High frequency of BRCA1, but not CHEK2 or NBS1 (NBN), founder mutations in Russian ovarian cancer patients

<p>Abstract</p> <p>Background</p> <p>A significant portion of ovarian cancer (OC) cases is caused by germ-line mutations in BRCA1 or BRCA2 genes. BRCA testing is cheap in populations with founder effect and therefore recommended for all patients with OC diagnosis. Recurrent mutations constitute the vast majority of BRCA defects in Russia, however their impact in OC morbidity has not been yet systematically studied. Furthermore, Russian population is characterized by a relatively high frequency of CHEK2 and NBS1 (NBN) heterozygotes, but it remains unclear whether these two genes contribute to the OC risk.</p> <p>Methods</p> <p>The study included 354 OC patients from 2 distinct, geographically remote regions (290 from North-Western Russia (St.-Petersburg) and 64 from the south of the country (Krasnodar)). DNA samples were tested by allele-specific PCR for the presence of 8 founder mutations (BRCA1 5382insC, BRCA1 4153delA, BRCA1 185delAG, BRCA1 300T>G, BRCA2 6174delT, CHEK2 1100delC, CHEK2 IVS2+1G>A, NBS1 657del5). In addition, literature data on the occurrence of BRCA1, BRCA2, CHEK2 and NBS1 mutations in non-selected ovarian cancer patients were reviewed.</p> <p>Results</p> <p>BRCA1 5382insC allele was detected in 28/290 (9.7%) OC cases from the North-West and 11/64 (17.2%) OC patients from the South of Russia. In addition, 4 BRCA1 185delAG, 2 BRCA1 4153delA, 1 BRCA2 6174delT, 2 CHEK2 1100delC and 1 NBS1 657del5 mutation were detected. 1 patient from Krasnodar was heterozygous for both BRCA1 5382insC and NBS1 657del5 variants.</p> <p>Conclusion</p> <p>Founder BRCA1 mutations, especially BRCA1 5382insC variant, are responsible for substantial share of OC morbidity in Russia, therefore DNA testing has to be considered for every OC patient of Russian origin. Taken together with literature data, this study does not support the contribution of CHEK2 in OC risk, while the role of NBS1 heterozygosity may require further clarification.</p

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Investigating the android apps' success: An empirical study

Measuring the success of software systems was not a trivial task in the past. Nowadays, mobile apps provide a uniform schema, i.e., the average ratings provided by the apps' users to gauge their success. While recent research has focused on examining the relationship between change-and fault-proneness and apps' lack of success, as well as qualitatively analyzing the reasons behind the apps' users dissatisfaction, there is little empirical evidence on the factors related to the success of mobile apps. In this paper, we explore the relationships between the mobile apps' success and a set of metrics that not only characterize the apps themselves but also the quality of the APIs used by the apps, as well as user attributes when they interact with the apps. In particular, we measure API quality in terms of bugs fixed in APIs used by apps and changes that occurred in the API methods. We examine different kinds of changes including changes in the interfaces, implementation, and exception handling. For user-related factors, we leverage the number of app's downloads and installations, and users' reviews. Through an empirical study of 474 free Android apps, we find that factors such as the number of users' reviews provided for an app, app's category and size appear to have an impact on the app's success

Code review quality: How developers see it

In a large, long-lived project, an efiective code review process is key to ensuring the long-term quality of the code base. In this work, we study code review practices of a large, open source project, and we investigate how the developers themselves perceive code review quality. We present a qualitative study that summarizes the results from a survey of 88 Mozilla core developers. The results provide developer insights into how they define review quality, what factors contribute to how they evaluate submitted code, and what challenges they face when performing review tasks. We found that the review quality is primarily associated with the thoroughness of the feedback, the reviewer's familiarity with the code, and the perceived quality of the code itself. Also, we found that while different factors are perceived to contribute to the review quality, reviewers often find it dificult to keep their technical skills up-to-date, manage personal priorities, and mitigate context switching

Mining twitter data for influenza detection and surveillance

Twitter - a social media platform - has gained phenomenal popularity among researchers who have explored its massive volumes of data to offer meaningful insights into many aspects of modern life. Twitter has also drawn great interest from public health community to answer many health-related questions regarding the detection and spread of certain diseases. However, despite the growing popularity of Twitter as an influenza detection source among researchers, healthcare officials do not seem to be as intrigued by the opportunities that social media offers for detecting and monitoring diseases. In this paper, we demonstrate that 1) Twitter messages (tweets) can be reliably classified based on influenza related keywords; 2) the spread of influenza can be predicted with high accuracy; and, 3) there is a way to monitor the spread of influenza in selected cities in real-time. We propose an approach to efficiently mine and extract data from Twitter streams, reliably classify tweets based on their sentiment, and visualize data via a real-time interactive map. Our study benefits not only aspiring researchers who are interested in conducting a study involving the analysis of Twitter data but also health sectors officials who are encouraged to incorporate the analysis of vast information from social media data sources, in particular, Twitter

Studying developer build issues and debugger usage via timeline analysis in visual studio IDE

Every day, most software developers use development tools to write, build, and maintain their code. The most crucial of such tools is the integrated development environment (IDE), in which developers create and build code. Therefore, it is important to understand how developers perform their work and what impact each action has on their workflow to further enhance their productivity. In this work, we study the KaVE dataset of developer interactions within the Microsoft Visual Studio IDE and analyze a number of topics extracted from the data. First, we propose a method for developing what we call "timelines" that chronologically map an individual development session, and from this, we study build failures, code debugger usage, and we propose a metric for measuring developer throughput. We find that the timeline analysis may prove to be an invaluable tool for developer self-assessment and key to uncovering problem areas regarding build failures. Moreover, we find that developers spend a significant amount of time debugging their code, utilizing features such as breakpoints to resolve issues. Finally, we see that the developer metric can be used for self assessment, giving value to the amount of effort, put forth by a developer, in a given session

Predicting questions' scores on stack overflow

Developer support forums are becoming more popular than ever. Crowdsourced knowledge is an essential resource for many developers yet it can raise concerns about the quality of the shared content. Most existing research efforts address the quality of answers posted by Q&A community members. In this paper, we explore the quality of questions and propose a method of predicting the score of questions on Stack Overflow based on sixteen factors related to questions' format, content and interactions that occur in the post. We performed an extensive investigation to understand the relationship between the factors and the scores of questions. The multiple regression analysis shows that the question's length of the code, accepted answer score, number of tags and the count of views, comments and answers are statistically significantly associated with the scores of questions. Our findings can offer insights to community-based Q&A sites for improving the content of the shared knowledge

Evaluating the performance of machine learning sentiment analysis algorithms in software engineering

In recent years, sentiment analysis has been aware within software engineering domain. While automated sentiment analysis has long been suffering from doubt of accuracy, the tool performance is unstable when being applied on datasets other than the original dataset for evaluation. Researchers also have the disagreements upon if machine learning algorithms perform better than conventional lexicon and rule based approaches. In this paper, we looked into the factors in datasets that may affect the evaluation performance, also evaluated the popular machine learning algorithms in sentiment analysis, then proposed a novel structure for automated sentiment tool combines advantages from both approaches

Synthesizing Knowledge from Software Development Artifacts

When software practitioners make day-to-day design decisions about their projects, they are guided by not only their intuition and experience, but also by the variety of software artifacts that are available to them. This chapter describes how lifecycle models can be used to build a useful and intuitive model of these development artifacts. Lifecycle models capture the dynamic nature of how such artifacts change over time in a graphical form that can be easily understood and communicated. We show how lifecycle models can be generated, and we present two industrial case studies where we applied lifecycle models to assess a project's code review process

Software analytics: Challenges and opportunities

Nowadays, software development projects produce a large number of software artifacts including source code, execution traces, end-user feedback, as well as informal documentation such as developers' discussions, change logs, Stack-Overflow, and code reviews. Such data embeds rich and significant knowledge about software projects, their quality and services, as well as the dynamics of software development. Most often, this data is not organized, stored, and presented in a way that is immediately useful to software developers and project managers to support their decisions. To help developers and managers understand their projects, how they evolve, as well as support them during their decision-making process, software analytics - use of analysis, data, and systematic reasoning for making decisions - has become an emerging field of modern data analysis. While results obtained from analytics-based solutions suggested so far are promising, there are still several challenges associated with the adoption of software analytics into software development processes, as well as the development and integration of analytics tools in practical settings. We therefore propose a tutorial on software analytics. The tutorial will start with an introduction of software analytics. Next, we will discuss the main challenges and opportunities associated with software analytics based on the examples from our own research. These examples will cover a range of topics leveraging software analytics. The topics include mobile apps quality, code review process and its quality, analytics for the software engineering Twitter space, as well as the use of analytics to solve scheduling problems in the cloud