A Novel Clustering Tree-based Video lookup Strategy for Supporting VCR-like Operations in MANETs

Mobile Peer-to-Peer (MP2P) network is a promising avenue for large-scale deployment of Video-on-Demand (VoD) applications over mobile ad-hoc networks (MANETs). In P2P VoD systems, fast search for resources is key determinants for improving the Quality of Service (QoS) due to the low delay of seeking resources caused by streaming interactivity. In this paper, we propose a novel Clustering Tree-based Video Lookup strategy for supporting VCR-like operations in MANETs (CTVL) CTVL selects the chunks with the high popularity as "overlay router" chunks to build the "virtual connection" with other chunks in terms of the popularities and external connection of video chunks. CTVL designs a new clustering strategy to group nodes in P2P networks and a maintenance mechanism of cluster structure, which achieves the high system scalability and fast resource search performance. Thorough simulation results also show how CTVL achieves higher average lookup success rate, lower maintenance cost, lower average end-to-end delay and lower packet loss ratio (PLR) in comparison with other state of the art solutions

Cryo-EM Structure of Mechanosensitive Channel YnaI Using SMA2000: Challenges and Opportunities

Mechanosensitive channels respond to mechanical forces exerted on the cell membrane and play vital roles in regulating the chemical equilibrium within cells and their environment. Highresolution structural information is required to understand the gating mechanisms of mechanosensitive channels. Protein-lipid interactions are essential for the structural and functional integrity of mechanosensitive channels, but detergents cannot maintain the crucial native lipid environment for purified mechanosensitive channels. Recently, detergent-free systems have emerged as alternatives for membrane protein structural biology. This report shows that while membrane-active polymer, SMA2000, could retain some native cell membrane lipids on the transmembrane domain of the mechanosensitive-like YnaI channel, the complete structure of the transmembrane domain of YnaI was not resolved. This reveals a significant limitation of SMA2000 or similar membrane-active copolymers. This limitation may come from the heterogeneity of the polymers and nonspecific interactions between the polymers and the relatively large hydrophobic pockets within the transmembrane domain of YnaI. However, this limitation offers development opportunities for detergent-free technology for challenging membrane proteins

The Plasma DIA-Based Quantitative Proteomics Reveals the Pathogenic Pathways and New Biomarkers in Cervical Cancer and High Grade Squamous Intraepithelial Lesion

Objective: The process of normal cervix changing into high grade squamous intraepithelial lesion (HSIL) and invasive cervical cancer is long and the mechanisms are still not completely clear. This study aimed to reveal the protein profiles related to HSIL and cervical cancer and find the diagnostic and prognostic molecular changes. Methods: Data-independent acquisition (DIA) analysis was performed to identify 20 healthy female volunteers, 20 HSIL and 20 cervical patients in a cohort to screen differentially expressed proteins (DEPs) for the HSIL and cervical cancer. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used for functional annotation of DEPs; the protein–protein interaction (PPI) and weighted gene co-expression network analysis (WGCNA) were performed for detection of key molecular modules and hub proteins. They were validated using the Enzyme-Linked Immunosorbent Assay (ELISA). Results: A total of 243 DEPs were identified in the study groups. GO and KEGG analysis showed that DEPs were mainly enriched in the complement and coagulation pathway, cholesterol metabolism pathway, the IL-17 signaling pathway as well as the viral protein interaction with cytokine and cytokine receptor pathway. Subsequently, the WGCNA analysis showed that the green module was highly correlated with the cervical cancer stage. Additionally, six interesting core DEPs were verified by ELISA, APOF and ORM1, showing nearly the same expression pattern with DIA. The area under the curve (AUC) of 0.978 was obtained by using ORM1 combined with APOF to predict CK and HSIL+CC, and in the diagnosis of HSIL and CC, the AUC can reach to 0.982. The high expression of ORM1 is related to lymph node metastasis and the clinical stage of cervical cancer patients as well as the poor prognosis. Conclusion: DIA-ELSIA combined analysis screened and validated two previously unexplored but potentially useful biomarkers for early diagnosis of HSIL and cervical cancer, as well as possible new pathogenic pathways and therapeutic targets

Merging cultures and disciplines to create a drug discovery ecosystem at Virginia commonwealth university: Medicinal chemistry, structural biology, molecular and behavioral pharmacology and computational chemistry

The Department of Medicinal Chemistry, together with the Institute for Structural Biology, Drug Discovery and Development, at Virginia Commonwealth University (VCU) has evolved, organically with quite a bit of bootstrapping, into a unique drug discovery ecosystem in response to the environment and culture of the university and the wider research enterprise. Each faculty member that joined the department and/or institute added a layer of expertise, technology and most importantly, innovation, that fertilized numerous collaborations within the University and with outside partners. Despite moderate institutional support with respect to a typical drug discovery enterprise, the VCU drug discovery ecosystem has built and maintained an impressive array of facilities and instrumentation for drug synthesis, drug characterization, biomolecular structural analysis and biophysical analysis, and pharmacological studies. Altogether, this ecosystem has had major impacts on numerous therapeutic areas, such as neurology, psychiatry, drugs of abuse, cancer, sickle cell disease, coagulopathy, inflammation, aging disorders and others. Novel tools and strategies for drug discovery, design and development have been developed at VCU in the last five decades; e.g., fundamental rational structure-activity relationship (SAR)-based drug design, structure-based drug design, orthosteric and allosteric drug design, design of multi-functional agents towards polypharmacy outcomes, principles on designing glycosaminoglycans as drugs, and computational tools and algorithms for quantitative SAR (QSAR) and understanding the roles of water and the hydrophobic effect

TFP4 structure aligned with structural homologs.

<p>(A) The top five, non-redundant, structural homologs as determined by the Dali server [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125376#pone.0125376.ref045" target="_blank">45</a>] aligned with the average TFP4 structure. TFP4 is shown in black and the structural homologs in grey (PDB ID: 1Y1B chain A, 1YU6 chain B, 1IY6 chain A, 1TBQ chain S, 2F3C chain I) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125376#pone.0125376.ref046" target="_blank">46</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125376#pone.0125376.ref048" target="_blank">48</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125376#pone.0125376.ref051" target="_blank">51</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125376#pone.0125376.ref052" target="_blank">52</a>]. The extra loops in TFP4 that are not found in the homologous structures are labeled, Loop 1 and Loop 2, with their corresponding amino acid sequences. (B) Sequence alignments based on the structural alignment of TFP4 and the homologous proteins were generated using the Dali server [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125376#pone.0125376.ref045" target="_blank">45</a>]. The Kazal-type consensus sequence is highlighted in bold with the active site residue indicated by the asterisk. Loops 1 and 2 are labeled with the corresponding residues are in bold.</p

The (A) DNA and (B) primary amino acid sequence of TFP4 cloned from the defense gland secretion of <i>C. formosanus</i> termite soldiers.

<p>The Kazal-type serine protease inhibitor consensus sequence is highlighted in grey with the active site residue, P₁, indicated with the asterisk at Met¹⁰. Laskowski and Kato’s nomenclature for the Kazal-type active loop is shown above the amino acid sequence [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125376#pone.0125376.ref021" target="_blank">21</a>]. Cystines and disulfide bonds are indicated by Roman numerals and lines.</p

Enzyme kinetic analysis of TFP4 inhibition of BTEE hydrolysis by chymotrypsin.

<p>The reaction velocities of chymotrypsin hydrolysis of BTEE at varying concentrations of BTEE and TFP4 are plotted. The points are the observed velocities, and the lines represent the best global fit of the data to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125376#pone.0125376.e001" target="_blank">Eq 1</a>, a competitive inhibition model. The K_i of TFP4 determined from the fit (91.3 ± 8.9 nM) and the correlation coefficient (R² = 0.9884) are displayed on the plot.</p