Aggregation and Representation in the European Parliament Party Groups

While members of the European Parliament are elected in national constituencies, their votes are determined by the aggregation of MEPs in multinational party groups. The uncoordinated aggregation of national party programmes in multinational EP party groups challenges theories of representation based on national parties and parliaments. This article provides a theoretical means of understanding representation by linking the aggregation of dozens of national party programmes in different EP party groups to the aggregation of groups to produce the parliamentary majority needed to enact policies. Drawing on an original data source of national party programmes, the EU Profiler, the article shows that the EP majorities created by aggregating MEP votes in party groups are best explained by cartel theories. These give priority to strengthening the EP’s collective capacity to enact policies rather than voting in accord with the programmes they were nationally elected to represent

Stakeholder Engagement in Sustainability Reporting: Evidence of Reputation Risk Management in Large Australian Companies

The objective of this research is to examine whether stakeholder engagement in sustainability reporting constitutes the process of managing reputation risks. This research utilises Shrives and Brennan’s (2017) framework of rhetorical strategies of non-compliance to obtain empirical evidence of reputation risk management in the context of stakeholder engagement in sustainability reporting. Quantitative and qualitative methods of content analysis were undertaken on 154 sustainability disclosures in both annual reports and sustainability reports of large Australian companies. This research finds that large Australian companies engage with their stakeholders to manage reputation risks: to increase market share and pre-empt social issues. It is evident that large Australian companies use several forms of rhetorical statements in their sustainability disclosures with respect to reputation risk management efforts. However, there is no evidence that they shirk responsibilities

Mua (HP0868) Is a Nickel-Binding Protein That Modulates Urease Activity in Helicobacter pylori

A novel mechanism aimed at controlling urease expression in Helicobacter pylori in the presence of ample nickel is described. Higher urease activities were observed in an hp0868 mutant (than in the wild type) in cells supplemented with nickel, suggesting that the HP0868 protein (herein named Mua for modulator of urease activity) represses urease activity when nickel concentrations are ample. The increase in urease activity in the Δmua mutant was linked to an increase in urease transcription and synthesis, as shown by quantitative real-time PCR, SDS-PAGE, and immunoblotting against UreAB. Increased urease synthesis was also detected in a Δmua ΔnikR double mutant strain. The Δmua mutant was more sensitive to nickel toxicity but more resistant to acid challenge than was the wild-type strain. Pure Mua protein binds 2 moles of Ni2+ per mole of dimer. Electrophoretic mobility shift assays did not reveal any binding of Mua to the ureA promoter or other selected promoters (nikR, arsRS, 5′ ureB-sRNAp). Previous yeast two-hybrid studies indicated that Mua and RpoD may interact; however, only a weak interaction was detected via cross-linking with pure components and this could not be verified by another approach. There was no significant difference in the intracellular nickel level between wild-type and mua mutant cells. Taken together, our results suggest the HP0868 gene product represses urease transcription when nickel levels are high through an as-yet-uncharacterized mechanism, thus counterbalancing the well-described NikR-mediated activation

A Review on the Young History of the Wind Power Short-term Prediction

This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art on models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly the same in order to compare two models (this fact makes it almost impossible to carry out a quantitative comparison between a huge number of models and methods). In place of a quantitative description, a qualitative approach is preferred for this review, remarking the contribution (and innovative aspect) of each model. On the basis of the review, some topics for future research are pointed out

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research

Reconstructing the demographic history of orang-utans using Approximate Bayesian Computation

Investigating how different evolutionary forces have shaped patterns of DNA variation within and among species requires detailed knowledge of their demographic history. Orang-utans, whose distribution is currently restricted to the South-East Asian islands of Borneo (Pongo pygmaeus) and Sumatra (Pongo abelii), have likely experienced a complex demographic history, influenced by recurrent changes in climate and sea levels, volcanic activities and anthropogenic pressures. Using the most extensive sample set of wild orang-utans to date, we employed an Approximate Bayesian Computation (ABC) approach to test the fit of 12 different demographic scenarios to the observed patterns of variation in autosomal, X-chromosomal, mitochondrial and Y-chromosomal markers. In the best-fitting model, Sumatran orang-utans exhibit a deep split of populations north and south of Lake Toba, probably caused by multiple eruptions of the Toba volcano. In addition, we found signals for a strong decline in all Sumatran populations ~24 ka, probably associated with hunting by human colonizers. In contrast, Bornean orang-utans experienced a severe bottleneck ~135 ka, followed by a population expansion and substructuring starting ~82 ka, which we link to an expansion from a glacial refugium. We showed that orang-utans went through drastic changes in population size and connectedness, caused by recurrent contraction and expansion of rainforest habitat during Pleistocene glaciations and probably hunting by early humans. Our findings emphasize the fact that important aspects of the evolutionary past of species with complex demographic histories might remain obscured when applying overly simplified models

AI-Based Chest CT Analysis for Rapid COVID-19 Diagnosis and Prognosis: A Practical Tool to Flag High-Risk Patients and Lower Healthcare Costs

peer reviewedEarly diagnosis of COVID-19 is required to provide the best treatment to our patients, to prevent the epidemic from spreading in the community, and to reduce costs associated with the aggravation of the disease. We developed a decision tree model to evaluate the impact of using an artificial intelligence-based chest computed tomography (CT) analysis software (icolung, icometrix) to analyze CT scans for the detection and prognosis of COVID-19 cases. The model compared routine practice where patients receiving a chest CT scan were not screened for COVID-19, with a scenario where icolung was introduced to enable COVID-19 diagnosis. The primary outcome was to evaluate the impact of icolung on the transmission of COVID-19 infection, and the secondary outcome was the in-hospital length of stay. Using EUR 20000 as a willingness-to-pay threshold, icolung is cost-effective in reducing the risk of transmission, with a low prevalence of COVID-19 infections. Concerning the hospitalization cost, icolung is cost-effective at a higher value of COVID-19 prevalence and risk of hospitalization. This model provides a framework for the evaluation of AI-based tools for the early detection of COVID-19 cases. It allows for making decisions regarding their implementation in routine practice, considering both costs and effects

The Role of Imaging in the Detection of Non-COVID-19 Pathologies during the Massive Screening of the First Pandemic Wave

peer reviewedDuring the COVID-19 pandemic induced by the SARS-CoV-2, numerous chest scans were carried out in order to establish the diagnosis, quantify the extension of lesions but also identify the occurrence of potential pulmonary embolisms. In this perspective, the performed chest scans provided a varied database for a retrospective analysis of non-COVID-19 chest pathologies discovered de novo. The fortuitous discovery of de novo non-COVID-19 lesions was generally not detected by the automated systems for COVID-19 pneumonia developed in parallel during the pandemic and was thus identified on chest CT by the radiologist. The objective is to use the study of the occurrence of non-COVID-19-related chest abnormalities (known and unknown) in a large cohort of patients having suffered from confirmed COVID-19 infection and statistically correlate the clinical data and the occurrence of these abnormalities in order to assess the potential of increased early detection of lesions/alterations. This study was performed on a group of 362 COVID-19-positive patients who were prescribed a CT scan in order to diagnose and predict COVID-19-associated lung disease. Statistical analysis using mean, standard deviation (SD) or median and interquartile range (IQR), logistic regression models and linear regression models were used for data analysis. Results were considered significant at the 5% critical level (p < 0.05). These de novo non-COVID-19 thoracic lesions detected on chest CT showed a significant prevalence in cardiovascular pathologies, with calcifying atheromatous anomalies approaching nearly 35.4% in patients over 65 years of age. The detection of non-COVID-19 pathologies was mostly already known, except for suspicious nodule, thyroid goiter and the ascending thoracic aortic aneurysm. The presence of vertebral compression or signs of pulmonary fibrosis has shown a significant impact on inpatient length of stay. The characteristics of the patients in this sample, both from a demographic and a tomodensitometric point of view on non-COVID-19 pathologies, influenced the length of hospital stay as well as the risk of intra-hospital death. This retrospective study showed that the potential importance of the detection of these non-COVID-19 lesions by the radiologist was essential in the management and the intra-hospital course of the patients