Quantitative structures of the fully hydrated fluid phases of dimyristoylphosphatidylcholine (DMPC) and dilauroylphosphatidylcholine (DLPC) were obtained at 30 degrees C. Data for the relative form factors F(q(z)) for DMPC were obtained using a combination of four methods. 1), Volumetric data provided F(0). 2), Diffuse x-ray scattering from oriented stacks of bilayers provided relative form factors |F(q(z))| for high q(z), 0.22 < q(z) < 0.8 A(-1). 3), X-ray scattering from extruded unilamellar vesicles with diameter 600 A provided |F(q(z))| for low q(z), 0.1 < q(z) < 0.3 A(-1). 4), Previous measurements using a liquid crystallographic x-ray method provided |F(2 pi h/D)| for h = 1 and 2 for a range of nearly fully hydrated D-spacings. The data from method 4 overlap and validate the new unilamellar vesicles data for DMPC, so method 4 is not required for DLPC or future studies. We used hybrid electron density models to obtain structural results from these form factors. Comparison of the model electron density profiles with that of gel phase DMPC provides areas per lipid A, 60.6 +/- 0.5 A(2) for DMPC and 63.2 +/- 0.5 A(2) for DLPC. Constraints on the model provided by volume measurements and component volumes obtained from simulations put the electron density profiles rho(z) and the corresponding form factors F(q(z)) on absolute scales. Various thicknesses, such as the hydrophobic thickness and the steric thickness, are obtained and compared to literature values.

Transfer free energy measurements of amino acids from water to the osmolytes, sucrose and sarcosine, were made as a function of osmolyte concentration. From these data, transfer free energies of the amino acid side chains were obtained, and the transfer free energy of the peptide backbone was determined from solubility measurements of diketopiperazine (DKP). Using static accessible surface evaluations of the native and unfolded states of ribonuclease A, solvent exposed side chain and peptide backbone areas were multiplied by their transfer free energies and summed in order to evaluate the transfer free energy of the native and unfolded states of the protein from water to the osmolyte solutions. The results reproduced the main features of the free energy profile determined for denaturation of proteins in the presence of osmolytes. The side chains were found collectively to favor exposure to the osmolyte in comparison to exposure in water, and in this sense the side chains favor protein unfolding. The major factor which opposes and overrides the side chain preference for denaturation and results in the stabilization of proteins observed in osmolytes is the highly unfavorable exposure of polypeptide backbone on unfolding. Except for urea and guanidine hydrochloride solutions, it is shown that all organic solvents (e.g., dioxane, ethanol, ethylene glycol) and solutes (osmolytes) for which transfer free energy measurements have been determined exhibit unfavorable transfer free energy of the peptide backbone.(ABSTRACT TRUNCATED AT 250 WORDS)

The structure of fully hydrated gel phase dimyristoylphosphatidylcholine lipid bilayers was obtained at 10 degrees C. Oriented lipid multilayers were used to obtain high signal-to-noise intensity data. The chain tilt angle and an estimate of the methylene electron density were obtained from wide angle reflections. The chain tilt angle is measured to be 32.3 +/- 0.6 degrees near full hydration, and it does not change as the sample is mildly dehydrated from a repeat spacing of D = 59.9 A to D = 56.5 A. Low angle diffraction peaks were obtained up to the tenth order for 17 samples with variable D and prepared by three different methods with different geometries. In addition to the usual Fourier reconstructions of the electron density profiles, model electron density profiles were fit to all the low angle data simultaneously while constraining the model to include the wide-angle data and the measured lipid volume. Results are obtained for area/lipid (A = 47.2 +/- 0.5 A(2)), the compressibility modulus (K(A) = 500 +/- 100 dyn/cm), various thicknesses, such as the hydrocarbon thickness (2D(C) = 30.3 +/- 0.2 A), and the head-to-head spacing (D(HH) = 40.1 +/- 0.1 A).

Purpose: Lung squamous cell carcinoma (SCC) is clinically and genetically heterogeneous, and current diagnostic practices do not adequately substratify this heterogeneity. A robust, biologically based SCC subclassification may describe this variability and lead to more precise patient prognosis and management. We sought to determine if SCC mRNA expression subtypes exist, are reproducible across multiple patient cohorts, and are clinically relevant.Experimental Design: Subtypes were detected by unsupervised consensus clustering in five published discovery cohorts of mRNA microarrays, totaling 382 SCC patients. An independent validation cohort of 56 SCC patients was collected and assayed by microarrays. A nearest-centroid subtype predictor was built using discovery cohorts. Validation cohort subtypes were predicted and evaluated for confirmation. Subtype survival outcome, clinical covariates, and biological processes were compared by statistical and bioinformatic methods.Results: Four lung SCC mRNA expression subtypes, named primitive, classical, secretory, and basal, were detected and independently validated (P < 0.001). The primitive subtype had the worst survival outcome (P < 0.05) and is an independent predictor of survival (P < 0.05). Tumor differentiation and patient sex were associated with subtype. The expression profiles of the subtypes contained distinct biological processes (primitive: proliferation; classical: xenobiotic metabolism; secretory: immune response; basal: cell adhesion) and suggested distinct pharmacologic interventions. Comparison with lung model systems revealed distinct subtype to cell type correspondence.Conclusions: Lung SCC consists of four mRNA expression subtypes that have different survival outcomes, patient populations, and biological processes. The subtypes stratify patients for more precise prognosis and targeted research.

The major facilitator superfamily (MFS) transporters are an ancient and widespread family of secondary active transporters. In Escherichia coli, the uptake of l-fucose, a source of carbon for microorganisms, is mediated by an MFS proton symporter, FucP. Despite intensive study of the MFS transporters, atomic structure information is only available on three proteins and the outward-open conformation has yet to be captured. Here we report the crystal structure of FucP at 3.1 Å resolution, which shows that it contains an outward-open, amphipathic cavity. The similarly folded amino and carboxyl domains of FucP have contrasting surface features along the transport path, with negative electrostatic potential on the N domain and hydrophobic surface on the C domain. FucP only contains two acidic residues along the transport path, Asp 46 and Glu 135, which can undergo cycles of protonation and deprotonation. Their essential role in active transport is supported by both in vivo and in vitro experiments. Structure-based biochemical analyses provide insights into energy coupling, substrate recognition and the transport mechanism of FucP.

Clustering methods provide a powerful tool for the exploratory analysis of high-dimension, low-sample size (HDLSS) data sets, such as gene expression microarray data. A fundamental statistical issue in clustering is which clusters are "really there," as opposed to being artifacts of the natural sampling variation. We propose SigClust as a simple and natural approach to this fundamental statistical problem. In particular, we define a cluster as data coming from a single Gaussian distribution and formulate the problem of assessing statistical significance of clustering as a testing procedure. This Gaussian null assumption allows direct formulation of p values that effectively quantify the significance of a given clustering. HDLSS covariance estimation for SigClust is achieved by a combination of invariance principles, together with a factor analysis model. The properties of SigClust are studied. Simulated examples, as well as an application to a real cancer microarray data set, show that the proposed method works remarkably well for assessing significance of clustering. Some theoretical results also are obtained.

Organisms and cellular systems which have adapted to stresses such as high temperature, desiccation, and urea-concentrating environments have responded by concentrating particular organic solutes known as osmolytes. These osmolytes are believed to confer protection to enzyme and other macromolecular systems against such denaturing stresses. Differential scanning calorimetric (DSC) experiments were performed on ribonuclease A and hen egg white lysozyme in the presence of varying concentrations of the osmolytes glycine, sarcosine, N,N-dimethylglycine, and betaine. Solutions containing up to several molar concentrations of these solutes were found to result in considerable increases in the thermal unfolding transition temperature (Tm) for these proteins. DSC scans of ribonuclease A in the presence of up to 8.2 M sarcosine resulted in reversible two-state unfolding transitions with Tm increases of up to 22 degrees C and unfolding enthalpy changes which were independent of Tm. On the basis of the thermodynamic parameters observed, 8.2 M sarcosine results in a stabilization free energy increase of 7.2 kcal/mol for ribonuclease A at 65 degrees C. This translates into more than a 45,000-fold increase in stability of the native form of ribonuclease A over that in the absence of sarcosine at this temperature. Catalytic activity measurements in the presence of 4 M sarcosine give kcat and Km values that are largely unchanged from those in the absence of sarcosine. DSC of lysozyme unfolding in the presence of these osmolytes also results in Tm increases of up to 23 degrees C; however, significant irreversibly occurs with this protein.(ABSTRACT TRUNCATED AT 250 WORDS)

Abstract. With its elegant margin theory and accurate classification performance, the Support Vector Machine (SVM) has been widely applied in both machine learning and statistics. Despite its success and popularity, it still has some drawbacks in certain situations. In particular, the SVM classifier can be very sensitive to outliers in the training sample. Moreover, the number of support vectors (SVs) can be very large in many applications. To solve these problems, [WL06] proposed a new SVM variant, the robust truncatedhinge-loss SVM (RSVM), which uses a truncated hinge loss. In this paper, we apply the operation of truncation on the multicategory hinge loss proposed by [LLW04]. We show that the proposed robust multicategory truncated-hingeloss SVM (RMSVM) is more robust to outliers and deliver more accurate classifiers using a smaller set of SVs than the original multicategory SVM (MSVM) proposed by [LLW04].