Dynamic graph-based search in unknown environments

A novel graph-based approach to search in unknown environments is presented. A virtual geometric structure is imposed on the environment represented in computer memory by a graph. Algorithms use this representation to coordinate a team of robots (or entities). Local discovery of environmental features cause dynamic expansion of the graph resulting in global exploration of the unknown environment. The algorithm is shown to have O(k.nH) time complexity, where nH is the number of vertices of the discovered environment and 1 <= k <= nH. A maximum bound on the length of the resulting walk is given

Gravitational Lensing by Nearby Clusters of Galaxies

We present an estimation of the expected number of arcs and arclets in a sample of nearby (z<0.1) clusters of galaxies, that takes into account the magnitude limit of the objects as well as seeing effects. We show that strong lensing effects are not common, but also they are not as rare as usually stated. Indeed, for a given cluster, they present a strong dependence with the magnitude limit adopted in the analysis and the seeing of the observations. We also describe the procedures and results of a search for lensing effects in a sample of 33 clusters spanning the redshift range of 0.014 to 0.076, representative of the local cluster distribution. This search produced two arc candidates. The first one is in A3408 (z=0.042), the same arc previously discovered by Campusano & Hardy (1996), with z=0.073 and associated to the brightest cluster galaxy. The second candidate is in the cluster A3266 (z=0.059) and is near a bright elliptical outside the cluster center, requiring the presence of a very massive sub-structure around this galaxy to be produced by gravitational lensing.Comment: 22 pages including 9 Figures and 2 Tables, submitted to A

Liquid-Solid Transition of Hard Spheres Under Gravity

We investigate the liquid-solid transition of two dimensional hard spheres in the presence of gravity. We determine the transition temperature and the fraction of particles in the solid regime as a function of temperature via Even-Driven molecular dynamics simulations and compare them with the theoretical predictions. We then examine the configurational statistics of a vibrating bed from the view point of the liquid-solid transition by explicitly determining the transition temperature and the effective temperature, T, of the bed, and present a relation between T and the vibration strength.Comment: 14 total pages, 4 figure

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even surpassed the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on 3D convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS), the most widespread autoimmune neuroinflammatory disease. MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients (n = 76) and healthy controls (n = 71). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of CNN models transparent, which could serve to justify classification decisions for clinical review, verify diagnosis-relevant features and potentially gather new disease knowledge

Kinetic and Modeling Studies of the Reaction S + H2S

Article on kinetic and modeling studies of the reaction S + H2S

Demonstration of Binding of Neuronal Calcium Sensor-1 to the Ca(v)2.1 P/Q-Type Calcium Channel

[Image: see text] In neurons, entry of extracellular calcium (Ca(2+)) into synaptic terminals through Ca(v)2.1 (P/Q-type) Ca(2+) channels is the driving force for exocytosis of neurotransmitter-containing synaptic vesicles. This class of Ca(2+) channel is, therefore, pivotal during normal neurotransmission in higher organisms. In response to channel opening and Ca(2+) influx, specific Ca(2+)-binding proteins associate with cytoplasmic regulatory domains of the P/Q channel to modulate subsequent channel opening. Channel modulation in this way influences synaptic plasticity with consequences for higher-level processes such as learning and memory acquisition. The ubiquitous Ca(2+)-sensing protein calmodulin (CaM) regulates the activity of all types of mammalian voltage-gated Ca(2+) channels, including the P/Q class, by direct binding to specific regulatory motifs. More recently, experimental evidence has highlighted a role for additional Ca(2+)-binding proteins, particularly of the CaBP and NCS families in the regulation of P/Q channels. NCS-1 is a protein found from yeast to humans and that regulates a diverse number of cellular functions. Physiological and genetic evidence indicates that NCS-1 regulates P/Q channel activity, including calcium-dependent facilitation, although a direct physical association between the proteins has yet to be demonstrated. In this study, we aimed to determine if there is a direct interaction between NCS-1 and the C-terminal cytoplasmic tail of the Ca(v)2.1 α-subunit. Using distinct but complementary approaches, including in vitro binding of bacterially expressed recombinant proteins, fluorescence spectrophotometry, isothermal titration calorimetry, nuclear magnetic resonance, and expression of fluorescently tagged proteins in mammalian cells, we show direct binding and demonstrate that CaM can compete for it. We speculate about how NCS-1/Ca(v)2.1 association might add to the complexity of calcium channel regulation mediated by other known calcium-sensing proteins and how this might help to fine-tune neurotransmission in the mammalian central nervous system

Functional connectome fingerprinting and stability in multiple sclerosis

BACKGROUND: Functional connectome fingerprinting can identify individuals based on their functional connectome. Previous studies relied mostly on short intervals between fMRI acquisitions. OBJECTIVE: This cohort study aimed to determine the stability of connectome-based identification and their underlying signatures in patients with multiple sclerosis and healthy individuals with long follow-up intervals. METHODS: We acquired resting-state fMRI in 70 patients with multiple sclerosis and 273 healthy individuals with long follow-up times (up to 4 and 9 years, respectively). Using functional connectome fingerprinting, we examined the stability of the connectome and additionally investigated which regions, connections and networks supported individual identification. Finally, we predicted cognitive and behavioural outcome based on functional connectivity. RESULTS: Multiple sclerosis patients showed connectome stability and identification accuracies similar to healthy individuals, with longer time delays between imaging sessions being associated with accuracies dropping from 89% to 76%. Lesion load, brain atrophy or cognitive impairment did not affect identification accuracies within the range of disease severity studied. Connections from the fronto-parietal and default mode network were consistently most distinctive, i.e., informative of identity. The functional connectivity also allowed the prediction of individual cognitive performances. CONCLUSION: Our results demonstrate that discriminatory signatures in the functional connectome are stable over extended periods of time in multiple sclerosis, resulting in similar identification accuracies and distinctive long-lasting functional connectome fingerprinting signatures in patients and healthy individuals

MRI Pattern Recognition in Multiple Sclerosis Normal-Appearing Brain Areas

Objective Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsing­remitting type) in lesioned areas, areas of normal­appearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR techniques. Methods A lesion mapping was carried out by an experienced neurologist for Turbo Inversion Recovery Magnitude (TIRM) images of individual subjects. Combining this mapping with templates from a neuroanatomic atlas, the TIRM images were segmented into three areas of homogenous tissue types (Lesions, NAGM, and NAWM) after spatial standardization. For each area, a linear Support Vector Machine algorithm was used in multiple local classification analyses to determine the diagnostic accuracy in separating MS patients from healthy controls based on voxel tissue intensity patterns extracted from small spherical subregions of these larger areas. To control for covariates, we also excluded group-specific biases in deformation fields as a potential source of information. Results Among regions containing lesions a posterior parietal WM area was maximally informative about the clinical status (96% accuracy, p<10−13). Cerebellar regions were maximally informative among NAGM areas (84% accuracy, p<10−7). A posterior brain region was maximally informative among NAWM areas (91% accuracy, p<10−10). Interpretation We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas. This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes. Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale

Parental phonological memory contributes to prediction of outcome of late talkers from 20 months to 4 years: a longitudinal study of precursors of specific language impairment

Background Many children who are late talkers go on to develop normal language, but others go on to have longer-term language difficulties. In this study, we considered which factors were predictive of persistent problems in late talkers. Methods Parental report of expressive vocabulary at 18 months of age was used to select 26 late talkers and 70 average talkers, who were assessed for language and cognitive ability at 20 months of age. Follow-up at 4 years of age was carried out for 24 late and 58 average talkers. A psychometric test battery was used to categorize children in terms of language status (unimpaired or impaired) and nonverbal ability (normal range or more than 1 SD below average). The vocabulary and non-word repetition skills of the accompanying parent were also assessed. Results Among the late talkers, seven (29%) met our criteria for specific language impairment (SLI) at 4 years of age, and a further two (8%) had low nonverbal ability. In the group of average talkers, eight (14%) met the criteria for SLI at 4 years, and five other children (8%) had low nonverbal ability. Family history of language problems was slightly better than late-talker status as a predictor of SLI.. The best predictors of SLI at 20 months of age were score on the receptive language scale of the Mullen Scales of Early Learning and the parent's performance on a non-word repetition task. Maternal education was not a significant predictor of outcome. Conclusions In this study, around three-quarters of late talkers did not have any language difficulties at 4 years of age, provided there was no family history of language impairment. A family history of language-literacy problems was found to be a significant predictor for persisting problems. Nevertheless, there are children with SLI for whom prediction is difficult because they did not have early language delay