Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions

Transforming Growth Factor: β Signaling Is Essential for Limb Regeneration in Axolotls

Axolotls (urodele amphibians) have the unique ability, among vertebrates, to perfectly regenerate many parts of their body including limbs, tail, jaw and spinal cord following injury or amputation. The axolotl limb is the most widely used structure as an experimental model to study tissue regeneration. The process is well characterized, requiring multiple cellular and molecular mechanisms. The preparation phase represents the first part of the regeneration process which includes wound healing, cellular migration, dedifferentiation and proliferation. The redevelopment phase represents the second part when dedifferentiated cells stop proliferating and redifferentiate to give rise to all missing structures. In the axolotl, when a limb is amputated, the missing or wounded part is regenerated perfectly without scar formation between the stump and the regenerated structure. Multiple authors have recently highlighted the similarities between the early phases of mammalian wound healing and urodele limb regeneration. In mammals, one very important family of growth factors implicated in the control of almost all aspects of wound healing is the transforming growth factor-beta family (TGF-β). In the present study, the full length sequence of the axolotl TGF-β1 cDNA was isolated. The spatio-temporal expression pattern of TGF-β1 in regenerating limbs shows that this gene is up-regulated during the preparation phase of regeneration. Our results also demonstrate the presence of multiple components of the TGF-β signaling machinery in axolotl cells. By using a specific pharmacological inhibitor of TGF-β type I receptor, SB-431542, we show that TGF-β signaling is required for axolotl limb regeneration. Treatment of regenerating limbs with SB-431542 reveals that cellular proliferation during limb regeneration as well as the expression of genes directly dependent on TGF-β signaling are down-regulated. These data directly implicate TGF-β signaling in the initiation and control of the regeneration process in axolotls

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Big Data and Causality

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory