Advancements in the Development of non-BET Bromodomain Chemical Probes

The bromodomain and extra terminal (BET) family of bromodomain-containing proteins (BCPs) have been the subject of extensive research over the past decade, resulting in a plethora of high-quality chemical probes for their tandem bromodomains. In turn, these chemical probes have helped reveal the profound biological role of the BET bromodomains and their role in disease, ultimately leading to a number of molecules in active clinical development. However, the BET subfamily represents just 8/61 of the known human bromodomains, and attention has now expanded to the biological role of the remaining 53 non-BET bromodomains. Rapid growth of this research area has been accompanied by a greater understanding of the requirements for an effective bromodomain chemical probe and has led to a number of new non-BET bromodomain chemical probes being developed. Advances since December 2015 are discussed, highlighting the strengths/caveats of each molecule, and the value they add toward validating the non-BET bromodomains as tractable therapeutic targets.     

PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates.     

Technique for finding and identifying filters that cut off OTDR lights in front of ONU from a central office

We report on a newly developed in-service measurement technique that can be used from a central office to find and identify any filter in front of an ONU on an optical fiber access network. Using this system, in-service tests can be performed because the test lights are modulated at a high frequency. Moreover, by using the equipment we developed, this confirmation operation can be performed continuously and automatically with existing automatic fiber testing systems. The developed technique is effective for constructing a fiber line testing system with an optical time domain reflectometer.     

Effect of irradiation and packaging type on sensory quality of chill-stored turkey breast fillets

A panel of experienced assessors was used to test the effect of irradiation at 2.5kGy (250krad) on the sensory quality of 1-day and 21-day-old turkey breast fillets stored at 1°C in either oxygen-permeable polythene or an oxygen-impermeable barrier film. In the application of Free Choice Profiling and Generalized Procrustes Analysis, assessors used their own terms to describe the appearance, odour and flavour of the samples.
The irradiated samples showed negligible growth of micro-organisms across 21 days of storage. Radiation treatment resulted in an intense pink colour in the raw and cooked samples, which was maintained during storage in oxygen-impermeable film, but decreased in samples exposed to oxygen during storage. Radiation also produced a set of unpleasant raw odour notes variously described as sour, rancid, mature, bad meat or putrid in the samples stored in oxygen-impermeable film. These notes were unlike the sulphurous notes previously associated with protein denaturation in irradiated chicken and were apparently distinct from the odour notes that developed in corresponding non-irradiated samples.     

Exploring teachers' perceptions on different CSCL script editing tools

Despite the apparent maturity of the learning design field, and the variety of tooling available to support it, adoption among the teacher community (one of its alleged main targets) is still low. There is a lack of research on teachers' perception and use of different technological learning design tools, as existing evaluations are often restricted to a single tool. In order to explore whether there are common factors hampering teacher adoption, and which tool features might appeal to different teachers, more studies involving multiple authoring tools are needed. This paper provides a first step in this direction, describing a mixed methods study performed around a professional development workshop with 18 university teachers from multiple disciplines. This workshop exposed teachers to two different authoring tools (WebCollage and EDIT2), as they learned to create computer-supported collaborative learning (CSCL) designs and implement them. The findings of our interpretive study (which included questionnaires, observations, or group discussion recordings) support the idea that there is no single tool or set of features that are globally perceived as better, although our evidence also highlights certain factors as important for participant teachers – amongst others, the integration of learning designs with the ICT platforms for enactment, as well as with other tools that they already use in their everyday practice.     

The Role of Deep Learning in Parking Space Identification andPredictionSystems

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.     

Quantum Fuzzy Regression Model for Uncertain Environment

In the era of big data, traditional regression models cannot deal with uncertain big data efficiently and accurately. In order to make up for this deficiency, this paper proposes a quantum fuzzy regression model, which uses fuzzy theory to describe the uncertainty in big data sets and uses quantum computing to exponentially improve the efficiency of data set preprocessing and parameter estimation. In this paper, data envelopment analysis (DEA) is used to calculate the degree of importance of each data point. Meanwhile, Harrow, Hassidim and Lloyd (HHL) algorithm and quantum swap circuits are used to improve the efficiency of high-dimensional data matrix calculation. The application of the quantum fuzzy regression model to small-scale financial data proves that its accuracy is greatly improved compared with the quantum regression model. Moreover, due to the introduction of quantum computing, the speed of dealing with high-dimensional data matrix has an exponential improvement compared with the fuzzy regression model. The quantum fuzzy regression model proposed in this paper combines the advantages of fuzzy theory and quantum computing which can efficiently calculate high-dimensional data matrix and complete parameter estimation using quantum computing while retaining the uncertainty in big data. Thus, it is a new model for efficient and accurate big data processing in uncertain environments.     

3-Methyl-α-himachalene: Proposed structure for novel homosesquiterpene sex pheromone ofLutzomyia longipalpis (diptera: Psychodidae) from Jacobina,Brazil

The principal behaviorally active volatile component (ca. 90% +) of the sex pheromone glands ofLutzomyia longipalpis from Jacobina. Brazil, has been isolated and characterized as a novel homosesquiterpene with the structure 3-methyl--himachalene (C₁₆H₂₆). A minor component (ca. 10%) of the gland extract has also been identified as the sesquiterpene-himachalene (C₁₅H₂₄). This work confirms that there are at least 3 members of theL. longipalpis species complex.     

Relating time complexity of protein folding simulation to approximations of folding time

Finkelstein and Badretdinov [A.V. Finkelstein, A.Y. Badretdinov, Rate of protein folding near the point of thermodynamic equilibrium between the coil and the most stable chain fold, Fold. Des. 2 (1997) 115-121] approximated the folding time of protein sequences of length n by exp(λ⋅n2/3±χ⋅n1/2/2)ns, where λ and χ are constants close to unity. Recently, Fu and Wang [B. Fu, W. Wang, A ²O(n1−1/d⋅logn) time algorithm for d-dimensional protein folding in the HP-model, in: J. Daz, J. Karhumäki, A. Lepistö, D. Sannella (Eds.), Proceedings of the 31st International Colloquium on Automata, Languages and Programming, in: Lecture Notes in Comput. Sci., vol. 3142, Springer-Verlag, Heidelberg, 2004, pp. 630-644] published an exp(O(n1−1/d)⋅lnn) algorithm for d-dimensional protein folding simulation in the HP-model, which is close to the folding time approximation by Finkelstein and Badretdinov and can be seen as a justification of the HP-model for investigating general complexity issues of protein folding. We propose a stochastic local search procedure that is based on logarithmic simulated annealing. We obtain that after (m/δ)^a⋅D Markov chain transitions the probability to be in a minimum energy conformation is at least 1−δ, where m?b(d)⋅n is the maximum neighbourhood size (b(d) small integer), a is a small constant, and D is the maximum value of the minimum escape height from local minima of the underlying energy landscape. We note that the time bound is instance-specific, and we conjecture D<n1−1/d as a worst case upper bound. We analyse experimentally on selected benchmark problems for the d=2 case.     

Profiling hospitals based on emergency readmission: a multilevel transition modelling approach

Emergency readmission is seen as an important part of the United Kingdom government policy to improve the quality of care that patients receive. In this context, patients and the public have the right to know how well different health organizations are performing. Most methods for profiling estimate the expected numbers of adverse outcomes (e.g. readmission, mortality) for each organization. A number of statistical concerns have been raised, such as the differences in hospital sizes and the unavailability of relevant data for risk adjustment. Having recognized these statistical concerns, a new framework known as the multilevel transition model is developed. Hospital specific propensities of the first, second and further readmissions are considered to be measures of performance, where these measures are used to define a new performance index. During the period 1997 and 2004, the national (English) hospital episodes statistics dataset comprise more than 5 million patient readmissions. Implementing a multilevel model using the complete population dataset could possibly take weeks to estimate the parameters. To resolve the problem, we extract 1000 random samples from the original data, where each random sample is likely to lead to differing hospital performance measures. For computational efficiency a Grid implementation of the model is developed. Analysing the output from the full 1000 sample, we noticed that 4 out of the 5 worst performing hospitals treating cancer patients were in London. These hospitals are known to be the leading NHS Trusts in England, providing diverse range of services to complex patients, and therefore it is inevitable to expect higher numbers of emergency readmissions.