Està subjecte a una llicència de Reconeixement-NoComercial-SenseObraDerivada 4.

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Assurance of transcript accuracy and quality in interview-based qualitative research is foundational for data accuracy and study validity. Based on our experience in a cross-cultural ethnographic study of women’s pelvic organ prolapse, we provide practical guidance to set up step-by-step interview transcription and translation protocols for team-based research on sensitive topics. Beginning with team decisions about level of detail in transcription, completeness, and accuracy, we operationalize the process of securing vendors to deliver the required quality of transcription and translation. We also share rubrics for assessing transcript quality and the team protocol for managing transcripts (assuring consistency of format, insertion of metadata, anonymization, and file labelling conventions) and procuring an acceptable initial translation of Spanish-language interviews. Accurate, complete, and systematically constructed transcripts in both source and target languages responds to the call for more transparency and reproducibility of scientific methods.

Background and ObjectivesThe detection of PML-RARα by real-time polymerase chain reaction (RQ-PCR) is becoming an important tool for monitoring minimal residual disease (MRD) in patients with acute promyelocytic leukemia (APL). However, its clinical value remains to be determined. Our aim was to analyze any associations between the risk of relapse and RQ-PCR results in different phases of treatment, comparing these data with those yielded by conventional qualitative reverse transcriptase-PCR. Design and MethodsFollow-up samples from 145 APL patients treated with the PETHEMA protocols were evaluated by the RQ-PCR protocol (Europe Against Cancer program) and by the RT-PCR method (BIOMED-1 Concerted Action). Hematologic and molecular relapses and relapse-free survival were recorded. We then looked for associations between relapse risk and RQ-PCR results. ResultsAfter induction therapy, no association was found between positive RQ-PCR results and relapse. The PCR result here did not imply any change in the scheduled therapy. After the third consolidation course, two out of three cases with positive RQ-PCR relapsed in contrast to 16 out of 119 (13%) patients with negative RQ-PCR. During maintenance therapy and out-of treatment, all patients with >10 PML-RARα normalized copy number (NCN) (n=19) relapsed while all patients with <1 NCN at the end of the study remained in hematologic remission (p<0.0001). In the intermediate group (NCN 1-10) (n=18), the relapse-free survival at 5 years was 60%. Hematologic relapses were predicted if a positive RQ-PCR result had been obtained in a follow-up sample within the previous 4 months. Interpretation and ConclusionsBased on the information provided by RQ-PCR in samples obtained after the end of consolidation and subsequently, a relapse risk stratification could be established for APL patients. This stratification divides patients into three groups: those at high risk of relapse, those with an intermediate risk and those with a low risk of relapse.

This study aims, firstly, to determine whether hotel categories worldwide can be inferred from features that are not taken into account by the institutions in charge of assigning such categories and, if so, to create a model to classify the properties offered by P2P accommodation platforms, similar to grading scheme categories for hotels, thus preventing opportunistic behaviours of information asymmetry and information overload. The characteristics of 33,000 hotels around the world and 18,000,000 reviews from Booking.com were collected automatically and, using the Support Vector Machine classification technique, we trained a model to assign a category to a given hotel. The results suggest that a hotel classification can usually be inferred by different criteria (number of reviews, price, score, and users' wish lists) that have nothing to do with the official criteria. Moreover, room prices are the most important feature for predicting the hotel category, followed by cleanliness and location.

Twitter has become a widely used social network to discuss ideas about many domains. This leads to a growing interest in understanding what are the major accepted or rejected opinions in different domains by social network users. At the same time, checking what are the topics that produce the most controversial discussions among users can be a good tool to discover topics that can be divisive, what can be useful, e.g., for policy makers. With the aim to automatically discover such information from Twitter discussions, we present an analysis system based on Valued Abstract Argumentation to model and reason about the accepted and rejected opinions. We consider different schemes to weight the opinions of Twitter users, such that we can tune the relevance of opinions considering different information sources from the social network. Towards having a fully automatic system, we also design a relation labeling system for discovering the relation between opinions. Regarding the underlying acceptability semantics, we use ideal semantics to compute accepted/rejected opinions. We define two measures over sets of accepted and rejected opinions to quantify the most controversial discussions. In order to validate our system, we analyze different real Twitter discussions from the political domain. The results show that different weighting schemes produce different sets of socially accepted opinions and that the controversy measures can reveal significant differences between discussions.

Cooling in the industry sector contributes significantly to the peak demand placed on an electrical utility grid. New electricity tariff structures include high charges for electricity consumption in peak hours which leads to elevated annual electricity costs for high-demanding consumers. Demand side management (DSM) is a promising solution to increase the energy efficiency among customers by reducing their electricity peak demand and consumption. In recent years, researchers have shown an increased interest in utilizing DSM techniques with thermal energy storage (TES) and solar PV technologies for peak demand reduction in industrial and commercial sectors. The main objective of the present study is to address the potential for applying optimization-based time-of-use DSM in the industry sector by using cold thermal energy storage and off-grid solar PV to decrease and shift peak electricity demands and to reduce the annual electricity consumption costs. The results show that when cold thermal energy storage and solar PV are coupled together higher annual electricity cost savings can be achieved compared to using these two technologies independently. Additionally, considerable reductions can be seen in electricity power demands in different tariff periods by coupling thermal energy storage with off-grid solar PV.