations of the ground-state energy and correlation functions in the cases of two-dimensional bosonic and fermonic systems.Comment: 7 pages, 5 figures, published versio

A Bayesian measurement error model for two-channel cell-based RNAi data with replicates

RNA interference (RNAi) is an endogenous cellular process in which small double-stranded RNAs lead to the destruction of mRNAs with complementary nucleoside sequence. With the production of RNAi libraries, large-scale RNAi screening in human cells can be conducted to identify unknown genes involved in a biological pathway. One challenge researchers face is how to deal with the multiple testing issue and the related false positive rate (FDR) and false negative rate (FNR). This paper proposes a Bayesian hierarchical measurement error model for the analysis of data from a two-channel RNAi high-throughput experiment with replicates, in which both the activity of a particular biological pathway and cell viability are monitored and the goal is to identify short hair-pin RNAs (shRNAs) that affect the pathway activity without affecting cell activity. Simulation studies demonstrate the flexibility and robustness of the Bayesian method and the benefits of having replicates in the experiment. This method is illustrated through analyzing the data from a RNAi high-throughput screening that searches for cellular factors affecting HCV replication without affecting cell viability; comparisons of the results from this HCV study and some of those reported in the literature are included.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS496 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Nemonoxacin (Taigexyn^®): A New Non-Fluorinated Quinolone

Nemonoxacin (Taigexyn®), a novel C-8-methoxy non-fluorinated quinolone, has been approved for use in community-acquired pneumonia (CAP) in Taiwan (2014) and mainland China (2016). The FDA granted nemonoxacin ‘qualified infectious disease product’ and ‘fast-track’ designations for CAP and acute bacterial skin and skin structure infection in December 2013. It possesses a broad spectrum of bactericidal activity against typical and atypical respiratory pathogens. In particular, nemonoxacin has activity against resistant Gram-positive cocci, including penicillin-resistant Streptococcus pneumoniae and methicillin-resistant Staphylococcus aureus. Oral nemonoxacin was compared with oral levofloxacin for efficacy and safety in three randomized, double-blinded, controlled Phase II–III clinical trials for the treatment of CAP. This article will review the microbiological profile of nemonoxacin against respiratory pathogens including S. pneumoniae and S. aureus, and microbiological outcome data from the three Phase II–III studies

Multi-Operator Fairness in Transparent RAN Sharing by Soft-Partition With Blocking and Dropping Mechanism

Radio access network (RAN) sharing has attracted significant attention from telecom operators as a means of accommodating data surges. However, current mechanisms for RAN sharing ignore the fairness issue among operators, and hence the RAN may be under- or over-utilized. Furthermore, the fairness among different operators cannot be guaranteed, since the RAN resources are distributed on a first come, first served basis. Accordingly, the present study proposes a “soft-partition with blocking and dropping” (SBD) mechanism that offers inter-operator fairness using a “soft-partition” approach. In particular, the operator subscribers are permitted to overuse the resources specified in the predefined service-level-agreement when the shared RAN is under-utilized, but are blocked (or even dropped) when the RAN is over-utilized. The simulation results show that SBD achieves an inter-operator fairness of 0.997, which is higher than that of both a hard-partition approach (0.98) and a no-partition approach (0.6) while maintaining a shared RAN utilization rate of 98%. Furthermore, SBD reduces the blocking rate from 35% (hard partition approach) to almost 0%, whereas controlling the dropping rate at 5%. Notably, the dropping rate can be reduced to almost 0% using a newly proposed bandwidth scale down procedure.This work was supported in part by H2020 collaborative Europe/Taiwan research project 5G-CORAL under Grant 761586, and in part by the Ministry of Science and Technology, Taiwan under Contract MOST 106-2218- E-009-018

Effects of job rotation and role stress among nurses on job satisfaction and organizational commitment

<p>Abstract</p> <p>Background</p> <p>The motivation for this study was to investigate how role stress among nurses could affect their job satisfaction and organizational commitment, and whether the job rotation system might encourage nurses to understand, relate to and share the vision of the organization, consequently increasing their job satisfaction and stimulating them to willingly remain in their jobs and commit themselves to the organization. Despite the fact that there have been plenty of studies on job satisfaction, none was specifically addressed to integrate the relational model of job rotation, role stress, job satisfaction, and organizational commitment among nurses.</p> <p>Methods</p> <p>With top managerial hospital administration's consent, questionnaires were only distributed to those nurses who had had job rotation experience. 650 copies of the questionnaire in two large and influential hospitals in southern Taiwan were distributed, among which 532 valid copies were retrieved with a response rate of 81.8%. Finally, the SPSS 11.0 and LISREL 8.54 (Linear Structural Relationship Model) statistical software packages were used for data analysis and processing.</p> <p>Results</p> <p>According to the nurses' views, the findings are as follows: (1) job rotation among nurses could have an effect on their job satisfaction; (2) job rotation could have an effect on organizational commitment; (3) job satisfaction could have a positive effect on organizational commitment; (4) role stress among nurses could have a negative effect on their job satisfaction; and (5) role stress could have a negative effect on their organizational commitment.</p> <p>Conclusion</p> <p>As a practical and excellent strategy for manpower utilization, a hospital could promote the benefits of job rotation to both individuals and the hospital while implementing job rotation periodically and fairly. And when a medical organization attempts to enhance nurses' commitment to the organization, the findings suggest that reduction of role ambiguity in role stress has the best effect on enhancing nurses' organizational commitment. The ultimate goal is to increase nurses' job satisfaction and encourage them to stay in their career. This would avoid the vicious circle of high turnover, which is wasteful of the organization's valuable human resources.</p

Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration

[[abstract]]Background Protein-ligand interactions are key processes in triggering and controlling biological functions within cells. Prediction of protein binding regions on the protein surface assists in understanding the mechanisms and principles of molecular recognition. In silico geometrical shape analysis plays a primary step in analyzing the spatial characteristics of protein binding regions and facilitates applications of bioinformatics in drug discovery and design. Here, we describe the novel software, PLB-SAVE, which uses parallel processing technology and is ideally suited to extract the geometrical construct of solid angles from surface atoms. Representative clusters and corresponding anchors were identified from all surface elements and were assigned according to the ranking of their solid angles. In addition, cavity depth indicators were obtained by proportional transformation of solid angles and cavity volumes were calculated by scanning multiple directional vectors within each selected cavity. Both depth and volume characteristics were combined with various weighting coefficients to rank predicted potential binding regions. Results Two test datasets from LigASite, each containing 388 bound and unbound structures, were used to predict binding regions using PLB-SAVE and two well-known prediction systems, SiteHound and MetaPocket2.0 (MPK2). PLB-SAVE outperformed the other programs with accuracy rates of 94.3% for unbound proteins and 95.5% for bound proteins via a tenfold cross-validation process. Additionally, because the parallel processing architecture was designed to enhance the computational efficiency, we obtained an average of 160-fold increase in computational time. Conclusions In silico binding region prediction is considered the initial stage in structure-based drug design. To improve the efficacy of biological experiments for drug development, we developed PLB-SAVE, which uses only geometrical features of proteins and achieves a good overall performance for protein-ligand binding region prediction. Based on the same approach and rationale, this method can also be applied to predict carbohydrate-antibody interactions for further design and development of carbohydrate-based vaccines. PLB-SAVE is available at http://save.cs.ntou.edu.tw.[[booktype]]電子