A study of feature combination for vehicle detection based on image processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification.

Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development.

Structural kinetics of transcription activation at the malT promoter of Escherichia coli by UV laser footprinting

We have studied the kinetics of transcriptional initiation and activation at the malT and malTp1 promoters of Escherichia coli using UV laser footprinting. Contrary to previous studies and because of the very rapid signal acquisition by this technique, we can obtain structural information about true reaction intermediates of transcription initiation. The consequences of adding a transcriptional activator, the cAMP receptor protein/cAMP complex (CRP), are monitored in real time, permitting us to assign specific interactions to the activation of discrete steps in transcription initiation. Direct protein–protein contacts between CRP and the RNA polymerase appeared very rapidly, followed by DNA melting around the −10 hexamer. CRP slightly increased the rate of this isomerization reaction but, more importantly, favored the establishment of additional contacts between the DNA upstream of the CRP binding site and RNA polymerase subsequent to open complex formation. These contacts make a major contribution to transcriptional activation by stabilizing open forms of the promoter complex, thereby indirectly accelerating promoter escape. The ensemble of the kinetic, structural signals demonstrated directly that CRP exerts most of its activating effects on the late stages of transcriptional initiation at the malT promoter.

A transient expansion of the native state precedes aggregation of recombinant human interferon-γ

Aggregation of proteins, even under conditions favoring the native state, is a ubiquitous problem in biotechnology and biomedical engineering. Providing a mechanistic basis for the pathways that lead to aggregation should allow development of rational approaches for its prevention. We have chosen recombinant human interferon-γ (rhIFN-γ) as a model protein for a mechanistic study of aggregation. In the presence of 0.9 M guanidinium hydrochloride, rhIFN-γ aggregates with first order kinetics, a process that is inhibited by addition of sucrose. We describe a pathway that accounts for both the observed first-order aggregation of rhIFN-γ and the effect of sucrose. In this pathway, aggregation proceeds through a transient expansion of the native state. Sucrose shifts the equilibrium within the ensemble of rhIFN-γ native conformations to favor the most compact native species over more expanded ones, thus stabilizing rhIFN-γ against aggregation. This phenomenon is attributed to the preferential exclusion of sucrose from the protein surface. In addition, kinetic analysis combined with solution thermodynamics shows that only a small (9%) expansion surface area is needed to form the transient native state that precedes aggregation. The approaches used here link thermodynamics and aggregation kinetics to provide a powerful tool for understanding both the pathway of protein aggregation and the rational use of excipients to inhibit the process.

The structure and precision of retinal spike trains

Assessing the reliability of neuronal spike trains is fundamental to an understanding of the neural code. We measured the reproducibility of retinal responses to repeated visual stimuli. In both tiger salamander and rabbit, the retinal ganglion cells responded to random flicker with discrete, brief periods of firing. For any given cell, these firing events covered only a small fraction of the total stimulus time, often less than 5%. Firing events were very reproducible from trial to trial: the timing jitter of individual spikes was as low as 1 msec, and the standard deviation in spike count was often less than 0.5 spikes. Comparing the precision of spike timing to that of the spike count showed that the timing of a firing event conveyed several times more visual information than its spike count. This sparseness and precision were general characteristics of ganglion cell responses, maintained over the broad ensemble of stimulus waveforms produced by random flicker, and over a range of contrasts. Thus, the responses of retinal ganglion cells are not properly described by a firing probability that varies continuously with the stimulus. Instead, these neurons elicit discrete firing events that may be the fundamental coding symbols in retinal spike trains.

Observation of strange kinetics in protein folding

Highly nonexponential folding kinetics in aqueous solution have been observed during temperature jump-induced refolding of two proteins, yeast phosphoglycerate kinase and a ubiquitin mutant. The observations are most easily interpreted in terms of downhill folding, which posits a heterogeneous ensemble of structures en route to the folded state. The data are also reconciled with exponential kinetics measured under different experimental conditions and with titration experiments indicating cooperative folding.

Modeling the dynamics of human hair cycles by a follicular automaton

The hair follicle cycle successively goes through the anagen, catagen, telogen, and latency phases, which correspond, respectively, to hair growth, arrest, shedding, and absence before a new anagen phase is initiated. Experimental observations collected over a period of 14 years in a group of 10 male volunteers, alopecic and nonalopecic, allowed us to determine the characteristics of scalp hair follicle cycles. On the basis of these observations, we propose a follicular automaton model to simulate the dynamics of human hair cycles. The automaton model is defined by a set of rules that govern the stochastic transitions of each follicle between the successive states anagen, telogen, and latency, and the subsequent return to anagen. The transitions occur independently for each follicle, after time intervals given stochastically by a distribution characterized by a mean and a variance. The follicular automaton model accounts both for the dynamical transitions observed in a single follicle and for the behavior of an ensemble of independently cycling follicles. Thus, the model successfully reproduces the evolution of the fractions of follicle populations in each of the three phases, which fluctuate around steady-state or slowly drifting values. We apply the follicular automaton model to the study of spatial patterns of follicular growth that result from a spatially heterogeneous distribution of parameters such as the mean duration of anagen phase. When considering that follicles die or miniaturize after going through a critical number of successive cycles, the model can reproduce the evolution to hair patterns similar to well known types of diffuse or androgenetic alopecia.

A method for directed evolution and functional cloning of enzymes

A general scheme is described for the in vitro evolution of protein catalysts in a biologically amplifiable system. Substrate is covalently and site specifically attached by a flexible tether to the pIII coat protein of a filamentous phage that also displays the catalyst. Intramolecular conversion of substrate to product provides a basis for selecting active catalysts from a library of mutants, either by release from or attachment to a solid support. This methodology has been developed with the enzyme staphylococcal nuclease as a model. An analysis of factors influencing the selection efficiency is presented, and it is shown that phage displaying staphylococcal nuclease can be enriched 100-fold in a single step from a library-like ensemble of phage displaying noncatalytic proteins. Additionally, this approach should allow one to functionally clone natural enzymes, based on their ability to catalyze specific reactions (e.g., glycosyl transfer, sequence-specific proteolysis or phosphorylation, polymerization, etc.) rather than their sequence- or structural homology to known enzymes.

Characterization of residual structure in the thermally denatured state of barnase by simulation and experiment: Description of the folding pathway

Residual structure in the denatured state of a protein may contain clues about the early events in folding. We have simulated by molecular dynamics the denatured state of barnase, which has been studied by NMR spectroscopy. An ensemble of 104 structures was generated after 2 ns of unfolding and following for a further 2 ns. The ensemble was heterogeneous, but there was nonrandom, residual structure with persistent interactions. Helical structure in the C-terminal portion of helix α1 (residues 13–17) and in helix α2 as well as a turn and nonnative hydrophobic clustering between β3 and β4 were observed, consistent with NMR data. In addition, there were tertiary contacts between residues in α1 and the C-terminal portion of the β-sheet. The simulated structures allow the rudimentary NMR data to be fleshed out. The consistency between simulation and experiment inspires confidence in the methods. A description of the folding pathway of barnase from the denatured to the native state can be constructed by combining the simulation with experimental data from φ value analysis and NMR.

Highly expressed and alien genes of the Synechocystis genome

Comparisons of codon frequencies of genes to several gene classes are used to characterize highly expressed and alien genes on the Synechocystis PCC6803 genome. The primary gene classes include the ensemble of all genes (average gene), ribosomal protein (RP) genes, translation processing factors (TF) and genes encoding chaperone/degradation proteins (CH). A gene is predicted highly expressed (PHX) if its codon usage is close to that of the RP/TF/CH standards but strongly deviant from the average gene. Putative alien (PA) genes are those for which codon usage is significantly different from all four classes of gene standards. In Synechocystis, 380 genes were identified as PHX. The genes with the highest predicted expression levels include many that encode proteins vital for photosynthesis. Nearly all of the genes of the RP/TF/CH gene classes are PHX. The principal glycolysis enzymes, which may also function in CO2 fixation, are PHX, while none of the genes encoding TCA cycle enzymes are PHX. The PA genes are mostly of unknown function or encode transposases. Several PA genes encode polypeptides that function in lipopolysaccharide biosynthesis. Both PHX and PA genes often form significant clusters (operons). The proteins encoded by PHX and PA genes are described with respect to functional classifications, their organization in the genome and their stoichiometry in multi-subunit complexes.

A complexity measure for selective matching of signals by the brain.

We have previously derived a theoretical measure of neural complexity (CN) in an attempt to characterize functional connectivity in the brain. CN measures the amount and heterogeneity of statistical correlations within a neural system in terms of the mutual information between subsets of its units. CN was initially used to characterize the functional connectivity of a neural system isolated from the environment. In the present paper, we introduce a related statistical measure, matching complexity (CM), which reflects the change in CN that occurs after a neural system receives signals from the environment. CM measures how well the ensemble of intrinsic correlations within a neural system fits the statistical structure of the sensory input. We show that CM is low when the intrinsic connectivity of a simulated cortical area is randomly organized. Conversely, CM is high when the intrinsic connectivity is modified so as to differentially amplify those intrinsic correlations that happen to be enhanced by sensory input. When the input is represented by an individual stimulus, a positive value of CM indicates that the limited mutual information between sensory sheets sampling the stimulus and the rest of the brain triggers a large increase in the mutual information between many functionally specialized subsets within the brain. In this way, a complex brain can deal with context and go "beyond the information given."

Changes in expression of putative antigens encoded by pigment genes in mouse melanomas at different stages of malignant progression.

Cutaneous melanomas of Tyr-SV40E transgenic mice (mice whose transgene consists of the tyrosinase promoter fused to the coding regions of simian virus 40 early genes) strikingly resemble human melanomas in their development and progression. Unlike human melanomas, the mouse tumors all arise in genetically identical individuals, thereby better enabling expression of specific genes to be characterized in relation to advancing malignancy. The products of pigment genes are of particular interest because peptides derived from these proteins have been reported to function as autoantigens with immunotherapeutic potential in some melanoma patients. However, the diminished pigmentation characteristic of many advanced melanomas raises the possibility that some of the relevant products may no longer be expressed in the most malignant cells. We have therefore investigated the contributions of several pigment genes in melanotic vs. relatively amelanotic components of primary and metastatic mouse melanomas. The analyses reveal marked differences within and among tumors in levels of mRNAs and proteins encoded by the wild-type alleles at the albino, brown, slaty, and silver loci. Tyrosinase (the protein encoded by the albino locus) was most often either absent or undetectable as melanization declined. The protein encoded by the slaty locus (tyrosinase-related protein 2) was the only one of those tested that was clearly present in all the tumor samples. These results suggest that sole reliance on targeting tyrosinase-based antigens might selectively favor survival of more malignant cells, whereas targeting the ensemble of the antigens tested might contribute toward a more inclusive and effective antimelanoma strategy.

Experimental tests of quantum nonlinear dynamics in atom optics

Cold atoms in optical potentials provide an ideal test bed to explore quantum nonlinear dynamics. Atoms are prepared in a magneto-optic trap or as a dilute Bose-Einstein condensate and subjected to a far detuned optical standing wave that is modulated. They exhibit a wide range of dynamics, some of which can be explained by classical theory while other aspects show the underlying quantum nature of the system. The atoms have a mixed phase space containing regions of regular motion which appear as distinct peaks in the atomic momentum distribution embedded in a sea of chaos. The action of the atoms is of the order of Planck's constant, making quantum effects significant. This tutorial presents a detailed description of experiments measuring the evolution of atoms in time-dependent optical potentials. Experimental methods are developed providing means for the observation and selective loading of regions of regular motion. The dependence of the atomic dynamics on the system parameters is explored and distinct changes in the atomic momentum distribution are observed which are explained by the applicable quantum and classical theory. The observation of a bifurcation sequence is reported and explained using classical perturbation theory. Experimental methods for the accurate control of the momentum of an ensemble of atoms are developed. They use phase space resonances and chaotic transients providing novel ensemble atomic beamsplitters. The divergence between quantum and classical nonlinear dynamics is manifest in the experimental observation of dynamical tunnelling. It involves no potential barrier. However a constant of motion other than energy still forbids classically this quantum allowed motion. Atoms coherently tunnel back and forth between their initial state of oscillatory motion and the state 180 out of phase with the initial state.

Prediction of subcellular localization using sequence-biased recurrent networks

Motivation: Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging. Results: We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively.

Fitness assessment of document model

Document classification is a supervised machine learning process, where predefined category labels are assigned to documents based on the hypothesis derived from training set of labelled documents. Documents cannot be directly interpreted by a computer system unless they have been modelled as a collection of computable features. Rogati and Yang [M. Rogati and Y. Yang, Resource selection for domain-specific cross-lingual IR, in SIGIR 2004: Proceedings of the 27th annual international conference on Research and Development in Information Retrieval, ACM Press, Sheffied: United Kingdom, pp. 154-161.] pointed out that the effectiveness of document classification system may vary in different domains. This implies that the quality of document model contributes to the effectiveness of document classification. Conventionally, model evaluation is accomplished by comparing the effectiveness scores of classifiers on model candidates. However, this kind of evaluation methods may encounter either under-fitting or over-fitting problems, because the effectiveness scores are restricted by the learning capacities of classifiers. We propose a model fitness evaluation method to determine whether a model is sufficient to distinguish positive and negative instances while still competent to provide satisfactory effectiveness with a small feature subset. Our experiments demonstrated how the fitness of models are assessed. The results of our work contribute to the researches of feature selection, dimensionality reduction and document classification.