Determining physiological reaction probabilities to noise events during sleep

Some of the activations that occur during sleep, e.g. awakening reactions, can be considered adverse effects of noise events (e.g., airplane overflights or train passings) during the night. The occurrence of such reactions is an important indicator of the sleep disturbing potential of the particular noise stimulus and it is often desired to exactly quantify that potential in terms of a probability. Awakenings are considered the strongest form of reaction to noise stimuli during sleep and are one of the most often adopted criteria in night time noise protection concepts. However, the correct determination of noise induced awakening probability has given rise to debate in the scientific community in recent years. Because during every night's sleep, spontaneous awakenings can occur at any time, it remains unknown in principle, whether a particular awakening observed during the presence of a noise stimulus was induced by that stimulus or emerged spontaneously. Nevertheless, correctly determining the awakening probability in question is key when it comes to forecasting noise effects during the night. This article introduces two definitions of reaction probability, discusses their advantages and disadvantages, and develops a model of the influence of the time window duration in which reactions of sleepers are screened on the calculated reaction probabilit

mmView: a web-based viewer of the mmCIF format

<p>Abstract</p> <p>Background</p> <p>Structural biomolecular data are commonly stored in the PDB format. The PDB format is widely supported by software vendors because of its simplicity and readability. However, the PDB format cannot fully address many informatics challenges related to the growing amount of structural data. To overcome the limitations of the PDB format, a new textual format mmCIF was released in June 1997 in its version 1.0. mmCIF provides extra information which has the advantage of being in a computer readable form. However, this advantage becomes a disadvantage if a human must read and understand the stored data. While software tools exist to help to prepare mmCIF files, the number of available systems simplifying the comprehension and interpretation of the mmCIF files is limited.</p> <p>Findings</p> <p>In this paper we present mmView - a cross-platform web-based application that allows to explore comfortably the structural data of biomacromolecules stored in the mmCIF format. The mmCIF categories can be easily browsed in a tree-like structure, and the corresponding data are presented in a well arranged tabular form. The application also allows to display and investigate biomolecular structures via an integrated Java application Jmol.</p> <p>Conclusions</p> <p>The mmView software system is primarily intended for educational purposes, but it can also serve as a useful research tool. The mmView application is offered in two flavors: as an open-source stand-alone application (available from <url>http://sourceforge.net/projects/mmview</url>) that can be installed on the user's computer, and as a publicly available web server.</p

Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

<p>Abstract</p> <p>Background</p> <p>Tuberculosis is a contagious disease caused by <it>Mycobacterium tuberculosis </it>(Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.</p> <p>Results</p> <p>We utilized physicochemical properties of compounds to train four supervised classifiers (Naïve Bayes, Random Forest, J48 and SMO) on three publicly available bioassay screens of Mtb inhibitors and validated the robustness of the predictive models using various statistical measures.</p> <p>Conclusions</p> <p>This study is a comprehensive analysis of high-throughput bioassay data for anti-tubercular activity and the application of machine learning approaches to create target-agnostic predictive models for anti-tubercular agents.</p

A sensing mechanism for the detection of carbon nanotubes using selective photoluminescent probes based on ionic complexes with organic dyes

The multifunctional properties of carbon nanotubes (CNTs) make them a powerful platform for unprecedented innovations in a variety of practical applications. As a result of the surging growth of nanotechnology, nanotubes present a potential problem as an environmental pollutant, and as such, an efficient method for their rapid detection must be established. Here, we propose a novel type of ionic sensor complex for detecting CNTs – an organic dye that responds sensitively and selectively to CNTs with a photoluminescent signal. The complexes are formed through Coulomb attractions between dye molecules with uncompensated charges and CNTs covered with an ionic surfactant in water. We demonstrate that the photoluminescent excitation of the dye can be transferred to the nanotubes, resulting in selective and strong amplification (up to a factor of 6) of the light emission from the excitonic levels of CNTs in the near-infrared spectral range, as experimentally observed via excitation-emission photoluminescence (PL) mapping. The chirality of the nanotubes and the type of ionic surfactant used to disperse the nanotubes both strongly affect the amplification; thus, the complexation provides sensing selectivity towards specific CNTs. Additionally, neither similar uncharged dyes nor CNTs covered with neutral surfactant form such complexes. As model organic molecules, we use a family of polymethine dyes with an easily tailorable molecular structure and, consequently, tunable absorbance and PL characteristics. This provides us with a versatile tool for the controllable photonic and electronic engineering of an efficient probe for CNT detection

Virtual Screening of Bioassay Data

Background: There are three main problems associated with the virtual screening of bioassay data. The first is access to freely-available curated data, the second is the number of false positives that occur in the physical primary screening process, and finally the data is highly-imbalanced with a low ratio of Active compounds to Inactive compounds. This paper first discusses these three problems and then a selection of Weka cost-sensitive classifiers (Naive Bayes, SVM, C4.5 and Random Forest) are applied to a variety of bioassay datasets. Results: Pharmaceutical bioassay data is not readily available to the academic community. The data held at PubChem is not curated and there is a lack of detailed cross-referencing between Primary and Confirmatory screening assays. With regard to the number of false positives that occur in the primary screening process, the analysis carried out has been shallow due to the lack of crossreferencing mentioned above. In six cases found, the average percentage of false positives from the High-Throughput Primary screen is quite high at 64%. For the cost-sensitive classification, Weka's implementations of the Support Vector Machine and C4.5 decision tree learner have performed relatively well. It was also found, that the setting of the Weka cost matrix is dependent on the base classifier used and not solely on the ratio of class imbalance. Conclusions: Understandably, pharmaceutical data is hard to obtain. However, it would be beneficial to both the pharmaceutical industry and to academics for curated primary screening and corresponding confirmatory data to be provided. Two benefits could be gained by employing virtual screening techniques to bioassay data. First, by reducing the search space of compounds to be screened and secondly, by analysing the false positives that occur in the primary screening process, the technology may be improved. The number of false positives arising from primary screening leads to the issue of whether this type of data should be used for virtual screening. Care when using Weka's cost-sensitive classifiers is needed - across the board misclassification costs based on class ratios should not be used when comparing differing classifiers for the same dataset

Examining the role of three sets of innovation attributes for determining adoption of the interbank mobile payment service

The interbank mobile payment service (IMPS) is a very recent technology in India that serves the very critical purpose of a mobile wallet. To account for the adoption and use of IMPS by the Indian consumers, this study seeks to compare three competing sets of attributes borrowed from three recognized pieces of work in the area of innovations adoption. This study aims to examine which of the three sets of attributes better predicts the adoption of IMPS in an Indian context. The research model is empirically tested and validated against the data gathered from 323 respondents from different cities in India. The findings are analysed using the SPSS analysis tool, which are then discussed to derive the key conclusions from this study. The research implications are stated, limitations listed and suggestions for future research on this technology are then finally made

Inherited liver shunts in dogs elucidate pathways regulating embryonic development and clinical disorders of the portal vein

Congenital disorders of the hepatic portal vasculature are rare in man but occur frequently in certain dog breeds. In dogs, there are two main subtypes: intrahepatic portosystemic shunts, which are considered to stem from defective closure of the embryonic ductus venosus, and extrahepatic shunts, which connect the splanchnic vascular system with the vena cava or vena azygos. Both subtypes result in nearly complete bypass of the liver by the portal blood flow. In both subtypes the development of the smaller branches of the portal vein tree in the liver is impaired and terminal branches delivering portal blood to the liver lobules are often lacking. The clinical signs are due to poor liver growth, development, and function. Patency of the ductus venosus seems to be a digenic trait in Irish wolfhounds, whereas Cairn terriers with extrahepatic portosystemic shunts display a more complex inheritance. The genes involved in these disorders cannot be identified with the sporadic human cases, but in dogs, the genome-wide study of the extrahepatic form is at an advanced stage. The canine disease may lead to the identification of novel genes and pathways cooperating in growth and development of the hepatic portal vein tree. The same pathways likely regulate the development of the vascular system of regenerating livers during liver diseases such as hepatitis and cirrhosis. Therefore, the identification of these molecular pathways may provide a basis for future proregenerative intervention

Therapeutic opportunities within the DNA damage response

The DNA damage response (DDR) is essential for maintaining the genomic integrity of the cell, and its disruption is one of the hallmarks of cancer. Classically, defects in the DDR have been exploited therapeutically in the treatment of cancer with radiation therapies or genotoxic chemotherapies. More recently, protein components of the DDR systems have been identified as promising avenues for targeted cancer therapeutics. Here, we present an in-depth analysis of the function, role in cancer and therapeutic potential of 450 expert-curated human DDR genes. We discuss the DDR drugs that have been approved by the US Food and Drug Administration (FDA) or that are under clinical investigation. We examine large-scale genomic and expression data for 15 cancers to identify deregulated components of the DDR, and we apply systematic computational analysis to identify DDR proteins that are amenable to modulation by small molecules, highlighting potential novel therapeutic targets

Digital payments adoption research: A meta-analysis for generalising the effects of attitude, cost, innovativeness, mobility and price value on behavioural intention

yesThe rapid evolution of mobile-based technologies and applications has led to the development of several different forms of digital payment methods (DPMs) but with limited enthusiasm in consumers for adopting them. Hence, several academic studies have already been conducted to examine the role of various antecedents that determines consumers’ intention to adopt DPMs. The degree of effect and significance of several antecedents found to be inconsistent across different studies. This provided us a basis for undertaking a meta-analysis of existing research for estimating the cumulative effect of such antecedents. Therefore, this study aims to perform a meta-analysis of five antecedents (i.e. attitude, cost, mobility, price value and innovativeness) for confirming their overall influence on intentions to adopt DPMs. The results of this study suggest that the cumulative effect of four out of five antecedents found to be significant while influence of price value was found insignificant on behavioural intentions. The recommendations drawn from this research would help to decide if and when to use such antecedents for predicting consumer intention to adopt DPMs