V 1.2/KV 3.1 channels and blocked K+ currents which lie downstream of ERK activation. Taken together, these findings underpin the role of AT2R as a novel target that regulates cell proliferation mediated by Ang II with implications for therapeutic intervention for regulation of neurogenesis.Angiotensin II (Ang II), known a potent vasoactive substance in the renin-angiotensin system in the brain, plays a critical role in systemic blood pressure control. However, increasing evidence indicated that the physiological role of Ang II go beyond its vasoactive effect. In the present study, we demonstrated that Ang II type-1 receptor (AT1R) and type-2 receptor (AT2R) were expressed in primary rat hippocampal neuronal stem cells (NSCs). Treatment of rat hippocampal NSCs with Ang II increased cell proliferation. Pretreatment of NSCs with specific AT2R, but not AT1R, antagonist significantly suppressed Ang II-induced cell proliferation. Furthermore, Ang II stimulated ERK and Akt phosphorylation in NSCs. Pretreatment of MEK inhibitor, but not PI3K inhibitor, inhibited Ang II-induced ERK phosphorylation as well as cell proliferation. In addition, stimulation of NSCs with Ang II decreased expression of K It is well known that Angiotensin II (Ang II) is the principal vasoactive substance of the renin-angiotensin-system (RAS) with a variety of physiological actions, including vasoconstriction, aldosterone release, and cell growth It is now well-known that neurogenesis is persistent in mammalian brain until adult Ang II has been clearly demonstrated to induce cell proliferation V) affect cell mitogenesis, which was initially reported in human T lymphocytes V play an important role in controlling proliferation in numerous types of cell, including glial cells, lymphocyte, endothelial cells, bereast and prostate cancer cells, and stem cell + current have been found in NSCs, such as IKDR , IA (encoded by KV 1.4), TEA-sensitive IKDR (encoded by KV 3.1) V 1.3 and KV 3.1 channels has been reported to increase NSC proliferation + current and K+ channel expression in different types of cell, including rostral ventrolateral medulla neurons V and A-type K+ currents + currents in cultured neurons V channels modulate Ang II-mediated proliferation in NSCs and, if so, AT1R or AT2R is involved in regulation of K+ currents.It has been demonstrated that voltage-gated potassium channels , α-Dendrotoxin (α-DTX), 4-Aminopyridine (4-AP) and tetrodotoxin (TTX) were purchased from Sigma Chemicals . The specific MEK1/2 inhibitor U0126 and PI3K inhibitor LY294002 was purchased from Calbiochem .Pregnant female Sprague Dawley rats were purchased from Charles River Laboratories, Inc. . All of animals were housed under conditions of constant temperature and humidity on a 12-h light, 12-h dark cycle, with lights on at 0700 h. Food and water were available ad libitum. All animal procedures were performed according to the protocols approved by the Institutional Animal Care and Use Committee of the University of Nebraska Medical Center.tal5 cells/well for 24-well plate, 1×104 cells/well for 96-well plate) and were used if found more than 90% nestin+ (a marker for progenitor cells).NSCs derived from the hippocampus of embryonic day18 (E18) fetus were cultured in substrate-free tissue culture T75 flasks as reported by Tian eNSCs were lysed using the Mammalian Cell Lysis kit . Cell lysis containing 20 µg of protein were subjected to electrophoresis on a 12% sodium dodecyl sulfate-ployacrylaminde (SDS) gel and blotted onto a polyvinylidene difluoride membrane with a semi-dry blotting apparatus. After blocking, membranes were then probed with antibodies recognizing the AT1R , AT2R , p-ERK , p-Akt  and β-actin . Secondary antibodies were alkaline phosphatase conjugated to goat anti mouse/rabbit IgG (1∶5000). Signals were detected by chemiluminescence . All of the Western blot experiments were repeated three times individually and representative blots are presented in the figures.Dissociated NSCs were plated on the coverslip. The cells at different time were fixed with 4% paraformaldehyde for 20 minutes at room temperature according to the experiment protocol. The cells were permeabilized and blocked with solution containing 10% normal goat serum (NGS), 0.3%Triton X-100 in PBS at room temperature for 2 hours. Next, the cells were incubated with primary antibody  in 10% NGS, 0.3%Triton X-100 in PBS at 4°C overnight. Following 3 washes with PBS, the cells were exposed to secondary fluorescence antibody for 2 hr. After washing with PBS for 3 times, the cells were mounted on the slide with anti-fade reagent with DAPI at room temperature. The slides were examined with a laser confocal microscope (Leica TSC STED).V channel after Ang II treatment was first normalized against GAPDH RNA as ΔCT, and then the relative expression was compared to untreated group using the ΔΔCT method for quantification in Opticon Monitor software . Relative fold changes were determined as RQ values. The primers were designed using online software at https://www.genscript.com/ssl-bin/app/primer and synthesized in the Eppley Cancer Institute Molecular Biology Core Laboratory on the campus of the University of Nebraska Medical Center.RNA was extracted using TRIZOL Reagent  according to the manufacturer’s instructions. The RNA quality and quantity was verified using NanoDrop 2000 . Total RNA was reverse-transcribed using the iScript cDNA synthesis kits  according to manufacturer’s protocols. Quantitative RT-PCR assays were carried out using SsoFast Eva Green Supermix RT-PCR kit . Relative quantification was analyzed using PTC-200  and Chromo 4 continuous fluorescence detector . In each experiment, GAPDH RNA was amplified as a reference standard. Expression data for different K+ currents in NSCs. This experiment was carried out using an Axopatch 200 A amplifier . Measurements of the K+ current were performed in episodic-stimulation mode. For the K+ current measurement, patch pipettes were filled with (in mM) 135 KCl, 5 EGTA, 10 HEPES, 2 MgCl2, 0.25 CaCl2, 1 ATP, 0.1 GTP and 15 glucose, pH 7.2. The extracellular solution consisted of the following composition (in mM): 134 NaCl, 5.4 KCl, 2 MgCl2, 10 HEPES, 10 glucose, 1.35 CaCl2, 0.3 NaH2PO4 and 0.3 CdCl2, pH 7.4. Na+ channels were blocked by TTX (3 µM). Cell membrane capacitance was determined by integrating the capacitive current evoked by a voltage step from 0 to 5 mV and dividing the resulting charge by the voltage step. Currents were not leak subtracted. Current traces were sampled at 10 kHz and filtered at 5 kHz. Holding potential was −80 mV. Current-voltage relations were elicited by test potentials over the range of −80 mV to +80 mV, with duration of 400 ms in 20 mV increments (5 s between steps). Peak currents were measured for each test potential.The whole cell patch-clamp technique was used to determine the effect of Ang II on K4 cells/well for 2 days and were pre-treated with AT1R antagonist-Losartan, AT2R antagonists-PD123319, MEK inhibitor-U0126 or PI3K inhibitor-LY294002 for 1 hr followed by subsequent treatment with Ang II for 48 hrs. Then, 200 µl of the CyQUANT® GR dye/cell-lysis buffer was added into each well and incubated in the CO2 incubator for 15 min. Without washing, fluorescence intensity of each well was obtained using a Dynatech MR5000 plate counter at excitation and emission wavelengths of 480 and 520 nm, respectively.Cell proliferation was measured by CyQUANT® NF Cell Proliferation Assay Kit . Briefly, NSCs dissociated from neurosphere were seeded in 96-well plates at a density of 105 cells/well. At this time, Ad5-SYN-AT2R-IRES-EGFP or Ad5-SYN-EGFP were added to the culture medium (5×107 infectious units per well), three days later followed by monitoring for AT2R expression using western-blot analyses based upon previous report NSCs dissociated from neurosphere were seeded in 24-well plates at a density of 10Data were expressed as mean ± SEM. Significance of differences between control and samples treated with various drugs was determined by one-way ANOVA followed by post hoc least significant difference (LSD) test. Values of P<0.05 were considered as statistically significant.To assess the effects of Ang II on the cell proliferation of NSCs, rat NSCs were exposed to varying concentrations of Ang II for 48 h and cell proliferation assessed by CyQuant assay. As shown in Since Ang II mediates signaling via binding to its cognate receptor, AT1R and AT2R, we next examined the expression pattern of AT1R and AT2R in rat NSCs. Interestingly, we observed increased expression of both AT1R  and AT2RERK/mitogen-activated protein kinase (MAPK) and PI3K/Akt pathway has been demonstrated to play a crucial role in cell proliferation + channel activity, Ang II has been shown to regulate both K+ current and K+ channel expression in different types of cell + channel expression and K+ current in NSCs. As shown in + channel, in which KV1.2 and KV 3.1 decreased more than 10 fold. Functional implication of K+ channel in NSC proliferation induced by Ang II was further corroborated using proliferation assays, wherein 4-AP , a specific KV channel blocker, increased NSC proliferation in a concentration-dependent manner  respectively, as shown in + channel in regulation of NSC proliferation.Since cell proliferation can be regulated by modulation of Kt manner . These fV channels mediate cell proliferation and that Ang II regulated K+ currents in other cell system, it was rationalized that K+ currents through AT2R is a pre-requisite for increased NSC proliferation mediated by Ang II. NSCs were treated with Ang II and CGP42112A for 48 hours, then subsequently assessed for K+ currents using whole-cell patch clamp. As shown in + currents in NSCs. The effect of Ang II on K+ currents was restored by pretreatment of PD123319, but not by losartan. These findings underpinned the role of AT2R in Ang II-mediated blockage of K+ currents. To further unravel the role of ERK pathway in Ang II-mediated blockage of K+ currents, NSCs were pretreated with the MEK inhibitor-U0126 and assessed for K+ currents. As shown in + currents.Based on the premise that KIt is well-recognized that new dentate granule cells are continuously generated from NSCs and are integrated into the existing hippocampal circuitry in the adult mammalian brain through an orchestrated process termed adult neurogenesis It is well-known that the cardiovascular and other actions of Ang II are mediated by AT1R and AT2R, which are seven transmembrane glycoproteins with 30% sequence similarity In this study, we observed that Ang II induced ERK and Akt phosphorylation. These signaling pathways have been shown to regulate NSC proliferation Another interesting finding herein was the observation that inhibition of PI3K/Akt failed to reverse Ang II-mediated NSC proliferation thereby ruling out the potential role of this pathway. It is well known that activation of PI3K/Akt and its downstream pathway-NF-κB plays a key role in enhancing NSC proliferation following exposure to a wide array of stimuli + channel in Ang II-mediated increased proliferation, thereby lending credence to previous reports indicating the involvement of K+ channels and K+ currents in cell proliferation + channel with the neural progenitor cells isolated from subventricular zone V channels were expressed in NSCs. Ang II is known to exert its action by regulation of K+ currents in neuronal cell system + currents in NSCs remains less clear. Herein we report that Ang II induced inhibition of K+ currents through activation of AT2R. Both AT1R and AT2R belong to the large family of G protein-coupled receptors with seven transmembrane domains + channels can be activated by G-protein-coupled receptors + current via the activation of AT1R + current via AT2R + current through AT2R + currents via AT2R, but not AT1R. A key finding here is that Ang II-induced inhibition of K+ currents via ERK activation. It was evident from our findings that Ang II-mediated inhibition of K+ currents is ERK-dependent activation, and ensuing NSC proliferation. To further address the relevance of KV channel in NSC proliferation, we test the effect of pan KV channel blocker and specific KV 1.2/KV 3.1 blocker in NSC proliferation. 4-AP, α-DTX and low dosage of TEA significantly increased NSC proliferation, while high dosage of TEA inhibited NSC proliferation. It is reported that high concentration of TEA induced mammalian neuroblastoma cell swelling and decreased cell proliferation V channel in NSC proliferation Another novel finding of this study is the role of KV channels, which in turn, resulted in increased NSC proliferation. Taken together, our findings suggest that although the two pathways were upregulated by Ang II, only ERK was involved in Ang II-mediated increased proliferation of NSCs. A better understanding of these molecular pathways could be critical for the understanding how Ang II play important role in the NSC proliferation in the central nervous system.In summary, activation of the AngII/AT2R axis resulted in stimulation of ERK pathways leading to inhibition of K


In conclusion, this study is the first to demonstrate that the infiltration of PD1 positive lymphocytes and PD-L1 expression in STS cells could be used as novel prognostic indicators for STS. Moreover, the evaluation of PD1- and PD-L1-positivity in STS is also available as possible criteria for selection of patients suitable for PD1-based immunotherapy. Recently, the possibility of PD1 pathway-targeted therapy has been extensively studied in various human malignant tumors. However, no previous study has investigated their potential application for soft-tissue sarcomas (STS). In this study, we evaluated the clinical impact of intra-tumoral infiltration of PD1-positive lymphocytes and PD-L1 expression in tumor cells in 105 cases of STS. Intra-tumoral infiltration of PD1-positive lymphocytes and PD-L1 expression were seen in 65% and 58% of STS, respectively. Both PD1-positivity and PD-L1 expression were significantly associated with advanced clinicopathological parameters such as higher clinical stage, presence of distant metastasis, higher histological grade, poor differentiation of tumor, and tumor necrosis. Moreover, both PD1-positivity and PD-L1 positivity were independent prognostic indicators of overall survival (OS) and event-free survival (EFS) of STS by multivariate analysis. In addition, the combined pattern of PD1- and PD-L1-positivity was also an independent prognostic indicator for OS and EFS by multivariate analysis. The patents with a PD1 Programmed death 1 (PD1) is a member of the CD28 receptor family and attenuates immune responses by negatively regulating T-cell proliferation and function . The inhSoft-tissue sarcomas (STS) account for less than 1% of human malignant tumors  and apprP = 0.063). When we performed additional analysis by combining PD1- and PD-L1-positivity (the PD1/PD-L1 pattern), it was significantly associated with the age of patients, tumor stage, depth of sarcoma, distant metastasis, histologic grade, tumor differentiation, mitotic count, and tumor necrosis  and EFS   and EFS  (P < 0.001) and EFS  , and PD1+/PD-L1+ groups were 90%, 74%, and 13%, respectively. The factors significantly correlated with OS and EFS by univariate analyses were the age of patients, tumor stage, depth of tumor, lymph node metastasis, distant metastasis, histological grade, tumor differentiation, mitotic count, tumor necrosis, intra-tumoral infiltration of PD1-positive lymphocytes, PD-L1 expression, and the PD1/PD-L1 pattern  Table 3Table 3. 7-6.803) . The exp6-6.036) . The PD1< 0.001) . The PD1P < 0.001) and EFS  (P < 0.001) and EFS   and high stage (stage III and IV) subgroups. In the low stage subgroup, the PD1/PD-L1 pattern was significantly associated with OS  . In the = 0.003) . The infiltration of PD1-positive lymphocytes, the expression of PD-L1, and the combined expression pattern of PD1 and PD-L1 in soft-tissue sarcomas are independent prognostic factors of shorter event-free survival and poorer overall survival P < 0.001) and a 3.174-fold  greater risk of shorter EFS (P < 0.001). In addition, the patients with PD-L1 expression had a 4.827-fold  greater risk of death (P < 0.001) and a 2.629-fold  greater risk of shorter EFS (P = 0.002). When we included the PD1/PD-L1 pattern and excluded PD1- and PD-L1 positivity from the multivariate analysis (Model 2), the PD1/PD-L1 pattern was also an independent prognostic indicator of OS and EFS of STS patients  greater risk of death , 1(weak staining), 2 (intermediate staining), and 3 (strong staining). The area of staining was scored as 0 (0-10% of the cells stained), 1 (11-33% of the cells stained), 2 (34-66% of the cells stained), and 3 (67-100% of the cells stained). Thereafter, the sum of intensity score and staining proportion score was used for further analysis. Subsequently, the immunohistochemical expression of PD-L1 was scored by adding the sum score of two different TMA cores. The maximum combined score was twelve and the minimum combined score was zero.The tissue microarray (TMA) established after review of original H&E slides. Two 3.0 mm cores were taken from the most intact solid area of the highest histological grade which did not showing necrosis or degenerative change. Immunohistochemical staining for PD1  and PD-L1  was performed on 4 µm thick sections of TMA slides after microwave antigen retrieval. For negative controls, tissue sections were incubated with antibody diluent  without primary antibody. Immunohistochemical scoring was performed by two pathologists (Jang KY and Kim KM) without the information of clinicopathological factors. To minimize inter-observer variability, two authors simultaneously counted PD1-positive cells and evaluated staining area and staining intensity for PD-L1 immunostaining with the consensus under the multi-viewing microscope . The consensus for immunostaining scoring was reached after discussion by two authors. To quantify tumor infiltrating PD1-positive lymphocytes, the number of PD1-positive lymphocytes were counted at the highest numbered five high-power fields (HPF) in each TMA core. Thereafter, the number of PD1-positive lymphocytes was scored by adding the number of PD1-positive lymphocytes of the two different TMA cores. The field diameter of HPF was 0.55 mm, and the area of one HPF was 0.238 mmversus the false positive rate (1 - specificity) for the determination of the death of patients, the cut-off level for the ideal test is presented as the sensitivity 1 and specificity 1 (AUC 1.000). Therefore, we choose the cut-off point at the highest AUC value. The cut-off point of the PD1 immunostaining was one PD1-positive lymphocyte in 10 HPF from two TMA cores. The area under the curve was 0.719 for PD1  of the receiver operating characteristic (ROC) curve at the highest positive likelihood ratio point for overall survival (OS) . Because for PD1 . When th for PD1 . The immFigure S1Immunohistochemical expression of PD-L1 in intra-tumoral non-neoplastic cells. A) In contrast to tumor cells, intra-tumoral non-neoplastic cells negative for PD-L1. B) In a PD-L1-negative case, tumor cells did not express PD-L1 but intra-tumoral endothelial cells are positive for PD-L1. C) Intra-tumoral endothelial cells (arrow) and inflammatory cells (arrow head) express PD-L1. Original magnification, x400.(TIF)Click here for additional data file.Figure S2Analysis of sensitivity and specificity of PD1 and PD-L1 score for the event of overall survival (death of the patient) by receiver operator characteristic curves. Arrow indicates a cut-off point for the number of intra-tumor PD1-positive lymphocytes and arrow head indicates a cut-off point for the PD-L1 immunostaining.(TIF)Click here for additional data file.Table S1The association between histologic type of soft-tissue sarcoma and tumor stage.(DOC)Click here for additional data file.Table S2Statistical data on receiver operating characteristic curves.(DOC)Click here for additional data file.


In this article, PtAg alloy nanoislands/graphene hybrid composites were prepared based on the self-organization of Au@PtAg nanorods on graphene sheets. Graphite oxides (GO) were prepared and separated to individual sheets using Hummer's method. Graphene nano-sheets were prepared by chemical reduction with hydrazine. The prepared PtAg alloy nanomaterial and the hybrid composites with graphene were characterized by SEM, TEM, and zeta potential measurements. It is confirmed that the prepared Au@PtAg alloy nanorods/graphene hybrid composites own good catalytic function for methanol electro-oxidation by cyclic voltammograms measurements, and exhibited higher catalytic activity and more stability than pure Au@Pt nanorods and Au@AgPt alloy nanorods. In conclusion, the prepared PtAg alloy nanoislands/graphene hybrid composites own high stability and catalytic activity in methanol electro-oxidation, so that it is one kind of high-performance catalyst, and has great potential in applications such as methanol fuel cells in near future. The unint years -10, fabrnt years -13, dye-nt years , lithiumnt years ,16, and nt years . Up to dnt years , epitaxint years ,20, chemnt years , and rednt years -26 have 3O4 nanoparticles (NPs) on graphene sheets. Zhang et al. [2 NPs on both sides of single layer graphene sheets as anode materials in Li-ion batteries. They found much higher retention of SnO2-graphene composite than commercial SnO2 powder after 50 cycles. Apart from these studies, a lot of efforts had been paid on metal oxide/graphene hybrid composites [In the field of electrochemistry, graphene is an excellent substrate to load active nanomaterials for energy applications due to its high conductivity, large surface area, flexibility, and chemical stability. For example, Dai and colleagues  made higg et al.  preparedmposites . Howevermposites -30. TherIn the field of catalysis, Pt (and Pd) is intensively applied in direct methanol fuel cells (DMFCs) ,32, becamAgn to represent the samples, and m and n are percentage determined by EDX.) The self-assembly technology enables loading a lot of Au NRs and Au@AgPt alloy nanorods on individual graphene sheets with uniform morphology. It was investigated that the prepared Au@AgPt alloy nanorods/graphene hybrid composites were used as a fuel cell electrocatalyst for methanol electro-oxidation. The utilization ratio of Pt was 23.4%, but its catalytic activity was 124 mA mg Pt-1, which was close to 162.5 mA mg Pt-1 (99.2% utilization ratio of Pt) reported previously [In this study, we reported to prepare PtAg alloy nanoislands/graphene hybrid composites based on the self-assembly of positively charged gold nanorods and Au@AgPt alloy nanorods on negatively charged graphene sheets. , chlorauric acid (HAuCl4·3H2O), silver nitrate (AgNO3), and potassium tetrachloroplatinate(II) (K2PtCl4), L-ascorbic acid (AA), methanol, sulfuric acid, potassium permanganate (KMnO4), hydrogenperoxide (H2O2), sodium nitrate (NaNO3), were purchased from Shanghai Sigma Company and used as-received. Milli-Q water (18 MΩ cm) was used for all solution preparations. All glassware used in the following procedures were cleaned in a bath of a piranha solution (H2SO4/30%H2O2 = 7:3 v/v) and boiling for 30 min.10000 mesh (dimension: 1.5µm) graphite, etyltrimethylammonium bromide (CTAB), PVP  were obtained from Alfa Company and used as-received. Sodium borohydride (NaBH-1) was obtained. PVP was used to prevent flocculation when reduced graphene oxide to graphene sheets. In a typical procedure for chemical conversion of graphene oxide to graphene (GN), 100 mL 8 mg mL-1 PVP solution was added to 50 mL 0.5 mg mL-1 GO solution, then stirred vigorously for more than 12 h. Afterward, 1.75 mL 0.5% hydrazine solution and 2 mL 2.5% ammonia solution were added. The mixture was stirred for 1 h at 95°C. After that, graphene was cooled at room temperature. The whole reduction process was repeated once more to reduce GO further. The stable black dispersion of GN was filtered under the condition of vacuum with 200 nm membrane as filter paper to collect it, at the same time it was washed with Milli-Q water (18 MΩ cm). Finally, the prepared GNs were dissolved in 50 mL water (0.5 mg mL-1).Graphene oxides (GO) were synthesized from flake graphite (1.5 µm graphite) using modified Hummer's method ,42. ThenAu@AgPt nanorods were prepared using an etching method described by Wu . The spe-1 GNs was added to 1 mL of the gold nanorods solution (0.5 mmol L-1) or Au@AgPt nanorods solution. The mixture solution was then shaken vigorously and sonicated for 30 s. Afterward, the mixture was left undisturbed and aged at room temperature for more than 24 h. The color of the solution changed from red (Au nanorods) or dark gray (Au@AgPt nanorods) to colorless, and the hybrid composites precipitated at the bottom of the vessel. Afterward, the precipitate was collected by centrifugation (12000 rpm for 5 min). Finally, the precipitate was redispersed in 100 µL water for electrochemical testing.A certain volume of 0.5 mg mL-1 H2SO4 and 2 mol L-1 CH3OH in the potential range of -0.25 to 1.0 V at a sweep rate of 50 mV s-1.UV-Vis-NIR absorption spectra were obtained from a Varian Cary 50 spectrophotometer. Scanning electron microscopy (SEM) images and energy dispersive X-ray (EDX) analysis were taken on a field emission scanning electron microscope . Transmission electron microscopy (TEM) images were captured on a JEM-2010/INCA OXFORD at an accelerating voltage of 200 kV. Zeta potential results were carried out on zeta potential/particle sizer (Nicom 380ZLS). CHI660C electrochemical workstation  was carried out for the electrochemical measurement. Cyclic voltammetry was performed in a three-electrode glass cell at room temperature. Glassy carbon (GC) electrode was used as working electrode. Before testing, the electrode was rejuvenated by polished with 0.3 and 0.05 µm alumina powders, respectively, then sonicated sequentially in alcohol, pure water in each for about 20 min. 5 μL as-prepared samples were drop-casted onto GC electrodes, and dried overnight in vacuum conditions. A platinum wire and an Ag/AgCl (saturated KCl) electrode were used as counter electrode and reference electrode, respectively. The electrolyte solution was purged with high-purity nitrogen for 30 min and protected under nitrogen during the measurements. Methanol was electro-oxidized in an electrolyte containing 0.5 mol L6H2O3 proposed by Boehm [Figure by Boehm . After r2+ ions were used, the nanodots of Ag-Pt alloy particles deposited almost on the two ends of Au NRs as shown in Figure 2+ ion increased, the nanodots of Ag-Pt alloy particles distributed uniformly on the surface of Au NRs. The amount of Ag and Pt in the shell layer was determined by EDX spectra. To mention the samples relatively easily, we used Au@PtmAgn to represent the samples. Here, m and n were percentage determined by EDX spectra.Figure -1) was mixed with Au NRs solution with different weight ratios  and sonicated for 15 min to form a homogeneous mixture. Self-assembly of positively charged gold nanorods and Au@AgPt alloy nanorods with negatively charged graphene sheets resulted in formation of heavier entities; therefore, after 24 h, precipitation could be found at the bottom of the vessel. For the front four samples , the corresponding supernatants were colorless. By contrast, the corresponding supernatants of the last two samples were still red color which suggested extensive Au NRs used. As shown in Figure Characterization of Au@PtAg alloy NRs/graphene hybrid composites was carried out by zeta potential test, SEM, and TEM. The zeta potential data were shown in Table TEM was also carried out for the sample of weight ratio 2:1 and 5:1 . Among the five test samples shown in Figure 0.64Ag0.36 NRs had the highest catalytic activity.In recent years, DMFCs have intensely been studied because of their numerous advantages, which include high-energy density, the ease of handling a liquid, low operating temperature, and their possible applications to micro-fuel cells. Electrocatalytic materials restricted the performance and application of DMFCs. Herein, cyclic voltammetry (CV) was carried out to investigate the electrocatalytic activity of various graphene/Au@PtAg NRs hybrids materials for the oxidation of methanol. Three samples of Au@PtAg alloy nanorods and one sample of Au@Pt nanorods were used to prepare graphene hybrids materials and measured. In the blank control test, cyclic voltammetry was carried out in 0.5 mol LIf) to the reverse current peak (Ib), If/Ib [QH is the charge consumed for the electrooxidation of adsorbed hydrogen; Qe is the elementary charge or charge of an electron; APt is the averaged atomic area of surface Pt atoms, which is 7.69 × 10-2 nm2 according to the atomic density of a Pt surface which is 1.3 × 1019 m-2; and WPt is the Pt loading at the working electrode. This equation is based on the well-established hydrogen-adsorption stoichiometry at a Pt surface (H: Pt = 1:1). Utilization of Pt was determined by Equation 2. Nt is Pt atom loading on the working electrode; Ns is utilizated Pt atom for electrooxidation [If/Ib value could be used to evaluate the catalyst tolerance to the poisoning species. Low If/Ib value indicates poor oxidation of methanol to carbon dioxide during the anodic sweep and excessive accumulation of carbonaceous residues on the catalyst surface. High If/Ib value shows the converse case.To gain more insights into the three catalysts, some electrochemical parameters such as electrochemically active surfaces (EAS) ,45, util), If/Ib -49 were xidation . If/Ib vIf/Ib) of the three graphene/Au@PtAg NRs hybrids materials  had high catalytic activity reached 124 mA mg Pt-1, which was just a bit lower than the sample of 99% Pt utilization in the sixth row. This result suggested graphene could enhance catalytic activity of Pt material. As Pt utilization was not high for our three samples tested in the experiment, if Pt utilization even enhanced, catalytic activity might even reach a new high platform. Furthermore, the ratio of If/Ib was all higher than the commercial E-TEK catalyst (0.74) [0.64Ag0.36 NRs (sample e) was tested by long-term repeated sweep by cyclic voltammetry in 0.5 mol L-1 H2SO4 with 2 mol L-1 CH3OH at 298 K , graphene/Au@Pt0.64Ag0.36 NRs (sample e) in this study is good electrode catalyst for methanol electro-oxidation.Electrochemical parameters  . It indi0.57Ag0.43 NRs (sample a) and the NRs hybrid compositions of graphene and Au@Pt0.57Ag0.43 NRs (sample b), whose results were shown in Figure 0.57Ag0.43 NRs without graphene), it was hard to find an oxidation peak in the first cycle . With cycles went on, oxidation peak current gradually appeared and increased. The 25th cycle of sample a was shown in Figure 0.57Ag0.43 NRs with graphene), obvious methanol-oxidation peaks were observed at 0.69 V in the forward sweep and at 0.49 V in the backward sweep , which were similar to that in the 25th cycle of sample a. For this reason, sample b had good oxidation current of methanol oxidation, and electrical excitation process was not needed.As mentioned above, graphene/Au@PtAg alloy NRs hybrid compositions were excellent materials for methanol electro-oxidation. To make out what role graphene played in the course, we done controlled experiment using pure Au@Ptad stripping voltammograms to explain the role graphene played in this reaction. The different state of CO adsorption on Pt/graphene was inferred to traditional Pt catalysts supported on carbon black [If/Ib were 1.46 and 1.24, respectively, for graphene/Au@PtAg alloy NRs hybrid compositions (the first sweep) and Au@PtAg alloy NRs (the 25th sweep) without graphene. The different onset potential and If/Ib value in backward sweep could be attributed to different CO adsorption state. The different CO adsorption state on graphene/Au@PtAg alloy NRs hybrid compositions and ordinary PtAg alloy NRs materials influenced the catalytic activity for methanol electrooxidation. Graphene in hybrid compositions could enhance anti-poisoning effect in the backward sweep. Graphene in the hybrid composition could change adsorption state of reactant, so the electrochemical process was affected. The higher oxidation peak in the first cycle of graphene/Au@PtAg alloy NRs hybrid compositions might result from the different interaction between graphene and methanol. Therefore, graphene in the hybrid compositions could improve the catalytic activity for methanol electrooxidation.Another important parameter to value catalytic activity of the samples is onset potential in electrical oxidation process. In forward sweep, all the samples had the same onset potentials (0.216 V). Otherwise, in backward sweep, sample b had frontier onset potentials (up to 124 mV) than sample a (without graphene). As mentioned above, the oxidation current of methanol oxidation in backward sweep represented the removal activity of the incompletely oxidized carbonaceous species  generated in the forward sweep. The frontier onset potentials of graphene/Au@PtAg alloy NRs hybrid compositions indicated easier remove of the incompletely oxidized carbonaceous species. This phenomenon was very similar to that discovered by Yoo et al. before. In their research, Yoo et al. had done COon black . In our In addition, graphene had the advantages of good dispersion, high conductivity, large surface area, flexibility, and chemical stability. The higher catalytic activity of graphene architecture was attributed to the larger surface area which led to large currents and good dispersion of Au@PtAg NRs on the surface. The good dispersion of Au@PtAg NRs on graphene would give reactants easy access to the catalytic active sites, which would help to improve proton diffusion and mass transport.In this study, PtAg alloy nanoislands/graphene hybrid composites based on self-assembling of Au@PtAg NRs on graphene sheets were successfully prepared. The high-loading Au@PtAg NRs distributed uniformly on the surface of graphene sheets. It is confirmed that PtAg alloy nanoislands/graphene hybrid composites own better catalytic activity and longer stabilization for methanol oxidation compared with traditional method. Because large-scale graphene can be prepared by chemical reduction of graphene oxide; therefore, the PtAg alloy nanoislands/graphene hybrid composites can be obtained by large scale with low cost; therefore, as-prepared PtAg alloy nanoislands/graphene hybrid composite has great potential in applications such as electro-catalyst for DMFCs in near future.The authors declare that they have no competing interests.LF carried out the whole study. GG participated in the taking of SEM images. PH participated in the taking of TEM images. XW, CZ, JZ participated in the discussion of this research. DC and SG participated in the design of the study and gave instruction of the study. All authors read and approved the final manuscript.Figure S1. UV-Vis-NIR absorption spectra of the Au NRs. Figure S2. TEM images of Au NRs (A) and Au NRs/graphene hybrid composites with weight ratios: 2:1 (B), 5:1 (C). Scale bar: 200 nm. Figure S3. Cyclic voltammetric curves of the following electrocatalysts: (a) graphene; (b) graphene/Au@Pt NRs; (c) graphene/Au@Pt0.34Ag0.66 NRs; (d) graphene/Au@Pt0.57Ag0.43 NRs; (e) graphene/Au@Pt0.64Ag0.36 NRs in 0.5 mol L-1 H2SO4 solution at 298 K. 0.64Ag0.36 NRs electrocatalyst over 200 cycles of methanol electrooxidationFigure S4. Stability of the graphene/Au@Pt.Click here for file


Biotechnology research is traditionally focused on individual microbial strains that are perceived to have the necessary metabolic functions, or the capability to have these functions introduced, to achieve a particular task. For many important applications, the development of such omnipotent microbes is an extremely challenging if not impossible task. By contrast, nature employs a radically different strategy based on synergistic combinations of different microbial species that collectively achieve the desired task. These natural communities have evolved to exploit the native metabolic capabilities of each species and are highly adaptive to changes in their environments. However, microbial communities have proven difficult to study due to a lack of suitable experimental and computational tools.With the advent of genome sequencing, omics technologies, bioinformatics and genome-scale modeling, researchers now have unprecedented capabilities to analyze and engineer the metabolism of microbial communities. The goal of this review is to summarize recent applications of genome-scale metabolic modeling to microbial communities. A brief introduction to lumped community models is used to motivate the need for genome-level descriptions of individual species and their metabolic interactions. The review of genome-scale models begins with static modeling approaches, which are appropriate for communities where the extracellular environment can be assumed to be time invariant or slowly varying. Dynamic extensions of the static modeling approach are described, and then applications of genome-scale models for design of synthetic microbial communities are reviewed. The review concludes with a summary of metagenomic tools for analyzing community metabolism and an outlook for future research. Recent advances in omics and genetic engineering technologies have resulted in novel techniques to interrogate and manipulate biological processes at varying levels of detail. The availability of such tools to analyze system-wide changes at the biomolecular level of genes, proteins and metabolites has created significant opportunities to understand cellular functions and to ultimately design processes that achieve a desired objective . Such tools have been increasingly used to understand, manipulate and model microbial communities for several practical applications including biorefineries, bioelectricity generation and bioremediation , 34.Microbial communities are ubiquitous, and microorganisms rarely function in isolation in the environment, even though most microbiology research is focused around the study of pure culture representatives. Hence, it is valuable to investigate the interactions between microorganisms so that we can extend our understanding of the physiology of pure culture representatives to communities. Genome sequencing of whole communities in natural environments (metagenomics) is now possible  and a weExtending modeling approaches to microbial communities is challenging partly due to the range of metabolic interactions that are possible, including competition, cross-feeding, syntrophy and mutualism. Additionally, the isolation of individual members of the community can be challenging despite advances in physiological techniques . While gIn this review, we focus on studies that have used genome-scale modeling approaches for analyzing and manipulating metabolism in microbial communities. We start with a brief introduction to lumped models, following which static approaches for genome-scale modeling of community metabolism are presented. Then extensions that account for community dynamics are discussed and the use of static and dynamic models for the design of synthetic communities are described. Finally, recent studies on analyzing metabolism in metagenomes are summarized and prospects for future research are discussed. This reviews aims to provide the reader with an overview of how metabolic modeling approaches are being applied for analyzing and engineering microbial communities. The reader is referred to other recent reviews , 15 for Escherichia coli was developed , have been developed. The dFBA approach links the steady-state intracellular metabolic flux distribution with the dynamic changes in the environment and allows the prediction of microbial growth, substrate utilization and product formation dynamics. Initially, dFBA was used to model the diauxic growth of bstrates , 33, 50.bstrates  and in sineering  of S. ce2 by metal reducers, sulfate reducers and methanogens, respectively. Hence, models of community metabolism have to account for the environment as a separate compartment in addition to the intracellular metabolic networks of individual organisms in the community. Most often, these interacting partners are assumed to grow together and this metabolic interaction is assumed to be at steady state. There have been several such “static genome-scale models” of microbial communities that are summarized in the next section.Microbial communities often involve species interactions where a metabolic by-product of one species is the substrate for other species and vice versa. In the environment, carbohydrates from organic matter are fermented to hydrogen, acids and alcohols by fermentative microbes and these electron donating byproducts are oxidized with electron acceptors such as Fe(III), sulfate and CODesulfovibrio vulgaris, and the methanogen, Methanococcus maripaludis . The autFreilich and Ruppin (2011)  extendedDesulfovibrio vulgaris, Geobacter sulfurreducens and Clostridium cellulolyticum. The predictions of carbon and electron balance were found to be consistent with experimental observations. The authors also investigated a hypothetical scenario where new species were added to this microbial community and methane production was simulated. These results suggest the potential for a model-based framework in designing synthetic microbial communities for practical applications in bioenergy and bioremediation.In most studies, the community objective has been assumed to be growth rate maximization of the individual microbes. Recently, Zomorrodi et al. (2012)  formulatWhile the static models described in the previous section have been shown to be valuable for understanding and analyzing metabolic interactions in microbial communities, the static approach does not allow prediction of the biomass concentrations of the individual species or account for time-varying changes in the intracellular and extracellular environments. Consequently, static genome-based models cannot easily predict the microbial composition in dynamic environments. Recently, dynamic flux balance analysis (dFBA) has been extended to microbial communities to predict time-varying interactions between species and their effects on the microbial composition. The extension involves coupling genome-scale metabolic models of the individual species, dynamic extracellular mass balances on each species, substrate and metabolic byproduct, and uptake kinetics for each substrate/species pair. Below several representative studies are summarized to illustrate the potential of the dFBA approach.Rhodoferax and Geobacter, in natural and acetate amended environments. In agreement with data, the simulations suggested that Geobacter species were fast growing, energetically inefficient organisms relative to the Rhodoferax species and would dominate the community at high acetate availabilities. The Rhodoferax species were predicted to be most efficient at utilizing acetate and to be dominant during acetate limitation. Salimi et al.   have devi et al.  used thiE. coli mutant strains ALS1008 and ZSC113, which have been engineered to have glucose and xylose only uptake, respectively. The simulations suggested that improvements in batch substrate consumption observed in a previous experimental study  engineering the metabolism of individual microbes in defined microbial communities; 2) manipulating the community by adding new microbes ; and 3) engineering the environment by introducing substrates that allow a specific species of interest to dominate  . Below set al. and a mercuric ion reducer  was required to degrade PCP in the presence of mercury as S. chlorophenolicum was sensitive to mercuric ion. Following a different approach, Klitgord and Segre  injection rates to allow sustained uranium bioremediation to occur in the environment. For the ZSC113/S. cerevisiae co-culture system, Hanly and Henson (Currently there are few reported examples of utilizing dynamic genome-scale metabolic models for designing the community environment. Zhuang et al.  have used Henson  used genThe studies reviewed in this paper clearly demonstrate the value of genome-scale models for understanding and engineering metabolic interactions in microbial communities. The availability of such rigorous and experimentally validated models will provide the opportunity to systematically optimize microbial communities for practical applications through the engineering of the individual species, addition of new species to existing communities and manipulation of the community composition and environment . IndividWhile this review has focused on organisms for which an annotated genome sequence is available, metabolic network reconstructions of metagenomes are increasingly possible. Such reconstructions of overall communities have been applied to environmental metagenomes and human microbiomes. For example, a metabolic network reconstructed from the metagenome of the English Channel has been used to define a metric that correlated the presence of metabolic enzymes with the metabolic turnover of the associated pathways . AdditioIn the future, we anticipate that such genome-scale models can be generalized to more than three species and therefore can be extended to the main members of natural communities. In addition, integration of these microbial community models with biogeochemical, physical processes in ecosystem models will allow improved prediction of community responses to environmental perturbations. Game theoretic approaches have been proposed to represent both the fitness of the individual members and changes in the environment due to microbial dynamics , 40. The


Clinicians search PubMed for answers to clinical questions although it is time consuming and not always successful.To determine if PubMed used with its Clinical Queries feature to filter results based on study quality would improve search success .We invited 528 primary care physicians to participate, 143 (27.1%) consented, and 111  completed the study. Participants answered 14 yes/no therapy questions and were given 4 of these  to search using either the PubMed main screen or PubMed Clinical Queries narrow therapy filter via a purpose-built system with identical search screens. Participants also picked 3 of the first 20 retrieved citations that best addressed each question. They were then asked to re-answer the original 14 questions.We found no statistically significant differences in the rates of correct or incorrect answers using the PubMed main screen or PubMed Clinical Queries. The rate of correct answers increased from 50.0% to 61.4% (95% CI 55.0%-67.8%) for the PubMed main screen searches and from 50.0% to 59.1% (95% CI 52.6%-65.6%) for Clinical Queries searches. These net absolute increases of 11.4% and 9.1%, respectively, included previously correct answers changing to incorrect at a rate of 9.5% (95% CI 5.6%-13.4%) for PubMed main screen searches and 9.1% (95% CI 5.3%-12.9%) for Clinical Queries searches, combined with increases in the rate of being correct of 20.5% (95% CI 15.2%-25.8%) for PubMed main screen searches and 17.7% (95% CI 12.7%-22.7%) for Clinical Queries searches.PubMed can assist clinicians answering clinical questions with an approximately 10% absolute rate of improvement in correct answers. This small increase includes more correct answers partially offset by a decrease in previously correct answers. Medline indexed 760,903 new articles in 2012, bringing their total to just under 20 million articles. The number of journals indexed by Medline has grown by 50% in the past 20 years [The 6S pyramid of evidence from health care research describes a range of tools and resources to assist physicians in accessing or retrieving relevant research evidence. The pyramid is structured so that original studies form the base and are topped by, in ascending order of clinical usefulness, synopses of studies, syntheses (systematic reviews), synopses of syntheses, summaries (evidence-driven online texts), and systems  . In addiResearch has shown that published original studies and reviews can provide clinicians with answers to their clinical questions -13 and lOther studies have reported negative effects of information searching on physician responses to clinical questions. McKibbon and Fridsma  found thSearch filters have been developed to help clinicians search the primary literature. These filters are rigorously developed and validated to increase the yield of clinically relevant articles based on research methods or clinical content. The Health Information Research Unit at McMaster University has developed filters for detecting primary studies for therapy ,20, diagThe objective of this pragmatic study was to determine if differences exist in the rate of correct answers to clinical questions when primary care physicians use the PubMed main screen or the Clinical Queries feature of PubMed for searches. Specifically, do searches done by primary care physicians through the PubMed main screen or through Clinical Queries give different rates of correctness of answers to clinical questions related to therapy?To assess if PubMed provided correct answers to clinical questions related to therapies, standard clinical questions with answers based on recent systematic reviews were developed. The reviews were selected from a database of clinical research from 125 journals preappraised for methodological rigor  and rateIn all, 24 standard questions were devised and iteratively tested on 3 physicians. The physicians were 2 experienced general practitioners and 1 experienced general internist. A fourth general internist also reviewed the questions. The physicians provided input on clinical applicability, perceived difficulty of the question, and relevance to practice for each question. Revised questions were then piloted on 2 general practitioners who provided further feedback. Questions were dropped if they were perceived by the clinicians as being too difficult or easy to answer, not relevant to general practice, or if the answer was perceived to be controversial. The remaining 14 questions are presented in Practicing physicians self-identified as general practitioners, family practitioners, or primary care general internal medicine practitioners who were registered with the McMaster Online Rating of Evidence (MORE)  system wParticipants were sent an Internet link to the survey that required them to sign into our information production system of high-quality clinical articles using their system passwords, which started the task . After pBecause our questions were treatment questions, we used the therapy category of the Research Methodology filter of Clinical Queries. We were interested in clinicians searching for answers to clinical questions; therefore, we used the narrow Clinical Queries. The narrow search filters are designed for clinical care because they retrieve a good proportion of potentially relevant citations while keeping the number of nonrelevant citations to a minimum (sensitivity of 93% and specificity of 97% ). The brFor each question, participants were asked to enter search terms into a textbox. If participants were unhappy with the retrieval, they could alter their search terms and submit a new search.After each of the 4 searches, the first 20 results were presented. The participant was blinded as to which PubMed interface the retrieval came from. They could view the abstract of the article in the same window by selecting the title of the article. Participants were asked to select the top 3 articles most important to forming/supporting their answer. After the 4 searches were performed and articles were selected, the participants were given the 14 questions again and asked to answer them with yes or no. The study was approved by the McMaster University Hamilton Health Sciences/Faculty of Health Sciences Research Ethics Board.The primary outcome of the study was the difference in the proportion of correct answers before and after searching. Secondary outcomes were the proportion of questions searched that went from incorrect to correct and correct to incorrect, the proportion of questions without searches that went from correct to incorrect and incorrect to correct, and the time taken to complete the project tasks.Based on previous studies, starting proportions of answers to clinical questions were 27% correct (n=557)  and 40% We anticipated an approximately 10% change in correct to incorrect answers; therefore, we set a 5% absolute difference between search modes as clinically interesting. This gave us a sample size of 522 searches for the correct group and 459 for the incorrect group to detect a 5% difference in search modes with 80% power.The Mantel-Haenszel test for matched pairs, stratified by participant and by question, was used to determine the odds ratio of changing a response by using Clinical Queries searches versus PubMed main screen searches. A posteriori we recognized that question 6 was a prognosis question rather than a treatment question. Given that the Clinical Queries searches used a therapy filter, we performed our analysis including this question and the sensitivity analysis without this question. In the entire dataset, only 1 of 29 participants (3%) presented with question 6 changed their answer .Articles selected by the participants as being relevant to answering their question were independently assessed in duplicate for methodological criteria outlined below. Disagreements were resolved through consensus.A therapy study is methodologically sound if it meets these 3 criteria:Random allocation of participants to comparison groups;Outcome assessment of at least 80% of those entering the investigation accounted for in one major analysis at any given follow-up assessment; andAnalysis consistent with study design.A systematic review of therapy studies is methodologically sound if it meets these 6 criteria:Explicit statement of clinical topic;Question refers to treatment;Methods are described in report body (not just the abstract);More than one major database searched or Cochrane CENTRAL searched;Explicit inclusion/exclusion criteria; andOne or more articles meet criteria set out for therapy studies (listed previously).During recruitment, 528 physicians were invited to take part in the study; 143 (27.1%) provided consent, 110 of whom (21.0% of those invited and 77.6% of those who consented) completed the study tasks (24 abandoned the task after the first search and 9 did not perform any searches). Two participants (1.8%) answered all 14 questions correctly; consequently, they were directed to search for only 2 questions. At baseline, participants answered 62.3% (95% CI 59.8%-64.7%) of the questions correctly.Time to complete the tasks was calculated based on the time the participant signed in to the website until the time they submitted the survey. If the participant logged off without clicking the submit button, the timer continued to count. As such, 16 observations were more than 100 minutes, ranging from 119 to 103,786 minutes (72 days). We selected a cutoff of 100 minutes as a likely point at which the tasks were not completed in 1 sitting. The remaining 95 participants completed the tasks within 6 to 76 minutes .During the study, 440 searches were executed, 222 (50.5%) answered correctly and 218 (49.5%) answered incorrectly initially. For questions selected for searching, baseline responses were 50.0% correct in both groups by design. After searching, responses were correct for 61.4% (95% CI 55.0%-67.8%) of questions for the PubMed main screen group, and 59.1% (95% CI 52.6%-65.6%) for the Clinical Queries group. We found no differences in the rate of answers going from incorrect to correct for the PubMed main screen searches  compared with the Clinical Queries searches  . Both seP>.05)  . SensitiP=.002).For questions answered before and after searching but without intervening searches, an average of 65.4% (95% CI 62.8%-68.0%) were correct at baseline, 64.6% (95% CI 62.0%-67.2%) were correct at the end of the study across the 14 questions, 4.0% (95% CI 2.3%-5.6%) went from correct to incorrect, and 3.1% (95% CI 2.2%-4.1%) went from incorrect to correct. There was variability in baseline performance across questions . WithoutClinical Queries were developed to retrieve clinically useful studies based on study design. Therapy filters retrieve citations based on the article being a randomized controlled trial. Therefore, we were interested in determining if the participants identified studies with strong methods  when presented with the first 20 retrievals. The participants were asked to identify the 3 most important articles that provided evidence to answer the clinical question they were addressing. Overall, the PubMed main page group tagged 334 articles as important and the Clinical Queries group tagged 321 articles. Although we sought to show that searches with PubMed Clinical Queries were associated with more correct answers to clinical questions than were PubMed main screen searches, we did not find any differences. This may be because we did not meet our sample size of approximately 1000 searches as originally calculated. Another explanation for these results may be the training and experience of the study participants. All were practicing clinicians and were registered with the MORE system, wherein they evaluate and rate clinician-ready health research studies. Also, this study was done on the Internet. The participants likely had strong computer and Internet skills and were probably skilled users of PubMed and the clinical research literature. Therefore, the study participants may be the least likely group of clinicians who could benefit from using Clinical Queries. Naïve users or new clinicians, such as interns and residents, or those clinicians less skilled at the assessment and application of research findings may derive more benefit from the Clinical Queries searches. Time is also a major factor in seeking answers to clinical questions. If the time to seek answers had been more tightly constrained in the study (we did not have time limits on the tasks) we may have found a larger difference between the correctness of the answers found with standalone PubMed searches and searches using Clinical Queries.P=.60).However, this study does show that PubMed, either on its own or using Clinical Queries, helps clinicians answer clinical questions with increased accuracy. For questions that clinicians answered twice without searching, the rate of correct answers stayed the same (65.4% correct at first answer and 64.6% correct on second answer). With searching, clinicians improved their rate of correct replies. Their answers went from 50.0% correct (set by the study) to 60.2% correct .The questions we used in the study were based on strong evidence from current systematic reviews, and they were pretested with various physician groups. However, despite these strengths, our questions were not questions that arose in the participants’ daily practices.Future research needs to be done to improve the quality of search tools and their ability to maximize the correct answers to questions while minimizing the answers that go from an initial correct answer to an incorrect answer. Focusing on specific groups of clinicians  or in certain situations  may also address the potential for automated assistance of PubMed searching. Other research has shown that an interface in PubMed leads to better question answering if the search entry screen required clinicians to enter concepts related to patients or populations, intervention, comparison, and outcome (PICO) aspects of the questions.  ComparisWe have shown that complex studies of searching can be done through the Internet. We also have reinforced that clinician searching in PubMed produces an absolute improvement of approximately 10% in clinician ability to correctly answer clinical questions. This 10% improvement is consistent with other similar studies ,17,18 an


In order to characterize the relationship between genes and inserted elements, we have examined the distribution of retrotransposons and their internal motifs within tissue-specific and housekeeping genes. We have identified that the previously established retrotransposon antisense bias decays at a linear rate across genes, resulting in an equal density of sense and antisense retrotransposons near the 3′-UTR. In addition, the decay of antisense bias across genes is less pronounced among tissue-specific genes. Our results provide support for the scenario in which this linear decay in antisense bias is established by natural selection shortly after retrotransposon integration, and that total antisense bias observed is above and beyond any bias introduced by the integration process itself. Finally, we provide an example of a retrotransposon acting as an eQTL on a coincident gene, highlighting one of several possible avenues through which insertions may modulate gene function.Retrotransposons comprise approximately half of the human genome and contribute to chromatin structure, regulatory motifs, and protein-coding sequences. Since retrotransposon insertions can disrupt functional genetic elements as well as introduce new sequence motifs to a region, they have the potential to affect the function of genes that harbour insertions as well as those nearby. Partly as a result of these effects, the distribution of retrotransposons across the genome is non-uniform and there are observed imbalances in the orientation of insertions with respect to the transcriptional direction of the containing gene. Although some of the factors underlying the observed distributions are understood, much of the variability remains unexplained. Detailed characterization of retrotransposon density in genes could help inform predictions of the functional consequence of  Retrotransposons (RT) make up approximately half of the human genome RTs contain DNA motifs that can act as binding sites for polymerases Previous studies have taken advantage of the high copy number of RTs to study their relationship with the host genome. These studies have begun to reveal the capacity of various genomic contexts to withstand retrotransposition. Within genes, certain regions are more susceptible to disruption by RTs than others The distribution of retrotransposons throughout the human genome shifted towards a non-uniform pattern over evolutionary time Given that polymorphic Alu and human-specific Alu already appear over-represented in the antisense orientation, we next wanted to determine how this antisense bias might be modified over time. To address this question, we examined the rate of change in both L1 and Alu frequencies within genes and the ratio of ASO/SO over time. All L1 and Alu annotated in the Hg19 reference genome were used for this analysis. As a proxy for time, RTs can be ranked by relative age of retrotransposition. To order subfamilies in approximate order of subfamily age, we made use of the average nucleotide distance of subfamily members to their consensus sequence . As detaWe first separately examined the frequency of L1 elements in each orientation across time. Most L1 subfamilies were depleted within genes , with thIn contrast to L1, the majority of Alu were over-represented in genes . After gCombined with the L1 data, these results indicated that L1 and Alu density within genes had increased over the course of evolutionary time in relation to intergenic frequencies, but the overall level of antisense bias remained largely unchanged.To determine how RT frequency was dependent upon an element's position in a gene, we calculated the frequency of all L1 and Alu within each gene decile both in the ASO and SO . We wereHere we further note that the slope of human-specific ASO Alu was significantly different from the slope calculated with all Alu , indicating that the decay in antisense bias across genes most likely arose over evolutionary time. Alternatively, and somewhat less parsimoniously, recently inserted elements may have significantly altered their insertion pattern compared to older elements due to shifts in RT targeting mechanisms.Since previous studies have shown that ASO RT density was inversely correlated with exon density, we next calculated the frequency of coding exons (CDS) within each decile. Indeed, CDS density increased at a similar linear rate across gene deciles . The firAlthough both are conserved, coding exons and UTRs have very different roles in the creation of a final gene product. It has been shown that exon density is highly predictive of RT frequency; however it is also possible that the 5′-UTR has a specific effect on RT abundance that is distinct from the effect imposed by coding exons. We therefore wanted to determine if the presence of a 5′-UTR was associated with altered RT frequency beyond what would be expected from exon enrichment alone.In our method of analysis the first and last deciles consistently contained a 5′- or 3′-UTR, whereas the internal deciles were variable as to the presence of a coding exon. The resulting frequency plots revealed that RTs were highly reduced within the first bin (containing the 5′-UTR) in comparison to the second bin. Since this could be a result of a decrease in the density of CDS exons as well as an altered effect by the 5′-UTR, we needed to distinguish the effects from one another. To accomplish this, we restricted analysis to genes that contained a second 5′-UTR in each decile. Examples of genes that contained a secondary 5′-UTR are depicted in L1 ASO, 0.04L1 SO, 0.49Alu ASO, 0.24Alu SO) , and so on for all ten deciles see . The fre4Alu SO) .Across all genes, we have shown that the second decile, that neighbours the 5′-UTR containing decile, contains the largest number of L1 and Alu. We therefore tested if L1 and Alu were similarly increased in the deciles surrounding the secondary 5′-UTRs. We found that the L1 and Alu were also reduced to a lower extent in the bins surrounding the 5′UTR . L1 nearWhen taken as a group, RT antisense bias decreased linearly across gene bounds. Different functional groups of genes, however, contain varied numbers of RTs. For example, housekeeping genes have a predictably higher level of Alu frequency than other gene categories To determine if differences in gene expression across tissue types was confounding this result, we tested the correlation between ASO frequency and exon density as a function of relative expression in housekeeping and tissue specific genes. There was no link between expression level and the correlation between ASO and exon density . FurtherAlthough the consensus sequences for Alu and L1 contain many functional DNA binding motifs, each individual RT in the genome is variable as to whether or not it contains these motifs. The loss or gain of a motif may occur by mutation at the nucleotide level or through retrotransposition of a parent element that has already lost or gained the motif sequence. To help determine how the functional motifs within Alu and L1 affect their frequency within genes, we analysed a subset of functional motifs found in the respective consensus sequences . The binSince we have shown that, at the level of entire elements, L1 and Alu frequencies are correlated with relative location within a gene, we examined the dependency of individual motifs on the relative location within the gene. To reduce the number of statistical tests (and hence required corrections), we restricted analysis to the bins that contained the extremes of Alu and L1 frequency. These were the first and last bins within the linear range of Alu/L1 frequency, which contained the most and least Alu and L1 respectively. Motif frequencies were normalized by the count of ASO or SO elements within the bin and then compared between bins . ContrarTo account for differences between gene groups we further classified subsets of genes into functional groups  . The relOverall, the frequencies of motifs found within L1 followed slightly different patterns. Motifs within L1 were significantly depleted within genes compared to motifs within Alu (t-test: p<1.3e-05) . They diThese results indicate that not only are whole RT elements non-randomly distributed within genes, but also functional motifs within Alu and L1 are further depleted. Furthermore, this depletion is sensitive to orientation and location of the retrotransposon within the gene as well as the tissue specificity of the gene itself.The human L1 consensus sequence is approximately 6 kb, although it varies by subfamily. During retrotransposition L1 are often truncated, resulting in an average size of 900 bp To test whether the linear decay of antisense oriented L1 was related to the size of the element, we calculated the proportions of L1<1 kb, 1–2 kb, 3–4 kb, and 5 kb+ versus expectation based on all L1. Fischer's exact test p-values were calculated between the count of observed size-specific L1 and the expectation within bins 1 and 2 since these bins had the largest effects of antisense bias. We found that full-length L1 (5 kb+) were greatly reduced in frequency near the 5′ region of the gene compared to expectation . This waPutku et al. have previously linked the presence of polymorphic RTs to differential gene expression of the WNK1 gene These results highlight the presence of predictive relationships between retrotransposons and the genes in which they reside. Primarily, we identified a linear decay of ASO RTs across the length of a gene that was correlated with exon density. The failure to observe a similar decay in polymorphic elements suggests this distribution is not present at the initial time of insertion and instead developed as a result of selective pressure relatively soon after insertion. The extent of antisense bias (ASO/SO), independent of gene decile, remained constant over time although the overall frequencies of RTs in genes steadily increased with increasing age. This is supported by previous cell line work which has shown that Alu may exhibit preferential insertion in the antisense orientation within a gene One factor strongly affecting the linear decay of ASO L1 retrotransposons is the length of the element. L1 that were truncated to <1 kb were substantially increased in frequency near the 5′-UTR when compared to full length L1. Although we postulate here that this was likely due to the size of the L1, it is also possible that the presence or absence of the bidirectional Pol-II promoter plays a role in maintenance of L1 near the 5′-UTR. Our analyses here cannot readily disentangle the effects of length, promoter presence/absence, and presence of motifs along the length of the element. Additional subfamily-specific analyses, adjusting for variability in promoter positions among L1 consensus sequences, will be required to tease apart these variables. Ideally, such analyses would be accompanied by experimental data examining the impact of various L1 insertion scenarios.Contrary to previous findings, RTs near 5′-UTRs were reduced in frequency, most significantly for L1 in the antisense orientation. Our analysis was capable of distinguishing this trend by removing the confounding effect of low exon density that is commonly present near 5′UTRs. Since the linearly decreasing antisense bias is highly correlated with CDS exons, this indicates that the commonly reported increase in antisense oriented RTs near the 5′-UTR is more likely due to a lack of CDS exons as opposed to a function of the UTR itself, while the overall reduction in elements in either orientation is attributable to the UTR itself.As previously reported, we observed a relative increase of RTs within genes over time. We consider it most likely that the increase in relative frequency of subfamilies within genes compared to intergenic is not a result of population fixation within genes, but, rather, a decrease of element numbers within intergenic regions. This could be explained by the relaxed purifying selection against deletion events within intergenic regions compared to genic One of the primary results of this study indicated that the bias towards antisense RTs near the 5′ UTR and the decay of bias toward the 3′ region of the gene occurred relatively soon after insertion, while the elements remained polymorphic within the human population. This was evidenced by the observation that the linear decay from 5′ to 3′ of the gene was not evident from more recently inserted, polymorphic elements. As noted above, we did not, however, detect a difference in the overall genic ASO/SO ratio between the polymorphic and non-polymorphic elements. This discrepancy could be attributed to the fact that the change in slope of decay was a more sensitive measurement than overall bias across the gene. If this explanation is correct, additional polymorphic element data from population screening should ultimately allow for the overall ASO/SO genic bias to be detectable. Based on our results the selective bias towards a linear decay of antisense orientation appears to be separate from the antisense bias introduced during the insertion process itself.One possible avenue, through which polymorphic elements may be influencing fitness, is through acting as eQTLs. It remains to be determined, however, if the elements in this case are directly impacting expression levels or simply tagging another functional variant in linkage disequilibrium with the element. The trends in RT distribution observed here may be helpful in predicting the effects of RTs insertions on a co-localized gene. Factors identified to be important in RT distribution were RT type, orientation with respect to the gene, proximity to a CDS, proximity to a 5′-UTR, and tissue specificity. While the biological mechanisms underlying these observations remain undetermined, it is plausible that the juxtaposition of ASO elements near intron-exon junctions interferes with splicing regulation Retrotransposon (RepeatMasker) coordinates were downloaded from the UCSC genome browser . Polymorphic Alu were taken from the supplemental data in the original publication Gene (RefSeq flat file track), and Retrotransposon (RepeatMasker) coordinates were downloaded from the UCSC genome browser . Each gene was subdivided into ten bins of equal size where bin1 began at the transcription start and decile 10 ended at the transcription stop. This method of binning resulted in a concentration of exons in the first and last bins. Further calculations took this bias into account. Repeats analyses were limited to recent inserts only where mentioned.L1 were ranked first by subfamily group by the average divergence from their respective consensus sequences, as calculated in 5′-UTR coordinates were selectively downloaded from the UCSC genome browser RefSeq Gene track. These coordinates were intersected with gene deciles to determine which 5′-UTRs were within gene boundaries. Genes were then grouped depending on which decile the 5′-UTR was located within.Counts of elements within each decile were tabulated using an in-house script that intersected gene locations with RepeatMasker annotations to tabulate strand orientation counts per decile. Frequencies of elements within deciles were defined as the count of elements in a decile  per total elements . For SO-only or ASO-only plots, subfamily counts were normalized by the intergenic frequencies of the respective subfamilies in order to account for variable amplification rates. Trend lines were obtained via linear regression. For the analysis of L1 and Alu maintenance in genes as a function of rank by milliDiv; milliDiv was calculated as the average base mismatches in parts per thousand, as tabulated in RepeatMasker . A minimum sample size for comparison between subfamilies was calculated using G*Power3 To correct for differences in genomic coverage within genes and intergenically, retrotransposon frequencies were calculated as a factor of their total length as opposed to count. Coverage in bp was tabulated for each element and summed per subfamily. Alu subfamilies were ranked according to age as per presence in humans.The values within genes were then divided by the total bp covered by similar elements intergenically. All values were normalized by the total bp in the queried region.Gene categories were grouped based on the calculations in To reduce multiple testing we restricted motif analysis to sequences that have been previously identified in Alu or L1. Exact matches, unless otherwise stated, were searched in the necessary region  using an in-house program. Counts of each motif were tabulated in each region and, where necessary, were normalized by the relative element density. Truncated L1 were identified by comparing every known L1  versus known L1 consensus sequences using a local BLAST alignment. The start position of each L1 was denoted as the start position within its respective consensus sequence.Polymorphic retrotransposons were filtered from the dataset in Dataset S1List of Polymorphic Retrotransposons and Human-Specific Retrotransposons adapted from and .(XLSX)Click here for additional data file.Dataset S2Ranked list of L1 and Alu subfamilies used in this study.(XLSX)Click here for additional data file.


The aim of the research article is to develop a representative volume element using finite elements to study the buckling stability of graphene/polymer nanocomposites. Research work exploring the full potential of graphene as filler for nanocomposites is limited in part due to the complex processes associated with the mixing of graphene in polymer. To overcome some of these issues, a multiscale modeling technique has been proposed in this numerical work. Graphene was herein modeled in the atomistic scale, whereas the polymer deformation was analyzed as a continuum. Separate representative volume element models were developed for investigating buckling in neat polymer and graphene/polymer nanocomposites. Significant improvements in buckling strength were observed under applied compressive loading when compared with the buckling stability of neat polymer. In the recent past, graphene has emerged as a potential candidate for developing nanocomposites with improved properties ,2. The eBuckling in isolated graphene sheets was modeled by several researchers -11. HoweIt is well established that the reinforcement of polymer with graphene increases the elastic modulus of the material which further improves buckling stability. The aim of this study is to propose a numerical model which can estimate the increase in buckling stability with different volume fractions of graphene and can further be extended to complex shapes and structures.It has been reported that achieving a uniform dispersion of two-dimensional graphene sheets in polymer is more challenging compared to the mixing of one-dimensional CNT. Moreover, the application of nanocomposites is not limited to simple structures, and the comprehension of material behavior in complex structures is restricted when employing experimental and analytical methods. Consequently, research efforts are increasingly focused on numerical approaches. To overcome some of the limitations that exist in experimental and analytical work, a multiscale representative volume element (RVE) is proposed in this paper to investigate buckling phenomena in graphene/polymer nanocomposites under the assumption that graphene is uniformly distributed in the polymer. To the knowledge of the present authors, no numerical model has been reported yet to study the effect of graphene on the buckling strength of nanocomposites. In the proposed technique, graphene was modeled in the atomistic scale, whereas polymer deformation was analyzed as a continuum.In this paper a finite element technique was employed in conjunction with molecular and continuum mechanics to simulate buckling in graphene/polymer nanocomposites. In the proposed RVE, the polymer, epoxy in this case, was modeled as a continuum phase whereas the deformation in graphene was evaluated using an atomistic description. Nonbonded interactions were considered as the load transfer mechanism or interphase between polymer and graphene. FE modeling was performed in the ANSYS (Version 13) software environment .To model graphene in the proposed RVE, it was assumed that graphene behaves like a space frame structure in which the covalent bonding between C-C atoms constitutes the load-bearing element while atoms act like a joint. A molecular-mechanics-based approach was employed to estimate properties for graphene in the atomistic scale. In the molecular form, potential energy of graphene can be represented asVbonded represents the bonded interaction between atoms and is considered primarily responsible for maintaining the structural integrity. The potential energy term ‘Vnonbonded’ includes electrostatic forces, e.g., van der Waals forces, which are considered weak in nature and can generally be neglected in comparison to bonded interactions. Li and Chou  when the structure is subjected to the reference unit load PR. In Equation 7 the total stiffness matrix [K0] is the assembled global stiffness matrix for the multiscale structure composed of beam, truss, and solid elements. Conventional assembly techniques can be used to obtain the global stiffness matrix.The pre-buckling analysis was performed in the finite element environment to compute the reference stresses Equation 8 represents the general solution that is obtained in the finite element analysis after solving the assembled matrices defined in Equation 7.Q*} obtained from Equation 8 are further post-processed to compute strains and corresponding stress values S* within each element using Equation 9, in which matrix [D] represents the elasticity matrix  while matrix [B] is a function of approximating polynomial or shape functions.The nodal displacement vectors {λ and v are the load factor and the eigenvector of displacements. The geometric stiffness matrix KG used in Equation 10 is defined with the help of Equation 11, where Γ is composed of derivatives of shape functions and S is a function of the stress S* estimated in the pre-buckling analysis.The post-buckling analysis corresponds to a general eigenvalue problem as defined by Equation 10, in which λmin can be determined, which is then employed to estimate the buckling force given by Equation 12.By solving Equation 10, the lowest possible value for the load factor The proposed RVE was employed to understand the buckling behavior of graphene in a polymer matrix when graphene was assumed to be uniformly distributed. A second separate RVE structure with equal dimensions was developed using finite element modeling to study the buckling in neat (homogenous) polymer.E) were then used in defining the homogenous rectangular plate with dimensions of the proposed RVE model. The buckling loads were calculated for this homogenous rectangular plate shown in Figure To validate the results of the proposed RVE, an alternative method (indirect method) was also employed in this paper. The proposed RVE model with boundary conditions as shown in Figure Px is the applied unidirectional compressive load, w is the displacement in the outward normal direction, and D is flexural rigidity . Boundary conditions were kept identical in all RVE models. Only the value of flexure rigidity was varied, which afforded the proposed RVE model the capability of yielding E for different graphene volume fractions.The analytical formulation to estimate the buckling load for a rectangular plate is given in Equation 13, where The boundary conditions along with dimensions for the proposed RVE model are shown in Figure The results obtained from the developed RVE structures were plotted in Figure In this study a representative volume element method was successfully employed to investigate the buckling phenomenon in graphene/polymer nanocomposites, where graphene was assumed to be uniformly distributed. Graphene was modeled in the atomistic scale and polymer as a continuum. A significantly enhanced buckling strength of graphene reinforced polymers was observed as compared to neat polymer, i.e., buckling strength of graphene/polymer nanocomposite improved by 26% with only 6% filler volume fraction.The authors declare that they have no competing interests.Both authors made equally valuable contributions to this paper. Both authors read and approved the final manuscript.