On exploiting task duplication in parallel program scheduling

One of the main obstacles in obtaining high performance from message-passing multicomputer systems is the inevitable communication overhead which is incurred when tasks executing on different processors exchange data. Given a task graph, duplication-based scheduling can mitigate this overhead by allocating some of the tasks redundantly on more than one processor. In this paper, we focus on the problem of using duplication in static scheduling of task graphs on parallel and distributed systems. We discuss five previously proposed algorithms and examine their merits and demerits. We describe some of the essential principles for exploiting duplication in a more useful manner and, based on these principles, propose an algorithm which outperforms the previous algorithms. The proposed algorithm generates optimal solutions for a number of task graphs. The algorithm assumes an unbounded number of processors. For scheduling on a bounded number of processors, we propose a second algorithm which controls the degree of duplication according to the number of available processors. The proposed algorithms are analytically and experimentally evaluated and are also compared with the previous algorithms     

Optimizing download time of embedded multimedia objects for Web browsing

Notoriously high delays in accessing Web pages loaded with massive multimedia objects are highly undesirable. Inspired by the requirements of news agencies and other information providers to include multimedia content in their pages, this paper proposes a new solution to the problem of minimizing the Web response time. We consider an environment that consists of a central multimedia repository and various sites physically dispersed. Our approach is based on simultaneous downloading of some of the embedded multimedia objects from the repository, and the rest from the regional servers. We propose a cost model to formalize the relative benefits of the proposed scheme, and design an algorithm that replicates multimedia objects so as to take advantage of concurrency in data transferring. An extensive simulation study evaluates the performance of the proposed replication policy under storage and processing capacity constraints, as well as with various network transfer rates. Comparisons are carried out with alternative schemes.     

Effects of helium massive gas injection level on disruption mitigation on EAST

In this study, NIMROD simulations are performed to investigate the effects of massive helium gas injection level on the induced disruption on EAST tokamak. It is demonstrated in simulations that two different scenarios of plasma cooling(complete cooling and partial cooling) take place for different amounts of injected impurities. For the impurity injection above a critical level, a single MHD activity is able to induce a complete core temperature collapse. For impurity injection below the critical level, a series of multiple minor disruptions occur before the complete thermal quench.     

A Novel L-Phenylalanine Dipeptide Inhibits the Growth and Metastasis of Prostate Cancer Cells via Targeting DUSP1 and TNFSF9

Prostate cancer (PCa) is a common malignant cancer of the urinary system. Drug therapy, chemotherapy, and radical prostatectomy are the primary treatment methods, but drug resistance and postoperative recurrence often occur. Therefore, seeking novel anti-tumor compounds with high efficiency and low toxicity from natural products can produce a new tumor treatment method. Matijin-Su [N-(N-benzoyl-L-phenylalanyl)-O-acetyl-L-phenylalanol, MTS] is a phenylalanine dipeptide monomer compound that is isolated from the Chinese ethnic medicine Matijin (Dichondra repens Forst.). Its derivatives exhibit various pharmacological activities, especially anti-tumor. Among them, the novel MTS derivative HXL131 has a significant inhibitory effect against prostate tumor growth and metastasis. This study is designed to investigate the effects of HXL131 on the growth and metastasis of human PCa cell lines PC3 and its molecular mechanism through in vitro experiments combined with proteomics, molecular docking, and gene silencing. The in vitro results showed that HXL131 concentration dependently inhibited PC3 cell proliferation, induced apoptosis, arrested cell cycle at the G2/M phase, and inhibited cell migration capacity. A proteomic analysis and a Western blot showed that HXL131 up-regulated the expression of proliferation, apoptosis, cell cycle, and migration-related proteins CYR61, TIMP1, SOD2, IL6, SERPINE2, DUSP1, TNFSF9, OSMR, TNFRSF10D, and TNFRSF12A. Molecular docking, a cellular thermal shift assay (CETSA), and gene silencing showed that HXL131 had a strong binding affinity with DUSP1 and TNFSF9, which are important target genes for inhibiting the growth and metastasis of PC3 cells. This study demonstrates that HXL131 exhibited excellent anti-prostate cancer activity and inhibited the growth and metastasis of prostate cancer cells by regulating the expression of DUSP1 and TNFSF9.     

Design of dual‐band microstrip patch antenna with right‐angle triangular aperture slot for energy transfer application

This work focusing on the dual‐band antenna design with rectifying circuit for energy transfer system technology for enhancement gain performance. The air gap technique is applied on this microstrip antenna design work to enhance the antenna gain. The work begins with designing and analyzing the antenna via the CST Microwave Studio software. After validation on acceptable performance in simulation side is obtained, the return loss, S₁₁ of the antenna is measured using vector network analyzer equipment. The rectifier circuit is used to convert the captured signal to DC voltage. This projected dual‐band antenna has successfully accomplished the target on return loss of ?44.707 dB and ?32.163 dB at dual resonant frequencies for 1.8 GHz and 2.4 GHz, respectively. This proposed antenna design benefits in low cost fabrication and has achieved high gain of 6.31 dBi and 7.82 dBi for dual‐band functioning frequencies.     

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

     

Regulation of Cell Signaling Pathways and Non-Coding RNAs by Baicalein in Different Cancers

Landmark discoveries in molecular oncology have provided a wide-angle overview of the heterogenous and therapeutically challenging nature of cancer. The power of modern ‘omics’ technologies has enabled researchers to deeply and comprehensively characterize molecular mechanisms underlying cellular functions. Interestingly, high-throughput technologies have opened new horizons for the design and scientific fool-proof evaluation of the pharmacological properties of targeted chemical compounds to tactfully control the activities of the oncogenic protein networks. Groundbreaking discoveries have galvanized the expansion of the repertoire of available pharmacopoeia to therapeutically target a myriad of deregulated oncogenic pathways. Natural product research has undergone substantial broadening, and many of the drugs which constitute the backbone of modern pharmaceuticals have been derived from the natural cornucopia. Baicalein has gradually gained attention because of its unique ability to target different oncogenic signal transduction cascades in various cancers. We have partitioned this review into different sub-sections to provide a broader snapshot of the oncogenic pathways regulated by baicalein. In this review, we summarize baicalein-mediated targeting of WNT/β-catenin, AKT/mTOR, JAK/STAT, MAPK, and NOTCH pathways. We also critically analyze how baicalein regulates non-coding RNAs (microRNAs and long non-coding RNAs) in different cancers. Finally, we conceptually interpret baicalein-mediated inhibition of primary and secondary growths in xenografted mice.     

A Multistage In Silico Study of Natural Potential Inhibitors Targeting SARS-CoV-2 Main Protease

Among a group of 310 natural antiviral natural metabolites, our team identified three compounds as the most potent natural inhibitors against the SARS-CoV-2 main protease (PDB ID: 5R84), M^pro. The identified compounds are sattazolin and caprolactin A and B. A validated multistage in silico study was conducted using several techniques. First, the molecular structures of the selected metabolites were compared with that of GWS, the co-crystallized ligand of M^pro, in a structural similarity study. The aim of this study was to determine the thirty most similar metabolites (10%) that may bind to the M^pro similar to GWS. Then, molecular docking against M^pro and pharmacophore studies led to the choice of five metabolites that exhibited good binding modes against the M^pro and good fit values against the generated pharmacophore model. Among them, three metabolites were chosen according to ADMET studies. The most promising M^pro inhibitor was determined by toxicity and DFT studies to be caprolactin A (292). Finally, molecular dynamics (MD) simulation studies were performed for caprolactin A to confirm the obtained results and understand the thermodynamic characteristics of the binding. It is hoped that the accomplished results could represent a positive step in the battle against COVID-19 through further in vitro and in vivo studies on the selected compounds.     

Pivotal Role of Phytohormones and Their Responsive Genes in Plant Growth and Their Signaling and Transduction Pathway under Salt Stress in Cotton

The presence of phyto-hormones in plants at relatively low concentrations plays an indispensable role in regulating crop growth and yield. Salt stress is one of the major abiotic stresses limiting cotton production. It has been reported that exogenous phyto-hormones are involved in various plant defense systems against salt stress. Recently, different studies revealed the pivotal performance of hormones in regulating cotton growth and yield. However, a comprehensive understanding of these exogenous hormones, which regulate cotton growth and yield under salt stress, is lacking. In this review, we focused on new advances in elucidating the roles of exogenous hormones (gibberellin (GA) and salicylic acid (SA)) and their signaling and transduction pathways and the cross-talk between GA and SA in regulating crop growth and development under salt stress. In this review, we not only focused on the role of phyto-hormones but also identified the roles of GA and SA responsive genes to salt stress. Our aim is to provide a comprehensive review of the performance of GA and SA and their responsive genes under salt stress, assisting in the further elucidation of the mechanism that plant hormones use to regulate growth and yield under salt stress.