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cells are resistant to multiple inducers of apoptosis. J Immunol.1998,160:1824–1830.PubMed 57. Keller P, Schaumberg F, Fischer SF, Häcker G, Groß U, Lüder CGK:Direct inhibition of cytochrome c -induced caspase activation in vitro by Toxoplasma gondii reveals novel mechanisms of interference with host cell apoptosis. FEMS Microbiology Letters2006,258:312–319.PubMedCrossRef 58. Molestina RE, Payne TM, Coppens I, Myosin Sinai AP:Activation of NF-κB by Toxoplasma gondii correlates with increased expression of antiapoptotic genes and localization of phosphorylated IκB to the parasitophorous vacuole membrane. Journal of Cell Science2003,116(21):4359–4371.PubMedCrossRef 59. Palmer GH, Machado JJ, Fernandez P, Heussler V, Perinat T, Dobbelaere DA:Parasite-mediated nuclear factor kappaB regulation in lymphoproliferation caused by Theileria parva infection. Proc Natl Acad Sci USA1997,94:12527–12532.PubMedCrossRef 60. Sohn KH, Lei R, Nemri A, Jones JDG:The downy mildew effector proteins ATR1 and ATR13 promote disease susceptibility in Arabidopsis thaliana.The Plant Cell2007,19:4077–4090.PubMedCrossRef 61.

Imaging Techniques MRI IWP-2 was obtained by the use of a 0.5 T superconductive

system (Gyroscan, Philips healthcare , Eindhoven, The Netherlands). MRI was performed using a neck-coil, 5-millimeter-thick slice, two acquisitions and a matrix of 256 × 256 pixels. The study consisted in spin-echo (SE) T1 sequences (TR 450 ms TE 20 ms) on multiple planes (axial and coronal or sagittal) selected in relation to the site of the tumours into the oral cavity and short-tau-inversion-recovery (STIR) sequences T2 weighted (TR 1800 ms; TE 100 ms; TI 10 ms) acquired on the axial plane. In addiction, AZD6738 clinical trial for the evaluation of the mandible, SE T1 sequences were acquired on coronal or axial plane with 3-millimetre-thick slices. After administration of gadopentate dimeglumine (Gd-DTPA, Magnevist, Bayern Shering Pharma AG, Berlin, Germany) at 0,2 mmol/kg, T1 fat-suppressed (SPIR) sequences

(TR 400 ms;TE 10 ms.) with an acquisition time of 1.43 min on axial planes and SE T1 sequences on multiple planes were used. MDCT examination was performed using a 4-slice MDCT scanner (Siemens Medical Solutions, Enlargen, Germany). The scans were performed with the patients supine with head first, using the following parameters: slice collimation 4 × 1;

tube voltage, 120 kV; effective mAs, 150; slice thickness 1 mm; reconstruction section thickness 1.5 mm; gantry rotation time 0.8 s; field of view (FOV) 35-50 cm. UnStaurosporine clinical trial enhanced MDCT images were at first obtained; successively contrast enhanced images were achieved during a late phase after a scan delay of 70s by prior intravenous administration of 110 ml of iodinated non-ionic contrast material (Iomeron 300 mg, Bracco Spa, Milan Italy) at a flow rate of 3 ml/s. Row data were reconstructed with both soft-tissue PAK5 and bone algorithms and MDCT-reformatted images in axial, coronal and sagittal planes were obtained. Image Analysis Images were analysed on a workstation commercially available which allows analysis of both MRI and MDCT images. MDCT diagnostic criteria used for the evaluation of the mandibular bone invasion were: (i) demonstration of cortical bone defects adjacent to the tumour, in order to determinate the cortical invasion, (ii) evidence of trabecular disruption continuous to the cortical bone erosion, in order to determinate the marrow involvement and (iii) MDCT infiltration signs of the inferior alveolar canal.

Methods Microcalorimetry For PRIMA-1MET in vitro our microcalorimetric studies we used a Setaram MicroDSC III differential scanning microcalorimeter, Joule effect factory calibrated. Outer thermostatic loop was provided by a Julabo F32-HE device operating in standard mode. 3D sensor protection was provided with Argon purge gas (99.99% SIAD – TP). Setsoft 2000 V 3.05 software was used for data acquisition and primary signal processing. In each experiment, a sample of 600 μL was introduced in a batch cell with

a capacity of 1 mL (with a maximum sample volume of 850 μL). Bacterial population We performed the microcalorimetric experiments on a strain of Staphylococcus epidermidis ATCC 12228. Culture medium Bacterial cultures were prepared MDV3100 molecular weight in trypticase soy broth (TSB) which is a mixture of Pancreatic digest of casein (17 g), NaCl (5 g), Papaic digest of soybean meal (3 g), K2HPO4 (2.5 g), Glucose (1.8 g) to 1 liter and a pH of 7.3 ± 0.2 at 25°C. The medium was autoclaved before use and microbiologically pure. For bacterial plating, isolation

and random sample checking of CB-839 clinical trial sterile conditions we used trypticase soy agar (TSA) which is a solid medium, with the same basic components as TSB. Sample preparation Discrete colonies of Staphylococcus epidermidis grown on TSA culture media were used to prepare TSB cultures. For bacterial growth, the liquid suspensions were kept overnight at 37°C in the Abiraterone research buy JulaboF32-HE thermostat. Subsequent inocula were prepared, with the desired transmittance measured at 600 nm (T600). Depending on the experiment, serial dilutions of the inoculum were performed. The transmittance measurements were made using blank TSB as reference. TSA calibration of transmittance indicated a concentration of ≈5×107 CFU/mL for the T600 = 95% suspension, the most frequently used dilution

within this study. The sample cells and their hermetically o-ring sealing caps were sterilized at 121°C and kept sealed until use. Procedure The sample cells were filled at room temperature and were hermetically silicon o-ring sealed. A batch cell containing 600 μL sterile TSB was used as reference for differential scanning microcalorimetry (μDSC). Two types of experiments to test signal reproducibility and variability were performed: a. Experiments on freshly prepared samples Samples were prepared as described above and introduced in the microcalorimeter immediately after preparation. They were allowed to reach thermal equilibrium at room temperature. The working temperature was reached with maximum heating rate then kept constant for the entire experiment and the signal was recorded. b.

(a) Nyquist plots of impedance data for HNF and NF cells. (b) Charge recombination resistance vs. chemical capacitance. Conclusions A simple, effective, and economical approach to improve the light harvesting of electrospun nanofibers has been Selumetinib reported in this Selleckchem LY294002 work. By employing hydrothermal route, nanorods are grown

on electrospun nanofibers. The resulting TiO2 nanostructures consist of both anatase and rutile phases. The secondary growth of nanorods is in [110] orientation and are single crystalline in nature, a characteristic which plays a significant role in reducing the charge transport resistance throughout the film. Upon integration of the synthesized nanostructures as photoanodes for solid-state dye-sensitized solar cells, the hierarchical nanofibers exhibit 2.14% efficiency with J sc and V oc values being 4.05 mA/cm2 and 0.92 V, respectively. The nanorods provide additional surface area for dye loading, which helps to improve the

light harvesting of the fibers by 41%. In addition to dye adsorption, the presence of larger number and densely packed dye molecules offers greater extent of screening between the electrons injected into the TiO2 conduction band and holes in spiro-OMeTAD. Owing to their crystallinity and packing density, the hierarchical nanofibers exhibit superior properties as compared to the plain nanofibers for solar cell application. These nanostructures can also be employed in fuel cells or in water splitting applications, where high surface area is required with efficient transport in 1D nanostructures. Furthermore, the combination of hierarchical nanofibers with CH3NH3PbI3, as a sensitizer CB-5083 nmr with Thalidomide high absorption coefficient, can lead to inexpensive yet high efficiency solid-state cells [32]. Authors’ information DS is currently doing her Ph.D. in Materials Science Engineering at Nanyang Technological University, Singapore. She did her M.Sc. in Advanced Materials (Nanotechnology) studies at University Ulm, Germany and her B.Tech. in Metallurgical and Materials Engineering at IIT-Roorkee. Currently, her research focus is on electrospinning organic/inorganic nanostructures and investigate their properties for solar energy

application. SA obtained her Ph.D. in electronics engineering in 2011 from National University of Singapore (NUS) on nanostructured materials for dye-sensitized solar cells. Currently, she is a research fellow under SM at Energy Research Institute (ERI@N), Nanyang Technological University (NTU). Her research is aimed at new synthesis pathways for porous inorganic nano-materials and perovskite materials for solar energy applications. SSP obtained his Ph.D. in Materials Science and Engineering in 2011 from Nanyang Technological University on novel intermediate temperature solid oxide fuel cell (SOFC) apatite electrolyte. Currently, he is a postdoctoral research associate working with Professor Mary Ryan and Dr. Stephen Skinner at Imperial College London.

The individual losses, each accounting for a fraction of energy diverted away from conversion to the desired product, are summarized in Table 3. Figure 2 shows the stack-up of losses affecting the conversion efficiencies. The large arrows shown in the bottom of the plot indicate the overall conversion efficiency, i.e., the fraction of photons captured and converted to product. Because the losses combine multiplicatively, showing the loss axis in logarithmic terms allows a proper relative comparison. As

shown in Fig. 2, various constraints result in nearly a 40% reduction in practical maximum conversion AICAR molecular weight efficiency for the direct process relative to the theoretical maximum for this process. Even so, the conversion efficiency for the direct process is about seven times selleck kinase inhibitor larger than that for an algal open pond. Note that these calculations do not account for downstream-processing efficiency. Also note CA4P cell line that the results presented in Fig. 2 show the potential for converting photons to product, but do not indicate the cost for building and operating facilities for implementing these processes. Fig. 2 Sum of individual contributions and accumulated photon losses for two fuel processes and a theoretical maximum for energy conversion. The losses are represented on a logarithmic scale and accumulated serially for the processes beginning with the percent of PAR in empirically

measured solar ground insolation. Total practical conversion efficiency after accounting for losses is indicated by the green arrows Figure 3 shows the relationship between the calculated energy conversions expressed for any liquid fuel in per barrel energy equivalents (bble). By using the photosynthetic efficiency calculated above, the extrapolated metric of barrel energy equivalents (bble is equal to 6.1 × 109 joule) and any product density expressed in kg/m3 and energy content, e.g., heating value in MJ/kg, the output of this analysis can be converted to areal productivity for any molecule produced from either an http://www.selleck.co.jp/products/Decitabine.html endogenous or

an engineered pathway. For example, the direct process, operating at the calculated 7.2% efficiency would yield 350 bble/acre/year. This equates to 15,000 gal alkane/acre/year where a C17 alkane has a heating value of 47.2 MJ/kg and density of 777 kg/m3. Given the flexibility of genome engineering to construct production organisms that make and secrete various fuel products, a similar calculation can be applied for any product synthesized via a recombinant enzymatic pathway and a productivity value extrapolated. By comparison on an energy basis, the practical efficiency of the algal biomass process would equal about 3,500 gal/acre/year of the target triglyceride (71 bble; heating value 41 MJ/kg; density 890 kg/m3). Note that 1 gal/acre/year is equivalent to 9.4 l/hectare/year. Fig.

Br.013 group [15]. Interestingly, at this regional scale, canSNPs and MLVA exhibited considerable congruence in identifying genetic groups. Specifically, canSNPs identified six subclades and MLVA identified five, albeit with slightly different but not phylogenetically inconsistent membership due to the

nature of the two different marker types. SNPs discovered from whole genome sequences click here will typically provide greater discrimination than MLVA, as seen in subclades B.Br.030/031, B.Br.031/032 and B.Br.Georgia (Table 2), and can even be used to identify specific strains [33]. However, discovering these rare SNPs requires whole genome sequencing whereas MLVA can identify nearly the same number of genetic groups by simply surveying a few highly polymorphic portions of the genome. At this regional scale, homoplasy does not appear to be much of a factor in obscuring phylogenetic signal for identifying

genetic groups using MLVA, although the relationships among those groups are less resolved as isolates from adjacent groups share MLVA genotypes. Together, SNPs and MLVA provide complementary approaches, by first accurately placing isolates in a phylogeny using SNPs and then discriminating among isolates within SNP-determined subclades using MLVA. This step-wise Selleckchem Evofosfamide approach has been termed Progressive Hierarchical Resolving Assays using Nucleic Acids (PHRANA) [24]. OSI-906 research buy Conclusions We describe a new subpopulation in the B.Br.013 group

from Georgia that is genetically and geographically distinct from the other B.Br.013 group subpopulations found in Europe. Members of this
age are endemic to parts of Eastern Europe and Western Chloroambucil Asia, though the complete geographic range remains unknown. The basal positioning of the Georgian lineage and its restricted geographic distribution illustrates the need for studies on additional Asian and East European isolates to gain a better understanding of the clonal expansion of F. tularensis subsp. holarctica. Methods Whole Genome Sequencing We sequenced a single Georgian isolate (F0673), representing the most common MLVA profile type of F. tularensis subsp. holarctica found in the country of Georgia (Chanturia, unpubl. data), using Illumina’s Genome Analyzer II (San Diego, CA). DNA from F0673 was prepared using a standard chloroform extraction protocol [34]. Library preparation for this isolate involved sonication of 5 μg genomic DNA to an average fragment size of 350 bp, followed by sample preparation and cluster generation protocols for paired-end reads from Illumina. The library was quantified using SYBR-based qPCR and primers modified from the adaptor sequence. The library was then run in two lanes of the flow cell to increase overall coverage. Read lengths were ca. 40 bp, with a final yield of 32 Gb of sequence for the entire run. Image analysis for base calling and alignments followed the methods of Craig and colleagues [35].

id., 8 May 1866. P.A. Karsten (H,

FFE 825, kleptotype). Notes selleck morphology Chaetomastia was introduced by Saccardo (1883) as a subgenus of Melanomma, and five species were included, i.e. M. canescens Speg., M. cucurbitarioides Speg., M. hirtulum (P. Karst.) Sacc., M. hispidulum Sacc. and M. pilosellum P. Karst. Berlese (1890) promoted it to genus rank. Subsequently, Chaetomastia hirtula (P. Karst.) Berl. was selected as the BI 10773 clinical trial lectotype species of the genus (Clements and Shear 1931). Chaetomastia has been regarded as having unitunicate asci (Eriksson and Hawksworth 1986, 1998; Eriksson 1999). However its bitunicate status was confirmed by Holm (1957). Holm (1957) treated C. hirtula as Melanomma hirtulum (P. Karst.) Sacc., and Leuchtmann (1985)

transferred this species to Montagnula sensu lato based on the ascospore morphology and the hyphae surrounding the ascomata. Barr (1987b) suggested that ascoma, peridium structure and ascospore characters pointed Montagnula sensu stricto to Phaeosphaeriaceae, while the characters of ascomata and peridium structure of Chaetomastia were thought to fit the definition of Dacampiaceae (Barr 1987b). In particular, the peridium and ascospore characters of C. hirtula are comparable with those of the generic type of Massariosphaeria (M. phaeospora). Thus, Barr (1989c) accepted Massariosphaeria sensu stricto and assigned the phragmosporous species of Massariosphaeria sensu lato PF299804 to Chaetomastia. Barr (2002) later assigned Chaetomastia to Teichosporaceae based on its saprobic or hypersaprobic lifestyle, occurring on woody stems and peridium structure, and this is widely followed (Eriksson 2006; Lumbsch and Huhndorf 2007). Currently, 11 species are accepted in this genus (http://​www.​indexfungorum.​org/​). Phylogenetic study None. Concluding

remarks Familial placement of Chaetomastia is undetermined currently but has been included in the Teichosporaceae by authoritative sources (Eriksson 2006; Lumbsch and Huhndorf 2007) or the Dacampiaceae (http://​www.​indexfungorum.​org/​). Chaetoplea (Sacc.) Clem., Gen. Fung. (Minneapolis): 275 (1931). (?Phaeosphaeriaceae) ≡ Pyrenophora subgen. Chaetoplea Sacc., Syll. fung. (Abellini) 2: 279 (1883). Generic description Habitat terrestrial, saprobic. Ascomata small to medium, immersed, erumpent to superficial, Fenbendazole globose to subglobose, papillate, ostiolate. Peridium not examined. Hamathecium of dense, long, narrowly cellular pseudoparaphyses. Asci 8-spored or 4-spored, bitunicate, fissitunicate, cylindro-clavate, with a thick, furcate pedicel. Ascospores ellipsoid or fusoid, pale brown to brown, phragmosporous or muriform. Anamorphs reported for genus: Microdiplodia-like (Barr 1990b). Literature: Barr 1981; 1987a; b; 1990b; Clements and Shear 1931; Ramaley and Barr 1995; Yuan and Barr 1994. Type species Chaetoplea calvescens (Fr.) Clem., Gen. Fung. (Minneapolis): 275 (1931). (Fig. 22) Fig.

Branch chain lengths of amylopectin determined by peak fraction showed polymerization degrees of 18 and 30 for short and long branches, respectively. The authors attributed variations in physical properties mainly to differences in amylose content and amylopectin structure (Jane et al. 1992). According Acadesine chemical structure to Leterme et al. (2005) the content of truly digestible protein in peach palm is 51 g kg−1 dry matter with 3.691 kcal kg−1 dry matter of digestible energy. Average values for the digestibility of dry matter, energy, starch and protein are 91, 87, 96 and 95 %, respectively. Varieties differed significantly only for starch. Quesada et al. (2011) reported a glycemic index of 35 mg dl−1 in peach palm mesocarp, which is low compared

to white bread. Foods with low glycemic index values are considered beneficial for patients with diabetes and coronary diseases, as released Caspase Inhibitor VI sugars are absorbed more slowly. Lipids Peach palm oil contains omega-3 (linolenic

acid), omega-6 (linoleic acid) and omega-9 (oleic acid) fatty acids. Oil content has been shown to increase as fruits mature, but with high variability between bunches and harvest seasons (Arkcoll and Aguiar 1984). Mono-unsaturated oleic acids predominated (except one outlier from French Guyana), and palmitic acid was found to be the most abundant saturated fatty acid. Among GSK1210151A purchase the essential fatty acids, linoleic acid was the most common (Table 5). Saturated fatty acids predominate in the seed, with very high content of lauric and myristic acids (Zumbado and Murillo 1984). Clement and Arkcoll (1991) have

evaluated potential breeding strategies for converting peach palm into an oil crop. This is especially important given the deficiency of omega-3 fatty acids in industrialized country diets, which contribute to the so-called “diseases of civilization”, including cardiovascular disease, cancer, and inflammatory and autoimmune diseases (Simopoulos 2004). There is strong evidence that increasing dietary omega-3 and other long-chain polyunsaturated fatty acids may ameliorate such diseases (Ruxton Phenylethanolamine N-methyltransferase et al. 2004; Gogus and Smith 2010). Table 5 Unsaturated and saturated fatty acid in peach palm (% of fatty acid) Country Brazil Brazil Colombia Costa Rica Costa Rica French Guiana French Guiana Unsatured fatty acids 53.3 53.7 59.4 45.6 69.9 63 12.9 Palmitoleic 16:1 (n − 7) 6.5 3.9–7.4 10.5 5.7–7.1 5.3 3.5 – Oleic 18:1 (n − 9) 41 42.8–60.8 47.5 32.6–47.8 50.3 54 12.9 Linoleic 18:2 (n − 6) 4.8 2.5–5.4 1.4 11.2–21.1 12.5 4.5 – Linolenic 18:3 (n − 3) 1 0.0–1.4 – 1.5-5.5 1.8 – – Satured fatty acids 46.3 39.2 40.6 – 29.6 37.5 85.5 Lauric 12:0 – – – – – – 60.6 Myristic 14:0 – – – – – – 18.9 Palmitic 16:0 44.8 24.1–42.3 40.2 30.5–40.3 29.6 32 6 Stearic 18:0 1.5 0.8–3.5 0.4 1.7–2.4 – 3 – Arachidic 20:0 – – – – – 2.5 – Source Gomes da Silva and Amelotti (1983) Yuyama et al. (2003) Zapata (1972) Fernández-Piedra et al. (1995) Hammond et al.

Operation of the LPI™ FlowCells – multi-step digestion

with PPS Silent® Surfactant PPS Silent® Surfactant (Protein Discovery) is a mass spectrometry compatible reagent designed for the extraction and solubilisation and improvement of in-solution enzymatic protein digestions of hydrophobic proteins. For the first digestion step with trypsin, the same procedure was followed as for the multi-step digestion method without PPS Silent® Surfactant as described above. For the second digestion step, trypsin was resuspended in 20 mM NH4HCO3 pH 8.0 to a final concentration of 5 μg ml-1. The resuspended trypsin was then used to resuspend PPS Silent® Surfactant to a final concentration of 0.1% (w/v). 700 μl of the trypsin containing PPS Silent® Surfactant was then injected

into the LPI™ FlowCell and then incubated at 37°C for 1 h. selleck products The tryptic peptides were collected by injecting 700 μl 20 mM NH4HCO3, pH 8 at the inlet port and collecting the eluant at the outlet port. Formic acid was added to the eluted peptides to a final concentration of 250 mM and incubated for 1 h at room temperature to inactivate the trypsin and cleave the PPS Silent® Surfactant from the sample. The sample was stored at -80°C for further analysis (see Additional File 3). Peptide analysis using liquid chromatography tandem mass spectrometry (LC-MS/MS) The peptide fraction collected 4SC-202 chemical structure from LPI™ FlowCell was subsequently analyzed separately by LC- MS/MS at the Proteomics Core Facility at the University of Gothenburg. Prior to analysis, the sample was centrifuged in vacuum to dryness and reconstituted in 20 μl 0.1% (v/v) formic acid in water. The sample was centrifuged at 13 000 g for 15 minutes and 17 μl was transferred to the autosampler of the LC-MS/MS system. For the liquid chromatography, an Agilent 1100 binary pump was used and the tryptic peptides were separated on a 200 × 0.05 mm i.d. fused silica column

packed in-house with 3 μm ReproSil-Pur C18-AQ particles (Dr. Maisch, GmbH, Ammerbuch, Germany). Two μl of the sample was injected and the peptides were first trapped on a precolumn (45 × 0.1 mm i.d.) packed with 3 μm C18-bonded particles. A 40 minute gradient of 10-50% (v/v) acetonitrile oxyclozanide in 0.2% (v/v) formic acid was used for separation of the peptides. The flow through the column was reduced by a split to approximately 100 nl min-1. Mass analyses were performed in a 7-Tesla LTQ-FT mass spectrometer (Hybrid Linear Trap Quadrupole – Fourier Transform; Thermo Electron) equipped with a nanospray source modified in-house. The instrument was operated in the data-dependent mode to automatically switch between MS and MS/MS acquisition. MS AICAR order spectra were acquired in the FT-ICR while MS/MS spectra were acquired in the LTQ-trap. For each scan of FT-ICR, the six most intense, double- or triple protonated ions were sequentially fragmented in the linear trap by collision induced dissociation (CID). Already fragmented target ions were excluded for MS/MS analysis for 6 seconds.

When the omnibus test was deemed significant, haplotype-specific test was performed. A conditional haplotype test that controlled for a particular haplotype among a set of haplotypes was also conducted to determine if that particular haplotype alone leads to the significant omnibus association result. Haploview 4.1 [43] was adopted to generate the haplotype block structure for the genotyped markers that passed the quality control requirements. LD is not calculated if markers are greater

than 500 kb apart. Statistical power was estimated by the “Case-Control for threshold-selected quantitative traits” module of the web-based Genetic Power Calculator (http://​pngu.​mgh.​harvard.​edu/​~purcell/​gpc/​qcc.​html) [44]. Bioinformatics analysis A comparative genomics approach was adopted to GSK1120212 determine potential functional elements in the candidate region associated with BMD variation. The chromosomal position of the region was submitted to the VISTA Genome browser. Pre-computed whole-genome alignment among large vertebrates, which had a high sensitivity in covering more than 90% of known exons, was available on the browser with timely update upon the release of new genome assemblies [45]. The sequence encompassing the significantly associated SNP was scanned against the weight matrices for vertebrates

that were publicly available on MatInspector [46]. Alpelisib order The optimized matrix threshold of a weight matrix was defined as the threshold that allowed a maximum of three matches in 10 kb of non-regulatory test sequences. The matrix similarity was calculated on-the-run by scanning the imported sequence against the relative frequency of each

nucleotide at a particular position in the matrix. Only potential binding sites with: (1) matrix similarity exceeding the optimized threshold; and (2) matrix similarity greater than 0.85 were considered good matches. Results Subject DNA Damage inhibitor characteristics The characteristics of the subjects are outlined in Table 2. Student’s t test was used to compare the mean age, height, weight, and BMD in the case- and control-group, without assuming equal variances. The covariates that showed significant differences between Tolmetin cases and controls were potential confounding factors for BMD variation. These were adjusted in the subsequent analysis as indicated in Table 2. Table 2 Characteristics and BMD measurements of the 1,080 subjects and the constituent 533 postmenopausal women   Whole study population Postmenopausal women Cases Controls p value (t test) Cases Controls p value (t test) Skeletal site: lumbar spine  Number 457 254 – 314 107 –  Age (year) 51.71 ± 13.78 49.56 ± 14.35 0.05 59.92 ± 5.90 63.55 ± 8.16 <0.01*  Height (m) 1.53 ± 0.06 1.576 ± 0.06 <0.01* 1.52 ± 0.057 1.55 ± 0.05 <0.01*  Weight (kg) 49.98 ± 7.22 60.34 ± 9.76 <0.01* 51.03 ± 7.43 62.45 ± 9.79 <0.01*  BMD (g/cm2) 0.