There were no significant differences between models in Table 3 (offspring level selleck CHIR99021 covariates) and their respective models (offspring level and parental level covariates) in Tables 6 and and77 (Wald chi-square = 42.6, p = .21). Table 6. Association Between Male Offspring Regular Smoking, Nicotine Dependence, and Suicidal Behavior in Males Adjusted for Father and Mother Suicidea, Fatherb and Mother Nicotine Dependencecd, and Father and Mother Conduct Disorder and Adjusted for Offspring … Table 7. Association Between Female Offspring Regular Smoking, Nicotine Dependence, and Suicidal Behavior in Females Adjusted for Father and Mother Suicidea, Fatherb and Mother Nicotine Dependencecd, and Father and Mother Conduct Disorder and Adjusted for Offspring …

Discussion In unadjusted analysis, in a cohort of 1,919 male and female offspring, we observed that increasing involvement in smoking was associated with increasing suicidal behavior such that ever smoking was associated with ideation among females and with ideation + plan among males. Regular smoking was more strongly associated with this measure of suicidal behavior and was significantly associated with ideation + plan among males and with ideation + plan + attempt or ideation + attempt in females. Last, nicotine dependence increased in strength of association from ideation through ideation + plan + attempt or ideation + attempt. Adjustment for offspring covariates attenuated the association between smoking status and levels of suicidal behavior; however, adjustment for paternal and maternal familial vulnerability did not mediate the effect between smoking status and levels of suicidal behaviors.

The familial contributions to suicidal behavior as well as the familial contribution to smoking were controlled in the present study. Though we are unable to determine the temporal direction of effect in the present design, we can conclude that the association exists above and beyond familial contributions to smoking and suicidal behavior. We also found the association remained after controlling for offspring conduct disorder, major depression, alcohol abuse/dependence, and illicit drug abuse/dependence but were attenuated, suggesting partial mediation by AV-951 these offspring level variables. The present design provides some of the strongest evidence to date that ever smoking, regular smoking, and nicotine dependence are all independent correlates of suicidal behavior above the influence of familial vulnerability. Our results extend the growing literature that establishes the association between smoking and suicidal behaviors (Breslau et al., 2005; Clarke et al., 2010; Kessler et al., 2009).

Responder controll table 1 by PET-CT scan was planned to be performed 4 weeks after initiation of the therapy. After 2 weeks under ambulatory pharmacological therapy the patient presented in the emergency room with an acute upper gastrointestinal bleeding. CT confirmed a dramatic bleeding from the upper GI tract necessitating mass blood transfusion (Fig. (Fig.1).1). Tumor size decreased to 7 �� 8 �� 12 cm within only 2 weeks of imatinib treatment. An angiographic CT showed the diffuse tumor bleeding supplied by the gastroduodenal artery and some branches of the superior mesenterial artery. The diffuse bleeding forbade a coiling of the vessels. During the emergency laparotomy an encapsulated tumor mass could be identified, originating from the descendent part of the duodenum and reaching both the pancreatic caput and the right flexure of the colon.

Obviously the giant tumor had led to a bleeding by arrosion of peripancreatic vessels. After ligation of the vessels supplying the mass a partial pancreaticoduodenectomy (Traverso-Longmire) was performed to resect the tumor (Fig. (Fig.2).2). Additionally a resection of the right hemicolon was performed due to tumor infiltration of the right curvature of the colon. Continuity was reconstructed by gastrojejunostomy (Traverso-Longmire) on the one hand and an end-to-side-pancreaticojejunostomy on the other hand. An ileotransversostomy was performed to reconstruct the gastrointestinal passage. Figure 1 Transversal (left) and coronal (right) CT scans of the abdomen reveal a cystic and necrotic tumor cavity 2 weeks after initiation of imatinib therapy.

Figure 2 Cross-section of the surgical specimen showing the tumor (asterisk) infiltrating the duodenal wall (arrows). Upon macroscopic examination the specimen showed a partially necrotic mesenchymal mass with a diameter of 9 cm, an infiltration of the duodenal wall leading to ulceration and perforation, an infiltration of the pancreas and two peripankreatic tumor islands (Fig. (Fig.2).2). There were no signs of metastases in locoregional lymphnodes. Histological examination of the tumour tissue revealed the typical appearance of a GIST composed of cells with spindle-shaped nuclei (Fig.(Fig.3D).3D). Immunohistochemically the tumour cells showed an expression of Vimentin (Fig. (Fig.3C)3C) and CD117 (Fig. (Fig.

3E),3E), a focal expression of CD34, smooth-muscle-actin (not shown) and a nuclear expression of the proliferation-associated Ki-67-antigen in approximately 5-10% of the tumour cells (Fig. (Fig.3F).3F). The tumour was negative for Batimastat S-100 and Keratin (not shown). Figure 3 A) GIST infiltrating adjacent Pancreas; asterisk = pancreatic glands and ducts, arrows = GIST [hematoxylin-eosin staining, Original magnification 50��]. B) GIST perforating into the lumen of the duodenum; asterisk = mucosa of the duodenum, arrowheads …

Henning Walczak (Tumor Immunology Unit, Division of Medicine, Imperial College, www.selleckchem.com/products/Bortezomib.html London, UK). Concanamycin A (CMA) and mevastatin were purchased from Sigma, while zoledronate was from Novartis Pharma, Basel, Switzerland. Generation of Polyclonal V��9V��2 T Cell Lines Polyclonal V��9V��2 T cell lines were generated by first enriching PBMC using a �æ� T cell isolation kit (Miltenyi Biotec, Bergisch Gladbach, Germany), followed by sorting single V��9V��2 T cells through a FACSAria (BD Biosciences) with specific mAbs. Cells (2��103) were then cultured into each well of round-bottom, 96-well plates containing 2��104 irradiated (40 Gy) allogeneic PBMC, 2��103 irradiated (70 Gy) EBV-transformed allogeneic B cells, 0.5 ��g/ml PHA (Sigma), and 200 U/ml recombinant interleukin 2 (Proleukin, Novartis Pharma).

Growing lines were expanded in 200 U/ml IL-2 and restimulated every 2 weeks. Usually, cells were collected after 4�C6 weeks of culture to be used for functional assays in vitro. Cytotoxic Assay Target colon CIC (105 cellsml) were pre-treated with 5-FU (2.5�C250 ��g/ml), DXR (0.025�C2.5 ��M) or zoledronate (0.5 ��M) for 24, 48 or 72 hrs. Cells were extensively washed in PBS and stained with CFSE (Merck, Milano, Italy) as follows: 50 ��l of CFSE were added to 1 ml of target sphere cell suspension (5��105 cells/ml) in PBS to obtain the final concentration of 2.5 ��M CFSE. The cells were incubated for 10 minutes at 37��C and gently mixed every 5 min. At the end of incubation, 1 ml of FBS was added to the cell suspension to stop the staining reaction and the cells were centrifuged at 600 g for 5 min at room temperature, washed twice with cold PBS and resuspended in serum-free medium.

V��9V��2 T cell lines were resuspended at the final concentrations of 106 and 2.5��106 cells/ml, were added to CFSE-stained target colon CICs (1��105) and co-cultures were maintained for 6 hrs a 37��C in presence of 5% of CO2. At the end of the incubation period, the cells were washed with PBS Dacomitinib and stained with 20 ��l of Propidium Iodide (PI, Sigma, 1 ��g/ml) for 10�C15 min in ice. Finally 100 ��l of cold PBS were added before acquisition on a FACSCalibur cytometer (BD Biosciences). The calculation of cytolytic activity was based on the degree of reduction of viable target cells with the ability to retain CFSE and exclude PI (CFSEhigh PI?), according to reference [27]. Blocking agents were used to evaluate the mechanisms of V��9V��2 T cell-mediated cytotoxicity of colon CICs. To evaluate the contribution of mevalonate metabolites tumor target cells were treated with mevastatin (25 ��M for 2 h) a selective upstream inhibitor of the mevalonate pathway.

No cases of NN displayed TaqMeth V over the optimal http://www.selleckchem.com/products/Sorafenib-Tosylate.html cut-offs for B4GALT1 and OSMR (100% specificity). The sensitivities of B4GALT1 and OSMR were 83% (25/30) and 80% (20/25), respectively. These results indicate that B4GALT1 and OSMR harbor cancer-specific methylation in CRC with high frequency. Therefore, we focused on B4GALT1 and OSMR for further study. We examined the methylation status of B4GALT1 and OSMR in 100 new pairs of CRC (PT) and corresponding normal (PN) tissues by TaqMan-MSP analysis (Fig. 1C). The TaqMeth V in PT ranged from 0 to 370.70 (median value 3.70) for B4GALT1 and from 0 to 749.27 (median value 59.24) for OSMR. The value in PN ranged from 0 to 9.70 (median value 0.26) for B4GALT1 and to 190.68 (median value 1.54) for OSMR. The overall methylation levels of B4GALT1 and OSMR detected in PT (22.

33��48.69 for B4GALT1 and 116.83��151.52 for OSMR, mean��SD, n=100) were also significantly higher than those in PN (0.90��1.37 for B4GALT1 and 6.49��21.52 for OSMR, mean��SD, n=100) (P<0.001). At the optimal cut-offs (values, 3.87 for B4GALT1 and 22.01 for OSMR) calculated from the ROC analysis (PT vs. PN) (Figure S4B), the specificity of the two genes was over 96%. The sensitivities of B4GALT1 and OSMR were 49% (49/100) and 80% (80/100), respectively. Taken together, B4GALT1 and OSMR were frequently methylated in primary CRC tissues but displayed absent or low levels of methylation in corresponding normal tissues. Next, we performed a blinded analysis of gene methylation analysis in stool DNA collected from patients with or without colon cancer.

The clinical status of the patients was not revealed until the analysis was completed. We also examined hypermethylation of SFRP1 as a positive control for detection of gene methylation in stool DNA [18]. The results of ROC analysis in the stool DNA are shown in Figure 2. Table 4 shows the sensitivity/specificity of each gene for colon cancer detected in stool DNA samples. SFRP1 methylation was not detected in patients with endoscopically normal colon (0%, 0/15), and was found in 55% (11/20) of CRC patients, at the optimal cut-off value calculated from the ROC analysis. Methylation of B4GALT1 and OSMR was detected in 64% (9/14) and 80% (16/20) of stool DNA samples from CRC patients, respectively. When methylation of SFRP1 and OSMR was combined, the sensitivity was 60% (12/20), and the specificity was 100% (0/15).

Discrimination between patients without cancer and CRC patients by SFRP1 or OSMR methylation was statistically significant (P<0.01). Figure 2 ROC curve Carfilzomib analysis in stool DNA. Table 4 Gene methylation detected in stool from colon cancer or non-cancer patients. OSMR methylation was tested in a blinded fashion in one more independent set of stool samples (no overlap with the samples in Table 4). Stool DNA from healthy control subjects who had no visual abnormalties in colonoscopy were included for this study.

Median IPI values were compared with the Wilcoxon two-sample test. Analyses were performed using SAS Proc Mixed selleck chemicals procedure. p Values less than .05 were considered to be statistically significant. Bonferroni corrections were applied to adjust for Type I error rates resulting from multiple comparisons, as appropriate. Generalized estimating equations, using SAS Proc Genmod, were used to examine which group (SS vs. CON) was more likely to have IPIs ��6 and ��10 s. We used random effects logistic regression analyses to compare differences between rapid smoking and non-rapid smoking groups. All statistical analyses were performed using SAS v. 9.1. Results Characteristics of the Sample Baseline clinical and sociodemographic data for the sample are described in Table 1.

SS were older and more likely to be men compared with CON (both p < .01). Although both groups smoked the same average number of cigarettes per day, SS had higher baseline expired CO (23.1 vs. 19.5; p < .05). On other characteristics including FTND total score, age of first smoking, number of past quit attempts, race/ethnicity, and education, there were no differences between groups. Serum nicotine and cotinine levels were significantly higher in SS compared with CON (31.3 vs. 24.4 ng/ml and 450.9 vs. 303.9 ng/ml, respectively; both p < .001). Mean 3HC/cotinine ratios were not different between groups (mean 0.54 vs. 0.49; p = .49). Table 1. Characteristics of the Sample Rapid Smoking As reported in Williams et al. (2011), SS differed significantly from CON on measures of smoking topography.

During the assessment period, data from 38,691 individual puffs (2,966 total cigarettes) were collected. SS smoked an average of 2.8 more puffs per cigarette than CON (both p < .001) in addition to smoking more cigarettes in the 24-hr testing session (mean 21.0, SS vs. 16.0, CON). The mean time between puffs or IPI was significantly shorter in SS (16.0 vs. 22.6 s; p < .001). Due to the skewed distribution of the data, we also examined the median IPI and found that it was shorter in SS than CON (9.3 vs.15.7 s; p < .001; Figure 1). Average total time to finish smoking a cigarette was shorter in SS compared with CON (4.5 vs. 5.5 min; p < .001). Figure 1 Distribution of interpuff interval scores. We examined the frequency of IPIs ��6 s. SS were twice as likely to have IPIs ��6 s than CON (OR = 2.32, 95% CI = 1.68, 3.20; p < .001). SS were also more likely to have IPIs Brefeldin_A ��10 when compared with CON (OR = 2.69, 95% CI = 2.24, 3.22; p < .001).

Given the separate and combined health effects of smoking and obesity, these findings have kinase assay several implications. First, these data document that obese treatment-seeking smokers are concerned about the weight gain that commonly accompanies efforts to quit smoking. Indeed obese smoker are more concerned than are their normal weight or overweight peers, and endorse feeling unable to manage weight without cigarettes. Thus, treatment approaches designed to address concerns about weight gain following smoking cessation (Levine, Marcus, & Perkins, 2003a; Levine, et al., 2010; Perkins, et al., 2001) may be a useful adjunct to cessation interventions among individuals of all weight categories. Second, the present findings are consistent with previous data on smoking and obesity.

For example, smoking may help to control a tendency toward overeating or binge eating as smokers with a history of binge eating report greater weight gain following cessation than those without a history of binge eating (White, Masheb, & Grilo, 2010) and were less successful in cessation treatment (White, Peters, & Toll, 2010). It also has been postulated that the rewarding value of food, which differs in obese individuals, relates to smoking behavior (Volkow, Wang, & Baler, 2011). A tendency to overeat after quitting or an increased experience of food as rewarding after smoking cessation suggests that obese smokers�� heightened concerns about weight gain postcessation may be justified. Another implication of the current data is that gender remains important in the development of intervention approaches for weight and smoking.

Across all weight categories, women endorsed stronger concerns about smoking-related weight gain. Thus, women may be more likely to benefit from strategies to address postcessation weight gain concerns than men. However, although weight concerns may be relevant to smokers of all pretreatment weight categories, and despite the fact that women endorsed stronger concern than did Batimastat men, among quitline users, depressive symptoms, which have been associated with cessation-related weight gain concerns (Levine, Marcus & Perkins, 2003b), were not associated with weight status or gender. There are several limitations to the current report. First, data were collected via telephone survey. Thus, the measure of depressive symptoms was brief and the data presented represent responses to screening questions rather than validated depressive symptom questionnaires. Second, weight and height were assessed solely by self-report. Although weight may be underreported by telephone (Gorber, Tremblay, Moher & Gorber, 2007), data suggest a reasonable correlation between self-report and clinic measured weight (Jeffery, 1996).

In the school context, only classmates�� modeling of smoking positively predicted selleck chemicals llc adolescent smoking, while none of the three social bond variables either moderated the modeling effect or had main effects on youth smoking. Exosystem model The addition of the set of neighborhood variables did not make a significant contribution to the model (Table 3, exosystem). Nevertheless, neighbors�� modeling of smoking was positively associated with the youth smoking trajectories. None of the neighborhood social bond variables predicted smoking or moderated the effect of neighbors�� smoking. With the addition of the neighborhood variables, all the significant relationships between adolescent smoking and the family, peer, and school context variables remained unchanged, although some significance levels were attenuated.

The interaction between social regulation and smoking modeling in the school context, however, became significant, such that the modeling effect was decreased when social regulation was greater rather than lower. The isolated nature of this change suggests the possibility that it was due to chance. Mesosystem models The cross-conte
Smokers hold a variety of beliefs, called expectancies, about the consequences of smoking including affect management (e.g., reducing negative affect and boredom and enhancing positive affect), weight management, and health effects. Reviews of research on smoking expectancies suggest that they play a significant role in smoking initiation, maintenance, and relapse (see Brandon, Juliano, & Copeland, 1999; Kassel, Stroud, & Paronis, 2003).

Smoking expectancies are formed in childhood prior to personal experience with smoking (Chassin, Presson, Sherman, & Edwards, 1991; Copeland Cilengitide et al., 2007) and predict smoking initiation or increases in smoking in adolescents and young adults (Cohen, McCarthy, Brown, & Myers, 2002; Wahl, Turner, Mermelstein, & Flay, 2005). Further, smoking expectancies differ by current smoking status (Brandon & Baker, 1991; Jeffries et al., 2004; Mullennix, Kilbey, Fisicaro, Farnworth, & Torrento, 2003), are associated with motivation to quit smoking (Pulvers et al., 2004), predict smoking cessation attempts (Rose, Chassin, Presson, & Sherman, 1996), and predict continued cessation and lapse or relapse after a quit attempt (Copeland, Brandon, & Quinn, 1995; Gwaltney, Shiffman, Balabanis, & Paty, 2005; Rose et al., 1996; Shadel & Mermelstein, 1993; Wetter et al., 1994). While expectancies are associated with success at smoking cessation, little is known about the ways that smoking expectancies may change over time, concomitant with changes in smoking status.

The parents or legal guardians of participating children (aged <16 years) gave their written informed consent prior to the collection of stool samples. At the end of the study, free treatment with albendazole (400 mg) was offered to selleck chemicals Ponatinib all study participants, and individuals infected with Schistosoma haematobium and/or S. mansoni were treated with praziquantel (40 mg/kg of body weight) according to the national treatment guidelines of C?te d’Ivoire. Field and laboratory procedures. The purpose and procedures of the study were explained to all participants. After written informed consent was obtained, stool collection containers were distributed, and participants were invited to provide a lime-sized sample of their own morning stool the following day. Stool samples were collected between 08:00 and 10:00 a.

m. and transferred to a laboratory in Taabo Cit��, 28 km east of L��l��bl��. All stool samples were examined with standard methods (Kato-Katz, Baermann, and Koga agar plate) (14, 18, 22) for soil-transmitted helminths, including Strongyloides stercoralis, and for S. mansoni infections. Sufficiently large stool samples were preserved in 5% formaldehyde and subjected to the Flotac-400 dual technique and the FECT to (i) diagnose helminth infections for prevalence assessment in the frame of the cross-sectional epidemiologic baseline survey for the Taabo HDSS and (ii) diagnose protozoon infections for the validation of the Flotac-400 dual technique for diagnosis of human intestinal protozoon infections. The latter objective is the primary focus of the work presented here.

First, about half of the preserved stool samples were utilized for preliminary investigations to standardize the Flotac-400 preparation protocol for the diagnosis of intestinal protozoa. Second, the remaining stool samples were analyzed by Flotac-400 and FECT, adhering to the standard protocols described below. The diagnostic accuracy of both techniques was assessed. Stool preparation procedures for intestinal protozoon diagnosis using Flotac-400 and FECT were as follows. A portion of 2 to 3 g of each sample was placed into a tube containing 10 ml of 5% formaldehyde. The stool material was stirred with a wooden spatula, and the tube was vigorously shaken in order to achieve a homogenized suspension of fecal material.

Tubes were labeled with unique identifiers, and the set of formaldehyde-preserved stool samples was forwarded to the Regional Center for Monitoring Parasites (CREMOPAR) in Eboli, Italy. After a preservation time of 10 to 12 weeks at room temperature, the stool samples Cilengitide were prepared and examined under a light microscope (Olympus CX21LED; Volketswil, Switzerland) by an experienced laboratory technician, adhering to standard protocols for FECT and the Flotac-400 dual technique. Each sample was resuspended by shaking and strained through a fine-mesh (250-��m) wire sieve.

In addition to the enzymatic properties of transcription, more than a generation of molecular biologists has elegantly described extensive transcriptional regulation networks controlling key phenotypes [25]. These selleck chem regulatory motifs are sensitive to changes in gene dose [26]. Feedback is an outstanding error-controlled regulator that detects deviations from the norm and implements corrective action. Feed-forward regulation differs in that it anticipates the possible effect of perturbations on the system rather than correcting the perturbation after the deviation occurs. This could operate if cells detect copy number and correct transcription levels before a quantitative error in transcript abundance is evident.

In male embryos, the sex determination hierarchy detects X chromosome number and leads to association of the MSL complex with the X chromosome before zygotic transcription is activated [27], as expected for a feed-forward regulator. However, MSL is selectively bound to transcribed genes [28], which is also consistent with feedback regulation. By examining the response of X chromosome genes to dose in the presence and absence of MSL, we show that X chromosome dosage compensation results from a combination of MSL-dependent feed-forward regulation based on anticipated effects from unbalanced gene dose and a more general and dynamic response to perceived gene dose. The latter could be due to negative feedback, buffering, or both. Results Segmental Aneuploidy in S2 Cells To determine the extent of aneuploidy in S2 cells, we performed next generation sequencing (DNA-Seq) and comparative genome hybridization (CGH).

These data confirmed the predicted male genotype of S2 cells, as the average sequence depth of the X chromosome (reads per kb per million reads, RPKM) was 54% of the autosome RPKM (Figures 1 and and2A2A). Figure 1 S2 cell DNA copy number. Figure 2 Expression at varying copy numbers. We also found that S2 cells exhibit numerous large regions of segmental aneuploidy (Figure 1, Figure S1, Table S1). Stepwise deviations from expected dose covered ~42% (~40.0 Mb) of the autosomes and ~17% (~3.8 Mb) of the X chromosome (Figure S1). The vast majority of the aneuploid segments showed an extra or lost copy. There was high congruence between DNA-Seq and CGH methods.

For example, we determined that >93% of calls for copy numbers between one and five made by DNA-Seq analysis were confirmed by CGH, even when comparing different lots of cells grown under slightly different conditions (Figure S2, Table S2). These data suggest that S2 cells are highly aneuploid but show a reasonably stable genotype. There was much more variability seen when Brefeldin_A copy number was greater than five (30% agreement between methods and cultures). This could be due to failure to call short segmental duplications or to repeat expansion/retraction in different cultures.

Figure 4 The relationship between the expression level of genes which participate in IFN production (TLR7, MyD88, IRAK1, and IRF7) in the liver of CH patients and IL28B genotype. Table 7 Quality of NR-prediction by DLDA with IFN related gene and IL28B polymorphism A.IFN+IL28B. Table 8 Quality of NR-prediction selleck chem by DLDA with IFN related gene only. Discussion Our comprehensive analysis identified 66 genes with expression levels that consistently differed depending on the drug response of 87 CH patients and 5 normal liver specimens (Figure 1). Comparing the gene expression pattern in NR and NL showed the expression levels of 31 genes were significantly different (Table 3). In addition, most genes with expression levels in NR that were higher or lower than in NL, also differed between NR and SVR.

Therefore, it is possible that innate immunity in the early period of HCV infection strongly influences IFN reaction. HCV infection induces the impairment of cell subset number and the function of plasmacytoid dendritic cells (PDC) and natural killer cells [15]. The amount of PDC, which are the most potent producers of antiviral Type-I and III IFN [16], decreased in patients’ peripheral blood [17], however, PDC was trapped in the HCV infected liver tissue. Therapeutic non-responders had increased PDC migration to inflammatory chemokines before therapy, compared with therapeutic responders [18]. This situation resulted in elevated expressions of IFN-related genes in the CH samples and was associated with their inability to eliminate the virus [19].

Inadequate expression of IFN related genes has been associated with several diseases. High expression of ISG can induce a refractory state in IFN therapy [20] and impaired IFN production leads to high risk of HCV-related hepatocarcinogenesis [21]. Lymphocyte IFN signaling was less responsive in patients with breast cancer, melanoma, and gastrointestinal cancer and these defects may represent a common cancer-associated mechanism of immune dysfunction. Alternately, since immunotherapeutic strategies require functional immune activation, such impaired IFN signaling may hinder therapeutic approaches designed to stimulate anti-tumor immunity [22]. In this way, the dysregulation of the IFN system can influence the progression of diseases and decrease curative effects.

Genes which participate in IFN production (TLR7, MyD88, IRAK1, and IRF7) did not show any significant difference in their expression Cilengitide level prior to CH combination therapy, and their level at the clinical outcome (Figure 4A and 4B). However, the gene expression pattern of down-stream IFN pathway genes (IFI27, IFI44, ISG15, MX1, and OAS1) was significantly different among SVR, R, and NR (Table 2). IFN is usually up-regulated in HCV infected cells; however in some cases, the mechanism that controls IFN becomes abnormal, and the expression levels of IFN and ISG remain high without any curative effect [23].