Structural combination of neural network models

Forecasts combinations normally use point forecasts that were obtained from different models or sources ([1], [2], [3]). This paper explores the incorporation of internal structure parameters of feed-forward neural network (NN) models as an approach to combine their forecasts via ensembles. First, the generated NN models that could be part of the ensembles are subject to a clustering algorithm that uses the structure parameters and, from each of the clusters obtained, a small set of models is selected and their forecasts are combined in a two-stage procedure. Secondly, in an alternative and simpler implementation, a subset of the generated NN models is selected by using several reference points in the model structure parameter space. The choice of the reference points is optimised through a genetic algorithm and the models selected are averaged. Hourly electricity demand time series is used to assess multi-step ahead forecasting performance for up to a 12 hours horizon. Results are compared against several statistical benchmarks, the average of the individual forecasts and the best models in the ensembles. Results show that the clusterbased (CB) structural combinations do better than the genetic algorithm (GA) structural combinations in outperforming the average forecast, which is the traditional point forecast from an ensemble

BacFITBase: A database to assess the relevance of bacterial genes during host infection

Bacterial infections have been on the rise world-wide in recent years and have a considerable impact on human well-being in terms of attributable deaths and disability-adjusted life years. Yet many mechanisms underlying bacterial pathogenesis are still poorly understood. Here, we introduce the BacFITBase database for the systematic characterization of bacterial proteins relevant for host infection aimed to enable the identification of new antibiotic targets. BacFITBase is manually curated and contains more than 90 000 entries with information on the contribution of individual genes to bacterial fitness under in vivo infection conditions in a range of host species. The data were collected from 15 different studies in which transposon mutagenesis was performed, including top-priority pathogens such as Acinetobacter baumannii and Campylobacter jejuni, for both of which increasing antibiotic resistance has been reported. Overall, BacFITBase includes information on 15 pathogenic bacteria and 5 host vertebrates across 10 different tissues. It is freely available at www.tartaglialab.com/bacfitbase

Flow and rent-based opportunity costs of water ecosystem service provision in a complex farming system

Unsustainable land uses present many challenges for securing ecosystem service provision. It is also difficult to estimate the cost of a transition to more sustainable land-management practices for individual landholders. The main cost to landholders is the opportunity costs, the income foregone when changing land use for continued or enhanced ecosystem service provision. Thus accurate estimation of opportunity costs and understanding their distribution are crucial starting points for determining the economic viability and design of any payment for ecosystem services (PES) scheme. We compare two opportunity cost approaches and examine the distribution of these costs for improving drinking water quality in a complex farming system in a Honduran forest catchment. Data for both approaches was collected through a survey applied to upstream catchment landholders. Our results indicate that the direct flow approach and the proxy rent approach provide comparable and consistent opportunity cost estimates. The mean net flow return ha-1 was US$1410, but this estimate was skewed, mainly by exceptionally high coffee returns and negative returns of land uses making a loss. This estimate would imply spending over US$2 million per annum for water conservation, but a revised estimate comes to US$257,057 per annum. Opportunity costs were found to vary according to differences in land use and landholder characteristics. High value cash crops upholding the local economy, such as coffee, entail much higher opportunity costs than for example cattle grazing. These results suggest that discriminate PES payments, that vary according to opportunity costs and thus discriminate between land uses and landholders, are essential. Water quality at our case study site could be managed sustainably by a scheme focusing on high-impact land uses with lower opportunity costs and closer to water sources

Barriers and facilitators experienced in collaborative prospective research in orthopaedic oncology

Recerca col·laborativa; Grup focal; Oncologia ortopèdicaCollaborative research; Focus group; Orthopaedic oncologyInvestigación colaborativa; Grupo focal; Oncología ortopédicaObjectives As tumours of bone and soft tissue are rare, multicentre prospective collaboration is essential for meaningful research and evidence-based advances in patient care. The aim of this study was to identify barriers and facilitators encountered in large-scale collaborative research by orthopaedic oncological surgeons involved or interested in prospective multicentre collaboration. Methods All surgeons who were involved, or had expressed an interest, in the ongoing Prophylactic Antibiotic Regimens in Tumour Surgery (PARITY) trial were invited to participate in a focus group to discuss their experiences with collaborative research in this area. The discussion was digitally recorded, transcribed and anonymised. The transcript was analysed qualitatively, using an analytic approach which aims to organise the data in the language of the participants with little theoretical interpretation. Results The 13 surgeons who participated in the discussion represented orthopaedic oncology practices from seven countries (Argentina, Brazil, Italy, Spain, Denmark, United States and Canada). Four categories and associated themes emerged from the discussion: the need for collaboration in the field of orthopaedic oncology due to the rarity of the tumours and the need for high level evidence to guide treatment; motivational factors for participating in collaborative research including establishing proof of principle, learning opportunity, answering a relevant research question and being part of a collaborative research community; barriers to participation including funding, personal barriers, institutional barriers, trial barriers, and administrative barriers and facilitators for participation including institutional facilitators, leadership, authorship, trial set-up, and the support of centralised study coordination. Conclusions Orthopaedic surgeons involved in an ongoing international randomised controlled trial (RCT) were motivated by many factors to participate. There were a number of barriers to and facilitators for their participation. There was a collective sense of fatigue experienced in overcoming these barriers, which was mirrored by a strong collective sense of the importance of, and need for, collaborative research in this field. The experiences were described as essential educational first steps to advance collaborative studies in this area. Knowledge gained from this study will inform the development of future large-scale collaborative research projects in orthopaedic oncology

Drug Dosage Individualization Based on a Random-Effects Linear Model

This article investigates drug dosage individualization when the patient population can be described with a random-effects linear model of a continuous pharmacokinetic or pharmacodynamic response. Specifically, we show through both decision-theoretic arguments and simulations that a published clinical algorithm may produce better individualized dosages than some traditional methods of therapeutic drug monitoring. Since empirical evidence suggests that the linear model may adequately describe drugs and patient populations, and linear models are easier to handle than the nonlinear models traditionally used in population pharmacokinetics, our results highlight the potential applicability of linear mixed models to dosage computations and personalized medicine

Structural Combination of Seasonal Exponential Smoothing Forecasts Applied to Load Forecasting

This article draws from research on ensembles in computational intelligence to propose structural combinations of forecasts, which are point forecast combinations that are based on information from the parameters of the individual models that generated the forecasts. Two types of structural combination are proposed which use seasonal exponential smoothing as base models, and are applied to forecast short-term electricity demand. Although forecasting performance may depend on how ensembles are generated, results show that the proposed combinations can outperform competitive benchmarks. The methods can be used to forecast other seasonal data and be extended to different types of forecasting models

A wot-based method for creating digital sentinel twins of iot devices

The data produced by sensors of IoT devices are becoming keystones for organizations to conduct critical decision-making processes. However, delivering information to these processes in real-time represents two challenges for the organizations: the first one is achieving a constant dataflow from IoT to the cloud and the second one is enabling decision-making processes to retrieve data from dataflows in real-time. This paper presents a cloud-based Web of Things method for creating digital twins of IoT devices (named sentinels).The novelty of the proposed approach is that sentinels create an abstract window for decision-making processes to: (a) find data (e.g., properties, events, and data from sensors of IoT devices) or (b) invoke functions (e.g., actions and tasks) from physical devices (PD), as well as from virtual devices (VD). In this approach, the applications and services of decision-making processes deal with sentinels instead of managing complex details associated with the PDs, VDs, and cloud computing infrastructures. A prototype based on the proposed method was implemented to conduct a case study based on a blockchain system for verifying contract violation in sensors used in product transportation logistics. The evaluation showed the effectiveness of sentinels enabling organizations to attain data from IoT sensors and the dataflows used by decision-making processes to convert these data into useful information

Bone Marrow Stem Cell Treatment for Ischemic Heart Disease in Patients with No Option of Revascularization: A Systematic Review and Meta-Analysis

PMCID: PMC3686792This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited