A large-scale comparative analysis of Coding Standard conformance in Open-Source Data Science projects

Background: Meeting the growing industry demand for Data Science requires cross-disciplinary teams that can translate machine learning research into production-ready code. Software engineering teams value adherence to coding standards as an indication of code readability, maintainability, and developer expertise. However, there are no large-scale empirical studies of coding standards focused specifically on Data Science projects. Aims: This study investigates the extent to which Data Science projects follow code standards. In particular, which standards are followed, which are ignored, and how does this differ to traditional software projects? Method: We compare a corpus of 1048 Open-Source Data Science projects to a reference group of 1099 non-Data Science projects with a similar level of quality and maturity. Results: Data Science projects suffer from a significantly higher rate of functions that use an excessive numbers of parameters and local variables. Data Science projects also follow different variable naming conventions to non-Data Science projects. Conclusions: The differences indicate that Data Science codebases are distinct from traditional software codebases and do not follow traditional software engineering conventions. Our conjecture is that this may be because traditional software engineering conventions are inappropriate in the context of Data Science projects.Comment: 11 pages, 7 figures. To appear in ESEM 2020. Updated based on peer revie

A method to search for long duration gravitational wave transients from isolated neutron stars using the generalized FrequencyHough

We describe a method to detect gravitational waves lasting $O(hours-days)$ emitted by young, isolated neutron stars, such as those that could form after a supernova or a binary neutron star merger, using advanced LIGO/Virgo data. The method is based on a generalization of the FrequencyHough (FH), a pipeline that performs hierarchical searches for continuous gravitational waves by mapping points in the time/frequency plane of the detector to lines in the frequency/spindown plane of the source. We show that signals whose spindowns are related to their frequencies by a power law can be transformed to coordinates where the behavior of these signals is always linear, and can therefore be searched for by the FH. We estimate the sensitivity of our search across different braking indices, and describe the portion of the parameter space we could explore in a search using varying fast Fourier Transform (FFT) lengths.Comment: 15 figure

When Therapy Dogs Provide Virtual Comfort: Exploring University Students’ Insights and Perspectives

With the proliferation of canine-assisted interventions and the emphasis placed on the impact of these sessions in bolstering the well-being of visitors to sessions, especially university students, it can be easy to overlook just how participating in one of these sessions is experienced by participants. Capturing participants’ experiences is important as this holds the potential to inform program design and delivery and elucidate mechanisms within the intervention that were found to be especially efficacious. Forging new empirical terrain, this study explored the insights and perceptions of 469 undergraduate students who participated in a virtual canine-assisted stress-reduction intervention at a mid-size western Canadian university. Participants were randomly assigned to synchronous or asynchronous and dog or no-dog conditions and were asked to share their views of their experience by rating statements and responding to open-ended prompts. Thematic content analysis of findings revealed that a virtual canine-assisted intervention was well received by participants. Participants in the synchronous condition with a dog reported more favorable well-being benefits, as compared with participants in the asynchronous condition with a dog and with participants in both the synchronous and asynchronous conditions without a dog. Implications of these findings hold relevance for supporting geographically remote students and students for whom attending virtual sessions is the only option given barriers preventing them from in-person attendance. Correspondingly, considerations of the role of the handler and of animal welfare are presented

IoT and Machine Learning Based Anomaly Detection in WSN for a Smart Greenhouse

Agriculture is the most crucial sector which raises the economy of every country; several techniques have been developed to control and monitor the environment in which a particular crop is growing. Famers need efficient support in terms of monitoring the temperature, the humidity, the water supply etc. However, the measurements provided by a wireless sensor network within a smart greenhouse are an essential aspect to take into consideration when it comes to evaluating the performance of sensor nodes used for controlling and monitoring the climatic condition (temperature, humidity, water supply, etc.). Therefore, this paper proposes a machine learning-based anomaly detection approach with the help of the DBSCAN algorithm of clustering to determine whether an unusual event has been found in the data. This approach allows farmers to ensure the reliability of the network. In this paper, we presented the description of the DBSCAN algorithm; we used an existing dataset that incorporates information about rose cultivation. With the used dataset, we introduced some noise, and we used MATLAB and Python to analyse and predict whether the introduced data is noise or not with DBSCAN. The performance of the algorithm after performing the prediction is 100% for two chosen features of the dataset and 75.4% for five features of the dataset in terms of precision

Cellular and Molecular Networking Within the Ecosystem of Cancer Cell Communication via Tunneling Nanotubes

Intercellular communication is vital to the ecosystem of cancer cell organization and invasion. Identification of key cellular cargo and their varied modes of transport are important considerations in understanding the basic mechanisms of cancer cell growth. Gap junctions, exosomes, and apoptotic bodies play key roles as physical modalities in mediating intercellular transport. Tunneling nanotubes (TNTs)—narrow actin-based cytoplasmic extensions—are unique structures that facilitate direct, long distance cell-to-cell transport of cargo, including microRNAs, mitochondria, and a variety of other sub cellular components. The transport of cargo via TNTs occurs between malignant and stromal cells and can lead to changes in gene regulation that propagate the cancer phenotype. More notably, the transfer of these varied molecules almost invariably plays a critical role in the communication between cancer cells themselves in an effort to resist death by chemotherapy and promote the growth and metastases of the primary oncogenic cell. The more traditional definition of “Systems Biology” is the computational and mathematical modeling of complex biological systems. The concept, however, is now used more widely in biology for a variety of contexts, including interdisciplinary fields of study that focus on complex interactions within biological systems and how these interactions give rise to the function and behavior of such systems. In fact, it is imperative to understand and reconstruct components in their native context rather than examining them separately. The long-term objective of evaluating cancer ecosystems in their proper context is to better diagnose, classify, and more accurately predict the outcome of cancer treatment. Communication is essential for the advancement and evolution of the tumor ecosystem. This interplay results in cancer progression. As key mediators of intercellular communication within the tumor ecosystem, TNTs are the central topic of this article

Repurposing Anti-Inflammasome NRTIs for Improving Insulin Sensitivity and Reducing Type 2 Diabetes Development

Innate immune signaling through the NLRP3 inflammasome is activated by multiple diabetes-related stressors, but whether targeting the inflammasome is beneficial for diabetes is still unclear. Nucleoside reverse-transcriptase inhibitors (NRTI), drugs approved to treat HIV-1 and hepatitis B infections, also block inflammasome activation. Here, we show, by analyzing five health insurance databases, that the adjusted risk of incident diabetes is 33% lower in patients with NRTI exposure among 128,861 patients with HIV-1 or hepatitis B (adjusted hazard ratio for NRTI exposure, 0.673; 95% confidence interval, 0.638 to 0.710; P \u3c 0.0001; 95% prediction interval, 0.618 to 0.734). Meanwhile, an NRTI, lamivudine, improves insulin sensitivity and reduces inflammasome activation in diabetic and insulin resistance-induced human cells, as well as in mice fed with high-fat chow; mechanistically, inflammasome-activating short interspersed nuclear element (SINE) transcripts are elevated, whereas SINE-catabolizing DICER1 is reduced, in diabetic cells and mice. These data suggest the possibility of repurposing an approved class of drugs for prevention of diabetes

A next-generation liquid xenon observatory for dark matter and neutrino physics

The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for weakly interacting massive particles, while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector

Daksha: On Alert for High Energy Transients

We present Daksha, a proposed high energy transients mission for the study of electromagnetic counterparts of gravitational wave sources, and gamma ray bursts. Daksha will comprise of two satellites in low earth equatorial orbits, on opposite sides of earth. Each satellite will carry three types of detectors to cover the entire sky in an energy range from 1 keV to >1 MeV. Any transients detected on-board will be announced publicly within minutes of discovery. All photon data will be downloaded in ground station passes to obtain source positions, spectra, and light curves. In addition, Daksha will address a wide range of science cases including monitoring X-ray pulsars, studies of magnetars, solar flares, searches for fast radio burst counterparts, routine monitoring of bright persistent high energy sources, terrestrial gamma-ray flashes, and probing primordial black hole abundances through lensing. In this paper, we discuss the technical capabilities of Daksha, while the detailed science case is discussed in a separate paper.Comment: 9 pages, 3 figures, 1 table. Additional information about the mission is available at https://www.dakshasat.in

A Next-Generation Liquid Xenon Observatory for Dark Matter and Neutrino Physics

The nature of dark matter and properties of neutrinos are among the mostpressing issues in contemporary particle physics. The dual-phase xenontime-projection chamber is the leading technology to cover the availableparameter space for Weakly Interacting Massive Particles (WIMPs), whilefeaturing extensive sensitivity to many alternative dark matter candidates.These detectors can also study neutrinos through neutrinoless double-beta decayand through a variety of astrophysical sources. A next-generation xenon-baseddetector will therefore be a true multi-purpose observatory to significantlyadvance particle physics, nuclear physics, astrophysics, solar physics, andcosmology. This review article presents the science cases for such a detector.<br