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Traditional metallic gratings and novel metamaterials are two basic kinds of candidates for perfect absorption. Comparatively speaking, metallic grating is the preferred choice for the same absorption effect because it is structurally simpler and more convenient to fabricate. However, to date, most of the perfect absorption effects achieved based on metamaterials are also available using an metallic grating except the tunable dual(multi)-band perfect absorption. To fill this gap, in this paper, by adding subgrooves on the rear surface as well as inside the grating slits to a free-standing metallic grating, tunable dual-band perfect absorption is also obtained for the first time. The grooves inside the slits is to tune the frequency of the Cavity Mode(CM) resonance which enhances the transmission and suppresses the reflectance simultaneously. The grooves on the rear surface give rise to the phase resonance which not only suppresses the transmission but also reinforces the reflectance depression effect. Thus, when the phase resonance and the frequency tunable CM resonance occur together, transmission and reflection can be suppressed simultaneously, dual-band nearly perfect absorption with tunable frequencies is obtained. To our knowledge, this perfect absorption phenomenon is achieved for the first time in a designed metallic grating structure.

To explore the multivariate nature of fMRI data and to consider the inter-subject brain response discrepancies, a multivariate and brain response model-free method is fundamentally required. Two such methods are presented in this paper by integrating a machine learning algorithm, the support vector machine (SVM), and the random effect model. Without any brain response modeling, SVM was used to extract a whole brain spatial discriminance map (SDM), representing the brain response difference between the contrasted experimental conditions. Population inference was then obtained through the random effect analysis (RFX) or permutation testing (PMU) on the individual subjects' SDMs. Applied to arterial spin labeling (ASL) perfusion fMRI data, SDM RFX yielded lower false-positive rates in the null hypothesis test and higher detection sensitivity for synthetic activations with varying cluster size and activation strengths, compared to the univariate general linear model (GLM)-based RFX. For a sensory-motor ASL fMRI study, both SDM RFX and SDM PMU yielded similar activation patterns to GLM RFX and GLM PMU, respectively, but with higher t values and cluster extensions at the same significance level. Capitalizing on the absence of temporal noise correlation in ASL data, this study also incorporated PMU in the individual-level GLM and SVM analyses accompanied by group-level analysis through RFX or group-level PMU. Providing inferences on the probability of being activated or deactivated at each voxel, these individual-level PMU-based group analysis methods can be used to threshold the analysis results of GLM RFX, SDM RFX or SDM PMU.

Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/. PMID:25928885

Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (DeltaDeltaG) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition. We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which DeltaDeltaG >or= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%. We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems

The compression behavior of a material cannot be accurately determined if the machine compliance is not accounted prior to the measurements. This work discusses the machine compliance during a compressibility test with fiberglass fabrics. The thickness variation was measured during loading and unloading cycles with a relaxation stage of 30 minutes between them. The measurements were performed using an indirect technique based on the comparison between the displacement at a free compression cycle and the displacement with a sample. Relating to the free test, it has been noticed the nonexistence of machine relaxation during relaxation stage. Considering relaxation or not, the characteristic curves for a free compression cycle can be overlapped precisely in the majority of the points. For the compression test with sample, it was noticed a non-physical decrease of about 30 µm during the relaxation stage, what can be explained by the greater fabric relaxation in relation to the machine relaxation. Beyond the technique normally used, another technique was used which allows a constant thickness during relaxation. Within this second method, machine displacement with sample is simply subtracted to the machine displacement without sample being imposed as constant. If imposed as a constant it will remain constant during relaxation stage and it will suddenly decrease after relaxation. If constantly calculated it will decrease gradually during relaxation stage. Independently of the technique used the final result will remain unchanged. The uncertainty introduced by this imprecision is about ±15 µm. 2b1af7f3a8