bluuebunny/crossref_metadata_2025_split huggingface dataset:
Graph based multi-scale neighboring topology deep learning for kidney and tumor segmentation
Authors: Xuan Ping , Bi Hanwen , Cui Hui , Jin Qiangguo , Zhang Tiangang , Tu Huawei , Cheng Peng , Li Changyang , Ning Zhiyu , guo Menghan , Duh Henry B L 
Published: November 2022

Abstract

Objective. Effective learning and modelling of spatial and semantic relations between image regions in various ranges are critical yet challenging in image segmentation tasks. Approach. We propose a novel deep graph reasoning model to learn from multi-order neighborhood topologies for volumetric image segmentation. A graph is first constructed with nodes representing image regions and graph topology to derive spatial dependencies and semantic connections across image regions. We propose a new node attribute embedding mechanism to formulate topological attributes for each image region node by performing multi-order random walks (RW) on the graph and updating neighboring topologies at different neighborhood ranges. Afterwards, multi-scale graph convolutional autoencoders are developed to extract deep multi-scale topological representations of nodes and propagate learnt knowledge along graph edges during the convolutional and optimization process. We also propose a scale-level attention module to learn the adaptive weights of topological representations at multiple scales for enhanced fusion. Finally, the enhanced topological representation and knowledge from graph reasoning are integrated with content features before feeding into the segmentation decoder. Main results. The evaluation results over public kidney and tumor CT segmentation dataset show that our model outperforms other state-of-the-art segmentation methods. Ablation studies and experiments using different convolutional neural networks backbones show the contributions of major technical innovations and generalization ability. Significance. We propose for the first time an RW-driven MCG with scale-level attention to extract semantic connections and spatial dependencies between a diverse range of regions for accurate kidney and tumor segmentation in CT volumes.
Improving ANAIS-112 sensitivity to DAMA/LIBRA signal with machine learning techniques
Authors: Coarasa I. , Apilluelo J. , Amaré J. , Cebrián S. , Cintas D. , García E. , Martínez M. , Oliván M.A. , Ortigoza Y. , Ortiz de Solórzano A. , Pardo T. , Puimedón J. , Salinas A. , Sarsa M.L. , Villar P. 
Published: November 2022

Abstract
The DAMA/LIBRA observation of an annual modulation in the detection rate compatible with that expected for dark matter particles from the galactic halo has accumulated evidence for more than twenty years. It is the only hint of a direct detection of the elusive dark matter, but it is in strong tension with the negative results of other very sensitive experiments, requiring ad-hoc scenarios to reconcile all the present experimental results. Testing the DAMA/LIBRA result using the same target material, NaI(Tl), removes the dependence on the particle and halo models and is the goal of the ANAIS-112 experiment, taking data at the Canfranc Underground Laboratory in Spain since August 2017 with 112.5 kg of NaI(Tl). At very low energies, the detection rate is dominated by non-bulk scintillation events and careful event selection is mandatory. This article summarizes the efforts devoted to better characterize and filter this contribution in ANAIS-112 data using a boosted decision tree (BDT), trained for this goal with high efficiency. We report on the selection of the training populations, the procedure to determine the optimal cut on the BDT parameter, the estimate of the efficiencies for the selection of bulk scintillation in the region of interest (ROI), and the evaluation of the performance of this analysis with respect to the previous filtering. The improvement achieved in background rejection in the ROI, but moreover, the increase in detection efficiency, push the ANAIS-112 sensitivity to test the DAMA/LIBRA annual modulation result beyond 3σ with three-year exposure, being possible to reach 5σ by extending the data taking for a few more years than the scheduled 5 years which were due in August 2022.
Study on the interaction mechanism of double-blade corona discharge with a large discharge gap
Authors: LI Dingchen , LI Chuan , LI Jiawei , YANG Wendi , XIAO Menghan , ZHANG Ming , YU Kexun 
Published: November 2022

Abstract
Multi-source corona discharge is a commonly used method to generate more charged particles, but the interaction mechanism between multiple discharge sources, which largely determines the overall discharge effect, has still not been studied much. In this work, a large-space hybrid model based on a hydrodynamic model and ion-transport model is adopted to study the interaction mechanism between discharge sources. Specifically, the effects of the number of electrodes, voltage level, and electrode spacing on the discharge characteristics are studied by taking a double-blade electrode as an example. The calculation results show that, when multiple discharge electrodes operate simultaneously, the superimposed electric field includes multiple components from the electrodes, making the ion distribution and current different from that under a single-blade electrode. The larger the distance between discharge electrodes, the weaker the interaction. When the electrode spacing d is larger than 4 cm, the interaction can be ignored. The results can guide the design of large discharge gap array electrodes to achieve efficient discharge.
#FleeingWuhan: Legitimation and Delegitimation Strategies in Hostile Online Discourse
Authors: Ho Janet 
Published: November 2022

AbstractFollowing the first coronavirus case reported to the World Health Organization in Wuhan in 2019 and the ensuing city-wide lockdown that was imposed, many people attempted to leave the city, culminating in a vigorous discourse on the dominant Chinese microblogging site, Weibo. This study seeks to examine how online participants discursively delegitimated and legitimated people who left Wuhan before the lockdown. Weibo posts with the hashtag #逃离武汉 (‘Fleeing Wuhan’) were collected, and delegitimation and legitimation strategies deployed by users were identified. My findings reveal that the delegitimators exploited moral evaluation and impersonal authority to highlight the construed unethicality and shamelessness of people who left Wuhan, whereas the legitimators used an array of strategies, including explanation and definition, to normalize their intentions and counter linguistic hostility. These findings also provide implications vis-à-vis the clustering of delegitimation strategies as well as their linkages with emotional appeals in online discourse.
A fast and powerful linear mixed model approach for genotype-environment interaction tests in large-scale GWAS
Authors: Zhong Wujuan , Chhibber Aparna , Luo Lan , Mehrotra Devan V , Shen Judong 
Published: November 2022

AbstractGenotype-by-environment interaction (GEI or GxE) plays an important role in understanding complex human traits. However, it is usually challenging to detect GEI signals efficiently and accurately while adjusting for population stratification and sample relatedness in large-scale genome-wide association studies (GWAS). Here we propose a fast and powerful linear mixed model-based approach, fastGWA-GE, to test for GEI effect and G + GxE joint effect. Our extensive simulations show that fastGWA-GE outperforms other existing GEI test methods by controlling genomic inflation better, providing larger power and running hundreds to thousands of times faster. We performed a fastGWA-GE analysis of ~7.27 million variants on 452 249 individuals of European ancestry for 13 quantitative traits and five environment variables in the UK Biobank GWAS data and identified 96 significant signals (72 variants across 57 loci) with GEI test P-values &lt; 1 × 10−9, including 27 novel GEI associations, which highlights the effectiveness of fastGWA-GE in GEI signal discovery in large-scale GWAS.
Gaussian universal likelihood ratio testing
Authors: Dunn Robin , Ramdas Aaditya , Balakrishnan Sivaraman , Wasserman Larry 
Published: November 2022

Summary
The classical likelihood ratio test based on the asymptotic chi-squared distribution of the log-likelihood is one of the fundamental tools of statistical inference. A recent universal likelihood ratio test approach based on sample splitting provides valid hypothesis tests and confidence sets in any setting for which we can compute the split likelihood ratio statistic, or, more generally, an upper bound on the null maximum likelihood. The universal likelihood ratio test is valid in finite samples and without regularity conditions. This test empowers statisticians to construct tests in settings for which no valid hypothesis test previously existed. For the simple, but fundamental, case of testing the population mean of $d$-dimensional Gaussian data with an identity covariance matrix, the classical likelihood ratio test itself applies. Thus, this setting serves as a perfect test bed to compare the classical likelihood ratio test against the universal likelihood ratio test. This work presents the first in-depth exploration of the size, power and relationships between several universal likelihood ratio test variants. We show that a repeated subsampling approach is the best choice in terms of size and power. For large numbers of subsamples, the repeated subsampling set is approximately spherical. We observe reasonable performance even in a high-dimensional setting, where the expected squared radius of the best universal likelihood ratio test’s confidence set is approximately 3/2 times the squared radius of the classical likelihood ratio test’s spherical confidence set. We illustrate the benefits of the universal likelihood ratio test through testing a nonconvex doughnut-shaped null hypothesis, where a universal inference procedure can have higher power than a standard approach.
‘The Great Oblivion’—An Autoethnographic Depiction of Social and Personal Recovery After Electro-convulsive Therapy (ECT)
Authors: Moberg Jennie 
Published: November 2022

Abstract
This article aims to shed light on cognitive disabilities after electroconvulsive therapy (ECT) from an expert-by-experience perspective. It illuminates the living conditions that may follow, with a particular focus on epistemic injustice, societal attitudes, narration and recovery. Since personal narratives about ECT are scant, an autoethnographic method was used through which I weave together my own experiences of ECT and the need for support from, for example, social services, with research in various nearby areas. These experiences were then placed in a wider psycho-social context. Three themes emerged when analysing own experiences of ECT, revealing both risks and important aspects of managing side effects such as cognitive disabilities: (i) At the social services office; (ii) Alienation in society; and (iii) Excerpt from a friendly conversation. Subjective experiences may broaden the understanding of a certain phenomenon which calls for greater (societal) knowledge regarding cognitive disabilities after ECT. It also emphasises the importance of different professionals, including social workers and psychiatric staff, working to promote recovery, legitimise users’ narratives and not seeing disabilities as given obstacles in everyday life.
Effort avoidance as a core mechanism of apathy in frontotemporal dementia
Authors: Le Bouc Raphaël , Borderies Nicolas , Carle Guilhem , Robriquet Chloé , Vinckier Fabien , Daunizeau Jean , Azuar Carole , Levy Richard , Pessiglione Mathias 
Published: November 2022

Abstract
Apathy is a core symptom in patients with behavioural variant frontotemporal dementia (bvFTD). It is defined by the observable reduction in goal-directed behaviour, but the underlying mechanisms are poorly understood. According to decision theory, engagement in goal-directed behaviour depends on a cost–benefit optimization trading off the estimated effort (related to the behaviour) against the expected reward (related to the goal). In this framework, apathy would thus result from either a decreased appetence for reward, or from an increased aversion to effort. Here, we phenotyped the motivational state of 21 patients with bvFTD and 40 matched healthy controls using computational analyses of behavioural responses in a comprehensive series of behavioural tasks, involving both expression of preference (comparing reward value and effort cost) and optimization of performance (adjusting effort production to the reward at stake). The primary finding was an elevated aversion to effort, consistent across preference and performance tasks in patients with bvFTD compared to controls. Within the bvFTD group, effort avoidance was correlated to cortical atrophy in the dorsal anterior cingulate cortex and to apathy score measured on a clinical scale. Thus, our results highlight elevated effort aversion (not reduced reward appetence) as a core dysfunction that might generate apathy in patients with bvFTD. More broadly, they provide novel behavioural tests and computational tools to identify the dysfunctional mechanisms producing motivation deficits in patients with brain damage.
Adolescent nicotine administration increases nicotinic acetylcholine receptor binding and functional connectivity in specific cortico-striatal-thalamic circuits
Authors: Keeley Robin J , Prillaman McKenzie E , Scarlata Miranda , Vrana Antonia , Tsai Pei-Jung , Gomez Juan L , Bonaventura Jordi , Lu Hanbing , Michaelides Michael , Stein Elliot A 
Published: November 2022

AbstractNicotine exposure is associated with regional changes in brain nicotinic acetylcholine receptors subtype expression patterns as a function of dose and age at the time of exposure. Moreover, nicotine dependence is associated with changes in brain circuit functional connectivity, but the relationship between such connectivity and concomitant regional distribution changes in nicotinic acetylcholine receptor subtypes following nicotine exposure is not understood. Although smoking typically begins in adolescence, developmental changes in brain circuits and nicotinic acetylcholine receptors following chronic nicotine exposure remain minimally investigated. Here, we combined in vitro nicotinic acetylcholine receptor autoradiography with resting state functional magnetic resonance imaging to measure changes in [3H]nicotine binding and α4ß2 subtype nicotinic acetylcholine receptor binding and circuit connectivity across the brain in adolescent (postnatal Day 33) and adult (postnatal Day 68) rats exposed to 6 weeks of nicotine administration (0, 1.2 and 4.8 mg/kg/day). Chronic nicotine exposure increased nicotinic acetylcholine receptor levels and induced discrete, developmental stage changes in regional nicotinic acetylcholine receptor subtype distribution. These effects were most pronounced in striatal, thalamic and cortical regions when nicotine was administered during adolescence but not in adults. Using these regional receptor changes as seeds, resting state functional magnetic resonance imaging identified dysregulations in cortico-striatal-thalamic-cortical circuits that were also dysregulated following adolescent nicotine exposure. Thus, nicotine-induced increases in cortical, striatal and thalamic nicotinic acetylcholine receptors during adolescence modifies processing and brain circuits within cortico-striatal-thalamic-cortical loops, which are known to be crucial for multisensory integration, action selection and motor output, and may alter the developmental trajectory of the adolescent brain. This unique multimodal study significantly advances our understanding of nicotine dependence and its effects on the adolescent brain.
Complement inhibition prevents glial nodal membrane injury in a GM1 antibody-mediated mouse model
Authors: Campbell Clare I , McGonigal Rhona , Barrie Jennifer A , Delaere Jolien , Bracke Laura , Cunningham Madeleine E , Yao Denggao , Delahaye Tim , Van de Walle Inge , Willison Hugh J 
Published: November 2022

AbstractThe involvement of the complement pathway in Guillain–Barré syndrome pathogenesis has been demonstrated in both patient biosamples and animal models. One proposed mechanism is that anti-ganglioside antibodies mediate neural membrane injury through the activation of complement and the formation of membrane attack complex pores, thereby allowing the uncontrolled influx of ions, including calcium, intracellularly. Calcium influx activates the calcium-dependent protease calpain, leading to the cleavage of neural cytoskeletal and transmembrane proteins and contributing to subsequent functional failure. Complement inhibition has been demonstrated to provide effective protection from injury in anti-ganglioside antibody-mediated mouse models of axonal variants of Guillain–Barré syndrome; however, the role of complement in the pathogenesis of demyelinating variants has yet to be established. Thus, it is currently unknown whether complement inhibition would be an effective therapeutic for Guillain–Barré syndrome patients with injuries to the Schwann cell membrane. To address this, we recently developed a mouse model whereby the Schwann cell membrane was selectively targeted with an anti-GM1 antibody resulting in significant disruption to the axo-glial junction and cytoplasmic paranodal loops, presenting as conduction block. Herein, we utilize this Schwann cell nodal membrane injury model to determine the relevance of inhibiting complement activation. We addressed the early complement component C2 as the therapeutic target within the complement cascade by using the anti-C2 humanized monoclonal antibody, ARGX-117. This anti-C2 antibody blocks the formation of C3 convertase, specifically inhibiting the classical and lectin complement pathways and preventing the production of downstream harmful anaphylatoxins (C3a and C5a) and membrane attack complexes. Here, we demonstrate that C2 inhibition significantly attenuates injury to paranodal proteins at the node of Ranvier and improves respiratory function in ex vivo and in vivo Schwann cell nodal membrane injury models. In parallel studies, C2 inhibition also protects axonal integrity in our well-established model of acute motor axonal neuropathy mediated by both mouse and human anti-GM1 antibodies. These data demonstrate that complement inhibition prevents injury in a Schwann cell nodal membrane injury model, which is representative of neuropathies associated with anti-GM1 antibodies, including Guillain–Barré syndrome and multifocal motor neuropathy. This outcome suggests that both the motor axonal and demyelinating variants of Guillain–Barré syndrome should be included in future complement inhibition clinical trials.
Functional connectome stability and optimality are markers of cognitive performance
Authors: Corriveau Anna , Yoo Kwangsun , Kwon Young Hye , Chun Marvin M , Rosenberg Monica D 
Published: November 2022

Abstract
Patterns of whole-brain fMRI functional connectivity, or connectomes, are unique to individuals. Previous work has identified subsets of functional connections within these patterns whose strength predicts aspects of attention and cognition. However, overall features of these connectomes, such as how stable they are over time and how similar they are to a group-average (typical) or high-performance (optimal) connectivity pattern, may also reflect cognitive and attentional abilities. Here, we test whether individuals who express more stable, typical, optimal, and distinctive patterns of functional connectivity perform better on cognitive tasks using data from three independent samples. We find that individuals with more stable task-based functional connectivity patterns perform better on attention and working memory tasks, even when controlling for behavioral performance stability. Additionally, we find initial evidence that individuals with more typical and optimal patterns of functional connectivity also perform better on these tasks. These results demonstrate that functional connectome stability within individuals and similarity across individuals predicts individual differences in cognition.
E-cigarette exposure augments murine abdominal aortic aneurysm development: role of Chil1
Authors: Mulorz Joscha , Spin Joshua M , Mulorz Pireyatharsheny , Wagenhäuser Markus Udo , Deng Alicia , Mattern Karin , Rhee Yae H , Toyama Kensuke , Adam Matti , Schelzig Hubert , Maegdefessel Lars , Tsao Philip S 
Published: November 2022

Abstract

Aims
Abdominal aortic aneurysm (AAA) is a common cardiovascular disease with a strong correlation to smoking, although underlying mechanisms have been minimally explored. Electronic cigarettes (e-cigs) have gained recent broad popularity and can deliver nicotine at comparable levels to tobacco cigarettes, but effects on AAA development are unknown.

Methods and results
We evaluated the impact of daily e-cig vaping with nicotine on AAA using two complementary murine models and found that exposure enhanced aneurysm development in both models and genders. E-cigs induced changes in key mediators of AAA development including cytokine chitinase-3-like protein 1 (CHI3L1/Chil1) and its targeting microRNA-24 (miR-24). We show that nicotine triggers inflammatory signalling and reactive oxygen species while modulating miR-24 and CHI3L1/Chil1 in vitro and that Chil1 is crucial to e-cig-augmented aneurysm formation using a knockout model.

Conclusions
In conclusion our work shows increased aneurysm formation along with augmented vascular inflammation in response to e-cig exposure with nicotine. Further, we identify Chil1 as a key mediator in this context. Our data raise concerns regarding the potentially harmful long-term effects of e-cig nicotine vaping.
Auctions with Privately Known Capacities: Understanding Competition Among Renewables
Authors: Fabra Natalia , Llobet Gerard 
Published: November 2022

Abstract
We study a multi-unit auction model in which bidders are privately informed about the maximum number of units they are willing to trade (which we refer to as ‘capacity’). No matter how big or small, private information on capacities changes the nature of the equilibrium as compared to when private information is on costs (or valuations). Also, the uniform-price and discriminatory auctions are not revenue equivalent, in contrast to when costs are independently drawn. In particular, with independently drawn capacities (and possibly costs), the discriminatory format reduces payments to firms relative to the uniform-price format. Our analysis is motivated by the performance of future electricity markets in which renewable energies will be predominant, but the set-up also applies to a variety of contexts (from central bank liquidity auctions to emissions trading).