Use of a structural alphabet to find compatible folds for amino acid sequences

The structural annotation of proteins with no detectable homologs of known 3D structure identified using sequence-search methods is a major challenge today. We propose an original method that computes the conditional probabilities for the amino-acid sequence of a protein to fit to known protein 3D structures using a structural alphabet, known as Protein Blocks (PBs). PBs constitute a library of 16 local structural prototypes that approximate every part of protein backbone structures. It is used to encode 3D protein structures into 1D PB sequences and to capture sequence to structure relationships. Our method relies on amino acid occurrence matrices, one for each PB, to score global and local threading of query amino acid sequences to protein folds encoded into PB sequences. It does not use any information from residue contacts or sequence-search methods or explicit incorporation of hydrophobic effect. The performance of the method was assessed with independent test datasets derived from SCOP 1.75A. With a Z-score cutoff that achieved 95% specificity (i.e., less than 5% false positives), global and local threading showed sensitivity of 64.1% and 34.2%, respectively. We further tested its performance on 57 difficult CASP10 targets that had no known homologs in PDB: 38 compatible templates were identified by our approach and 66% of these hits yielded correctly predicted structures. This method scales-up well and offers promising perspectives for structural annotations at genomic level. It has been implemented in the form of a web-server that is freely available at http://www.bo-protscience.fr/forsa.

From Workstations to Workbenches: Towards Predicting Physicochemically Viable Protein-Protein Interactions Across a Host and a Pathogen

The understanding of protein-protein interactions is indispensable in comprehending most of the biological processes in a cell. Small-scale experiments as well as large-scale high-throughput techniques over the past few decades have facilitated identification and analysis of protein-protein interactions which form the basis of much of our knowledge on functional and regulatory aspects of proteins. However, such rich catalog of interaction data should be used with caution when establishing protein-protein interactions in silico, as the high-throughput datasets are prone to false positives. Numerous computational means developed to pursue genome-wide studies on protein-protein interactions at times overlook the mechanistic and molecular details, thus questioning the reliability of predicted protein-protein interactions. We review the development, advantages, and shortcomings of varied approaches and demonstrate that by providing a structural viewpoint in terms of shape complementarity and interaction energies at protein-protein interfaces coupled with information on expression and localization of proteins homologous to an interacting pair, it is possible to assess the credibility of predicted interactions in biological context. With a focus on human pathogen Mycobacterium tuberculosis H37Rv, we show that such scrupulous use of details at the molecular level can predict physicochemically viable protein-protein interactions across host and pathogen. Such predicted interactions have the potential to provide molecular basis of probable mechanisms of pathogenesis and hence open up ways to explore their usefulness as targets in the light of drug discovery. (c) 2014 IUBMB Life, 66(11):759-774, 2014

Trace Driven Dynamic Deadlock Detection and Reproduction

Dynamic analysis techniques have been proposed to detect potential deadlocks. Analyzing and comprehending each potential deadlock to determine whether the deadlock is feasible in a real execution requires significant programmer effort. Moreover, empirical evidence shows that existing analyses are quite imprecise. This imprecision of the analyses further void the manual effort invested in reasoning about non-existent defects. In this paper, we address the problems of imprecision of existing analyses and the subsequent manual effort necessary to reason about deadlocks. We propose a novel approach for deadlock detection by designing a dynamic analysis that intelligently leverages execution traces. To reduce the manual effort, we replay the program by making the execution follow a schedule derived based on the observed trace. For a real deadlock, its feasibility is automatically verified if the replay causes the execution to deadlock. We have implemented our approach as part of WOLF and have analyzed many large (upto 160KLoC) Java programs. Our experimental results show that we are able to identify 74% of the reported defects as true (or false) positives automatically leaving very few defects for manual analysis. The overhead of our approach is negligible making it a compelling tool for practical adoption.

Zero-Crossings Based Spectrum Sensing Under Noise Uncertainties

In this work, the hypothesis testing problem of spectrum sensing in a cognitive radio is formulated as a Goodness-of-fit test against the general class of noise distributions used in most communications-related applications. A simple, general, and powerful spectrum sensing technique based on the number of weighted zero-crossings in the observations is proposed. For the cases of uniform and exponential weights, an expression for computing the near-optimal detection threshold that meets a given false alarm probability constraint is obtained. The proposed detector is shown to be robust to two commonly encountered types of noise uncertainties, namely, the noise model uncertainty, where the PDF of the noise process is not completely known, and the noise parameter uncertainty, where the parameters associated with the noise PDF are either partially or completely unknown. Simulation results validate our analysis, and illustrate the performance benefits of the proposed technique relative to existing methods, especially in the low SNR regime and in the presence of noise uncertainties.

Do we see what we should see? Describing non-covalent interactions in protein structures including precision

The power of X-ray crystal structure analysis as a technique is to `see where the atoms are'. The results are extensively used by a wide variety of research communities. However, this `seeing where the atoms are' can give a false sense of security unless the precision of the placement of the atoms has been taken into account. Indeed, the presentation of bond distances and angles to a false precision (i.e. to too many decimal places) is commonplace. This article has three themes. Firstly, a basis for a proper representation of protein crystal structure results is detailed and demonstrated with respect to analyses of Protein Data Bank entries. The basis for establishing the precision of placement of each atom in a protein crystal structure is non-trivial. Secondly, a knowledge base harnessing such a descriptor of precision is presented. It is applied here to the case of salt bridges, i.e. ion pairs, in protein structures; this is the most fundamental place to start with such structure-precision representations since salt bridges are one of the tenets of protein structure stability. Ion pairs also play a central role in protein oligomerization, molecular recognition of ligands and substrates, allosteric regulation, domain motion and alpha-helix capping. A new knowledge base, SBPS (Salt Bridges in Protein Structures), takes these structural precisions into account and is the first of its kind. The third theme of the article is to indicate natural extensions of the need for such a description of precision, such as those involving metalloproteins and the determination of the protonation states of ionizable amino acids. Overall, it is also noted that this work and these examples are also relevant to protein three-dimensional structure molecular graphics software.

Solid-state optical absorption from optimally tuned time-dependent range-separated hybrid density functional theory

We present a framework for obtaining reliable solid-state charge and optical excitations and spectra from optimally tuned range-separated hybrid density functional theory. The approach, which is fully couched within the formal framework of generalized Kohn-Sham theory, allows for the accurate prediction of exciton binding energies. We demonstrate our approach through first principles calculations of one- and two-particle excitations in pentacene, a molecular semiconducting crystal, where our work is in excellent agreement with experiments and prior computations. We further show that with one adjustable parameter, set to produce the known band gap, this method accurately predicts band structures and optical spectra of silicon and lithium fluoride, prototypical covalent and ionic solids. Our findings indicate that for a broad range of extended bulk systems, this method may provide a computationally inexpensive alternative to many-body perturbation theory, opening the door to studies of materials of increasing size and complexity.

Lesion Detection in Breast Ultrasound Images Using Tissue Transition Analysis

Breast cancer is one of the leading cause of cancer related deaths in women and early detection is crucial for reducing mortality rates. In this paper, we present a novel and fully automated approach based on tissue transition analysis for lesion detection in breast ultrasound images. Every candidate pixel is classified as belonging to the lesion boundary, lesion interior or normal tissue based on its descriptor value. The tissue transitions are modeled using a Markov chain to estimate the likelihood of a candidate lesion region. Experimental evaluation on a clinical dataset of 135 images show that the proposed approach can achieve high sensitivity (95 %) with modest (3) false positives per image. The approach achieves very similar results (94 % for 3 false positives) on a completely different clinical dataset of 159 images without retraining, highlighting the robustness of the approach.

Mass spectrometry based characterization of Hb Beckman variant in a falsely elevated HbA(1c) sample

Glycated hemoglobin (HbA(1c)) is a `gold standard' biomarker for assessing the glycemic index of an individual. HbA(1c) is formed due to nonenzymatic glycosylation at N-terminal valine residue of the P-globin chain. Cation exchange based high performance liquid chromatography (CE HPLC) is mostly used to quantify HbA(1c), in blood sample. A few genetic variants of hemoglobin and post-translationally modified variants of hemoglobin interfere with CE HPLC-based quantification,. resulting in its false positive estimation. Using mass spectrometry, we analyzed a blood sample with abnormally high HbA(1c) (52.1%) in the CE HPLC method. The observed HbA(1c) did not corroborate the blood glucose level of the patient. A mass spectrometry based bottom up proteomics approach, intact globin chain mass analysis, and chemical modification of the proteolytic peptides identified the presence of Hb Beckman, a genetic variant of hemoglobin, in the experimental sample. A similar surface area to charge ratio between HbA(1c) and Hb Beckman might have resulted in the coelution of the variant with HbA(1c) in CE HPLC. Therefore, in the screening of diabetes mellitus through the estimation of HbA(1c), it is important to look for genetic variants of hemoglobin in samples that show abnormally high glycemic index, and HbA(1c) must be estimated using an alternative method. (C) 2015 Elsevier Inc. All rights reserved.

Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data

This study presents a comprehensive evaluation of five widely used multisatellite precipitation estimates (MPEs) against 1 degrees x 1 degrees gridded rain gauge data set as ground truth over India. One decade observations are used to assess the performance of various MPEs (Climate Prediction Center (CPC)-South Asia data set, CPC Morphing Technique (CMORPH), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks, Tropical Rainfall Measuring Mission's Multisatellite Precipitation Analysis (TMPA-3B42), and Global Precipitation Climatology Project). All MPEs have high detection skills of rain with larger probability of detection (POD) and smaller ``missing'' values. However, the detection sensitivity differs from one product (and also one region) to the other. While the CMORPH has the lowest sensitivity of detecting rain, CPC shows highest sensitivity and often overdetects rain, as evidenced by large POD and false alarm ratio and small missing values. All MPEs show higher rain sensitivity over eastern India than western India. These differential sensitivities are found to alter the biases in rain amount differently. All MPEs show similar spatial patterns of seasonal rain bias and root-mean-square error, but their spatial variability across India is complex and pronounced. The MPEs overestimate the rainfall over the dry regions (northwest and southeast India) and severely underestimate over mountainous regions (west coast and northeast India), whereas the bias is relatively small over the core monsoon zone. Higher occurrence of virga rain due to subcloud evaporation and possible missing of small-scale convective events by gauges over the dry regions are the main reasons for the observed overestimation of rain by MPEs. The decomposed components of total bias show that the major part of overestimation is due to false precipitation. The severe underestimation of rain along the west coast is attributed to the predominant occurrence of shallow rain and underestimation of moderate to heavy rain by MPEs. The decomposed components suggest that the missed precipitation and hit bias are the leading error sources for the total bias along the west coast. All evaluation metrics are found to be nearly equal in two contrasting monsoon seasons (southwest and northeast), indicating that the performance of MPEs does not change with the season, at least over southeast India. Among various MPEs, the performance of TMPA is found to be better than others, as it reproduced most of the spatial variability exhibited by the reference.

1D Tight-Binding Models Render Quantum First Passage Time ``Speakable''

The calculation of First Passage Time (moreover, even its probability density in time) has so far been generally viewed as an ill-posed problem in the domain of quantum mechanics. The reasons can be summarily seen in the fact that the quantum probabilities in general do not satisfy the Kolmogorov sum rule: the probabilities for entering and non-entering of Feynman paths into a given region of space-time do not in general add up to unity, much owing to the interference of alternative paths. In the present work, it is pointed out that a special case exists (within quantum framework), in which, by design, there exists one and only one available path (i.e., door-way) to mediate the (first) passage -no alternative path to interfere with. Further, it is identified that a popular family of quantum systems - namely the 1d tight binding Hamiltonian systems - falls under this special category. For these model quantum systems, the first passage time distributions are obtained analytically by suitably applying a method originally devised for classical (stochastic) mechanics (by Schroedinger in 1915). This result is interesting especially given the fact that the tight binding models are extensively used in describing everyday phenomena in condense matter physics.

Entropy Constrained Exemplar-based Image Inpainting

Image inpainting is the process of filling the unwanted region in an image marked by the user. It is used for restoring old paintings and photographs, removal of red eyes from pictures, etc. In this paper, we propose an efficient inpainting algorithm which takes care of false edge propagation. We use the classical exemplar based technique to find out the priority term for each patch. To ensure that the edge content of the nearest neighbor patch found by minimizing L-2 distance between patches, we impose an additional constraint that the entropy of the patches be similar. Entropy of the patch acts as a good measure of edge content. Additionally, we fill the image by considering overlapping patches to ensure smoothness in the output. We use structural similarity index as the measure of similarity between ground truth and inpainted image. The results of the proposed approach on a number of examples on real and synthetic images show the effectiveness of our algorithm in removing objects and thin scratches or text written on image. It is also shown that the proposed approach is robust to the shape of the manually selected target. Our results compare favorably to those obtained by existing techniques

Lack of Critical Slowing Down Suggests that Financial Meltdowns Are Not Critical Transitions, yet Rising Variability Could Signal Systemic Risk

Complex systems inspired analysis suggests a hypothesis that financial meltdowns are abrupt critical transitions that occur when the system reaches a tipping point. Theoretical and empirical studies on climatic and ecological dynamical systems have shown that approach to tipping points is preceded by a generic phenomenon called critical slowing down, i.e. an increasingly slow response of the system to perturbations. Therefore, it has been suggested that critical slowing down may be used as an early warning signal of imminent critical transitions. Whether financial markets exhibit critical slowing down prior to meltdowns remains unclear. Here, our analysis reveals that three major US (Dow Jones Index, S&P 500 and NASDAQ) and two European markets (DAX and FTSE) did not exhibit critical slowing down prior to major financial crashes over the last century. However, all markets showed strong trends of rising variability, quantified by time series variance and spectral function at low frequencies, prior to crashes. These results suggest that financial crashes are not critical transitions that occur in the vicinity of a tipping point. Using a simple model, we argue that financial crashes are likely to be stochastic transitions which can occur even when the system is far away from the tipping point. Specifically, we show that a gradually increasing strength of stochastic perturbations may have caused to abrupt transitions in the financial markets. Broadly, our results highlight the importance of stochastically driven abrupt transitions in real world scenarios. Our study offers rising variability as a precursor of financial meltdowns albeit with a limitation that they may signal false alarms.

Clean localization super-resolution microscopy for 3D biological imaging

We propose clean localization microscopy (a variant of fPALM) using a molecule filtering technique. Localization imaging involves acquiring a large number of images containing single molecule signatures followed by one-to-one mapping to render a super-resolution image. In principle, this process can be repeated for other z-planes to construct a 3D image. But, single molecules observed from off-focal planes result in false representation of their presence in the focal plane, resulting in incorrect quantification and analysis. We overcome this with a single molecule filtering technique that imposes constraints on the diffraction limited spot size of single molecules in the image plane. Calibration with sub-diffraction size beads puts a natural cutoff on the actual diffraction-limited size of single molecules in the focal plane. This helps in distinguishing beads present in the focal plane from those in the off-focal planes thereby providing an estimate of the single molecules in the focal plane. We study the distribution of actin (labeled with a photoactivatable CAGE 552 dye) in NIH 3T3 mouse fibroblast cells. (C) 2016 Author(s).

Evaluación de una mezcla de cepas de Rhizobium bajo diferentes niveles de fertilización en tres variedades de frijol rojo (Phaseolus vulgaris L.) en Nicaragua

El ensayo se realizó en la Estación Experimental La Compañía, Masatepe, en la época de postrera (octubre-diciembre) de 1998. Los objetivos fueron: Determinar el crecimiento y rendimiento de tres cultivares de frijol, utilizando la técnica de inoculación en bolsas de polietileno y evaluar el rendimiento de tres cultivares de frijol, utilizando la técnica de inoculación y diferentes niveles de fertilización en condiciones de campo. Se montaron dos ensayos, uno en bolsas de polietileno con diseño en parcelas divididas en BCA con 40 observaciones y el ensayo en campo con diseño en parcelas subdivididas con cuatro réplicas. Los factores evaluados fueron; A: variedades (a1: Door-364, a2: Compañia y •a3: Criolla), B: inoculante con cepa de Rhizobium (b1: Semilla Inoculada y b2: Semilla Sin Inocular) y C: niveles de fertilizantes (c1: 127 kg/ha de completo (12-24-12) a la siembra, C2: 127 kg/ha de completo (12-24-12) a la siembra más 63.5 kg/ha de urea a los 25 días después de la siembra y c3: 127 kg/ha de completo (12-24-12) a la siembra más 127 kg/ha de urea a los 25 días después de la siembra),• este último factor no se evaluó en el ensayo en bolsas. En el experimento en bolsas la variedad Door-364 obtuvo el mayor peso seco y la variedad Criolla el mayor rendimiento entre los demás cultivares. En el factor inoculante con cepa de Rhizobium las plantas no inoculadas superaron en altura, peso seco de plantas y rendimientos a las plantas inoculadas. En el experimento en campo la variedad Door-364 superó en número de vainas por planta, granos por vaina, peso seco de paja y rendimiento en campo a las demás variedades, los cultivares Compañia y Criolla presentaron mayor peso en mil granos y población por hectárea respectivamente. Las plantas inoculadas superaron a las plantas no inoculadas en todas las variables con excepción del peso seco de paja. El nivel de fertilizantes 127 kg/ha de la formula completa (l2-24-12) a la siembra más 127 kg/ha de urea a los 25 días después de la siembra obtuvo más vainas por planta, peso de mil granos, rendimientos y peso seco de paja. La dosis de 127 kg/ha de la fórmula completa (12-24-12) a la siembra más 63.5 kg/ha de urea a los 25 días después de la siembra presentó el mayor número de plantas por hectárea y granos por vaina, en esta última variable la dosis de 127 kg/ha de la fórmula completa (12-24-12) a la siembra obtuvo el mismo resultado.

Image-based localization and pose recovery using scale invariant features

In this paper, we propose a vision based mobile robot localization strategy. Local scale-invariant features are used as natural landmarks in unstructured and unmodified environment. The local characteristics of the features we use prove to be robust to occlusion and outliers. In addition, the invariance of the features to viewpoint change makes them suitable landmarks for mobile robot localization. Scale-invariant features detected in the first exploration are indexed into a location database. Indexing and voting allow efficient recognition of global localization. The localization result is verified by epipolar geometry between the representative view in database and the view to be localized, thus the probability of false localization will be decreased. The localization system can recover the pose of the camera mounted on the robot by essential matrix decomposition. Then the position of the robot can be computed easily. Both calibrated and un-calibrated cases are discussed and relative position estimation based on calibrated camera turns out to be the better choice. Experimental results show that our approach is effective and reliable in the case of illumination changes, similarity transformations and extraneous features. © 2004 IEEE.