However, a 30-day treatment of chimeric mice with erlotinib, a small molecule that specifically inhibits EGF-receptor

activity, did not prevent but only delayed the kinetics of infection. In conclusion, we show here that the human monoclonal antibody mAb16-71 can efficiently block in vitro and in vivo infection by multiple HCV genotypes. In addition, we demonstrate that blockade of SR-BI after infection can prevent rapid virus spread through the liver parenchyma, presumably by interfering with SR-BI-dependent cell-free as well as direct cell-to-cell HCV transmission. Therefore, targeting SR-BI may represent a superior strategy for anti-HCV immunotherapy to prevent the emergence of escape mutants and virus rebound during or following antiviral therapy, and to prevent allograft GSK1120212 in vivo infection in chronically infected HCV patients undergoing orthotopic

liver transplantation. We thank Dr. Veronique Stove and Yvonne Geybels for the analysis of mouse plasma and Dr. Robert H. Purcell (NIH) and Dr. Jens Bukh (NIH; CO-HEP, Copenhagen) for providing plasma from acutely infected chimpanzees. Additional Supporting Information may be found in the online version of this article. “
“During bile duct ligation (BDL), the growth of large cholangiocytes is regulated by the cyclic adenosine monophosphate (cAMP)/extracellular signal-regulated kinase 1/2 (ERK1/2) pathway and is closely associated with increased secretin receptor (SR) expression. Although it has been suggested that SR modulates cholangiocyte growth, direct evidence for secretin-dependent proliferation is lacking. SR wild-type (WT) (SR+/+) or SR knockout (SR−/−) mice underwent sham surgery or BDL for 3 or 7 days. We evaluated selleck chemicals llc SR expression, cholangiocyte proliferation, and apoptosis in liver sections and proliferating cell nuclear antigen (PCNA) protein expression and ERK1/2 phosphorylation in purified large cholangiocytes from WT and SR−/− BDL mice. Normal WT mice were treated with secretin (2.5 nmoles/kg/day by way of osmotic minipumps for 1 week), and biliary mass was learn more evaluated. Small and large cholangiocytes were

used to evaluate the in vitro effect of secretin (100 nM) on proliferation, protein kinase A (PKA) activity, and ERK1/2 phosphorylation. SR expression was also stably knocked down by short hairpin RNA, and basal and secretin-stimulated cAMP levels (a functional index of biliary growth) and proliferation were determined. SR was expressed by large cholangiocytes. Knockout of SR significantly decreased large cholangiocyte growth induced by BDL, which was associated with enhanced apoptosis. PCNA expression and ERK1/2 phosphorylation were decreased in large cholangiocytes from SR−/− BDL compared with WT BDL mice. In vivo administration of secretin to normal WT mice increased ductal mass. In vitro, secretin increased proliferation, PKA activity, and ERK1/2 phosphorylation of large cholangiocytes that was blocked by PKA and mitogen-activated protein kinase kinase inhibitors.

Autophagy has been implicated in a variety of important physiopathological processes, such as neurodegeneration, cancer, viral infections, inflammatory disorders, and liver disease.26 The mitochondrion is one of the organelles that can become targets for autophagic degradation in a process known as mitophagy, which

is specifically induced by nutrient deprivation, reduced ATP generation, mitochondrial membrane depolarization, triggering of the mitochondria permeability transition (MPT), and oxidative stress.27 In fact, compelling evidence has emerged indicating that the removal of mitochondria is a highly regulated and organelle-specific process, and mitophagic signaling has only very recently come to light.15 To our knowledge, the present study is the first to address the relationship between NNRTI-induced toxicity and induction of autophagy. We have documented the induction of autophagy and, in CX-4945 nmr particular, mitophagy in hepatic cells treated with EFV, the most commonly used NNRTI. Nevirapine, the other NNRTI, was not evaluated, as previous studies in this model have shown that it lacks a direct mitochondrial

effect.14 Autophagy was assessed using several approaches. We employed TEM to study mitochondrial morphology and to detect the presence of autophagic vacuoles, as this continues to be the most sensitive and widely employed technique for these purposes.23 We also studied LC3-II, the only protein known to be specifically localized to autophagic structures throughout the entire autophagic process, from the phagophore to the lysosomal degradation.28 Nevertheless, it is important learn more to point out that increases in LC3-II levels have been associated not only with an enhanced autophagosome synthesis but also with a reduced autophagosome turnover. This is relevant to our results because, whereas moderate EFV concentrations (10 and 25 μM) triggered

a normal autophagic flux, the highest concentration (50 μM), which produced severe mitochondrial damage, was associated with a delayed or an inhibited autophagic flux. Such an effect may be due to a reduced fusion between compartments and/or impaired lysosomal proteolysis. Interestingly, this may also explain the increased mitochondrial mass we observed in cells treated with the same concentration this website of EFV, because an impaired mitochondrial clearance can result in an apparently enhanced mass of these organelles. In connection with this, it is relevant to stress that this increase in the mitochondrial mass occurs in the absence of true mitochondrial biogenesis, as shown by the lack of changes in the mtDNA/nDNA ratio in EFV-treated Hep3B cells.13 Autophagy is related to cell death, but this relationship is still not well understood. Stress or injury signals can activate both autophagy and cell death pathways in which the role of the former can vary depending on the context.

7C) and Pgc-1α (Fig. 7D) messenger RNAs (mRNAs) more in WT than in Ass+/− mice and no differences were observed in Acc mRNA (not shown). CPT-I is the rate-limiting enzyme in fatty acid catabolism for the conversion of long-chain fatty acids into long-chain acylcarnitines, whereas CPT-II is responsible for the release

of long-chain fatty acids from carnitine, inside the mitochondrial matrix, for fatty acid β-oxidation. 21 Although no changes were observed by ethanol binge drinking (not shown), Cpt1 mRNA (Fig. 7E) and CPT-II protein (Fig. 7F) were induced in chronic ethanol feeding in both WT and Ass+/− mice. The ratio of free carnitine (C0) to long-chain acylcarnitine (C16+C18) is an indicator of CPT-I activity. Ass+/− mice had higher MLN0128 order CPT-I activity (lower C0/C16+C18 ratio) (control group: 32.7 ± 12.2; ethanol group: 31.2 ± 5.8) compared BGB324 chemical structure with WT mice (control group: 52.8 ± 15.6; ethanol group: 56.0 ± 19.7) but chronic ethanol feeding did not affect CPT-I activity (P < 0.05 for Ass+/− versus WT). However, CPT-II protein expression was significantly

increased by ethanol feeding in WT mice compared with Ass+/− mice (Fig. 7F); hence, fatty acid β-oxidation was impaired in chronic ethanol-fed Ass+/− mice. Thus, although Ass+/− mice may have efficient fatty acid transport into the mitochondria for β-oxidation, the decrease in CPT-II under chronic ethanol drinking impaired the efficiency of this pathway, leading to fat accumulation. Up-regulation of NOS2 along with generation of RNS plays a major role in alcohol-induced liver learn more injury. 22 The overwhelming research on the production of NO· has been focused on the different isoforms of NOS. However, a renewal of interest in the regulation of ASS has recently emerged as a result of its possible rate-limiting

role for high-output NO· synthesis. 2 Using an integrated proteomics and systems biology approach we identified NOS2 along with ASS—the rate-limiting enzyme in the urea and L-citrulline/NO· cycles—as significantly coinduced under chronic ethanol consumption in rodents, which was also validated in human samples. In addition, ASS, ASL, ARG1, and 3-NT residues were up-regulated in both hepatocytes isolated from chronic ethanol-fed rats and in ALD and cirrhosis patients. Moreover, NOS2 was regulated by altering ASS expression in hepatocytes. Treatment with L-citrulline, an inducer of ASS, increased the expression of both ASS and NOS2, whereas downregulation of ASS by siRNA or other inhibitors significantly reduced NOS2 expression. Because the urea cycle is key for hepatic amino acid content, this result suggested that ASS may control NOS2 by modulating substrate availability in the L-citrulline/NO· cycle. Thus, the correlation between both enzymes and the induction of nitrosative stress prompted us to study the contribution of ASS to the pathogenesis ALD using in vivo models of ethanol binge and chronic ethanol drinking.

7C) and Pgc-1α (Fig. 7D) messenger RNAs (mRNAs) more in WT than in Ass+/− mice and no differences were observed in Acc mRNA (not shown). CPT-I is the rate-limiting enzyme in fatty acid catabolism for the conversion of long-chain fatty acids into long-chain acylcarnitines, whereas CPT-II is responsible for the release

of long-chain fatty acids from carnitine, inside the mitochondrial matrix, for fatty acid β-oxidation. 21 Although no changes were observed by ethanol binge drinking (not shown), Cpt1 mRNA (Fig. 7E) and CPT-II protein (Fig. 7F) were induced in chronic ethanol feeding in both WT and Ass+/− mice. The ratio of free carnitine (C0) to long-chain acylcarnitine (C16+C18) is an indicator of CPT-I activity. Ass+/− mice had higher CH5424802 CPT-I activity (lower C0/C16+C18 ratio) (control group: 32.7 ± 12.2; ethanol group: 31.2 ± 5.8) compared Y-27632 solubility dmso with WT mice (control group: 52.8 ± 15.6; ethanol group: 56.0 ± 19.7) but chronic ethanol feeding did not affect CPT-I activity (P < 0.05 for Ass+/− versus WT). However, CPT-II protein expression was significantly

increased by ethanol feeding in WT mice compared with Ass+/− mice (Fig. 7F); hence, fatty acid β-oxidation was impaired in chronic ethanol-fed Ass+/− mice. Thus, although Ass+/− mice may have efficient fatty acid transport into the mitochondria for β-oxidation, the decrease in CPT-II under chronic ethanol drinking impaired the efficiency of this pathway, leading to fat accumulation. Up-regulation of NOS2 along with generation of RNS plays a major role in alcohol-induced liver click here injury. 22 The overwhelming research on the production of NO· has been focused on the different isoforms of NOS. However, a renewal of interest in the regulation of ASS has recently emerged as a result of its possible rate-limiting

role for high-output NO· synthesis. 2 Using an integrated proteomics and systems biology approach we identified NOS2 along with ASS—the rate-limiting enzyme in the urea and L-citrulline/NO· cycles—as significantly coinduced under chronic ethanol consumption in rodents, which was also validated in human samples. In addition, ASS, ASL, ARG1, and 3-NT residues were up-regulated in both hepatocytes isolated from chronic ethanol-fed rats and in ALD and cirrhosis patients. Moreover, NOS2 was regulated by altering ASS expression in hepatocytes. Treatment with L-citrulline, an inducer of ASS, increased the expression of both ASS and NOS2, whereas downregulation of ASS by siRNA or other inhibitors significantly reduced NOS2 expression. Because the urea cycle is key for hepatic amino acid content, this result suggested that ASS may control NOS2 by modulating substrate availability in the L-citrulline/NO· cycle. Thus, the correlation between both enzymes and the induction of nitrosative stress prompted us to study the contribution of ASS to the pathogenesis ALD using in vivo models of ethanol binge and chronic ethanol drinking.

In our dataset, a threshold concentration of 370 pg/mL revealed the optimal combination of specificity (80%) and sensitivity (56%) in predicting SVR patients. We then determined our optimal IP-10 level to correctly predict both SVR as well as nonresponse. A threshold value of 550 pg/mL yielded the

highest rate of true positives or negatives (69%), and correlated well with the 600 pg/mL cutoff (68% true positives or negatives predicted in our dataset). Finally, logistic regression analysis Ibrutinib datasheet of pretreatment IP-10 concentrations enabled fitting the probability of SVR for specific IP-10 levels measured in individual patients, and demonstrated a highly significant effect of IP-10 (P< 0.0001; Supporting Fig. 1, gray curve). When comparing pretreatment IP-10 serum levels of CA and AA patients, no significant differences were observed in separate analyses of responders (P = 0.75) and nonresponders (P = 0.97) (Table 1).

The significant (P = 0.015) difference in baseline serum IP-10 level between CA and AA patients that was observed in the overall FK506 molecular weight study cohort can most likely be explained by the unbalanced composition of the cohort (IFN treatment response rate in the CA subgroup was 75% versus 40% in the AA subgroup). The highly significant difference in IP-10 serum level between responders and nonresponders to IFN therapy was found both in CA and AA patients (Table 1). Logistic regression analyses of baseline IP-10 levels were used to generate treatment response curves for CA and AA patients (Supporting Fig. 1). The response curves for AA and CA patients revealed a significant effect of both IP-10 (P< 0.0001) and race (P< 0.0001), but no significant interaction between IP-10 and race (P = 0.08). Of the 210 patients genotyped, 30% were CC, 49% were CT, and 21% were TT. A significant association between IL28B

check details genotype and treatment response was observed: corresponding SVR rates were 87% for CC, 50% for CT, and 39% for TT (P< 0.0001) (Table 2). For CA patients, 49% were CC with an SVR of 91%, 41% were CT with an SVR of 67%, and 10% were TT with an SVR of 45% (P< 0.001). For AA patients, only 9% were CC with an SVR of 67%, 58% were CT with an SVR of 35%, and 33% were TT with an SVR of 36% (P = 0.20). Mean serum IP-10 levels were similar for all patients regardless of IL28B genotype both in CA patients (P = 0.27) and AA patients (P = 0.58) (Fig. 2). This lack of correlation between serum IP-10 and IL28B genotype indicates that the associations with SVR observed for both of these markers are independent. Using the 600 pg/mL cutoff for pretreatment IP-10 levels, the SVR rate for our cohort of patients with both serum IP-10 and IL28B genotype data available (n = 210) was 69% for those with a low IP-10 level (<600 pg/mL) and 35% for those with a high IP-10 level (>600 pg/mL) (P< 0.0001).

In our dataset, a threshold concentration of 370 pg/mL revealed the optimal combination of specificity (80%) and sensitivity (56%) in predicting SVR patients. We then determined our optimal IP-10 level to correctly predict both SVR as well as nonresponse. A threshold value of 550 pg/mL yielded the

highest rate of true positives or negatives (69%), and correlated well with the 600 pg/mL cutoff (68% true positives or negatives predicted in our dataset). Finally, logistic regression analysis Selleckchem Selumetinib of pretreatment IP-10 concentrations enabled fitting the probability of SVR for specific IP-10 levels measured in individual patients, and demonstrated a highly significant effect of IP-10 (P< 0.0001; Supporting Fig. 1, gray curve). When comparing pretreatment IP-10 serum levels of CA and AA patients, no significant differences were observed in separate analyses of responders (P = 0.75) and nonresponders (P = 0.97) (Table 1).

The significant (P = 0.015) difference in baseline serum IP-10 level between CA and AA patients that was observed in the overall buy Ferrostatin-1 study cohort can most likely be explained by the unbalanced composition of the cohort (IFN treatment response rate in the CA subgroup was 75% versus 40% in the AA subgroup). The highly significant difference in IP-10 serum level between responders and nonresponders to IFN therapy was found both in CA and AA patients (Table 1). Logistic regression analyses of baseline IP-10 levels were used to generate treatment response curves for CA and AA patients (Supporting Fig. 1). The response curves for AA and CA patients revealed a significant effect of both IP-10 (P< 0.0001) and race (P< 0.0001), but no significant interaction between IP-10 and race (P = 0.08). Of the 210 patients genotyped, 30% were CC, 49% were CT, and 21% were TT. A significant association between IL28B

see more genotype and treatment response was observed: corresponding SVR rates were 87% for CC, 50% for CT, and 39% for TT (P< 0.0001) (Table 2). For CA patients, 49% were CC with an SVR of 91%, 41% were CT with an SVR of 67%, and 10% were TT with an SVR of 45% (P< 0.001). For AA patients, only 9% were CC with an SVR of 67%, 58% were CT with an SVR of 35%, and 33% were TT with an SVR of 36% (P = 0.20). Mean serum IP-10 levels were similar for all patients regardless of IL28B genotype both in CA patients (P = 0.27) and AA patients (P = 0.58) (Fig. 2). This lack of correlation between serum IP-10 and IL28B genotype indicates that the associations with SVR observed for both of these markers are independent. Using the 600 pg/mL cutoff for pretreatment IP-10 levels, the SVR rate for our cohort of patients with both serum IP-10 and IL28B genotype data available (n = 210) was 69% for those with a low IP-10 level (<600 pg/mL) and 35% for those with a high IP-10 level (>600 pg/mL) (P< 0.0001).

Studies addressing physiological races, mating types and RAPD analysis were carried out on 82 isolates of P. xanthii sampled in 34 cucurbit www.selleckchem.com/products/AP24534.html fields from Apulia (southern Italy). A set of eight differential melon genotypes were used to discriminate physiological races of the fungus. In particular, 13% of the tested isolates belonged to physiological race 2 FR, 30% to race 5, 25% to race 1, 10% to race 3, 5% to race 4, 1% to race 0 and 16% to undetermined races,

whereas only one of the two mating types (MAT1-2) of the fungus was detected, and RAPD analysis showed a quite broad variation within fungal isolates. “
“Sensitivity of 159 isolates of Zymoseptoria tritici collected from durum wheat fields in Tunisia in 2012 was analysed towards pyraclostrobin, fluxapyroxad, epoxiconazole, metconazole, prochloraz and tebuconazole using microtiter tests. All isolates Alvelestat molecular weight were found to be highly sensitive to pyraclostrobin with EC50 <0.01 mg/l with the exception of three isolates from the same field with higher EC50 values (>0.5 mg/l). These three isolates carried a mutation in

the cytochrome b gene encoding the G143A substitution. This is the first report of quinone outside inhibitors (QoI) resistance in Z. tritici in Tunisia. Sensitivity towards r fluxapyroxad was in a narrow range with EC50 values ranging between 0.013 and 0.125 mg/l, which can serve as baseline sensitivity data for the future. Demethylation inhibitors sensitivity varied across a broad range with the data indicating a slight shift in sensitivity when compared to a previous study on the 2010 population. No highly sensitive strains were isolated from samples from fields, which had received click here three or four DMI applications. “
“AFLP analysis was carried out to assess genetic variability

and determine the population structure of the sugarcane rust Puccinia melanocephala in northwest Argentina. Molecular data were also used to clarify whether genetic variation was correlated with host variation and/or the geographic distribution of the disease. Bulk rust uredospores were collected in the field, and both the geographical area and the infected host sugarcane cultivar were recorded. A total of 538 AFLP markers generated with 20 primer combinations were used to perform the genetic analysis. The percentage of polymorphic loci was quite high (85.7%), considering that P. melanocephala only reproduces asexually. Cluster analysis (UPGMA) and principal co-ordinate analysis (PCoA) grouped populations from distinct geographic and host origins, suggesting that neither geographical region nor sugarcane variety constrains the relationships among the populations. This finding was corroborated by a lack of significant correlation between genetic distance and geographic distance (r = 0.057; P = 0.285).

As illustrated in Fig. 3F, IFN-γ treatment inhibited the HM781-36B cost expression of α-SMA and TGF-β1 in 2-week CCl4 mice but not in 10- or 12-week CCl4 mice. STAT1 was phosphorylated in isolated HSCs of the IFN-γ–treated 2-week group, but not in HSCs of the IFN-γ–treated

10- or 12-week groups. Finally, expression of SOCS1 protein, a negative regulator of STAT1,16 in HSCs was up-regulated in 2-week CCl4 mice after IFN-γ treatment. HSCs isolated from 10- or 12-week CCl4 mice had higher basal levels of SOCS1 protein than those from 2-week CCl4 mice, which were not further up-regulated after IFN-γ treatment (Fig. 3F). To further understand the underlying mechanism of suppressed NK cell function observed in advanced liver fibrosis, day 4 (D4) (early activated) or day 8 (D8) (intermediately activated) cultured HSCs were cocultured with liver NK cells for 24 hours. After coculturing with HSCs, IFN-γ

production by NK cells was significantly Selleckchem Linsitinib increased in coculturing with D4 HSCs or with D8 HSCs. Higher levels of IFN-γ were observed when cocultured with D4 HSCs than those with D8 HSCs (Fig. 4A). Coculture studies of IFN-γ–deficient cells suggest that the source of IFN-γ production is from NK cells (Fig. 4B). Furthermore, incubation with NKG2D neutralizing antibody diminished IFN-γ production in the coculture experiments (Fig. 4C), suggesting that activated HSCs induce IFN-γ production by NK cells through an NKG2D-dependent mechanism. Expression of TGF-β protein was significantly higher in D8 HSCs compared with D4 HSCs (Fig. 4D). Because TGF-β is a potent inhibitor for NK cells,7, 17 we hypothesized that TGF-β1 produced by cocultured HSCs may inhibit IFN-γ production and cytotoxicity of NK

cells. As illustrated in Fig. 4E, incubation with TGF-β neutralizing antibody markedly enhanced NK cell cytotoxicity against D8 HSCs as well as D4 HSCs (albeit to a lesser extent). In addition, selleck products TGF-β antibody treatment increased IFN-γ production by NK cells when cocultured with D8 HSCs but did not affect IFN-γ production in coculture experiment with D4 HSCs (Fig. 4F). Furthermore, the addition of TGF-β1 ligand suppressed the cytotoxicity of NK cells against D4 and D8 HSCs (Supporting Fig. 4). Although IFN-γ–mediated STAT1 activation has been well documented in HSCs,6, 11, 12, 18 the aforementioned experiments show that IFN-γ activation of STAT1 in HSCs from livers with advanced liver fibrosis appears to be disrupted (Fig. 3F). To study the underlying mechanisms responsible for the disruption, IFN-γ–mediated inhibitory cell proliferation and activation of STAT1 were compared on D4 and D8 HSCs. As shown in Fig. 5A, IFN-γ treatment suppressed cell proliferation of D4 HSCs, but not D8 HSCs. Western blotting showed that IFN-γ induced STAT1 activation (phosphorylated STAT1) in D4 HSCs, but this activation was markedly attenuated in D8 HSCs (Fig. 5B and Supporting Fig. 5A).

Caruntu – Advisory Committees or Review Panels: MSD, Abbvie, Jans-sen, BMS, Roche Won Young Tak

– Advisory Committees or Review Panels: Gilead Korea; Grant/ Research Support: SAMIL Pharma; Speaking and Teaching: Bayer Korea Magdy selleck screening library Elkashab – Advisory Committees or Review Panels: GSK INC, GILEAD SCIENCES INC, Roche Canada; Speaking and Teaching: GILEAD SCIENCES INC Wan-Long Chuang – Advisory Committees or Review Panels: Gilead, Roche, Abbvie, MSD; Speaking and Teaching: BMS Joerg Petersen – Advisory Committees or Review Panels: Bristol-Myers Squibb, Gilead, Novartis, Merck, Bristol-Myers Squibb, Gilead, Novartis, Merck; Grant/ Research Support: Roche, GlaxoSmithKline, Roche, GlaxoSmithKline; Speaking and Teaching: Abbott, Tibotec, Merck, Abbott, Tibotec, Akt inhibitor Merck Eduardo B. Martins – Employment: Gilead Sciences, Inc.; Stock Shareholder: Gilead Sciences, Inc. Phillip Dinh – Employment: Gilead Sciences; Stock Shareholder: Gilead Sciences Amoreena C. Corsa – Employment: Gilead Sciences Inc.; Stock Shareholder: Gilead Sciences Inc. Prista Charuworn – Employment: Gilead Sciences; Stock Shareholder: Gilead Sciences Mani Subramanian – Employment: Gilead Sciences John G.

McHutchison – Employment: Gilead Sciences; Stock Shareholder: Gilead Sciences Maria Buti – Advisory Committees or Review Panels: Gilead, Janssen, Vertex, MSD; Grant/Research Support: Gilead, Janssen; Speaking and Teaching: learn more Gil-ead, Janssen, Vertex, Novartis Giovanni B. Gaeta – Advisory Committees or Review Panels: Janssen, Merck, Abbvie, Roche; Speaking and Teaching: BMS, Gilead George V. Papatheodoridis

– Advisory Committees or Review Panels: Janssen, Abbvie, Boehringer Ingelheim, Novartis, BMS, Gilead, Roche, MSD; Consulting: Roche; Grant/Research Support: BMS, Gilead, Roche, Abbvie, Janssen; Speaking and Teaching: Janssen, Novartis, BMS, Gilead, Roche, MSD, Abbvie Robert Flisiak – Advisory Committees or Review Panels: Gilead, Merck, Roche, Bristol Myers Squibb, Janssen, Novartis, Abbvie; Grant/Research Support: Roche, Bristol Myers Squibb, Janssen, Novartis, Gilead, Vertex, Merck; Speaking and Teaching: Janssen, Merck, Roche, Bristol Myers Squibb, Gilead, Abbvie Henry Lik-Yuen Chan – Advisory Committees or Review Panels: Gilead, MSD, Bristol-Myers Squibb, Roche, Novartis Pharmaceutical; Speaking and Teaching: Echosens, Abbvie The following people have nothing to disclose: Sang Hoon Ahn, Fehmi Tabak, Rajiv Mehta PURPOSE: CDC and the U.S.

A TDR was attached to the belt to position it as close to the dugong as possible. The length of the tether and the buoyancy of the cylinder allowed the satellite antenna to be exposed above the NVP-LDE225 water when a dugong was in water <3 m or swimming near the surface very slowly, thereby maximizing uplink opportunities. The whole tracking apparatus was retrieved as described in Sheppard et al. (2006). Data from TDRs were decoded using software provided by the manufacturer.

We used custom software to preprocess the data by identifying the level of the water surface (zero-offset) and removing dugong spikes, i.e., biologically implausible rapid changes in depth (Appendix S1A, Hagihara et al. 2011). Dive data collected within 5 min of a GPS and QFP fix were then subsampled (10 min in total, 5 min before and PI3K inhibitor after each fix, Appendix S1B). To avoid any potential postrelease behavioral responses, we only used data recorded ≥3 d after the day of tag deployment. However, no apparent changes in diving patterns were observed in the

3 d after deployment; capture and handling did not appear to trigger a flight response and dugongs stayed in the vicinity of the capture area (Sheppard et al. 2006; RH, unpublished data). Bathymetric models and tidal records (Maritime Safety Queensland, Department of Transport and Main Roads) were used to estimate the water depth at the time and geographic location for each fix. The bathymetric models were of 100 m resolution and generated by Sheppard (2008) in Hervey Bay and by Beaman (2010) in Moreton Bay. The depth at the location of each fix was identified by importing the bathymetric models and location fixes into ArcGIS 9.3.1 (Environmental find more Systems Research Institute 2009). Tidal heights were added or subtracted to the depth on the bathymetric charts to calculate the water depth experienced by the dugong at the time of each fix. Tidal range in Hervey Bay was ca. 4 m and in Moreton Bay 2.6 m during the deployment periods. We assumed

that estimated water depths remained constant for the 10 min around each fix. Previous experiments using dugong replicas found that the availability of dugongs varies with levels of turbidity and sea state (Pollock et al. 2006). Following Pollock et al. (2006), we examined the proportion of time dugongs spent in two detection zones: 0–1.5 m of the surface for turbid water and Beaufort sea state 3 (rougher conditions with very few whitecaps); and 0–2.5 m of the surface for clear water and sea state ≤2 (calm conditions with no whitecaps). We assigned “1” when a depth measurement was recorded within each of the detection zones and “0” when a depth measurement was recorded outside of the detection zone. The proportion of time dugongs spent in each detection zone was calculated by the sum of these numbers divided by the number of depth records.