Highest scoring network after interactive network analysis of gene expression changes induced after hep-ATIII treatment of SIV-infected rhesus macaques.

<p>The highest scoring primary transcriptional network activated by hep-ATIII treatment of chronically infected rhesus macaques, at a time point when viral replication is inhibited by hep-ATIII (day 7), is shown. Rx (orange): potential medication treatment options, BM (green): possible biomarkers. Network analysis was performed using Ingenuity 8.0 software. Explanation for symbols is given in <b>Figure S2</b>.</p

<i>In vivo</i> anti-viral activity of ET-ATIII in rhesus macaques.

<p>Chronically SIV_mac239 infected rhesus macaques (n = 2) were treated with 1.5 ml ET-ATIII (0.3 nmol/kg encapsulated hep-ATIII) at indicated time points depicted by arrows. (<b>A</b>) Viral load of ET-ATIII treated animals as RNA copies/ml. (<b>B</b>) Log₁₀ reduction of viral load. Vehicle liposomes were used as a control. Viral load was measured and compared to animals before treatment (day 0). Data are shown as mean ± S.E.</p

Characterization of heparin-activated ATIII.

<p>(<b>A</b>) Sepharcyl S100 ÄKTA FPLC of purified hep-ATIII. A 9–14 ml fraction was separated, termed as hep-ATIII and used for our experiments. Detection at 260 nm for protein detection and 280 nm for heparin detection is shown. (<b>B</b>) Hep-ATIII purity and molecular weight were also determined by SDS-PAGE and silver staining (Bio-Rad kit) of a 15% slab gel. For molecular weight determination a low-range protein molecular weight marker (Bio-Rad) was used. Lane 1∶2 µg hep-ATIII; lane 2: phosphorylase B (97 kDa), serum albumin (69 kDa), carbonic anhydrase (31 kDa), trypsin inhibitor (21.5 kDa), aprotinin (6.5 kDa).</p

<i>In vivo</i> anti-viral activity of non-activated and heparin-activated ATIII in rhesus macaques.

<p>(<b>A</b>) Viral loads as RNA copies/ml of chronically SIV_mac239 infected rhesus macaques treated with 0.8 µmol/kg non-activated ATIII (n = 3) and (<b>B</b>) corresponding log₁₀ reduction of viral load of same treatment group. (<b>C</b>) Viral loads as RNA copies/ml of chronically SIV_mac239 infected rhesus macaques treated with 0.6 µmol/kg heparin-activated ATIII (n = 3) and (<b>D</b>) corresponding log₁₀ reduction of viral load of same treatment group. Administration via i. v. inoculation is shown at indicated time points depicted by arrows. Viral load was measured and compared to animals before treatment (day 0). *, <i>P</i><0.05, paired T-test, compared to pre-treatment (day 0). Data are shown as mean ± S.E.</p

Effect of heparin-activated ATIII in pseudovirus inhibition assay.

<p>Pseudoviruses with clade B envelopes (in <b>A</b> and <b>C</b>) and clade C envelopes (in <b>B</b> and <b>D</b>) were treated with three fold dilutions of heparin-activated ATIII (hep-ATIII), unmodified ATIII and heparin. Percentage of inhibition was calculated by comparing of residual luciferase activity and untreated control. Experiments were done in triplicates. Data are shown as mean ± S.E.</p

IC50 of hep-ATIII in different liposome formulations.

<p>IC50 of hep-ATIII in different liposome formulations.</p

Second-highest scoring network after interactive network analysis of gene expression changes induced after hep-ATIII treatment of SIV-infected rhesus macaques.

<p>The second-highest scored network activated by hep-ATIII treatment of chronically SIV-infected rhesus macaques, at a time point when viral replication is inhibited by hep-ATIII (day 7), is shown. Network analysis was performed using Ingenuity 8.0 software. Explanation for symbols is given in <b>Figure S2</b>.</p

TCR:pMHC measurements.

<p>DRMs were purified from total CD8⁺ T cells sorted from seven chronically-infected SIVsmE660-infected monkeys. The DRMs were evaluated for specific binding, measured in resonance units (RU), to pMHC monomers constructed with p11C, p54E660, and p68A epitope peptides and Mamu-A*01. Data are representative of the binding observed from all seven monkeys evaluated. A) Initial experiments to detect p11C, p54E660, and p68A monomer binding to DRMs. 100 μg/mL p11C (red), 100 μg/mL p54E660 (blue), and 150 μg/mL p68A (green) pMHC monomer binding are overlaid from experiments conducted on separate Biacore Chips. Binding of the p68A:Mamu-A*01 monomer to the DRMs was not observed at any concentration of monomer tested (25–200 μg/mL). B) Titrations of p11C (top) and p54E660 (bottom) peptide:Mamu-A*01 monomers for calculation of binding kinetics and affinity. Overlaid sensograms of the binding of p11C and p54E660 pMHC monomers to DRMs purified from total CD8⁺ T cells are shown. Monomer binding was evaluated using pMHC concentrations ranging from 25 to 200 μg/mL. The black curve shows the Langmuir fitted curve that was used to calculate kinetics. C) Detection of p68A peptide:Mamu-A*01 monomer binding to DRMs. p68A-specific CD8⁺ T cells were collected from multiple tetramer-specific flow cytometric cell sorts and pooled for DRM purification. Titrations of p68A pMHC monomers were performed at concentrations ranging from 150 to 1000 μg/mL. Binding to DRMs from monkey ZD57 at 1000 μg/mL is shown and is representative of the four monkeys that were evaluated. All readings have been normalized by subtracting the binding of the control monomer TL8 run at the same concentrations as the experimental monomers.</p

Frequencies of p11C- and p54AS-specific CD8⁺ T cells and plasma viral loads during primary infection.

<p>A) Mean frequencies of the p11C- and p54AS-specific CD8⁺ T cells in peripheral blood. * significant at <i>p</i>≤0.05. B) Plasma SIV RNA levels in the peripheral blood.</p

Recent Advances in Mathematical Programming with Semi-continuous Variables and Cardinality Constraint

Abstract Mathematical programming problems with semi-continuous variables and cardinality constraint have many applications, including production planning, portfolio selection, compressed sensing and subset selection in regression. This class of problems can be modeled as mixed-integer programs with special structures and are in general NP-hard. In the past few years, based on new reformulations, approximation and relaxation techniques, promising exact and approximate methods have been developed. We survey in this paper these recent developments for this challenging class of mathematical programming problems.