bluuebunny/crossref_metadata_2025_split huggingface dataset:
OntoScene, A Logic-Based Scene Interpreter: Implementation and Application in the Rock Art Domain
Authors: BRIOLA DANIELA , MASCARDI VIVIANA , GIOSEFFI MASSIMILIANO 
Published: January 2020

AbstractWe present OntoScene, a framework aimed at understanding the semantics of visual scenes starting from the semantics of their elements and the spatial relations holding between them. OntoScene exploits ontologies for representing knowledge and Prolog for specifying the interpretation rules that domain experts may adopt, and for implementing the SceneInterpreter engine. Ontologies allow the designer to formalize the domain in a reusable way and make the system modular and interoperable with existing multiagent systems, while Prolog provides a solid basis to define complex rules of interpretation in a way that can be affordable even for people with no background in Computational Logics. The domain selected for experimenting OntoScene is that of prehistoric rock art, which provides us with a fascinating and challenging testbed.
Solving Advanced Argumentation Problems with Answer Set Programming
Authors: BREWKA GERHARD , DILLER MARTIN , HEISSENBERGER GEORG , LINSBICHLER THOMAS , WOLTRAN STEFAN 
Published: January 2020

AbstractPowerful formalisms for abstract argumentation have been proposed, among them abstract dialectical frameworks (ADFs) that allow for a succinct and flexible specification of the relationship between arguments and the GRAPPA framework which allows argumentation scenarios to be represented as arbitrary edge-labeled graphs. The complexity of ADFs and GRAPPA is located beyond NP and ranges up to the third level of the polynomial hierarchy. The combined complexity of Answer Set Programming (ASP) exactly matches this complexity when programs are restricted to predicates of bounded arity. In this paper, we exploit this coincidence and present novel efficient translations from ADFs and GRAPPA to ASP. More specifically, we provide reductions for the five main ADF semantics of admissible, complete, preferred, grounded, and stable interpretations, and exemplify how these reductions need to be adapted for GRAPPA for the admissible, complete, and preferred semantics.
Latent factors of adverse childhood experiences and adult-onset asthma
Authors: Ospina Maria B. , Serrano-Lomelin Jesus A. , Amjad Sana , Hicks Anne , Giesbrecht Gerald F. 
Published: January 2020

AbstractAsthma is a chronic respiratory disease with complex etiology. Adverse childhood experiences (ACEs) have been linked to asthma in adulthood. Underlying potential mechanisms for the ACE-asthma relationship include stress-induced inflammatory pathways and immune dysregulation. We conducted a cross-sectional secondary data analysis of the 2013 Alberta ACE Survey to explore the relationship between latent ACE factors and self-reported adult asthma. We evaluated the underlying correlation structure among eight different ACEs using exploratory factor analysis. We conducted a logistic regression model to evaluate whether ACE factors retained from the factor analysis predicted self-reported asthma in adulthood. Results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). We analyzed ACE survey results from 1207 participants. Factor analysis yielded four ACE latent factors: factor 1/relational violence, factor 2/negative home environment, factor 3/illness at home, and factor 4/sexual abuse. Results of the logistic regression showed that experiencing sexual abuse (OR: 3.23; 95% CI: 1.89, 5.23), relational violence (OR: 1.99; 95% CI: 1.17, 3.38), and being exposed to a negative home environment (OR: 1.86; 95% CI: 1.03, 3.35) were predictive of a diagnosis of asthma in adulthood, whereas living in a household with someone experiencing illness did not show an effect (OR: 1.38; 95% CI: 0.75, 2.56). Factor analysis provides an effectual approach to understand the long-term impact of ACEs on respiratory health. Our findings have important implications to understand the developmental origins of asthma in adulthood and inform interventions aimed at reducing the lasting negative impact of childhood adversities on future respiratory health.
The New Deal, the Deserving Poor, and the First Public Housing Residents in New York City
Authors: Allen Ryan , Van Riper David 
Published: January 2020

ABSTRACTBetween 1934 and the time of the 1940 Census, the US government built and leased 30,151 units of public housing, but we know little about the residents who benefited from this housing. We use a unique methodology that compares addresses of five public housing developments to complete-count data from the 1940 Census to identify residents of public housing in New York City at the time of the census. We compare these residents to the larger pool of residents living in New York City in 1940 who were eligible to apply for the housing to assess how closely housing authorities adhered to the intent of the National Industrial Recovery Act (1933) and the Housing Act of 1937. This comparison produces a picture of whom public housing administrators considered deserving of this public benefit at the dawn of the public housing program in the United States. Results indicate a shift toward serving households with lower incomes over time. All the developments had a consistent preference for households with a “nuclear family” structure, but policies favoring racial segregation and other discretion on the part of housing authorities for tenant selection created distinct populations across housing developments. Households headed by a naturalized citizen were favored over households headed by a native-born citizen in nearly all the public housing projects. This finding suggests a more nuanced understanding of who public housing administrators considered deserving of the first public housing than archival research accounts had previously indicated.
First Paleomagnetic Result From the Early Permian Volcanic Rocks in Northeastern Mongolia: Evolutional Implication for the Paleo‐Asian Ocean and the Mongol‐Okhotsk Ocean
Authors: Zhao Pan , Appel Erwin , Xu Bei , Sukhbaatar Turbold 
Published: January 2020

AbstractThe Mongolia Block (MOB), which is now sandwiched by the Siberia Craton (SIB) and the North China Craton (NCC), plays an essential role for understanding the late stage evolution of the Paleo‐Asian Ocean and the early stage evolution of the Mongol‐Okhotsk Ocean. Here, a paleomagnetic study is performed for the first time on the Early Permian volcanic strata in the Bayandun region of northeastern Mongolia and the data are used to uncover the late Paleozoic paleoposition of the MOB and better understand the evolution of both oceans. Zircon U‐Pb dating results reveal an emplacement age of 283 ± 3 Ma for the studied volcanic strata. Rock magnetic analyses identify that titanium‐poor magnetite is the main magnetic carrier. Characteristic remanent magnetization isolated from seven sites shows consistent reverse polarity, corresponding to the Permo‐Carboniferous (Kiaman) Reverse Superchron. Site‐mean directions pass fold tests, and an Early Permian paleomagnetic pole is calculated for the MOB at λ/φ = −14.9°N/76.8°E (A95 = 5.7°) with N = 7 sites. Comparison with published Permian paleomagnetic poles from surrounding blocks indicates that (1) the MOB should have welded with the NCC before the Early Permian or was at least very close to it. (2) The welded MOB‐NCC was separated from the SIB by the Mongol‐Okhotsk Ocean with ~30° latitudinal difference during the Early Permian. (3) Significant vertical‐axis strike‐slip related rotations occurred within and along the margins of the unified MOB‐NCC due to the far‐field stress effect produced by posterior orogenic events.
A 100‐year Geoelectric Hazard Analysis for the U.S. High‐Voltage Power Grid
Authors: Lucas G. M. , Love J. J. , Kelbert A. , Bedrosian P. A. , Rigler E. J. 
Published: January 2020

AbstractA once‐per‐century geoelectric hazard map is created for the U.S. high‐voltage power grid. A statistical extrapolation from 31 years of magnetic field measurements is made by identifying 84 geomagnetic storms with the Kp and Dst indices. Data from 24 geomagnetic observatories, 1,079 magnetotelluric survey sites, and 17,258 transmission lines are utilized to perform a geoelectric hazard analysis with the most comprehensive data publicly available. With these data, we estimate once‐per‐century geoelectric fields at the magnetotelluric survey sites and calculate the theoretical voltages within transmission lines in the U.S. power grid. Once‐per‐century geoelectric field strengths span more than 3 orders of magnitude from a minimum of 0.02 V/km at a site in Idaho to a maximum of 27.2 V/km at a site in Maine, with nearly 30% of the surveyed land area exceeding 1 V/km. We show the influence that geoelectric field polarization has on geoelectric hazards when viewed on a power transmission network. The calculated transmission line voltages can approach 1,000 V in some transmission lines. Four regions in the United States with particularly notable geoelectric hazards are identified and discussed: the East Coast, Pacific Northwest, Upper Midwest, and the Denver metropolitan area.
Detection of VLF Attenuation in the Earth‐Ionosphere Waveguide Caused by X‐Class Solar Flares Using aGlobal Lightning Location Network
Authors: Anderson T. S. , McCarthy M. P. , Holzworth R. H. 
Published: January 2020

AbstractSolar flares, energetic particles, and Earth‐impacting coronal mass ejections enhance ionization in the lower ionosphere, inhibiting radio wave propagation in the Earth‐ionosphere waveguide (EIWG). This enhanced ionization is observed locally by ionosondes and GPS/GNSS receivers, but spatial coverage of these observations is limited by receiver location. Very low frequency (VLF) propagation studies have previously been performed to assess the impact of space weather on the EIWG; however, these studies are typically limited by small numbers of fixed VLF transmitters and receivers and observe only the region of the EIWG along propagation paths between transmitters and receivers. Here, we use global lightning as a VLF source and an existing lightning detection network as a receiver. By mapping sferic propagation paths between lightning strokes and numerous network stations and considering how this distribution of paths changes during solar events, we can identify attenuation regions in the EIWG caused by space weather. We describe the VLF response in the EIWG to two X‐class solar flares and compare mapped attenuation regions with those provided by the NOAA D‐Region Absorption Prediction model. The identified attenuation regions associated with these flares match the D‐Region Absorption Prediction‐predicted regions well in both spatial extent and onset timing. Measurements of VLF attenuation caused by solar flares can provide ground‐truth confirmation of modeled attenuation and can inform the detection efficiency of lightning location networks. This analysis also paves the way for real‐time VLF attenuation mapping in the EIWG.
Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non‐Gaussian Hydraulic Conductivities
Authors: Mo Shaoxing , Zabaras Nicholas , Shi Xiaoqing , Wu Jichun 
Published: January 2020

AbstractInverse modeling for the estimation of non‐Gaussian hydraulic conductivity fields in subsurface flow and solute transport models remains a challenging problem. This is mainly due to the non‐Gaussian property, the nonlinear physics, and the fact that many repeated evaluations of the forward model are often required. In this study, we develop a convolutional adversarial autoencoder (CAAE) to parameterize non‐Gaussian conductivity fields with heterogeneous conductivity within each facies using a low‐dimensional latent representation. In addition, a deep residual dense convolutional network (DRDCN) is proposed for surrogate modeling of forward models with high‐dimensional and highly complex mappings. The two networks are both based on a multilevel residual learning architecture called residual‐in‐residual dense block. The multilevel residual learning strategy and the dense connection structure ease the training of deep networks, enabling us to efficiently build deeper networks that have an essentially increased capacity for approximating mappings of very high complexity. The CAAE and DRDCN networks are incorporated into an iterative ensemble smoother to formulate an inversion framework. The numerical experiments performed using 2‐D and 3‐D solute transport models illustrate the performance of the integrated method. The obtained results indicate that the CAAE is a robust parameterization method for non‐Gaussian conductivity fields with different heterogeneity patterns. The DRDCN is able to obtain accurate approximations of the forward models with high‐dimensional and highly complex mappings using relatively limited training data. The CAAE and DRDCN methods together significantly reduce the computation time required to achieve accurate inversion results.
Dorsal and ventral striatal dopamine D1 and D2 receptors differentially modulate distinct phases of serial visual reversal learning
Authors: Sala-Bayo Júlia , Fiddian Leanne , Nilsson Simon R. O. , Hervig Mona E. , McKenzie Colin , Mareschi Alexis , Boulos Maria , Zhukovsky Peter , Nicholson Janet , Dalley Jeffrey W. , Alsiö Johan , Robbins Trevor W. 
Published: January 2020

AbstractImpaired cognitive flexibility in visual reversal-learning tasks has been observed in a wide range of neurological and neuropsychiatric disorders. Although both human and animal studies have implicated striatal D2-like and D1-like receptors (D2R; D1R) in this form of flexibility, less is known about the contribution they make within distinct sub-regions of the striatum and the different phases of visual reversal learning. The present study investigated the involvement of D2R and D1R during the early (perseverative) phase of reversal learning as well as in the intermediate and late stages (new learning) after microinfusions of D2R and D1R antagonists into the nucleus accumbens core and shell (NAcC; NAcS), the anterior and posterior dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) on a touchscreen visual serial reversal-learning task. Reversal learning was improved after dopamine receptor blockade in the nucleus accumbens; the D1R antagonist, SCH23390, in the NAcS and the D2R antagonist, raclopride, in the NAcC selectively reduced early, perseverative errors. In contrast, reversal learning was impaired by D2R antagonism, but not D1R antagonism, in the dorsal striatum: raclopride increased errors in the intermediate phase after DMS infusions, and increased errors across phases after DLS infusions. These findings indicate that D1R and D2R modulate different stages of reversal learning through effects localised to different sub-regions of the striatum. Thus, deficits in behavioral flexibility observed in disorders linked to dopamine perturbations may be attributable to specific D1R and D2R dysfunction in distinct striatal sub-regions.
Monocarboxylate transporter 1 blockade with AZD3965 inhibits lipid biosynthesis and increases tumour immune cell infiltration
Authors: Beloueche-Babari Mounia , Casals Galobart Teresa , Delgado-Goni Teresa , Wantuch Slawomir , Parkes Harold G. , Tandy Debbie , Harker James A. , Leach Martin O. 
Published: January 2020

AbstractBackgroundMonocarboxylate transporter 1 (MCT1) is a regulator of cell metabolism and a therapeutic target for cancer treatment. Understanding the changes in tumour function accompanying MCT1 inhibition will better characterise the anti-tumour effects of MCT1 inhibitors, potentially enabling the identification of pharmacodynamic biomarkers for the clinical development of these agents.MethodsWe assessed the impact of the MCT1 inhibitor AZD3965 on tumour metabolism and immune cell infiltration as key determinants of tumour biological function in the MCT1-dependent Raji B cell lymphoma model.ResultsTreatment of Raji xenograft-bearing severe combined immunodeficiency mice with AZD3965 led to inhibition of tumour growth paralleled with a decrease in tumour choline, as detected by non-invasive in vivo proton nuclear magnetic resonance spectroscopy. This effect was attributed to inhibition of phosphocholine de novo synthesis following decreased choline kinase α protein and messenger RNA expression that correlated with the AZD3965-induced build-up in intracellular lactate. These changes were concomitant with increased tumour immune cell infiltration involving dendritic and natural killer cells.ConclusionsOur data provide new insights into the metabolic and cellular changes that occur in the tumour microenvironment following MCT1 blockade, which may contribute to the anti-tumour activity of AZD3965 and could have potential as pharmacodynamic biomarkers of MCT1 inhibition.
Improved early detection of ovarian cancer using longitudinal multimarker models
Authors: Whitwell Harry J. , Worthington Jenny , Blyuss Oleg , Gentry-Maharaj Aleksandra , Ryan Andy , Gunu Richard , Kalsi Jatinderpal , Menon Usha , Jacobs Ian , Zaikin Alexey , Timms John F. 
Published: January 2020

Abstract
Background
Ovarian cancer has a poor survival rate due to late diagnosis and improved methods are needed for its early detection. Our primary objective was to identify and incorporate additional biomarkers into longitudinal models to improve on the performance of CA125 as a first-line screening test for ovarian cancer.

Methods
This case–control study nested within UKCTOCS used 490 serial serum samples from 49 women later diagnosed with ovarian cancer and 31 control women who were cancer-free. Proteomics-based biomarker discovery was carried out using pooled samples and selected candidates, including those from the literature, assayed in all serial samples. Multimarker longitudinal models were derived and tested against CA125 for early detection of ovarian cancer.

Results
The best performing models, incorporating CA125, HE4, CHI3L1, PEBP4 and/or AGR2, provided 85.7% sensitivity at 95.4% specificity up to 1 year before diagnosis, significantly improving on CA125 alone. For Type II cases (mostly high-grade serous), models achieved 95.5% sensitivity at 95.4% specificity. Predictive values were elevated earlier than CA125, showing the potential of models to improve lead time.

Conclusions
We have identified candidate biomarkers and tested longitudinal multimarker models that significantly improve on CA125 for early detection of ovarian cancer. These models now warrant independent validation.
Intratumoral heterogeneity and clonal evolution in liver cancer
Authors: Losic Bojan , Craig Amanda J. , Villacorta-Martin Carlos , Martins-Filho Sebastiao N. , Akers Nicholas , Chen Xintong , Ahsen Mehmet E. , von Felden Johann , Labgaa Ismail , DʹAvola Delia , Allette Kimaada , Lira Sergio A. , Furtado Glaucia C. , Garcia-Lezana Teresa , Restrepo Paula , Stueck Ashley , Ward Stephen C. , Fiel Maria I. , Hiotis Spiros P. , Gunasekaran Ganesh , Sia Daniela , Schadt Eric E. , Sebra Robert , Schwartz Myron , Llovet Josep M. , Thung Swan , Stolovitzky Gustavo , Villanueva Augusto 
Published: January 2020

AbstractClonal evolution of a tumor ecosystem depends on different selection pressures that are principally immune and treatment mediated. We integrate RNA-seq, DNA sequencing, TCR-seq and SNP array data across multiple regions of liver cancer specimens to map spatio-temporal interactions between cancer and immune cells. We investigate how these interactions reflect intra-tumor heterogeneity (ITH) by correlating regional neo-epitope and viral antigen burden with the regional adaptive immune response. Regional expression of passenger mutations dominantly recruits adaptive responses as opposed to hepatitis B virus and cancer-testis antigens. We detect different clonal expansion of the adaptive immune system in distant regions of the same tumor. An ITH-based gene signature improves single-biopsy patient survival predictions and an expression survey of 38,553 single cells across 7 regions of 2 patients further reveals heterogeneity in liver cancer. These data quantify transcriptomic ITH and how the different components of the HCC ecosystem interact during cancer evolution.
A compact single channel interferometer to study vortex beam propagation through scattering layers
Authors: Lathika Sruthy J. , Anand Vijayakumar , Bhattacharya Shanti 
Published: January 2020

AbstractWe propose and demonstrate a single channel interferometer that can be used to study how vortex beams propagate through a scatterer. The interferometer consists of a multifunctional diffractive optical element (MDOE) synthesized by the spatial random multiplexing of a Fresnel zone plate and a spiral Fresnel zone plate with different focal lengths. The MDOE generates two co-propagating beams, such that only the beam carrying orbital angular momentum is modulated by an annular stack of thin scatterers located at the focal plane of the Fresnel zone plate, while the other beam passes through the centre of the annulus without any modulation. The interference pattern is recorded at the focal plane of the spiral Fresnel zone plate. The scattering of vortex beams through stacks consisting of different number of thin scatterers was studied using the proposed optical setup. Conflicting results have been reported earlier on whether higher or lower charge beams suffer more deterioration. The proposed interferometer provides a relatively simple and compact means of experimentally studying propagation of vortex beams through scattering medium.
A multimodal neuroimaging classifier for alcohol dependence
Authors: Guggenmos Matthias , Schmack Katharina , Veer Ilya M. , Lett Tristram , Sekutowicz Maria , Sebold Miriam , Garbusow Maria , Sommer Christian , Wittchen Hans-Ulrich , Zimmermann Ulrich S. , Smolka Michael N. , Walter Henrik , Heinz Andreas , Sterzer Philipp 
Published: January 2020

AbstractWith progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality – grey-matter density – by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence.