Posterior Interosseous Artery Adipofascial Flap for the Management of Distal Radioulnar Joint Osteoarthritis.

LEVEL OF EVIDENCE: Level IV-therapeutic.

Life cycle assessment to evaluate the integral water cycle in industrial supply: A real case study.

Wastewater recycling technologies are developed in areas where the necessity of water resources cannot be satisfied by natural sources. Nevertheless, nowadays trends and European Union Plans show an increasing interest on using these technologies to reduce environmental impacts. This manuscript aims to address the question of the real environmental results of using these technologies and the differences between each specific case using the Life Cycle Assessment (LCA) methodology. A real case study is analyzed to answer this question: the integral water cycle of a northern of Spain, comparing a traditional water supply system (system I), and an alternative wastewater regeneration plant (system II). System II presents a higher impact for all categories (between 1.2 and 37 times higher), except for land use, where it is reduced by 53 %. These results show a larger impact produced by the alternative system due to higher energy and chemical product consumption. Energy consumption is the main factor causing the highest impact in most of the impact categories for both studied systems, including the one associated to the water resource consumption. It accounts for at least 50 % of the total impact for each system in 7 of the 16 evaluated impact categories. In terms of climate change, energy consumption is not particularly significant in system I, but it is for system II, where it represents around 50 % of that impact. In the categories where the impact is not determined by energy consumption, chemical product consumption and waste and discharge treatment are the most relevant factors. In this sense, this paper highlights the importance of analysing each case specifically and underscores the usefulness of using LCA methodology as a tool to improve decision-making in resource management, with water resources emerging as a crucial focal point.

Inclusion of key social indices for a comparative assessment of the sustainability of the life cycle of current and future electricity generation in Spain: A proposed methodology.

The purpose of this work is to offer a methodology for the introduction and consolidation of social indexes within a Life Cycle Sustainability Assessment for the evaluation of large-scale electricity production. This methodology is based on an interrelation of the UNEP subcategories with the global indicator framework for the Sustainable Development Goals, resulting in 9 categories of social impact quantified by 10 indexes. To evaluate the introduction of this methodology in an LCSA a study case is used. It is applied to obtain social indexes for the 2019, 2030 and 2050 Spanish electricity scenarios, which involve different electricity generation and storage technologies: involve coal, natural gas, nuclear, hydropower, wind, solar photovoltaic, concentrated solar, biomass and storage technologies. Within the study case, an environmental life cycle assessment is also conducted, while the impacts of the economic dimension are estimated by the levelized cost of energy. This study reveals that even before important qualitative information is brought to light, as should be done in any decision-making process, there are important gaps that can be filled in terms of quantitative information in an LCSA. To do so, it provides with a methodology to expand the number of social indexes normally used in this type of assessments with the purpose of contributing to decision processes with clarity and transparency. Labour rights, salary differences for the same position, diversity in executive positions, child labour, and R&D expenditure complement the previously used occupational injuries, fatalities due to large accidents, and direct employment. The proposed methodology is applied to a Spanish case study, demonstrating the benefits and disadvantages associated with the modification of the electricity mix. However, it also highlights significant information gaps in availability and transparency that need to be addressed in future research, as part of the path towards standardizing LCSA.

Boosting ridge for the extreme learning machine globally optimised for classification and regression problems.

This paper explores the boosting ridge (BR) framework in the extreme learning machine (ELM) community and presents a novel model that trains the base learners as a global ensemble. In the context of Extreme Learning Machine single-hidden-layer networks, the nodes in the hidden layer are preconfigured before training, and the optimisation is performed on the weights in the output layer. The previous implementation of the BR ensemble with ELM (BRELM) as base learners fix the nodes in the hidden layer for all the ELMs. The ensemble learning method generates different output layer coefficients by reducing the residual error of the ensemble sequentially as more base learners are added to the ensemble. As in other ensemble methodologies, base learners are selected until fulfilling ensemble criteria such as size or performance. This paper proposes a global learning method in the BR framework, where base learners are not added step by step, but all are calculated in a single step looking for ensemble performance. This method considers (i) the configurations of the hidden layer are different for each base learner, (ii) the base learners are optimised all at once, not sequentially, thus avoiding saturation, and (iii) the ensemble methodology does not have the disadvantage of working with strong classifiers. Various regression and classification benchmark datasets have been selected to compare this method with the original BRELM implementation and other state-of-the-art algorithms. Particularly, 71 datasets for classification and 52 for regression, have been considered using different metrics and analysing different characteristics of the datasets, such as the size, the number of classes or the imbalanced nature of them. Statistical tests indicate the superiority of the proposed method in both regression and classification problems in all experimental scenarios.

Nonlinear physics opens a new paradigm for accurate transcription start site prediction.

There is evidence that DNA breathing (spontaneous opening of the DNA strands) plays a relevant role in the interactions of DNA with other molecules, and in particular in the transcription process. Therefore, having physical models that can predict these openings is of interest. However, this source of information has not been used before either in transcription start sites (TSSs) or promoter prediction. In this article, one such model is used as an additional information source that, when used by a machine learning (ML) model, improves the results of current methods for the prediction of TSSs. In addition, we provide evidence on the validity of the physical model, as it is able by itself to predict TSSs with high accuracy. This opens an exciting avenue of research at the intersection of statistical mechanics and ML, where ML models in bioinformatics can be improved using physical models of DNA as feature extractors.

The challenge of community mental health interventions with patients, relatives, and health professionals during the COVID-19 pandemic: a real-world 9-month follow-up study.

Since the beginning of the COVID-19 pandemic, the need to implement protocols that respond to the mental health demands of the population has been demonstrated. The PASMICOR programme started in March 2020, involving a total of 210 requests for treatment. Out of those subjects, the intervention was performed in 53 patients with COVID-19 without history of past psychiatric illness, 57 relatives and 60 health professionals, all of them within the area of Salamanca (Spain). Interventions were carried out by professionals of the public mental health service mostly by telephone. Depending on clinical severity, patients received basic (level I) or complex psychotherapeutic care combined with psychiatric care (level II). The majority of attended subjects were women (76.5%). Anxious-depressive symptoms were predominant, although sadness was more frequent in patients, insomnia in relatives and anxiety and fear in health professionals. 80% of the sample, particularly most of the health professionals, required a high-intensity intervention (level II). Nearly 50% of the people treated were discharged after an average of 5 interventions. Providing early care to COVID-19 patients, relatives and professionals by using community mental health resources can help to reduce the negative impact of crises, such as the pandemic, on the most affected population groups.

Total hip reconstruction after sarcoma resection in children with a free vascularized fibula without osteotomy of the bone flap: Technique description and case series.

BACKGROUND: Hip joint reconstruction following intra-articular resection of the femoral head in children is a highly demanding challenge. We aimed to describe the outcomes of hip reconstruction in paediatric patients with a free fibular epiphyso-diaphyseal flap based on both anterior tibial and peroneal vessels within a radius allograft. PATIENTS AND METHODS: Four patients underwent hip reconstruction following this technique between 2013 and 2020 at La Paz University Hospital (Madrid, Spain). The postoperative follow-up period ranged between 12 months and seven years. Two of the patients were diagnosed with Ewing's sarcoma and two with osteosarcoma. The median age at the time of surgery was eight years (six to nine). RESULTS: Three patients remained to be disease-free at the time of this study, and one died shortly after surgery, so he was excluded from the reconstruction analysis of results. No postoperative complications requiring reintervention were recorded. Imaging studies (X-ray and MRI) showed three-dimensional growth of the flap and integration of the allograft. Mean leg length discrepancy was 1.3 cm (0 to 2.3). At the last follow-up visit, all patients were able to ambulate. CONCLUSION: For children following oncological resection of the femoral head, reconstruction with a vascularized epiphyso-diaphyseal fibula flap combined with radius allograft is a safe option. This procedure provides encouraging functional results and avoids the complications of previously published techniques or implants.

Global Negative Correlation Learning: A Unified Framework for Global Optimization of Ensemble Models.

Ensembles are a widely implemented approach in the machine learning community and their success is traditionally attributed to the diversity within the ensemble. Most of these approaches foster diversity in the ensemble by data sampling or by modifying the structure of the constituent models. Despite this, there is a family of ensemble models in which diversity is explicitly promoted in the error function of the individuals. The negative correlation learning (NCL) ensemble framework is probably the most well-known algorithm within this group of methods. This article analyzes NCL and reveals that the framework actually minimizes the combination of errors of the individuals of the ensemble instead of minimizing the residuals of the final ensemble. We propose a novel ensemble framework, named global negative correlation learning (GNCL), which focuses on the optimization of the global ensemble instead of the individual fitness of its components. An analytical solution for the parameters of base regressors based on the NCL framework and the global error function proposed is also provided under the assumption of fixed basis functions (although the general framework could also be instantiated for neural networks with nonfixed basis functions). The proposed ensemble framework is evaluated by extensive experiments with regression and classification data sets. Comparisons with other state-of-the-art ensemble methods confirm that GNCL yields the best overall performance.

Floating Search Methodology for Combining Classification Models for Site Recognition in DNA Sequences.

Recognition of the functional sites of genes, such as translation initiation sites, donor and acceptor splice sites and stop codons, is a relevant part of many current problems in bioinformatics. The best approaches use sophisticated classifiers, such as support vector machines. However, with the rapid accumulation of sequence data, methods for combining many sources of evidence are necessary as it is unlikely that a single classifier can solve this problem with the best possible performance. A major issue is that the number of possible models to combine is large and the use of all of these models is impractical. In this paper we present a methodology for combining many sources of information to recognize any functional site using "floating search", a powerful heuristics applicable when the cost of evaluating each solution is high. We present experiments on four functional sites in the human genome, which is used as the target genome, and use another 20 species as sources of evidence. The proposed methodology shows significant improvement over state-of-the-art methods. The results show an advantage of the proposed method and also challenge the standard assumption of using only genomes not very close and not very far from the human to improve the recognition of functional sites.

Adherence to Long-Term Oxygen Therapy. Influence of Tobacco Use. / Cumplimiento de la oxigenoterapia crónica domiciliaria. Influencia del consumo de tabaco.

INTRODUCTION: Domiciliary oxygen therapy (DOT) is a treatment that requires a high level of cooperation from patients due to the time it takes every day. A high level of non-compliance has been determined among patients receiving DOT. The aim of our study was to assess the level of non-compliance and the influence of active tobacco consumption on compliance. MATERIAL AND METHODS: Patients were monitored in the home using direct and indirect methods, to assess both compliance and tobacco consumption. RESULTS: The level of non-compliance detected by indirect methods was 22.6%, and 66.3% by direct methods. Tobacco consumption determined by indirect methods was 5.8%-8%, depending on the method used, and 16.2% when CO in exhaled air ≥10ppm was established as an indicator of tobacco use. The group of smokers complied with oxygen therapy for a significantly fewer number of hours per day (P<.001) than non-smokers. CONCLUSIONS: There is a high level of therapeutic non-compliance and a significant percentage of patients receiving DOT continue to smoke. Compliance must be monitored, and the correct use of DOT must be emphasized. Additional efforts should also be made to help smokers with DOT to stop smoking, since continued smoking impacts negatively on therapeutic non-compliance.

Now is the Time for a Postracial Medicine: Biomedical Research, the National Institutes of Health, and the Perpetuation of Scientific Racism.

The consideration of racial differences in the biology of disease and treatment options is a hallmark of modern medicine. However, this time-honored medical tradition has no scientific basis, and the premise itself, that is, the existence of biological differences between the commonly known races, is false inasmuch as races are only sociocultural constructions. It is time to rid medical research of the highly damaging exercise of searching for supposed racial differences in the biological manifestations of disease. The practice not only condoned but required by the National Institutes of Health (NIH) of utilizing racial identification as a demographic characteristic with assumed biological implications is at best badly flawed, and at worst unintentionally contributes to perpetuating the fallacy of natural differences between persons of different skin color, which has been used in the past to advance the cause of racial discrimination.

Stepwise approach for combining many sources of evidence for site-recognition in genomic sequences.

BACKGROUND: Recognizing the different functional parts of genes, such as promoters, translation initiation sites, donors, acceptors and stop codons, is a fundamental task of many current studies in Bioinformatics. Currently, the most successful methods use powerful classifiers, such as support vector machines with various string kernels. However, with the rapid evolution of our ability to collect genomic information, it has been shown that combining many sources of evidence is fundamental to the success of any recognition task. With the advent of next-generation sequencing, the number of available genomes is increasing very rapidly. Thus, methods for making use of such large amounts of information are needed. RESULTS: In this paper, we present a methodology for combining tens or even hundreds of different classifiers for an improved performance. Our approach can include almost a limitless number of sources of evidence. We can use the evidence for the prediction of sites in a certain species, such as human, or other species as needed. This approach can be used for any of the functional recognition tasks cited above. However, to provide the necessary focus, we have tested our approach in two functional recognition tasks: translation initiation site and stop codon recognition. We have used the entire human genome as a target and another 20 species as sources of evidence and tested our method on five different human chromosomes. The proposed method achieves better accuracy than the best state-of-the-art method both in terms of the geometric mean of the specificity and sensitivity and the area under the receiver operating characteristic and precision recall curves. Furthermore, our approach shows a more principled way for selecting the best genomes to be combined for a given recognition task. CONCLUSIONS: Our approach has proven to be a powerful tool for improving the performance of functional site recognition, and it is a useful method for combining many sources of evidence for any recognition task in Bioinformatics. The results also show that the common approach of heuristically choosing the species to be used as source of evidence can be improved because the best combinations of genomes for recognition were those not usually selected. Although the experiments were performed for translation initiation site and stop codon recognition, any other recognition task may benefit from our methodology.

Improving translation initiation site and stop codon recognition by using more than two classes.

MOTIVATION: The recognition of translation initiation sites and stop codons is a fundamental part of any gene recognition program. Currently, the most successful methods use powerful classifiers, such as support vector machines with various string kernels. These methods all use two classes, one of positive instances and another one of negative instances that are constructed using sequences from the whole genome. However, the features of the negative sequences differ depending on the position of the negative samples in the gene. There are differences depending on whether they are from exons, introns, intergenic regions or any other functional part of the genome. Thus, the positive class is fairly homogeneous, as all its sequences come from the same part of the gene, but the negative class is composed of different instances. The classifier suffers from this problem. In this article, we propose the training of different classifiers with different negative, more homogeneous, classes and the combination of these classifiers for improved accuracy. RESULTS: The proposed method achieves better accuracy than the best state-of-the-art method, both in terms of the geometric mean of the specificity and sensitivity and the area under the receiver operating characteristic and precision recall curves. The method is tested on the whole human genome. The results for recognizing both translation initiation sites and stop codons indicated improvements in the rates of both false-negative results (FN) and false-positive results (FP). On an average, for translation initiation site recognition, the false-negative ratio was reduced by 30.2% and the FP ratio decreased by 10.9%. For stop codon prediction, FP were reduced by 41.4% and FN by 31.7%. AVAILABILITY AND IMPLEMENTATION: The source code is licensed under the General Public License and is thus freely available. The datasets and source code can be obtained from http://cib.uco.es/site-recognition. CONTACT: npedrajas@uco.es.

A scalable memetic algorithm for simultaneous instance and feature selection.

Instance selection is becoming increasingly relevant due to the huge amount of data that is constantly produced in many fields of research. At the same time, most of the recent pattern recognition problems involve highly complex datasets with a large number of possible explanatory variables. For many reasons, this abundance of variables significantly harms classification or recognition tasks. There are efficiency issues, too, because the speed of many classification algorithms is largely improved when the complexity of the data is reduced. One of the approaches to address problems that have too many features or instances is feature or instance selection, respectively. Although most methods address instance and feature selection separately, both problems are interwoven, and benefits are expected from facing these two tasks jointly. This paper proposes a new memetic algorithm for dealing with many instances and many features simultaneously by performing joint instance and feature selection. The proposed method performs four different local search procedures with the aim of obtaining the most relevant subsets of instances and features to perform an accurate classification. A new fitness function is also proposed that enforces instance selection but avoids putting too much pressure on removing features. We prove experimentally that this fitness function improves the results in terms of testing error. Regarding the scalability of the method, an extension of the stratification approach is developed for simultaneous instance and feature selection. This extension allows the application of the proposed algorithm to large datasets. An extensive comparison using 55 medium to large datasets from the UCI Machine Learning Repository shows the usefulness of our method. Additionally, the method is applied to 30 large problems, with very good results. The accuracy of the method for class-imbalanced problems in a set of 40 datasets is shown. The usefulness of the method is also tested using decision trees and support vector machines as classification methods.

OligoIS: Scalable Instance Selection for Class-Imbalanced Data Sets.

In current research, an enormous amount of information is constantly being produced, which poses a challenge for data mining algorithms. Many of the problems in extremely active research areas, such as bioinformatics, security and intrusion detection, or text mining, share the following two features: large data sets and class-imbalanced distribution of samples. Although many methods have been proposed for dealing with class-imbalanced data sets, most of these methods are not scalable to the very large data sets common to those research fields. In this paper, we propose a new approach to dealing with the class-imbalance problem that is scalable to data sets with many millions of instances and hundreds of features. This proposal is based on the divide-and-conquer principle combined with application of the selection process to balanced subsets of the whole data set. This divide-and-conquer principle allows the execution of the algorithm in linear time. Furthermore, the proposed method is easy to implement using a parallel environment and can work without loading the whole data set into memory. Using 40 class-imbalanced medium-sized data sets, we will demonstrate our method's ability to improve the results of state-of-the-art instance selection methods for class-imbalanced data sets. Using three very large data sets, we will show the scalability of our proposal to millions of instances and hundreds of features.

Nephrogenic systemic fibrosis: incidence, associations, and effect of risk factor assessment--report of 33 cases.

PURPOSE: To describe the presentation and clinical course of patients with nephrogenic systemic fibrosis (NSF) at a large acute-care hospital, to evaluate the overall incidence of NSF, and to assess the effect of a hospital-wide policy regarding gadolinium-based contrast agent (GBCA) use on NSF incidence. MATERIALS AND METHODS: A review of all cases of NSF observed at an institution from 2003 to 2008 was conducted. This HIPAA-compliant study was approved by the institutional review board. The informed consent requirement was waived. Demographics, medical history, and associated conditions were recorded. Radiologic procedures were evaluated if they were performed within 1 year prior to NSF onset. GBCA use was assessed by checking the electronic database for each procedure. The incidence of NSF was compared before and after implementation of an institutional policy designed to assess risk of NSF prior to GBCA use. RESULTS: All 33 patients with NSF (mean age, 49 years; age range, 15-78 years) had advanced renal failure (estimated glomerular filtration rate < 15 mL/min/1.73 m(2)) when the GBCA was injected. Twenty-six patients had severe chronic or end-stage renal disease, and seven had acute renal failure. The mean interval between contrast material injection and NSF onset was 29 days +/- 25 (standard deviation) (range, 4-112 days). The overall incidence of NSF was 36.5 cases per 100,000 gadolinium-enhanced magnetic resonance (MR) examinations between 2003 and 2006 and four cases per 100,000 gadolinium-enhanced MR examinations between 2007 and 2008 after screening for NSF risk was instituted (Fisher exact test, P = .001). Five patients developed NSF in the peritransplant period, and four underwent a catheter-based radiographic procedure with administration of a GBCA. CONCLUSION: Common associations of GBCA MR imaging and NSF were acute and severe chronic renal failure and liver or renal transplantation. Screening procedures performed before MR imaging to determine which patients were at risk of developing NSF appear to reduce the incidence of this complication and further support the belief that NSF is associated with GBCA administration.