Traffic crashes at toll plazas in Hong Kong

Poisson regression was used to identify the significant contributory factors to traffic crashes at toll plaza areas in Hong Kong. Information on the crash incidences and traffic volume at the toll plaza areas of ten tolled roads in Hong Kong during 1998-2003 were obtained from the traffic accident database system and annual traffic census of the Transport Department of the Government of Hong Kong Special Administrative Region. These data, together with the geometric and operational characteristics, including toll plaza width, carriageway width, degree of road curvature, road gradient, and toll booth configuration, were incorporated into two aggregated crash predictive models for different traffic directions. The results revealed that the crash likelihood of inbound traffic was increased significantly with downward slope and the crash likelihood of outbound traffic increased with the degree of road curvature.published_or_final_versio

Using keystroke logging to understand writers’ processes on a reading-into-writing test

Background Integrated reading-into-writing tasks are increasingly used in large-scale language proficiency tests. Such tasks are said to possess higher authenticity as they reflect real-life writing conditions better than independent, writing-only tasks. However, to effectively define the reading-into-writing construct, more empirical evidence regarding how writers compose from sources both in real-life and under test conditions is urgently needed. Most previous process studies used think aloud or questionnaire to collect evidence. These methods rely on participants’ perceptions of their processes, as well as their ability to report them. Findings This paper reports on a small-scale experimental study to explore writers’ processes on a reading-into-writing test by employing keystroke logging. Two L2 postgraduates completed an argumentative essay on computer. Their text production processes were captured by a keystroke logging programme. Students were also interviewed to provide additional information. Keystroke logging like most computing tools provides a range of measures. The study examined the students’ reading-into-writing processes by analysing a selection of the keystroke logging measures in conjunction with students’ final texts and interview protocols. Conclusions The results suggest that the nature of the writers’ reading-into-writing processes might have a major influence on the writer’s final performance. Recommendations for future process studies are provided

Risk-Averse Matchings over Uncertain Graph Databases

A large number of applications such as querying sensor networks, and analyzing protein-protein interaction (PPI) networks, rely on mining uncertain graph and hypergraph databases. In this work we study the following problem: given an uncertain, weighted (hyper)graph, how can we efficiently find a (hyper)matching with high expected reward, and low risk? This problem naturally arises in the context of several important applications, such as online dating, kidney exchanges, and team formation. We introduce a novel formulation for finding matchings with maximum expected reward and bounded risk under a general model of uncertain weighted (hyper)graphs that we introduce in this work. Our model generalizes probabilistic models used in prior work, and captures both continuous and discrete probability distributions, thus allowing to handle privacy related applications that inject appropriately distributed noise to (hyper)edge weights. Given that our optimization problem is NP-hard, we turn our attention to designing efficient approximation algorithms. For the case of uncertain weighted graphs, we provide a $\frac{1}{3}$-approximation algorithm, and a $\frac{1}{5}$-approximation algorithm with near optimal run time. For the case of uncertain weighted hypergraphs, we provide a $\Omega(\frac{1}{k})$-approximation algorithm, where $k$ is the rank of the hypergraph (i.e., any hyperedge includes at most $k$ nodes), that runs in almost (modulo log factors) linear time. We complement our theoretical results by testing our approximation algorithms on a wide variety of synthetic experiments, where we observe in a controlled setting interesting findings on the trade-off between reward, and risk. We also provide an application of our formulation for providing recommendations of teams that are likely to collaborate, and have high impact.Comment: 25 page

Bistability in Apoptosis by Receptor Clustering

Apoptosis is a highly regulated cell death mechanism involved in many physiological processes. A key component of extrinsically activated apoptosis is the death receptor Fas, which, on binding to its cognate ligand FasL, oligomerize to form the death-inducing signaling complex. Motivated by recent experimental data, we propose a mathematical model of death ligand-receptor dynamics where FasL acts as a clustering agent for Fas, which form locally stable signaling platforms through proximity-induced receptor interactions. Significantly, the model exhibits hysteresis, providing an upstream mechanism for bistability and robustness. At low receptor concentrations, the bistability is contingent on the trimerism of FasL. Moreover, irreversible bistability, representing a committed cell death decision, emerges at high concentrations, which may be achieved through receptor pre-association or localization onto membrane lipid rafts. Thus, our model provides a novel theory for these observed biological phenomena within the unified context of bistability. Importantly, as Fas interactions initiate the extrinsic apoptotic pathway, our model also suggests a mechanism by which cells may function as bistable life/death switches independently of any such dynamics in their downstream components. Our results highlight the role of death receptors in deciding cell fate and add to the signal processing capabilities attributed to receptor clustering.Comment: Accepted by PLoS Comput Bio

Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Given that the molecular machines control mRNA levels of multiple genes, we expect genetic variation in the components of these machines would influence multiple genes in a similar fashion. In this study we show that this assumption is correct by using correlation of mRNA levels measured independently in the brain, kidney or liver of multiple, genetically typed, mice strains to detect shared genetic influences. These correlating groups of genes (CGG) have collective properties that account for 40-90% of the variability of their constituent genes and in some cases, but not all, contain genes encoding functionally related proteins. Critically, we show that the genetic influences are essentially tissue specific and consequently the same genetic variations in the one animal may up-regulate a CGG in one tissue but down-regulate the same CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. The implication of this study is that this class of genetic variation can result in complex inter- and intra-individual and tissue differences and that this will create substantial challenges to the investigation of phenotypic outcomes, particularly in humans where multiple tissues are not readily available.

&#xa

Synthesis and Spectral Studies of CdTe–Dendrimer Conjugates

In order to couple high cellular uptake and target specificity of dendrimer molecule with excellent optical properties of semiconductor nanoparticles, the interaction of cysteine-capped CdTe quantum dots with dendrimer was investigated through spectroscopic techniques. NH2-terminated dendrimer molecule quenched the photoluminescence of CdTe quantum dots. The binding constants and binding capacity were calculated, and the nature of binding was found to be noncovalent. Significant decrease in luminescence intensity of CdTe quantum dots owing to noncovalent binding with dendrimer limits further utilization of these nanoassemblies. Hence, an attempt is made, for the first time, to synthesize stable, highly luminescent, covalently linked CdTe–Dendrimer conjugate in aqueous medium using glutaric dialdehyde (G) linker. Conjugate has been characterized through Fourier transform infrared spectroscopy and transmission electron microscopy. In this strategy, photoluminescence quantum efficiency of CdTe quantum dots with narrow emission bandwidths remained unaffected after formation of the conjugate

RAIphy: Phylogenetic classification of metagenomics samples using iterative refinement of relative abundance index profiles

Background: Computational analysis of metagenomes requires the taxonomical assignment of the genome contigs assembled from DNA reads of environmental samples. Because of the diverse nature of microbiomes, the length of the assemblies obtained can vary between a few hundred bp to a few hundred Kbp. Current taxonomic classification algorithms provide accurate classification for long contigs or for short fragments from organisms that have close relatives with annotated genomes. These are significant limitations for metagenome analysis because of the complexity of microbiomes and the paucity of existing annotated genomes. Results: We propose a robust taxonomic classification method, RAIphy, that uses a novel sequence similarity metric with iterative refinement of taxonomic models and functions effectively without these limitations. We have tested RAIphy with synthetic metagenomics data ranging between 100 bp to 50 Kbp. Within a sequence read range of 100 bp-1000 bp, the sensitivity of RAIphy ranges between 38%-81% outperforming the currently popular composition-based methods for reads in this range. Comparison with computationally more intensive sequence similarity methods shows that RAIphy performs competitively while being significantly faster. The sensitivityspecificity characteristics for relatively longer contigs were compared with the PhyloPythia and TACOA algorithms. RAIphy performs better than these algorithms at varying clade-levels. For an acid mine drainage (AMD) metagenome, RAIphy was able to taxonomically bin the sequence read set more accurately than the currently available methods, Phymm and MEGAN, and more accurately in two out of three tests than the much more computationally intensive method, PhymmBL. Conclusions: With the introduction of the relative abundance index metric and an iterative classification method, we propose a taxonomic classification algorithm that performs competitively for a large range of DNA contig lengths assembled from metagenome data. Because of its speed, simplicity, and accuracy RAIphy can be successfully used in the binning process for a broad range of metagenomic data obtained from environmental samples

Ontological addiction: classification, etiology, and treatment

Despite the fact that there is increasing integration of Buddhist principles and practices into Western mental health and applied psychological disciplines, there appears to be limited understanding in Western psychology of the assumptions that underlie a Buddhist model of mental illness. The concept of ontological addiction was introduced and formulated in order to narrow some of the disconnect between Buddhist and Western models of mental illness, and to foster effective assimilation of Buddhist practices and principles into mental health research and practice. Ontological addiction refers to the maladaptive condition whereby an individual is addicted to the belief that they inherently exist. The purposes of the present paper are to: (i) classify ontological addiction in terms of its definition, symptoms, prevalence, and functional consequences, (ii) examine the etiology of the condition, and (iii) appraise both the traditional Buddhist and contemporary empirical literature in order to outline effective treatment strategies. An assessment of the extent to which ontological addiction meets the clinical criteria for addiction suggests that ontological addiction is a chronic and valid – albeit functionally distinct (i.e., when compared to chemical and behavioral addictions) – form of addiction. However, despite the protracted and pervasive nature of the condition, recent empirical findings add support to ancient Buddhist teachings and suggest that addiction to selfhood can be overcome by a treatment process involving phases of: (i) becoming aware of the imputed self, (ii) deconstructing the imputed self, and (iii) reconstructing a dynamic and non-dual self