Wood Recognition Using Image Texture Features

Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, novel, yet very powerful approach for wood recognition. The method is suitable for wood database applications, which are of great importance in wood related industries and administrations. At the feature extraction stage, a set of features is extracted from Mask Matching Image (MMI). The MMI features preserve the mask matching information gathered from the HLAC methods. The texture information in the image can then be accurately extracted from the statistical and geometrical features. In particular, richer information and enhanced discriminative power is achieved through the length histogram, a new histogram that embodies the width and height histograms. The performance of the proposed approach is compared to the state-of-the-art HLAC approaches using the wood stereogram dataset ZAFU WS 24. By conducting extensive experiments on ZAFU WS 24, we show that our approach significantly improves the classification accuracy.     

Multi-Scale Compositionality: Identifying the Compositional Structures of Social Dynamics Using Deep Learning

Objective

Social media exhibit rich yet distinct temporal dynamics which cover a wide range of different scales. In order to study this complex dynamics, two fundamental questions revolve around (1) the signatures of social dynamics at different time scales, and (2) the way in which these signatures interact and form higher-level meanings.

Method

In this paper, we propose the Recursive Convolutional Bayesian Model (RCBM) to address both of these fundamental questions. The key idea behind our approach consists of constructing a deep-learning framework using specialized convolution operators that are designed to exploit the inherent heterogeneity of social dynamics. RCBM’s runtime and convergence properties are guaranteed by formal analyses.

Results

Experimental results show that the proposed method outperforms the state-of-the-art approaches both in terms of solution quality and computational efficiency. Indeed, by applying the proposed method on two social network datasets, Twitter and Yelp, we are able to identify the compositional structures that can accurately characterize the complex social dynamics from these two social media. We further show that identifying these patterns can enable new applications such as anomaly detection and improved social dynamics forecasting. Finally, our analysis offers new insights on understanding and engineering social media dynamics, with direct applications to opinion spreading and online content promotion.     

Contact-Free Palm-Vein Recognition Based on Local Invariant Features

Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs), respectively, which demonstrate the effectiveness of the proposed approach.     

Recognition of Non-Harmonic Natural Sounds by Small Mammals Using Competitive Training

Animals recognize biologically relevant sounds, such as the non-harmonic sounds made by some predators, and respond with adaptive behaviors, such as escaping. To clarify which acoustic parameters are used for identifying non-harmonic, noise-like, broadband sounds, guinea pigs were conditioned to a natural target sound by introducing a novel training procedure in which 2 or 3 guinea pigs in a group competed for food. A set of distinct behavioral reactions was reliably induced almost exclusively to the target sound in a 2-week operant training. When fully conditioned, individual animals were separately tested for recognition of a set of target-like sounds that had been modified from the target sound, with spectral ranges eliminated or with fine or coarse temporal structures altered. The results show that guinea pigs are able to identify the noise-like non-harmonic natural sounds by relying on gross spectral compositions and/or fine temporal structures, just as birds are thought to do in the recognition of harmonic birdsongs. These findings are discussed with regard to similarities and dissimilarities to harmonic sound recognition. The results suggest that similar but not identical processing that requires different time scales might be used to recognize harmonic and non-harmonic sounds, at least in small mammals.     

Geometric Recognition as a Tool for Predicting Structures of Molecular Complexes

Summary The structures of two binary complexes (the TEM-1/BLIP complex and the trypsin/amiloride complex) were predicted prior to their experimental determination and compared to the corresponding experimental structures when these became available. In both predictions the rigid-body geometric docking algorithm ranked the correct solution among the top ones. Additional information concerning the structure and chemical character of the binding site of one of the molecules in the complex was used to single out the correct solution. The results indicate that the combination of geometric surface matching with biochemical information produces a useful tool for structure prediction.     

Geometric Recognition as a Tool for Predicting Structures of Molecular Complexes

The structures of two binary complexes (the TEM-1/BLIP complex and the trypsin/amiloride complex) were predicted prior to their experimental determination and compared to the corresponding experimental structures when these became available. In both predictions the rigid-body geometric docking algorithm ranked the correct solution among the top ones. Additional information concerning the structure and chemical character of the binding site of one of the molecules in the complex was used to single out the correct solution. The results indicate that the combination of geometric surface matching with biochemical information produces a useful tool for structure prediction.     

Manufacturing Of Robust Natural Fiber Preforms Utilizing Bacterial Cellulose as Binder

A novel method of manufacturing rigid and robust natural fiber preforms is presented here. This method is based on a papermaking process, whereby loose and short sisal fibers are dispersed into a water suspension containing bacterial cellulose. The fiber and nanocellulose suspension is then filtered (using vacuum or gravity) and the wet filter cake pressed to squeeze out any excess water, followed by a drying step. This will result in the hornification of the bacterial cellulose network, holding the loose natural fibers together.Our method is specially suited for the manufacturing of rigid and robust preforms of hydrophilic fibers. The porous and hydrophilic nature of such fibers results in significant water uptake, drawing in the bacterial cellulose dispersed in the suspension. The bacterial cellulose will then be filtered against the surface of these fibers, forming a bacterial cellulose coating. When the loose fiber-bacterial cellulose suspension is filtered and dried, the adjacent bacterial cellulose forms a network and hornified to hold the otherwise loose fibers together.The introduction of bacterial cellulose into the preform resulted in a significant increase of the mechanical properties of the fiber preforms. This can be attributed to the high stiffness and strength of the bacterial cellulose network. With this preform, renewable high performance hierarchical composites can also be manufactured by using conventional composite production methods, such as resin film infusion (RFI) or resin transfer molding (RTM). Here, we also describe the manufacturing of renewable hierarchical composites using double bag vacuum assisted resin infusion.     

Multi-Scale Analysis of the European Airspace Using Network Community Detection

We show that the European airspace can be represented as a multi-scale traffic network whose nodes are airports, sectors, or navigation points and links are defined and weighted according to the traffic of flights between the nodes. By using a unique database of the air traffic in the European airspace, we investigate the architecture of these networks with a special emphasis on their community structure. We propose that unsupervised network community detection algorithms can be used to monitor the current use of the airspace and improve it by guiding the design of new ones. Specifically, we compare the performance of several community detection algorithms, both with fixed and variable resolution, and also by using a null model which takes into account the spatial distance between nodes, and we discuss their ability to find communities that could be used to define new control units of the airspace.     

Robust Classification of Small-Molecule Mechanism of Action Using a Minimalist High-Content Microscopy Screen and Multidimensional Phenotypic Trajectory Analysis

Phenotypic screening through high-content automated microscopy is a powerful tool for evaluating the mechanism of action of candidate therapeutics. Despite more than a decade of development, however, high content assays have yielded mixed results, identifying robust phenotypes in only a small subset of compound classes. This has led to a combinatorial explosion of assay techniques, analyzing cellular phenotypes across dozens of assays with hundreds of measurements. Here, using a minimalist three-stain assay and only 23 basic cellular measurements, we developed an analytical approach that leverages informative dimensions extracted by linear discriminant analysis to evaluate similarity between the phenotypic trajectories of different compounds in response to a range of doses. This method enabled us to visualize biologically-interpretable phenotypic tracks populated by compounds of similar mechanism of action, cluster compounds according to phenotypic similarity, and classify novel compounds by comparing them to phenotypically active exemplars. Hierarchical clustering applied to 154 compounds from over a dozen different mechanistic classes demonstrated tight agreement with published compound mechanism classification. Using 11 phenotypically active mechanism classes, classification was performed on all 154 compounds: 78% were correctly identified as belonging to one of the 11 exemplar classes or to a different unspecified class, with accuracy increasing to 89% when less phenotypically active compounds were excluded. Importantly, several apparent clustering and classification failures, including rigosertib and 5-fluoro-2’-deoxycytidine, instead revealed more complex mechanisms or off-target effects verified by more recent publications. These results show that a simple, easily replicated, minimalist high-content assay can reveal subtle variations in the cellular phenotype induced by compounds and can correctly predict mechanism of action, as long as the appropriate analytical tools are used.     

Image Generation Using Bidirectional Integral Features for Face Recognition with a Single Sample per Person

In face recognition, most appearance-based methods require several images of each person to construct the feature space for recognition. However, in the real world it is difficult to collect multiple images per person, and in many cases there is only a single sample per person (SSPP). In this paper, we propose a method to generate new images with various illuminations from a single image taken under frontal illumination. Motivated by the integral image, which was developed for face detection, we extract the bidirectional integral feature (BIF) to obtain the characteristics of the illumination condition at the time of the picture being taken. The experimental results for various face databases show that the proposed method results in improved recognition performance under illumination variation.     

Robust Identification of Local Adaptation from Allele Frequencies

Comparing allele frequencies among populations that differ in environment has long been a tool for detecting loci involved in local adaptation. However, such analyses are complicated by an imperfect knowledge of population allele frequencies and neutral correlations of allele frequencies among populations due to shared population history and gene flow. Here we develop a set of methods to robustly test for unusual allele frequency patterns and correlations between environmental variables and allele frequencies while accounting for these complications based on a Bayesian model previously implemented in the software Bayenv. Using this model, we calculate a set of “standardized allele frequencies” that allows investigators to apply tests of their choice to multiple populations while accounting for sampling and covariance due to population history. We illustrate this first by showing that these standardized frequencies can be used to detect nonparametric correlations with environmental variables; these correlations are also less prone to spurious results due to outlier populations. We then demonstrate how these standardized allele frequencies can be used to construct a test to detect SNPs that deviate strongly from neutral population structure. This test is conceptually related to F_ST and is shown to be more powerful, as we account for population history. We also extend the model to next-generation sequencing of population pools—a cost-efficient way to estimate population allele frequencies, but one that introduces an additional level of sampling noise. The utility of these methods is demonstrated in simulations and by reanalyzing human SNP data from the Human Genome Diversity Panel populations and pooled next-generation sequencing data from Atlantic herring. An implementation of our method is available from http://gcbias.org.     

Simulation of Left Atrial Function Using a Multi-Scale Model of the Cardiovascular System

During a full cardiac cycle, the left atrium successively behaves as a reservoir, a conduit and a pump. This complex behavior makes it unrealistic to apply the time-varying elastance theory to characterize the left atrium, first, because this theory has known limitations, and second, because it is still uncertain whether the load independence hypothesis holds. In this study, we aim to bypass this uncertainty by relying on another kind of mathematical model of the cardiac chambers. In the present work, we describe both the left atrium and the left ventricle with a multi-scale model. The multi-scale property of this model comes from the fact that pressure inside a cardiac chamber is derived from a model of the sarcomere behavior. Macroscopic model parameters are identified from reference dog hemodynamic data. The multi-scale model of the cardiovascular system including the left atrium is then simulated to show that the physiological roles of the left atrium are correctly reproduced. This include a biphasic pressure wave and an eight-shaped pressure-volume loop. We also test the validity of our model in non basal conditions by reproducing a preload reduction experiment by inferior vena cava occlusion with the model. We compute the variation of eight indices before and after this experiment and obtain the same variation as experimentally observed for seven out of the eight indices. In summary, the multi-scale mathematical model presented in this work is able to correctly account for the three roles of the left atrium and also exhibits a realistic left atrial pressure-volume loop. Furthermore, the model has been previously presented and validated for the left ventricle. This makes it a proper alternative to the time-varying elastance theory if the focus is set on precisely representing the left atrial and left ventricular behaviors.     

Systems Pharmacology Dissection of Multi-Scale Mechanisms of Action for Herbal Medicines in Stroke Treatment and Prevention

Annually, tens of millions of first-ever strokes occur in the world; however, currently there is lack of effective and widely applicable pharmacological treatments for stroke patients. Herbal medicines, characterized as multi-constituent, multi-target and multi-effect, have been acknowledged with conspicuous effects in treating stroke, and attract extensive interest of researchers although the mechanism of action is yet unclear. In this work, we introduce an innovative systems-pharmacology method that combines pharmacokinetic prescreening, target fishing and network analysis to decipher the mechanisms of action of 10 herbal medicines like Salvia miltiorrhizae, Ginkgo biloba and Ephedrae herba which are efficient in stroke treatment and prevention. Our systematic analysis results display that, in these anti-stroke herbal medicines, 168 out of 1285 constituents with the favorable pharmacokinetic profiles might be implicated in stroke therapy, and the systematic use of these compounds probably acts through multiple mechanisms to synergistically benefit patients with stroke, which can roughly be classified as preventing ischemic inflammatory response, scavenging free radicals and inhibiting neuronal apoptosis against ischemic cerebral damage, as well as exhibiting lipid-lowering, anti-diabetic, anti-thrombotic and antiplatelet effects to decrease recurrent strokes. Relying on systems biology-based analysis, we speculate that herbal medicines, being characterized as the classical combination therapies, might be not only engaged in multiple mechanisms of action to synergistically improve the stroke outcomes, but also might be participated in reducing the risk factors for recurrent strokes.     

Lipase-catalyzed Synthesis of Capsaicin Analogs Using Natural Oils as an Acyl Donor

Synthesis of capsaicin analogs using natural oils as an acyl donor has been achieved by condensation with vanillylamine using Lipase D, Lipase R or Novozym 435 as a catalyst. When olive oil was employed, the major product was olvanil (1, 15.8–20.3%). Safflower oil gave a mixture of two major products, 1 (11.4–12.6%) and linoleoyl vanillylamide (2, 12.5–13.4%). The major product from perilla oil was linolenoyl vanillylamide (3, 7.5–10.0%).     

Quantifying Histological Features of Cancer Biospecimens for Biobanking Quality Assurance Using Automated Morphometric Pattern Recognition Image Analysis Algorithms

Biorepository-supported translational research depends on high-quality, well-annotated specimens. Histopathology assessment contributes insight into how representative lesions are for research objectives. Feasibility of documenting histological proportions of tumor and stroma was studied in an effort to enhance information regarding biorepository tissue heterogeneity. Using commercially available software, unique spatial-spectral algorithms were developed for applying automated pattern recognition morphometric image analysis to quantify histologic tumor and nontumor tissue areas in biospecimen tissue sections. Measurements were acquired successfully for 75/75 (100%) lymphomas, 76/77 (98.7%) osteosarcomas, and 60/70 (85.7%) melanomas. The percentage of tissue area occupied by tumor varied among patients and tumor types and was distributed around medians of 94% [interquartile range (IQR)=14%] for lymphomas, 84% for melanomas (IQR=24%), and 39% for osteosarcomas (IQR=44%). Within-patient comparisons from a subset, including multiple individual patient specimens, revealed ≤12% median coefficient of variation (CV) for lymphomas and melanomas. Phenotypic heterogeneity of osteosarcomas resulted in 33% median CV. Uniformly applied, tumor-specific pattern recognition software permits automated tissue-feature quantification. Furthermore, dispersion analyses of area measurements across collections, as well as of multiple specimens from individual patients, support using limited tissue slices to gauge features for some tumor types. Quantitative image analysis automation is anticipated to minimize variability associated with routine biorepository pathologic evaluations and enhance biomarker discovery by helping to guide the selection of study-appropriate specimens.     

Comparison of Protein Structures in Solution Using Local Conformations Derived from NMR Data: Application to Cytochrome c

Abstract

Structural comparisons of proteins in solution are often required to examine structure-functional relationships, study structural effects of mutations or distinguish between various forms of the same molecule under different conditions. A nuclear magnetic resonance (NMR) based probabilistic strategy is presented and used to study the structural differences between the two redox states of cytochrome C in solution. A probabilistic approach is employed to calculate the main chain conformations of horse ferro- and ferricytochrome C in solution, based on the published sequential d connectivity data. Conformational differences between the two oxidation states of horse cytochrome C in solution are found to be statistically significant. The largest changes in conformation are at residues Lys27, Thr28, Leu32, Gln42, Thr47, Tyr48, Thr49, Glu69, Lys72, Met80, Phe82, Ile85 and Lys86, all of which are close to the heme (within 14 Å of the heme iron in the high resolution Xray structure of tuna cytochrome c). We suggest that these conformational changes may modulate local dipole moments and hence influence the interactions of cytochrome C with its physiological redox partners during the electron transfer process. The oxidation state dependent conformational differences are found to be much greater in solution than in the crystalline state, and the solution and crystal structures differ significantly in regions close to the heme. These results suggest that the highly charged nature of cytochrome C makes this protein particularly sensitive to the ionic strength of its environment and leads to differences between crystal and solution structures in the same oxidation state. In such cases, crystal structures must be used with caution for modeling molecular interactions in vivo. More generally, this analysis indicates that the determination of accurate local conformations based on nmr data can provide useful information about structure-functional aspects of proteins in solution.     

Decoding the Anti-Trypanosoma cruzi Action of HIV Peptidase Inhibitors Using Epimastigotes as a Model

Background

Aspartic peptidase inhibitors have shown antimicrobial action against distinct microorganisms. Due to an increase in the occurrence of Chagas'' disease/AIDS co-infection, we decided to explore the effects of HIV aspartic peptidase inhibitors (HIV-PIs) on Trypanosoma cruzi, the etiologic agent of Chagas'' disease.

Methodology and Principal Findings

HIV-PIs presented an anti-proliferative action on epimastigotes of T. cruzi clone Dm28c, with IC₅₀ values ranging from 0.6 to 14 µM. The most effective inhibitors, ritonavir, lopinavir and nelfinavir, also had an anti-proliferative effect against different phylogenetic T. cruzi strains. The HIV-PIs induced some morphological alterations in clone Dm28c epimastigotes, as reduced cell size and swollen of the cellular body. Transmission electron microscopy revealed that the flagellar membrane, mitochondrion and reservosomes are the main targets of HIV-PIs in T. cruzi epimastigotes. Curiously, an increase in the epimastigote-into-trypomastigote differentiation process of clone Dm28c was observed, with many of these parasites presenting morphological alterations including the detachment of flagellum from the cell body. The pre-treatment with the most effective HIV-PIs drastically reduced the interaction process between epimastigotes and the invertebrate vector Rhodnius prolixus. It was also noted that HIV-PIs induced an increase in the expression of gp63-like and calpain-related molecules, and decreased the cruzipain expression in epimastigotes as judged by flow cytometry and immunoblotting assays. The hydrolysis of a cathepsin D fluorogenic substrate was inhibited by all HIV-PIs in a dose-dependent manner, showing that the aspartic peptidase could be a possible target to these drugs. Additionally, we verified that ritonavir, lopinavir and nelfinavir reduced drastically the viability of clone Dm28c trypomastigotes, causing many morphological damages.

Conclusions and Significance

The results contribute to understand the possible role of aspartic peptidases in T. cruzi physiology, adding new in vitro insights into the possibility of exploiting the use of HIV-PIs in the clinically relevant forms of the parasite.