The stuff that motor chunks are made of: Spatial instead of motor representations?

In order to determine how participants represent practiced, discrete keying sequences in the discrete sequence production task, we had 24 participants practice two six-key sequences on the basis of two pre-learned six-digit numbers. These sequences were carried out by fingers of the left (L) and right (R) hand with between-hand transitions always occurring between the second and third, and the fifth and sixth responses. This yielded the so-called LLRRRL and RRLLLR sequences. Early and late in practice, the keypad used for the right hand was briefly relocated from the front of the participants to 90° at their right side. The results indicate that after 600 practice trials, executing a keying sequence relies heavily on a spatial cross-hand representation in a trunk- or head-based reference frame that after about only 15 trials is fully adjusted to the changed hand location. The hand location effect was not found with the last sequence element. This is attributed to the application of explicit knowledge. The between-hand transitions appeared to induce initial segmentation in some of the participants, but this did not consolidate into a concatenation point of successive motor chunks

Phase 2 Study of Dabrafenib Plus Trametinib in Patients With BRAF V600E-Mutant Metastatic NSCLC:Updated 5-Year Survival Rates and Genomic Analysis

Introduction: Dabrafenib plus trametinib was found to have robust antitumor activity in patients with BRAF V600E-mutant metastatic NSCLC (mNSCLC). We report updated survival analysis of a phase 2 study (NCT01336634) with a minimum of 5-year follow-up and updated genomic data. Methods: Pretreated (cohort B) and treatment-naive (cohort C) patients with BRAF V600E-mutant mNSCLC received dabrafenib 150 mg twice daily and trametinib 2 mg once daily. The primary end point was investigator-assessed overall response rate per Response Evaluation Criteria in Solid Tumors version 1.1. Secondary end points were duration of response, progression-free survival, overall survival, and safety. Results: At data cutoff, for cohorts B (57 patients) and C (36 patients), the median follow-up was 16.6 (range: 0.5–78.5) and 16.3 (range: 0.4–80) months, overall response rate (95% confidence interval [CI]) was 68.4% (54.8–80.1) and 63.9% (46.2–79.2), median progression-free survival (95% CI) was 10.2 (6.9–16.7) and 10.8 (7.0–14.5) months, and median overall survival (95% CI) was 18.2 (14.3–28.6) and 17.3 (12.3–40.2) months, respectively. The 4- and 5-year survival rates were 26% and 19% in pretreated patients and 34% and 22% in treatment-naive patients, respectively. A total of 17 patients (18%) were still alive. The most frequent adverse event was pyrexia (56%). Exploratory genomic analysis indicated that the presence of coexisting genomic alterations might influence clinical outcomes in these patients; however, these results require further investigation. Conclusions: Dabrafenib plus trametinib therapy was found to have substantial and durable clinical benefit, with a manageable safety profile, in patients with BRAF V600E-mutant mNSCLC, regardless of previous treatment

Requirements Engineering for Well-Being, Aging, and Health:An Overview for Practitioners

Well-Being, Aging, and Health (WBAH) are important aspects of life that affect us all. The requirements for WBAH systems have also become a topic of common interest for researchers from different disciplines. This is unsurprising, given that health-related expenses often represent about 10% of a country's gross domestic product, according to the World Health Organizatio

Crowd out the competition. Gaining market advantage through crowd-based requirements engineering

MyERP is a fictional developer of an Enterprise Resource Planning (ERP) system. Driven by the competition, they face the challenge of losing market share if they fail to de-ploy a Software as a Service (SaaS) ERP system to the European market quickly, but with high quality product. This also means that the requirements engineering (RE) activities will have to be performed efficiently and provide solid results. An additional problem they face is that their (potential) stakeholders are physically distributed, it makes sense to consider them a "crowd". This competition paper suggests a Crowd-based RE approach that first identifies the crowd, then collects and analyzes their feedback to derive wishes and needs, and validate the results through prototyping. For this, techniques are introduced that have so far been rarely employed within RE, but more "traditional" RE techniques, will also be integrated and/or adapted to attain the best possible result in the case of MyERP

Vicus - a persona for towns: Towards innovation management through co-creation and predictive situation analytics

Digitization of towns in concepts such as "Smart Cities" pose a new challenge for software engineering. Software is usually developed for selected target groups, represented as personas with clear requirements. But solutions for cities or smaller towns affect all its inhabitants directly, so that performing stakeholder identification and prioritization based on characterizations such as personas will not do justice to all these stakeholders. Conversely, the government, industry and citizens of each town have specific interests, properties and needs, which can and must be understood when realizing new solutions for a town. This paper proposes a new elicitation and analysis method in the form of a so-called Vicus, through which a town can be characterized by gathering relevant information about a town. The Vicus facilitates the generation of new creative ideas as well as the reuse of already successfully implemented solutions in towns that reveal similar patterns in their characterization

An Overview of User Feedback Classification Approaches

Online user feedback about software products is a promising source of user requirements. To allow scaling analyses to large amounts of user feedback, research on Crowd-based Requirements Engineering (CrowdRE) seeks to tailor natural language processing (NLP) techniques to Requirements Engineering (RE). Various frameworks have been proposed, but it remains largely unclear why particular NLP techniques are better suited for CrowdRE than others, which makes it hard to make a well-founded choice for a technique. We found that CrowdRE research most often uses machine learning (ML) and has so far applied twelve clusters of ML algorithms and seven clusters of ML features. The prevalent algorithm–feature pair is Na¨ıve Bayes with Bag of Words – Term Frequency (BOW-TF), followed by Support Vector Machines (SVM) with BOW-TF. An initial comparison of the reported precision and recall suggests that classifications in RE need further improvement. Our research presents a preliminary overview of the current landscape of automated classification techniques for RE whose results may inspire researchers to apply new strategies to advance research in this field, or to include ML models they had not considered previously in their benchmarks