On the Detection of Reviewer-Author Collusion Rings From Paper Bidding

A major threat to the peer-review systems of computer science conferences is the existence of "collusion rings" between reviewers. In such collusion rings, reviewers who have also submitted their own papers to the conference work together to manipulate the conference's paper assignment, with the aim of being assigned to review each other's papers. The most straightforward way that colluding reviewers can manipulate the paper assignment is by indicating their interest in each other's papers through strategic paper bidding. One potential approach to solve this important problem would be to detect the colluding reviewers from their manipulated bids, after which the conference can take appropriate action. While prior work has developed effective techniques to detect other kinds of fraud, no research has yet established that detecting collusion rings is even possible. In this work, we tackle the question of whether it is feasible to detect collusion rings from the paper bidding. To answer this question, we conduct empirical analysis of two realistic conference bidding datasets, including evaluations of existing algorithms for fraud detection in other applications. We find that collusion rings can achieve considerable success at manipulating the paper assignment while remaining hidden from detection: for example, in one dataset, undetected colluders are able to achieve assignment to up to 30% of the papers authored by other colluders. In addition, when 10 colluders bid on all of each other's papers, no detection algorithm outputs a group of reviewers with more than 31% overlap with the true colluders. These results suggest that collusion cannot be effectively detected from the bidding using popular existing tools, demonstrating the need to develop more complex detection algorithms as well as those that leverage additional metadata (e.g., reviewer-paper text-similarity scores)

EwE-F 1.0: an implementation of Ecopath with Ecosim in Fortran 95/2003 for coupling and integration with other models

Abstract. Societal and scientific challenges foster the implementation of the ecosystem approach to marine ecosystem analysis and management, which is a comprehensive means of integrating the direct and indirect effects of multiple stressors on the different components of ecosystems, from physical to chemical and biological and from viruses to fishes and marine mammals. Ecopath with Ecosim (EwE) is a widely used software package, which offers capability for a dynamic description of the multiple interactions occurring within a food web, and, potentially, a crucial component of an integrated platform supporting the ecosystem approach. However, being written for the Microsoft .NET framework, seamless integration of this code with Fortran-based physical and/or biogeochemical oceanographic models is technically not straightforward. In this work we release a re-coding of EwE in Fortran (EwE-F). We believe that the availability of a Fortran version of EwE is an important step towards setting up coupled/integrated modelling schemes utilising this widely adopted software because it (i) increases portability of the EwE models and (ii) provides additional flexibility towards integrating EwE with Fortran-based modelling schemes. Furthermore, EwE-F might help modellers using the Fortran programming language to get close to the EwE approach. In the present work, first fundamentals of EwE-F are introduced, followed by validation of EwE-F against standard EwE utilising sample models. Afterwards, an end-to-end (E2E) ecological representation of the Gulf of Trieste (northern Adriatic Sea) ecosystem is presented as an example of online two-way coupling between an EwE-F food web model and a biogeochemical model. Finally, the possibilities that having EwE-F opens up are discussed

The iPlant Collaborative: Cyberinfrastructure for Plant Biology

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services

Comparative Prognostic Accuracy of Clinical and Inflammation- or Nutrition-Based Scores in Older Adults with Community-Acquired Pneumonia

Merve Eksioglu,1 Burcu Azapoglu Kaymak,1 Ebru Unal Akoglu,1 Selman Faruk Akyıldız,1 Ramazan Sivil,2 Tuba Cimilli Ozturk1 1Department of Emergency Medicine, University of Health Sciences, Fatih Sultan Mehmet Education and Research Hospital, Istanbul, Turkey; 2Department of Emergency Medicine, University of Health Sciences, Antalya Education and Research Hospital, Antalya, TurkeyCorrespondence: Merve Eksioglu, Department of Emergency Medicine, University of Health Sciences, Fatih Sultan Mehmet Education and Research Hospital, Hastane Sokak No: 1/9 İçerenköy, Ataşehir, Istanbul, 34752, Turkey, Tel +90 216 578 30 00; +90 505 295 36 87, Email [email protected]: This study aimed to assess the prognostic accuracy of the Glasgow Prognostic Score (GPS), modified Glasgow Prognostic Score (mGPS), and C-reactive protein/albumin ratio (CAR) in predicting 30-day mortality and intensive care unit (ICU) admission compared with the Pneumonia Severity Index (PSI) and CURB-65 in older adults with community-acquired pneumonia (CAP).Patients and Methods: This retrospective, single-center cohort study was conducted in a tertiary emergency department. Patients aged ≥ 65 years with CAP were included. Exclusion criteria were hospital- or ventilator-associated pneumonia, pneumonia mimics, and immunocompromised status. GPS and mGPS were calculated using CRP > 10 mg/L and albumin < 35 g/L. ROC and logistic regression analyses were performed.Results: A total of 349 patients (mean age: 77.96 ± 8.42 years; 52.7% men) were included. The 30-day mortality and ICU admission rates were 19.5% and 27.2%, respectively. For predicting mortality, the GPS showed an AUC of 0.753 (95% CI: 0.690– 0.816), sensitivity of 75.0%, specificity of 73.3%, PPV of 43.9%, and NPV of 92.4%. mGPS had an AUC of 0.747 (95% CI: 0.679– 0.814), sensitivity 77.9%, specificity 73.3%, PPV 45.2%, and NPV 93.2%. The CAR yielded an AUC of 0.677 (95% CI: 0.604– 0.751), sensitivity of 82.4%, specificity of 45.6%, PPV of 29.5%, and NPV of 91.4%. For ICU admission, the AUCs were 0.770 (GPS), 0.757 (mGPS), and 0.676 (CAR). The PSI demonstrated the highest predictive accuracy (AUC: 0.884 for mortality, 0.919 for ICU admission), followed by CURB-65 (AUC: 0.848 and 0.879, respectively). Independent predictors of 30-day mortality included acute confusion, lower PaO2/FiO2 ratio, low systolic blood pressure, reduced hemoglobin levels, and Alzheimer’s disease or dementia.Conclusion: The PSI and CURB-65 demonstrated superior prognostic accuracy. GPS and mGPS showed moderate performance, whereas CAR exhibited the lowest overall discriminative ability for both outcomes.Keywords: geriatric emergency care, community-acquired pneumonia, prognostic scores, pneumonia severity index, PSI, glasgow prognostic score, GPS, C-reactive protein to albumin ratio, CA

Improving shared decision-making about cancer treatment through design-based data-driven decision-support tools and redesigning care paths:an overview of the 4D PICTURE project

Background: Patients with cancer often have to make complex decisions about treatment, with the options varying in risk profiles and effects on survival and quality of life. Moreover, inefficient care paths make it hard for patients to participate in shared decision-making. Data-driven decision-support tools have the potential to empower patients, support personalized care, improve health outcomes and promote health equity. However, decision-support tools currently seldom consider quality of life or individual preferences, and their use in clinical practice remains limited, partly because they are not well integrated in patients' care paths.Aim and objectives: The central aim of the 4D PICTURE project is to redesign patients' care paths and develop and integrate evidence-based decision-support tools to improve decision-making processes in cancer care delivery. This article presents an overview of this international, interdisciplinary project.Design, methods and analysis: In co-creation with patients and other stakeholders, we will develop data-driven decision-support tools for patients with breast cancer, prostate cancer and melanoma. We will support treatment decisions by using large, high-quality datasets with state-of-the-art prognostic algorithms. We will further develop a conversation tool, the Metaphor Menu, using text mining combined with citizen science techniques and linguistics, incorporating large datasets of patient experiences, values and preferences. We will further develop a promising methodology, MetroMapping, to redesign care paths. We will evaluate MetroMapping and these integrated decision-support tools, and ensure their sustainability using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework. We will explore the generalizability of MetroMapping and the decision-support tools for other types of cancer and across other EU member states.Ethics: Through an embedded ethics approach, we will address social and ethical issues.Discussion: Improved care paths integrating comprehensive decision-support tools have the potential to empower patients, their significant others and healthcare providers in decision-making and improve outcomes. This project will strengthen health care at the system level by improving its resilience and efficiency.Improving the cancer patient journey and respecting personal preferences: an overview of the 4D PICTURE projectThe 4D PICTURE project aims to help cancer patients, their families and healthcare providers better undertstand their options. It supports their treatment and care choices, at each stage of disease, by drawing on large amounts of evidence from different types of European data. The project involves experts from many different specialist areas who are based in nine European countries. The overall aim is to improve the cancer patient journey and ensure personal preferences are respected

Influence Propagation: Patterns, Model and a Case Study

When a free, catchy application shows up, how quickly will people notify their friends about it? Will the enthusiasm drop exponentially with time, or oscillate? What other patterns emerge? Here we answer these questions using data from the Polly telephone-based application, a large influence network of 72,000 people, with about 173,000 interactions, spanning 500MB of log data and 200 GB of audio data. We report surprising patterns, the most striking of which are: (a) the Fizzle pattern, i.e., excitement about Polly shows a power-law decay over time with exponent of -1.2; (b) the Rendezvous pattern, that obeys a power law (we explain Rendezvous in the text); (c) the Dispersion pattern, we find that the more a person uses Polly, the fewer friends he will use it with, but in a reciprocal fashion. Finally, we also propose a generator of influence networks, which generate networks that mimic our discovered patterns © 2014 Springer International Publishing.Y

Improving shared decision-making about cancer treatment through design-based data-driven decision-support tools and redesigning care paths: an overview of the 4D PICTURE project

Background:Patients with cancer often have to make complex decisions about treatment, with the options varying in risk profiles and effects on survival and quality of life. Moreover, inefficient care paths make it hard for patients to participate in shared decision-making. Data-driven decision-support tools have the potential to empower patients, support personalized care, improve health outcomes and promote health equity. However, decision-support tools currently seldom consider quality of life or individual preferences, and their use in clinical practice remains limited, partly because they are not well integrated in patients’ care paths.Aim and objectives:The central aim of the 4D PICTURE project is to redesign patients’ care paths and develop and integrate evidence-based decision-support tools to improve decision-making processes in cancer care delivery. This article presents an overview of this international, interdisciplinary project.Design, methods and analysis:In co-creation with patients and other stakeholders, we will develop data-driven decision-support tools for patients with breast cancer, prostate cancer and melanoma. We will support treatment decisions by using large, high-quality datasets with state-of-the-art prognostic algorithms. We will further develop a conversation tool, the Metaphor Menu, using text mining combined with citizen science techniques and linguistics, incorporating large datasets of patient experiences, values and preferences. We will further develop a promising methodology, MetroMapping, to redesign care paths. We will evaluate MetroMapping and these integrated decision-support tools, and ensure their sustainability using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework. We will explore the generalizability of MetroMapping and the decision-support tools for other types of cancer and across other EU member states.Ethics:Through an embedded ethics approach, we will address social and ethical issues.Discussion:Improved care paths integrating comprehensive decision-support tools have the potential to empower patients, their significant others and healthcare providers in decision-making and improve outcomes. This project will strengthen health care at the system level by improving its resilience and efficiency.Development and application of statistical models for medical scientific researc

Benign skin disease with pustules in the newborn

The neonatal period comprises the first four weeks of life. It is a period of adaptation where the skin often presents several changes: transient lesions, resulting from a physiological response, others as a consequence of transient diseases and some as markers of severe disorders. The presence of pustules in the skin of the newborn is always a reason for the family and for the assisting doctor to be worried, since the newborn is especially vulnerable to bacterial, viral or fungal infection. However, the majority of neonatal skin pustules is not infectious, comprising the benign neonatal pustulosis. Benign neonatal pustuloses are a group of clinical disease characterized by pustular eruptions in which a contagious agent is not responsible for its etiology. The most common ones are erythema toxicum neonatorum, the transient neonatal pustular melanosis and the benign cephalic pustulosis. These dermatoses are usually benign, asymptomatic and self-limited. It is important that the dermatologist and the neonatologist can identify benign and transient lesions, those caused by genodermatoses, and especially differentiate between neonates with systemic involvement from those with benign skin lesions, avoiding unnecessary diagnostic tests and worries

Analyzing the effectiveness of graph metrics for anomaly detection in online social networks

Online social networks can be modelled as graphs; in this paper, we analyze the use of graph metrics for identifying users with anomalous relationships to other users. A framework is proposed for analyzing the effectiveness of various graph theoretic properties such as the number of neighbouring nodes and edges, betweenness centrality, and community cohesiveness in detecting anomalous users. Experimental results on real-world data collected from online social networks show that the majority of users typically have friends who are friends themselves, whereas anomalous users’ graphs typically do not follow this common rule. Empirical analysis also shows that the relationship between average betweenness centrality and edges identifies anomalies more accurately than other approaches