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2021 ◽  
Michael S Jones ◽  
Zhenchen Zhu ◽  
Aahana Bajracharya ◽  
Austin Luor ◽  
Jonathan E Peelle

Subject motion during fMRI can affect our ability to accurately measure signals of interest. In recent years, frame censoring—that is, statistically excluding motion-contaminated data within the general linear model using nuisance regressors—has appeared in several task-based fMRI studies as a mitigation strategy. However, there have been few systematic investigations quantifying its efficacy. In the present study, we compared the performance of frame censoring to several other common motion correction approaches for task-based fMRI using open data and reproducible workflows. We analyzed eight datasets available on representing eleven distinct tasks in child, adolescent, and adult participants. Performance was quantified using maximum t-values in group analyses, and ROI-based mean activation and split-half reliability in single subjects. We compared frame censoring to the use of 6 and 24 canonical motion regressors, wavelet despiking, robust weighted least squares, and untrained ICA-based denoising. Thresholds used to identify censored frames were based on both motion estimates (FD) and image intensity changes (DVARS). Relative to standard motion regressors, we found consistent improvements for modest amounts of frame censoring (e.g., 1-2% data loss), although these gains were frequently comparable to what could be achieved using other techniques. Importantly, no single approach consistently outperformed the others across all datasets and tasks. These findings suggest that although frame censoring can improve results, the choice of a motion mitigation strategy depends on the dataset and the outcome metric of interest.

2021 ◽  
Vol 25 ◽  
Ciro Alberto Amaya-Guio ◽  
Lina Patricia Navas ◽  
Cesar Humberto Torres-Gonzalez

Objective: Propose a methodology to determine the number of medical students who can rotate, for the practice of medicine, in a university hospital, so that the quality of training processes and in-patient care are assured. Materials and Methods: A three-step procedure is presented, in order to find the number of students that the institution can accept simultaneously. Results: The method is based on an integer linear model and it was implemented to assess installed capacity of General Surgery service at Hospital Universitario Clínica San Rafael, increasing in two students (33 %) the training capacity. Conclusions: The proposed methodology not only guaranties the quality of training processes and in-patient care, but also generates other intangible results such as having a more agile way of planning, reducing the planification time. The methodology is easily extended to other services within hospitals.

Thomas W. O’Gorman

2021 ◽  
Vol 13 (19) ◽  
pp. 11080
Svetlana Ratner ◽  
Konstantin Gomonov ◽  
Inna Lazanyuk ◽  
Svetlana Revinova

Historically, the development of the circular economy (CE) proceeds from the CE 1.0 stage, characterized by attention to waste management and recycling, to the CE 2.0 stage with an emphasis on resource efficiency and eco-efficiency, to the current CE 3.0 stage, in which the key factor to a company’s success is the business model. However, not all countries of the world simultaneously began transforming the national economy from a linear model to a circular one; many are still at the CE 1.0 and CE 2.0 stages, and do not have a developed system of institutions supporting the circular economy. In Russia, the concept of a circular economy has not yet received recognition in society and government; the stage of its development can be defined as CE 2.0. This study compares the barriers and drivers of CE development in the EU countries, a group of countries with a well-developed institutional support system, and in Russia, a country that does not have such a system. The study reveals that the most significant difference between countries with mature systems of institutional support and Russia lies in the regulatory sphere and in information and awareness about new available technologies and ways to increase resource efficiency, commercial attractiveness, and organizational feasibility. Changes in the first sphere are impossible without the participation of the national authorities; however, changes in the information sphere are feasible even without the government’s support. The actors in such changes can be international companies with access to resource-efficient new technologies and processes for organizing business.

2021 ◽  
pp. 1-35
M. Lindholm ◽  
R. Richman ◽  
A. Tsanakas ◽  
M.V. Wüthrich

Abstract We consider the following question: given information on individual policyholder characteristics, how can we ensure that insurance prices do not discriminate with respect to protected characteristics, such as gender? We address the issues of direct and indirect discrimination, the latter resulting from implicit learning of protected characteristics from nonprotected ones. We provide rigorous mathematical definitions for direct and indirect discrimination, and we introduce a simple formula for discrimination-free pricing, that avoids both direct and indirect discrimination. Our formula works in any statistical model. We demonstrate its application on a health insurance example, using a state-of-the-art generalized linear model and a neural network regression model. An important conclusion is that discrimination-free pricing in general requires collection of policyholders’ discriminatory characteristics, posing potential challenges in relation to policyholder’s privacy concerns.

2021 ◽  
pp. 197140092110490
Ludger Feyen ◽  
Peter Schott ◽  
Hendrik Ochmann ◽  
Marcus Katoh ◽  
Patrick Haage ◽  

Purpose Clinical outcomes vary considerably among individuals with vessel occlusion of the posterior circulation. In the present study we evaluated machine learning algorithms in their ability to discriminate between favourable and unfavourable outcomes in patients with endovascular treatment of acute ischaemic stroke of the posterior circulation. Methods This retrospective study evaluated three algorithms (generalised linear model, K-nearest neighbour and random forest) to predict functional outcomes at dismissal of 30 patients with acute occlusion of the basilar artery who were treated with thrombectomy. Input variables encompassed baseline as well as peri and postprocedural data. Favourable outcome was defined as a modified Rankin scale score of 0–2 and unfavourable outcome was defined as a modified Rankin scale score of 3–6. The performance of the algorithms was assessed with the area under the receiver operating curve and with confusion matrixes. Results Successful reperfusion was achieved in 83%, with 30% of the patients having a favourable outcome. The area under the curve was 0.93 for the random forest model, 0.86 for the K-nearest neighbour model and 0.78 for the generalised linear model. The accuracy was 0.69 for the generalised linear model and 0.84 for the random forest and the K nearest neighbour models. Conclusion Favourable and unfavourable outcomes at dismissal of patients with acute ischaemic stroke of the posterior circulation can be predicted immediately after the follow-up non-enhanced computed tomography using machine learning.

2021 ◽  
Vol 22 (1) ◽  
Guomin Zhang ◽  
Rongsheng Wang ◽  
Juntao Ma ◽  
Hongru Gao ◽  
Lingwei Deng ◽  

Abstract Background Heilongjiang Province is a high-quality japonica rice cultivation area in China. One in ten bowls of Chinese rice is produced here. Increasing yield is one of the main aims of rice production in this area. However, yield is a complex quantitative trait composed of many factors. The purpose of this study was to determine how many genetic loci are associated with yield-related traits. Genome-wide association studies (GWAS) were performed on 450 accessions collected from northeast Asia, including Russia, Korea, Japan and Heilongjiang Province of China. These accessions consist of elite varieties and landraces introduced into Heilongjiang Province decade ago. Results After resequencing of the 450 accessions, 189,019 single nucleotide polymorphisms (SNPs) were used for association studies by two different models, a general linear model (GLM) and a mixed linear model (MLM), examining four traits: days to heading (DH), plant height (PH), panicle weight (PW) and tiller number (TI). Over 25 SNPs were found to be associated with each trait. Among them, 22 SNPs were selected to identify candidate genes, and 2, 8, 1 and 11 SNPs were found to be located in 3′ UTR region, intron region, coding region and intergenic region, respectively. Conclusions All SNPs detected in this research may become candidates for further fine mapping and may be used in the molecular breeding of high-latitude rice.

2021 ◽  
Vol 11 (1) ◽  
Sini Sulkama ◽  
Jenni Puurunen ◽  
Milla Salonen ◽  
Salla Mikkola ◽  
Emma Hakanen ◽  

AbstractAttention-deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder impairing the quality of life of the affected individuals. The domestic dog can spontaneously manifest high hyperactivity/impulsivity and inattention which are components of human ADHD. Therefore, a better understanding of demographic, environmental and behavioural factors influencing canine hyperactivity/impulsivity and inattention could benefit both humans and dogs. We collected comprehensive behavioural survey data from over 11,000 Finnish pet dogs and quantified their level of hyperactivity/impulsivity and inattention. We performed generalised linear model analyses to identify factors associated with these behavioural traits. Our results indicated that high levels of hyperactivity/impulsivity and inattention were more common in dogs that are young, male and spend more time alone at home. Additionally, we showed several breed differences suggesting a substantial genetic basis for these traits. Furthermore, hyperactivity/impulsivity and inattention had strong comorbidities with compulsive behaviour, aggressiveness and fearfulness. Multiple of these associations have also been identified in humans, strengthening the role of the dog as an animal model for ADHD.

Huilin Zhou ◽  
Huimin Zheng ◽  
Qiegen Liu ◽  
Jian Liu ◽  
Yuhao Wang

Abstract Electromagnetic inverse-scattering problems (ISPs) are concerned with determining the properties of an unknown object using measured scattered fields. ISPs are often highly nonlinear, causing the problem to be very difficult to address. In addition, the reconstruction images of different optimization methods are distorted which leads to inaccurate reconstruction results. To alleviate these issues, we propose a new linear model solution of generative adversarial network-based (LM-GAN) inspired by generative adversarial networks (GAN). Two sub-networks are trained alternately in the adversarial framework. A linear deep iterative network as a generative network captures the spatial distribution of the data, and a discriminative network estimates the probability of a sample from the training data. Numerical results validate that LM-GAN has admirable fidelity and accuracy when reconstructing complex scatterers.

Seth M. Hirsh ◽  
Sara M. Ichinaga ◽  
Steven L. Brunton ◽  
J. Nathan Kutz ◽  
Bingni W. Brunton

Time-delay embedding and dimensionality reduction are powerful techniques for discovering effective coordinate systems to represent the dynamics of physical systems. Recently, it has been shown that models identified by dynamic mode decomposition on time-delay coordinates provide linear representations of strongly nonlinear systems, in the so-called Hankel alternative view of Koopman (HAVOK) approach. Curiously, the resulting linear model has a matrix representation that is approximately antisymmetric and tridiagonal; for chaotic systems, there is an additional forcing term in the last component. In this paper, we establish a new theoretical connection between HAVOK and the Frenet–Serret frame from differential geometry, and also develop an improved algorithm to identify more stable and accurate models from less data. In particular, we show that the sub- and super-diagonal entries of the linear model correspond to the intrinsic curvatures in the Frenet–Serret frame. Based on this connection, we modify the algorithm to promote this antisymmetric structure, even in the noisy, low-data limit. We demonstrate this improved modelling procedure on data from several nonlinear synthetic and real-world examples.

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