Dendritic Spine Shape Analysis: A Clustering Perspective

Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis. We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types.Comment: Accepted for BioImageComputing workshop at ECCV 201

SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

Pathogen detection, testing, and control in fresh broccoli sprouts

BACKGROUND: The recent increased interest in consuming green vegetable sprouts has been tempered by the fact that fresh sprouts can in some cases be vehicles for food-borne illnesses. They must be grown according to proper conditions of sanitation and handled as a food product rather than as an agricultural commodity. When sprouts are grown in accordance with the criteria proposed from within the sprout industry, developed by regulatory agencies, and adhered to by many sprouters, green sprouts can be produced with very low risk. Contamination may occur when these guidelines are not followed. METHODS: A one year program of microbial hold-and-release testing, conducted in concert with strict seed and facility cleaning procedures by 13 U.S. broccoli sprout growers was evaluated. Microbial contamination tests were performed on 6839 drums of sprouts, equivalent to about 5 million consumer packages of fresh green sprouts. RESULTS: Only 24 (0.75%) of the 3191 sprout samples gave an initial positive test for Escherichia coli O157:H7 or Salmonella spp., and when re-tested, 3 drums again tested positive. Composite testing (e.g., pooling up to 7 drums for pathogen testing) was equally sensitive to single drum testing. CONCLUSION: By using a "test-and-re-test" protocol, growers were able to minimize crop destruction. By pooling drums for testing, they were also able to reduce testing costs which now represent a substantial portion of the costs associated with sprout growing. The test-and-hold scheme described herein allowed those few batches of contaminated sprouts to be found prior to packaging and shipping. These events were isolated, and only safe sprouts entered the food supply

A pilot study of IL-1 inhibition by anakinra in acute gout

Monosodium urate crystals stimulate monocytes and macrophages to release IL-1β through the NALP3 component of the inflammasome. The effectiveness of IL-1 inhibition in hereditary autoinflammatory syndromes with mutations in the NALP3 protein suggested that IL-1 inhibition might also be effective in relieving the inflammatory manifestations of acute gout. The effectiveness of IL-1 inhibition was first evaluated in a mouse model of monosodium urate crystal-induced inflammation. IL-1 inhibition prevented peritoneal neutrophil accumulation but TNF blockade had no effect. Based on these findings, we performed a pilot, open-labeled study (trial registration number ISRCTN10862635) in 10 patients with gout who could not tolerate or had failed standard antiinflammatory therapies. All patients received 100 mg anakinra daily for 3 days. All 10 patients with acute gout responded rapidly to anakinra. No adverse effects were observed. IL-1 blockade appears to be an effective therapy for acute gouty arthritis. The clinical findings need to be confirmed in a controlled study

How Accessible Was Information about H1N1 Flu? Literacy Assessments of CDC Guidance Documents for Different Audiences

We assessed the literacy level and readability of online communications about H1N1/09 influenza issued by the Centers for Disease Control and Prevention (CDC) during the first month of outbreak. Documents were classified as targeting one of six audiences ranging in technical expertise. Flesch-Kincaid (FK) measure assessed literacy level for each group of documents. ANOVA models tested for differences in FK scores across target audiences and over time. Readability was assessed for documents targeting non-technical audiences using the Suitability Assessment of Materials (SAM). Overall, there was a main-effect by audience, F(5, 82) = 29.72, P<.001, but FK scores did not vary over time, F(2, 82) = .34, P>.05. A time-by-audience interaction was significant, F(10, 82) = 2.11, P<.05. Documents targeting non-technical audiences were found to be text-heavy and densely-formatted. The vocabulary and writing style were found to adequately reflect audience needs. The reading level of CDC guidance documents about H1N1/09 influenza varied appropriately according to the intended audience; sub-optimal formatting and layout may have rendered some text difficult to comprehend

HIV-1 gp120 Induces Expression of IL-6 through a Nuclear Factor-Kappa B-Dependent Mechanism: Suppression by gp120 Specific Small Interfering RNA

In addition to its role in virus entry, HIV-1 gp120 has also been implicated in HIV-associated neurocognitive disorders. However, the mechanism(s) responsible for gp120-mediated neuroinflammation remain undefined. In view of increased levels of IL-6 in HIV-positive individuals with neurological manifestations, we sought to address whether gp120 is involved in IL-6 over-expression in astrocytes. Transfection of a human astrocyte cell line with a plasmid encoding gp120 resulted in increased expression of IL-6 at the levels of mRNA and protein by 51.3±2.1 and 11.6±2.2 fold respectively; this effect of gp120 on IL-6 expression was also demonstrated using primary human fetal astrocytes. A similar effect on IL-6 expression was observed when primary astrocytes were treated with gp120 protein derived from different strains of X4 and R5 tropic HIV-1. The induction of IL-6 could be abrogated by use of gp120-specific siRNA. Furthermore, this study showed that the NF-κB pathway is involved in gp120-mediated IL-6 over-expression, as IKK-2 and IKKβ inhibitors inhibited IL-6 expression by 56.5% and 60.8%, respectively. These results were also confirmed through the use of NF-κB specific siRNA. We also showed that gp120 could increase the phosphorylation of IκBα. Furthermore, gp120 transfection in the SVGA cells increased translocation of NF-κB from cytoplasm to nucleus. These results demonstrate that HIV-1 gp120-mediated over-expression of IL-6 in astrocytes is one mechanism responsible for neuroinflammation in HIV-infected individuals and this is mediated by the NF-κB pathway

Genetic Architecture of a Reinforced, Postmating, Reproductive Isolation Barrier between Neurospora Species Indicates Evolution via Natural Selection

A role for natural selection in reinforcing premating barriers is recognized, but selection for reinforcement of postmating barriers remains controversial. Organisms lacking evolvable premating barriers can theoretically reinforce postmating isolation, but only under restrictive conditions: parental investment in hybrid progeny must inhibit subsequent reproduction, and selected postmating barriers must restore parents' capacity to reproduce successfully. We show that reinforced postmating isolation markedly increases maternal fitness in the fungus Neurospora crassa, and we detect the evolutionary genetic signature of natural selection by quantitative trait locus (QTL) analysis of the reinforced barrier. Hybrid progeny of N. crassa and N. intermedia are highly inviable. Fertilization by local N. intermedia results in early abortion of hybrid fruitbodies, and we show that abortion is adaptive because only aborted maternal colonies remain fully receptive to future reproduction. In the first QTL analysis of postmating reinforcement in microbial eukaryotes, we identify 11 loci for abortive hybrid fruitbody development, including three major QTLs that together explain 30% of trait variance. One of the major QTLs and six QTLs of lesser effect are found on the mating-type determining chromosome of Neurospora. Several reinforcement QTLs are flanked by genetic markers showing either segregation distortion or non-random associations with alleles at other loci in a cross between N. crassa of different clades, suggesting that the loci also are associated with local effects on same-species reproduction. Statistical analysis of the allelic effects distribution for abortive hybrid fruitbody development indicates its evolution occurred under positive selection. Our results strongly support a role for natural selection in the evolution of reinforced postmating isolation in N. crassa