Tracking Target Signal Strengths on a Grid using Sparsity

Multi-target tracking is mainly challenged by the nonlinearity present in the measurement equation, and the difficulty in fast and accurate data association. To overcome these challenges, the present paper introduces a grid-based model in which the state captures target signal strengths on a known spatial grid (TSSG). This model leads to \emph{linear} state and measurement equations, which bypass data association and can afford state estimation via sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of the novel model, two types of sparsity-cognizant TSSG-KF trackers are developed: one effects sparsity through $\ell_1$-norm regularization, and the other invokes sparsity as an extra measurement. Iterative extended KF and Gauss-Newton algorithms are developed for reduced-complexity tracking, along with accurate error covariance updates for assessing performance of the resultant sparsity-aware state estimators. Based on TSSG state estimates, more informative target position and track estimates can be obtained in a follow-up step, ensuring that track association and position estimation errors do not propagate back into TSSG state estimates. The novel TSSG trackers do not require knowing the number of targets or their signal strengths, and exhibit considerably lower complexity than the benchmark hidden Markov model filter, especially for a large number of targets. Numerical simulations demonstrate that sparsity-cognizant trackers enjoy improved root mean-square error performance at reduced complexity when compared to their sparsity-agnostic counterparts.Comment: Submitted to IEEE Trans. on Signal Processin

A bank of unscented Kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics

Characterization of complex networks: A survey of measurements

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of measurements for inclusion are welcomed by the author

Systemic inflammatory response syndrome in adult patients with nosocomial bloodstream infections due to enterococci

BACKGROUND: Enterococci are the third leading cause of nosocomial bloodstream infection (BSI). Vancomycin resistant enterococci are common and provide treatment challenges; however questions remain about VRE's pathogenicity and its direct clinical impact. This study analyzed the inflammatory response of Enterococcal BSI, contrasting infections from vancomycin-resistant and vancomycin-susceptible isolates. METHODS: We performed a historical cohort study on 50 adults with enterococcal BSI to evaluate the associated systemic inflammatory response syndrome (SIRS) and mortality. We examined SIRS scores 2 days prior through 14 days after the first positive blood culture. Vancomycin resistant (n = 17) and susceptible infections (n = 33) were compared. Variables significant in univariate analysis were entered into a logistic regression model to determine the affect on mortality. RESULTS: 60% of BSI were caused by E. faecalis and 34% by E. faecium. 34% of the isolates were vancomycin resistant. Mean APACHE II (A2) score on the day of BSI was 16. Appropriate antimicrobials were begun within 24 hours in 52%. Septic shock occurred in 62% and severe sepsis in an additional 18%. Incidence of organ failure was as follows: respiratory 42%, renal 48%, hematologic 44%, hepatic 26%. Crude mortality was 48%. Progression to septic shock was associated with death (OR 14.9, p < .001). There was no difference in A2 scores on days -2, -1 and 0 between the VRE and VSE groups. Maximal SIR (severe sepsis, septic shock or death) was seen on day 2 for VSE BSI vs. day 8 for VRE. No significant difference was noted in the incidence of organ failure, 7-day or overall mortality between the two groups. Univariate analysis revealed that AP2>18 at BSI onset, and respiratory, cardiovascular, renal, hematologic and hepatic failure were associated with death, but time to appropriate therapy >24 hours, age, and infection due to VRE were not. Multivariate analysis revealed that hematologic (OR 8.4, p = .025) and cardiovascular failure (OR 7.5, p = 032) independently predicted death. CONCLUSION: In patients with enterococcal BSI, (1) the incidence of septic shock and organ failure is high, (2) patients with VRE BSI are not more acutely ill prior to infection than those with VSE BSI, and (3) the development of hematologic or cardiovascular failure independently predicts death

Time Pressure Modulates Electrophysiological Correlates of Early Visual Processing

BACKGROUND: Reactions to sensory events sometimes require quick responses whereas at other times they require a high degree of accuracy-usually resulting in slower responses. It is important to understand whether visual processing under different response speed requirements employs different neural mechanisms. METHODOLOGY/PRINCIPAL FINDINGS: We asked participants to classify visual patterns with different levels of detail as real-world or non-sense objects. In one condition, participants were to respond immediately, whereas in the other they responded after a delay of 1 second. As expected, participants performed more accurately in delayed response trials. This effect was pronounced for stimuli with a high level of detail. These behavioral effects were accompanied by modulations of stimulus related EEG gamma oscillations which are an electrophysiological correlate of early visual processing. In trials requiring speeded responses, early stimulus-locked oscillations discriminated real-world and non-sense objects irrespective of the level of detail. For stimuli with a higher level of detail, oscillatory power in a later time window discriminated real-world and non-sense objects irrespective of response speed requirements. CONCLUSIONS/SIGNIFICANCE: Thus, it seems plausible to assume that different response speed requirements trigger different dynamics of processing

Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies

<p>Abstract</p> <p>Background</p> <p>The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Biclustering algorithms also have important applications in sample classification where, for instance, tissue samples can be classified as cancerous or normal. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the "best" grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters.</p> <p>Results</p> <p>In this article, we present a rigorous approach to biclustering, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix so as to globally minimize the dissimilarity metric. The physical permutations of the rows and columns of the data matrix can be modeled as either a network flow problem or a traveling salesman problem. Cluster boundaries in one dimension are used to partition and re-order the other dimensions of the corresponding submatrices to generate biclusters. The performance of OREO is tested on (a) metabolite concentration data, (b) an image reconstruction matrix, (c) synthetic data with implanted biclusters, and gene expression data for (d) colon cancer data, (e) breast cancer data, as well as (f) yeast segregant data to validate the ability of the proposed method and compare it to existing biclustering and clustering methods.</p> <p>Conclusion</p> <p>We demonstrate that this rigorous global optimization method for biclustering produces clusters with more insightful groupings of similar entities, such as genes or metabolites sharing common functions, than other clustering and biclustering algorithms and can reconstruct underlying fundamental patterns in the data for several distinct sets of data matrices arising in important biological applications.</p

AP2α controls the dynamic balance between miR-126&126∗ and miR-221&222 during melanoma progression

Accumulating evidences have shown the association between aberrantly expressed microRNAs (miRs) and cancer, where these small regulatory RNAs appear to dictate the cell fate by regulating all the main biological processes. We demonstrated the responsibility of the circuitry connecting the oncomiR-221&222 with the tumor suppressors miR-126&126∗ in melanoma development and progression. According to the inverse correlation between endogenous miR-221&222 and miR-126&126∗, respectively increasing or decreasing with malignancy, their enforced expression or silencing was sufficient for a reciprocal regulation. In line with the opposite roles of these miRs, protein analyses confirmed the reverse expression pattern of miR-126&126∗-targeted genes that were induced by miR-221&222. Looking for a central player in this complex network, we revealed the dual regulation of AP2α, on one side directly targeted by miR-221&222 and on the other a transcriptional activator of miR-126&126∗. We showed the chance of restoring miR-126&126∗ expression in metastatic melanoma to reduce the amount of mature intracellular heparin-binding EGF like growth factor, thus preventing promyelocytic leukemia zinc finger delocalization and maintaining its repression on miR-221&222 promoter. Thus, the low-residual quantity of these two miRs assures the release of AP2α expression, which in turn binds to and induces miR-126&126∗ transcription. All together these results point to an unbalanced ratio functional to melanoma malignancy between these two couples of miRs. During progression this balance gradually moves from miR-126&126∗ toward miR-221&222. This circuitry, besides confirming the central role of AP2α in orchestrating melanoma development and/or progression, further displays the significance of these miRs in cancer and the option of utilizing them for novel therapeutics

A double-blind placebo-controlled trial of azithromycin to reduce mortality and improve growth in high-risk young children with non-bloody diarrhoea in low resource settings: the Antibiotics for Children with Diarrhoea (ABCD) trial protocol

Background Acute diarrhoea is a common cause of illness and death among children in low- to middle-income settings. World Health Organization guidelines for the clinical management of acute watery diarrhoea in children focus on oral rehydration, supplemental zinc and feeding advice. Routine use of antibiotics is not recommended except when diarrhoea is bloody or cholera is suspected. Young children who are undernourished or have a dehydrating diarrhoea are more susceptible to death at 90 days after onset of diarrhoea. Given the mortality risk associated with diarrhoea in children with malnutrition or dehydrating diarrhoea, expanding the use of antibiotics for this subset of children could be an important intervention to reduce diarrhoea-associated mortality and morbidity. We designed the Antibiotics for Childhood Diarrhoea (ABCD) trial to test this intervention. Methods ABCD is a double-blind, randomised trial recruiting 11,500 children aged 2–23 months presenting with acute non-bloody diarrhoea who are dehydrated and/or undernourished (i.e. have a high risk for mortality). Enrolled children in Bangladesh, India, Kenya, Malawi, Mali, Pakistan and Tanzania are randomised (1:1) to oral azithromycin 10 mg/kg or placebo once daily for 3 days and followed-up for 180 days. Primary efficacy endpoints are all-cause mortality during the 180 days post-enrolment and change in linear growth 90 days post-enrolment. Discussion Expanding the treatment of acute watery diarrhoea in high-risk children to include an antibiotic may offer an opportunity to reduce deaths. These benefits may result from direct antimicrobial effects on pathogens or other incompletely understood mechanisms including improved nutrition, alterations in immune responsiveness or improved enteric function. The expansion of indications for antibiotic use raises concerns about the emergence of antimicrobial resistance both within treated children and the communities in which they live. ABCD will monitor antimicrobial resistance. The ABCD trial has important policy implications. If the trial shows significant benefits of azithromycin use, this may provide evidence to support reconsideration of antibiotic indications in the present World Health Organization diarrhoea management guidelines. Conversely, if there is no evidence of benefit, these results will support the current avoidance of antibiotics except in dysentery or cholera, thereby avoiding inappropriate use of antibiotics and reaffirming the current guidelines. Trial registration Clinicaltrials.gov, NCT03130114. Registered on April 26 2017

Reconstruction of cellular variability from spatiotemporal patterns of Dictyostelium discoideum

Variability in cell properties can be an important driving mechanism behind spatiotemporal patterns in biological systems, as the degree of cell-to-cell differences determines the capacity of cells to locally synchronize and, consequently, form patterns on a larger spatial scale. In principle, certain features of spatial patterns emerging with time may be regulated by variability or, more specifically, by certain constellations of cell-to-cell differences. Similarly, measuring variability in a system (i.e. the spatial distribution of cell-cell differences) may help predict properties of later-stage patterns