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In this data article, we present a dataset containing match scores from major international competitions for 12 popular team ball sports: basketball, cricket, field hockey, futsal, handball, ice hockey, lacrosse, roller hockey, rugby, soccer, volleyball, and water polo. The dataset was obtained by web scraping data available on Wikipedia pages and includes the following information related to individual matches: the year of the competition edition when a match occurred, the names of the two opposing teams, their respective scores, and the name of the winning team. Our match score dataset provides researchers in the field of sports analytics with valuable data that can be used to compute team statistics, develop team ranking and rating systems, infer patterns and trends in a team's performance across the edition years, build predictive models to forecast the outcome of future matches, and evaluate the performance of machine learning algorithms.

Explainable Artificial Intelligence (XAI) is a growing area of research that aims to improve the interpretability of the not-so-informative black-box models. However, it is currently difficult to categorize an existing method in terms of its intrinsic characteristics and explainability. We provide a new unified yet simple taxonomy for the categorization of XAI methods and present the explainability methods currently being applied in finance applications. For both purposes, we present two separate systematic literature reviews: an anthological search for surveys on XAI methods in order to present a unified taxonomy, followed by an exposition of the XAI methods currently in use that have been found. We also concisely define the existing explainability methods using the proposed categories based on the ones most commonly addressed in the reviewed literature and pinpoint specific XAI methods being used in practical applications in Finance.

This data article describes two datasets with hotel demand data. One of the hotels (H1) is a resort hotel and the other is a city hotel (H2). Both datasets share the same structure, with 31 variables describing the 40,060 observations of H1 and 79,330 observations of H2. Each observation represents a hotel booking. Both datasets comprehend bookings due to arrive between the 1st of July of 2015 and the 31st of August 2017, including bookings that effectively arrived and bookings that were canceled. Since this is hotel real data, all data elements pertaining hotel or costumer identification were deleted. Due to the scarcity of real business data for scientific and educational purposes, these datasets can have an important role for research and education in revenue management, machine learning, or data mining, as well as in other fields.

Traffic accidents in urban areas lead to reduced quality of life and added pressure in the cities’ infra-structures. In the context of smart city data is becoming available that allows a deeper analysis of the phenomenon. We propose a data fusion process from different information sources like road accidents, weather conditions, local authority reports tools, traffic, fire brigade. These big data analytics allow the creation of knowledge for local municipalities using local data. Data visualizations allow big picture overview. This paper presents an approach to the geo-referenced accident-hotspots identification. Using ArcGIS Pro, we apply Kernel Density and Hot Spot Analysis (Getis-Ord Gi*) tools, identifying the existence of black spots in terms of location and context conditions, and evaluate the possible human, environmental and circumstantial factors that may influence the severity of accidents. The results were validated by an expert committee. This approach can be applied to other cites wherever this data is available.

Booking cancellations have a substantial impact in demandmanagement decisions in the hospitality industry. Cancellations limit the production of accurate forecasts, a critical tool in terms of revenue management performance. To circumvent the problems caused by booking cancellations, hotels implement rigid cancellation policies and overbooking strategies, which can also have a negative influence on revenue and reputation. Using data sets from four resort hotels and addressing booking cancellation prediction as a classification problem in the scope of data science, authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%. This demonstrates that despite what was assumed by Morales and Wang (2010) it is possible to predict with high accuracy whether a booking will be canceled. Results allow hotel managers to accurately predict net demand and build better forecasts, improve cancellation policies, define better overbooking tactics and thus use more assertive pricing and inventory allocation strategies.

Booking cancellations negatively contribute to the production of accurate forecasts, which comprise a critical tool in the hospitality industry. Research has shown that with today’s computational power and advanced machine learning algorithms it is possible to build models to predict bookings cancellation likelihood. However, the effectiveness of these models has never been evaluated in a real environment. To fill this gap and investigate how these models can be implemented in a decision support system and its impact on demand-management decisions, a prototype was built and deployed in two hotels. The prototype, based on an automated machine learning system designed to learn continuously, lead to two important research contributions. First, the development of a training method and weighting mechanism designed to capture changes in cancellations patterns over time and learn from previous days’ predictions hits and errors. Second, the creation of a new measure – Minimum Frequency – to measure the precision of predictions over time. From a business standpoint, the prototype demonstrated its effectiveness, with results exceeding 84% in accuracy, 82% in precision, and 88% in Area Under the Curve (AUC). The system allowed hotels to predict their net demand and thus making better decisions about which bookings to accept and reject, what prices to make, and how many rooms to oversell. The systematic prediction of bookings with high probability of being canceled allowed hotels to reduce cancellations by 37 percentage points by acting to avoid their cancellation.

OBJECTIVE:The increased survival in Systemic Lupus Erythematosus (SLE) patients implies the development of chronic damage, occurring in up to 50% of cases. Its prevention is a major goal in the SLE management. We aimed at predicting chronic damage in a large monocentric SLE cohort by using neural networks. METHODS:We enrolled 413 SLE patients (M/F 30/383; mean age ± SD 46.3±11.9 years; mean disease duration ± SD 174.6 ± 112.1 months). Chronic damage was assessed by the SLICC/ACR Damage Index (SDI). We applied Recurrent Neural Networks (RNNs) as a machine-learning model to predict the risk of chronic damage. The clinical data sequences registered for each patient during the follow-up were used for building and testing the RNNs. RESULTS:At the first visit in the Lupus Clinic, 35.8% of patients had an SDI≥1. For the RNN model, two groups of patients were analyzed: patients with SDI = 0 at the baseline, developing damage during the follow-up (N = 38), and patients without damage (SDI = 0). We created a mathematical model with an AUC value of 0.77, able to predict damage development. A threshold value of 0.35 (sensitivity 0.74, specificity 0.76) seemed able to identify patients at risk to develop damage. CONCLUSION:We applied RNNs to identify a prediction model for SLE chronic damage. The use of the longitudinal data from the Sapienza Lupus Cohort, including laboratory and clinical items, resulted able to construct a mathematical model, potentially identifying patients at risk to develop damage.

Aim of the study: To introduce and describe FlorNExT®, a free cloud computing application to estimate growth and yield of maritime pine (Pinus pinaster Ait.) even-aged stands in the Northeast of Portugal (NE Portugal). Area of study: NE Portugal. Material and methods: FlorNExT® implements a dynamic growth and yield modelling framework which integrates transition functions for dominant height (site index curves) and basal area, as well as output functions for tree and stand volume, biomass, and carbon content. Main results: FlorNExT® is freely available from any device with an Internet connection at: http://flornext.esa.ipb.pt/. Research highlights: This application has been designed to make it possible for any stakeholder to easily estimate standing volume, biomass, and carbon content in maritime pine stands from stand data, as well as to estimate growth and yield based on four stand variables: age, density, dominant height, and basal area. FlorNExT® allows planning thinning treatments. FlorNExT® is a fundamental tool to support forest mobilization at local and regional scales in NE Portugal. Keywords: forest management; maritime pine; forest modelling; knowledge transfer tool.

A 45-year-old woman with a history of renal carcinoma was observed for facial, cervical and truncal flesh-colored papules. Relatives had similar skin findings and a brother had repeated episodes of pneumothorax. The computerized tomography scan revealed multiple cysts on both lungs. A skin biopsy revealed a perifollicular fibroma. The clinical diagnosis of Birt-Hogg-Dubé syndrome (BHDS) was corroborated by identification of a novel frameshift c.573delGAinsT (p.G191fsX31) mutation in heterozygosity on exon 6 of the folliculin gene. The presence of multiple and typical benign hair follicle tumors highlights the role of the dermatologist in the diagnosis of this rare genodermatosis that is associated with an increased risk of renal cell cancer and pulmonary cysts, warranting personal and familial follow-up and counseling.

The efficient use of vinasse, the primary byproduct of sugarcane ethanol production, is important for the economic and environmental sustainability of the industry. Few studies have quantified the soil health response to long‐term vinasse application, even though this byproduct is generally applied as a potassium (K) source in sugarcane fields. The Soil Management Assessment Framework (SMAF) was used to assess the integrated soil health response in soils with contrasting textures. Chemical, physical, and biological indicators were selected, measured, and integrated into a soil health index for clay‐ and sandy‐textured soils in Brazil. Overall, the application of vinasse improved soil health components in both soils. The results showed that the benefits of vinasse go beyond increasing the K content. Vinasse application showed increased soil organic carbon content, nutrient recycling, and soil physical quality. The long‐term application of vinasse increased the soil health from 49% to 62% in the clayey soil and from 43% to 61% in the sandy clay soil. The findings therefore revealed the potential of vinasse application to reduce the need for synthetic fertilizer and promote the circular economy and soil health regardless of soil type. This study verifies that the long‐term application of vinasse to sandy‐ and clay‐texture soils in Brazil has both economic and environmental benefits because it recycles an important ethanol byproduct and enhances soil health. [ABSTRACT FROM AUTHOR]

Abstract Background Co-occurring mutations in pairs of genes can pinpoint clinically relevant subgroups of cancer. Most colorectal cancers (CRCs) are microsatellite stable (MSS) and have few frequent mutations. Large patient cohorts and broad genomic coverage are needed for comprehensive co-mutation profiling. Methods Co-mutations were identified in a population-based Swedish cohort analyzed by whole-genome sequencing (n=819 stage I-IV MSS CRCs). Prognostic value was further evaluated in a publicly available dataset of clinically sequenced metastatic CRCs (MSK-IMPACT; n=934 MSS). Multivariable Cox proportional hazards analyses with clinicopathological parameters were performed for locoregional (stage I-III) and metastatic (stage IV and recurrent) cancers separately. Results Prevalent co-mutations were detected in 23 unique gene pairs, 20 of which included APC, TP53, KRAS and/or PIK3CA. Several co-mutations involving APC were associated with good overall survival in locoregional CRC, including APC-TCF7L2 (multivariable HR: 0.49, 95% CI 0.27-0.89). This co-mutation was prognostic also in metastatic cancers (multivariable HR: 0.49 and 0.37, 95% CI: 0.24-0.98 and 0.17-0.82 in the Swedish and MSK cohorts, respectively). APC-SOX9 co-mutations were mutually exclusive with APC-TCF7L2, and the co-mutations combined had stronger prognostic associations than APC alone in both metastatic cohorts. BRAF p.V600E-RNF43 co-mutations were associated with poor overall and recurrence-free survival in locoregional CRC (multivariable HR: 4.13 and 3.2, 95% CI: 1.78-9.54 and 1.53-8.04, respectively). Conclusions We report a genome-wide evaluation of co-occurring mutations in MSS CRCs, and suggest that co-mutations can improve the prognostic stratification compared to single mutations alone.

Stochastic alternating algorithms for bi-objective optimization are considered when optimizing two conflicting functions for which optimization steps have to be applied separately for each function. Such algorithms consist of applying a certain number of steps of gradient or subgradient descent on each single objective at each iteration. In this paper, we show that stochastic alternating algorithms achieve a sublinear convergence rate of O (1 / T) , under strong convexity, for the determination of a minimizer of a weighted-sum of the two functions, parameterized by the number of steps applied on each of them. An extension to the convex case is presented for which the rate weakens to O (1 / T) . These rates are valid also in the non-smooth case. Importantly, by varying the proportion of steps applied to each function, one can determine an approximation to the Pareto front. [ABSTRACT FROM AUTHOR]

Abstract Background Ephrin (EPH) receptors have been implicated in tumorigenesis and metastasis, but the functional understanding of mutations observed in human cancers is limited. We previously demonstrated reduced cell compartmentalisation for somatic EPHB1 mutations found in metastatic colorectal cancer cases. We therefore integrated pan-cancer and pan-EPH mutational data to prioritise recurrent EPHB1 mutations for functional studies to understand their contribution to cancer development and metastasis. Methods Here, 79,151 somatic mutations in 9,898 samples of 33 different tumour types were analysed with a bioinformatic pipeline to find 3D-mutated cluster pairs and hotspot mutations in EPH receptors. From these, 15 recurring EPHB1 mutations were stably expressed in colorectal cancer followed by confocal microscopy based in vitro compartmentalisation assays and phospho-proteome analysis. Results The 3D-protein structure-based bioinformatics analysis resulted in 63% EPHB1 mutants with compartmentalisation phenotypes vs 43% for hotspot mutations. Whereas the ligand-binding domain mutations C61Y, R90C, and R170W, the fibronectin domain mutation R351L, and the kinase domain mutation D762N displayed reduced to strongly compromised cell compartmentalisation, the kinase domain mutations R743W and G821R enhanced this phenotype. While mutants with reduced compartmentalisation also had reduced ligand induced receptor phosphorylation, the enhanced compartmentalisation was not linked to receptor phosphorylation level. Phosphoproteome mapping pinpointed the PI3K pathway and PIK3C2B phosphorylation in cells harbouring mutants with reduced compartmentalisation. Conclusions This is the first integrative study of pan-cancer EPH receptor mutations followed by in vitro validation, a robust way to identify cancer-causing mutations, uncovering EPHB1 mutation phenotypes and demonstrating the utility of protein structure-based mutation analysis in characterization of novel cancer genes. Video Abstract

Elephantopus mollis, popularly known as elephant paw or sussuaiá, is a species that has recently emerged as a weed affecting grain crops. This study aimed to evaluate the differential sensitivity of biotypes of E. mollis obtained from agricultural and nonagricultural areas and to determine the best combinations of herbicides applied at different stages of development for controlling this species. Three experiments were conducted. In the differential sensitivity experiment, 10 herbicides were used in 2 biotypes in 2 stages of development. The dose-response experiment was conducted using 8 doses of 7 herbicides. The field experiment was conducted in an area with weed issues to validate the results of previous experiments. The biotype from the agricultural area showed lower sensitivity to herbicides than the biotype from the nonagricultural area. The contact herbicides initially presented better control; however, the weed shortly showed re-growth due to the presence of buds in the plant crown. Isolated application of 2.4-D amine at doses between 1005 and 1675 g a.e. ha-1 demonstrated 25% control. When 2,4-D amine was followed by paraquat application (400 g a.i. ha-1), the control observed was between 51% and 68%. The best treatment for the control of completely developed weeds is 2,4-D amine + glyphosate mixture (1340 + 1080 g a.e. ha-1), followed by sequential paraquat application. [ABSTRACT FROM AUTHOR]

Purpose: To identify nonpharmacological clinically effective interventions for constipation in adults. Methods: A systematic review of experimental studies of nonpharmacological interventions addressing participants' management of constipation using samples of adults over 18 years of age was conducted. In evaluating the methodological quality of the eligible studies, we used the assumptions of the Cochrane Collaboration, and for the reporting of items in the systematic review we used the Model of Preferential Reporting Items for Systematic Reviews and Meta‐Analyses. The protocol of this review was recorded in the International Prospective Register of Systematic Reviews of the University of York under number 43693. Results: This review included 12 randomized controlled trials. Nonpharmacological effective interventions for the resolution of constipation were identified: individualized intervention based on the participant's modifiable risk factors of constipation promoting literacy in health; educational measures in dietary modification and lifestyle; and abdominal massage. Conclusions: Specific nonpharmacological interventions are crucial for nurses' clinical practice and of major importance for clients and families. Evidence on these interventions in resolving constipation is still scarce and fails to provide evidence‐based data to support nursing clinical practice. Clinical Relevance: Personal lifestyles, comorbidities, medication, and sedentary habits are likely to be risk factors in constipation. Thus, it is important to invest in nonpharmacological interventions that promote changes in behavior regarding prevention or resolution of constipation. Moreover, nursing researchers worldwide should conduct research for clinical practice regarding the fundamentals of care. [ABSTRACT FROM AUTHOR]

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