bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling algorithmic bias: simplicial complexes and evolving network topologies
Authors: Pansanella Valentina , Rossetti Giulio , Milli Letizia 
Published: August 2022

AbstractEvery day, people inform themselves and create their opinions on social networks. Although these platforms have promoted the access and dissemination of information, they may expose readers to manipulative, biased, and disinformative content—co-causes of polarization/radicalization. Moreover, recommendation algorithms, intended initially to enhance platform usage, are likely to augment such phenomena, generating the so-calledAlgorithmic Bias. In this work, we propose two extensions of the Algorithmic Bias model and analyze them on scale-free and Erdős–Rényi random network topologies. Our first extension introduces a mechanism of link rewiring so that the underlying structure co-evolves with the opinion dynamics, generating theAdaptive Algorithmic Biasmodel. The second one explicitly models a peer-pressure mechanism where a majority—if there is one—can attract a disagreeing individual, pushing them to conform. As a result, we observe that the co-evolution of opinions and network structure does not significantly impact the final state when the latter is much slower than the former. On the other hand, peer pressure enhances consensus mitigating the effects of both “close-mindedness” and algorithmic filtering.
Ghost Hunting in the Broken Archives: Re-Historicizing Digital Education in an Institutional Context
Authors: Gallagher Michael , Nicol Stuart , Breines Markus 
Published: August 2022

Abstract
Digital education is often presented as breaking from tradition. A failure to account for how digital education emerges from historical institutional activity is problematic insofar as this activity continues to circulate through the present and future, appearing and disappearing in often unexpected ways. Using Derrida’s hauntology as a theoretical lens, this paper traces how a digital education initiative at the University of Edinburgh in 2003 carried through to the creation of a course to train teachers to teach online in 2019, which in turn informed the university’s response to the pandemic in 2020. Working in a broadly autoethnographic way alongside archival document analysis, several findings emerged. First, hauntology provides a mechanism for institutions to trace their own histories and to note how these histories, often hidden in archives or carried forward into the present by hosts, inform their present and future trajectories. Second, broken archives, those that have ceased to function as active repositories but are disconnected from institutional domains and ontologies, shut due to absent gatekeepers, or merely forgotten, contribute​ to the sudden and often unexpected emergence of hauntings in present and future trajectories. Third, curation of the archive is an act of reinterpretation, one that troubles historical narratives and introduces new hauntings. All these findings assert a re-historicizing of digital education by emphasising the hauntings from the past that inform its emergent present and contested future, countering many of the ahistorical imaginaries of digital education.
Marketing Ultra-Processed Food and Beverages to Children in Latin America: Business Responsibilities and State Duties
Authors: Guarnizo-Peralta Diana 
Published: August 2022

AbstractExposure to the marketing of ultra-processed food and beverages has been proven to be detrimental to children’s health. This article explores this issue from a business and human rights perspective, with the purpose of understanding businesses’ responsibilities and states’ duties with respect to the deliberate marketing of ultra-processed products to children. To this end, this article refers to the three pillars of the United Nations Guiding Principles on Business and Human Rights, as well as to international human rights law. Its analysis looks not only at the normative content of obligations, responsibilities and rights under international law, but also at their implementation and at current challenges within the Latin American context.
The Coupling of Soil Water and Leaf Phenology Observed by Satellite During Australian Bushfires in 2019–2020
Authors: Liu Yuqing , Shi Wenzhong , Zhang Min , Hao Ming , Zhang Hua , Sun Yangjie 
Published: August 2022

AbstractWater and energy fluxes at the interface between the surface of the earth and the atmosphere depend strongly on soil moisture. Surface soil moisture (SSM) participates in natural phenomena such as evaporation, infiltration and runoff. From 2019 to 2020, bushfires in Australia caused changes in the runoff of surface water, the transpiration of vegetation, and evapotranspiration of the soil, subsequently affecting the soil water cycle. However, the dynamics of surface soil water content during large‐scale bushfires are still not completely understood. Here, we report on patterns of seasonal variation in SSM and vegetation leaf phenology in Australia, identified using L‐band radiometric measurements of soil moisture taken by the soil moisture and ocean salinity mission from 2011 to 2020. This study reveals a decrease in the leaf area index in affected regions of New South Wales, Victoria, Queensland, Western Australia, Australian Capital Territory, Northern Territory, and Tasmania. The observed seasonal variations in SSM are highly synchronous with those of leaf phenology in northern and northeastern Australia. However, the development of the leaf area lags behind that of SSM for up to 90 days in the southeastern subtropical woodlands. The time lag between leaf area development and SSM in the southwest is about 60 days. Bushfires can lead to the decrease of evapotranspiration hence shortening the time lag between SSM and leaf phenology. Our results offer insights into the variations in SSM during bushfires, the relationship between SSM and leaf phenology in Australia, and lay a foundation for improving models of ecological hydrology during bushfires.
An Analysis of the Stability and Trends in the LST_cci Land Surface Temperature Datasets Over Europe
Authors: Good E. J. , Aldred F. M. , Ghent D. J. , Veal K. L. , Jimenez C. 
Published: August 2022

AbstractLong‐term satellite land surface temperature (LST) data are desirable to augment 2m air temperatures (T2m) measured in situ and as an independent measure of surface temperature change. However, previous studies show variable agreement between LST and T2m time series. The objective of this study is to assess the stability and trends in six new LST data sets from the European Space Agency's Climate Change Initiative for LST (LST_cci). LST anomalies are compared with homogenized station T2m anomalies over Europe, which verifies all six data sets are well coupled (LST vs T2m anomaly correlations and slopes: 0.6–0.9). The temporal stability of the LST_cci data is assessed through a comparison with the T2m anomaly time series. Only the LST_cci data sets for the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Advanced Along‐Track Scanning Radiometer (AATSR) appear stable; the MODIS/Terra, ATSR‐2, and multisensor InfraRed and MicroWave data sets show non‐climatic discontinuities associated with changes in sensor and/or drift over time. For MODIS/Aqua (2002–2018), significant trends in LST of 0.64–0.66 K/decade compare well with the equivalent T2m trends of 0.52–0.59 K/decade. The LST and T2m trends for AATSR (2002–2012) are found to be statistically insignificant, likely due to the comparatively short study period and specific years available for analysis. No evidence is found to suggest that trends calculated using cloud‐free InfraRed observations are affected by clear‐sky bias. This study suggests that satellite LST data can be used to assess warming trends over land and for other climate applications if the required homogeneity is assured.
Observations of Electron Vorticity and Phase Space Holes in the Magnetopause Reconnection Separatrix
Authors: Ahmadi Narges , Eriksson Stefan , Newman David , Andersson Laila , Ergun Robert E. , Wilder Frederick D. 
Published: August 2022

AbstractWe report Magnetospheric Multiscale (MMS) observations of two electron vortices with a roughly 10 km or 2–3 electron skin depths cross‐section across the magnetospheric separatrix of a dayside magnetopause reconnection structure. The enhanced electron vorticity, which is caused by a significant velocity shear in the electron flows streaming in opposite directions along northward‐directed magnetic field lines, leads to perturbations perpendicular to the reconnecting magnetic field on the order of 1–2 nT. The high electron vorticity periods are also associated with electron phase space holes having the largest bipolar parallel electric field amplitudes ∼25 mV/m observed across this exhaust. Less intense ∼10 mV/m electron phase space holes propagate along a northward magnetic field away from the X‐line within a northward reconnection exhaust. The electron phase space holes with the largest bipolar parallel electric fields are observed to propagate toward the X‐line within discrete channels of southward and super‐Alfvénic 1,500 km/s electron flows in the magnetospheric separatrix layer.
Independent replication of advanced brain age in mild cognitive impairment and dementia: detection of future cognitive dysfunction
Authors: Karim Helmet T. , Aizenstein Howard J. , Mizuno Akiko , Ly Maria , Andreescu Carmen , Wu Minjie , Hong Chang Hyung , Roh Hyun Woong , Park Bumhee , Lee Heirim , Kim Na-Rae , Choi Jin Wook , Seo Sang Won , Choi Seong Hye , Kim Eun-Joo , Kim Byeong C. , Cheong Jae Youn , Lee Eunyoung , Lee Dong-gi , Cho Yong Hyuk , Moon So Young , Son Sang Joon 
Published: August 2022

AbstractWe previously developed a novel machine-learning-based brain age model that was sensitive to amyloid. We aimed to independently validate it and to demonstrate its utility using independent clinical data. We recruited 650 participants from South Korean memory clinics to undergo magnetic resonance imaging and clinical assessments. We employed a pretrained brain age model that used data from an independent set of largely Caucasian individuals (n = 757) who had no or relatively low levels of amyloid as confirmed by positron emission tomography (PET). We investigated the association between brain age residual and cognitive decline. We found that our pretrained brain age model was able to reliably estimate brain age (mean absolute error = 5.68 years, r(650) = 0.47, age range = 49–89 year) in the sample with 71 participants with subjective cognitive decline (SCD), 375 with mild cognitive impairment (MCI), and 204 with dementia. Greater brain age was associated with greater amyloid and worse cognitive function [Odds Ratio, (95% Confidence Interval {CI}): 1.28 (1.06–1.55), p = 0.030 for amyloid PET positivity; 2.52 (1.76–3.61), p < 0.001 for dementia]. Baseline brain age residual was predictive of future cognitive worsening even after adjusting for apolipoprotein E e4 and amyloid status [Hazard Ratio, (95% CI): 1.94 (1.33–2.81), p = 0.001 for total 336 follow-up sample; 2.31 (1.44–3.71), p = 0.001 for 284 subsample with baseline Clinical Dementia Rating ≤ 0.5; 2.40 (1.43–4.03), p = 0.001 for 240 subsample with baseline SCD or MCI]. In independent data set, these results replicate our previous findings using this model, which was able to delineate significant differences in brain age according to the diagnostic stages of dementia as well as amyloid deposition status. Brain age models may offer benefits in discriminating and tracking cognitive impairment in older adults.
Role of necroptosis in airflow limitation in chronic obstructive pulmonary disease: focus on small-airway disease and emphysema
Authors: Liu Chanjing , Li Peijun , Zheng Jiejiao , Wang Yingqi , Wu Weibing , Liu Xiaodan 
Published: August 2022

AbstractAirflow limitation with intractable progressive mechanisms is the main disease feature of chronic obstructive pulmonary disease (COPD). The pathological process of airflow limitation in COPD involves necroptosis, a form of programmed necrotic cell death with pro-inflammatory properties. In this paper, the correlations of small-airway disease and emphysema with airflow limitation in COPD were firstly reviewed; then, based on this, the effects of necroptosis on small-airway disease and emphysema were analysed, and the possible mechanisms of necroptosis causing airflow limitation in COPD were explored. The results showed that airflow limitation is caused by a combination of small-airway disease and emphysema. In addition, toxic particulate matter stimulates epithelial cells to trigger necroptosis, and necroptosis promotes the expulsion of cell contents, the abnormal hyperplasia of pro-inflammatory mediators and the insufficient clearance of dead cells by macrophages; these processes, coupled with the interaction of necroptosis and oxidative stress, collectively result in small-airway disease and emphysema in COPD.
Treatment of allergic eosinophilic asthma through engineered IL-5-anchored chimeric antigen receptor T cells
Authors: Chen Sisi , Chen Gaoying , Xu Fang , Sun Beibei , Chen Xinyi , Hu Wei , Li Fei , Syeda Madiha Zahra , Chen Haixia , Wu Youqian , Wu Peng , Jing Ruirui , Geng Xinwei , Zhang Lingling , Tang Longguang , Li Wen , Chen Zhihua , Zhang Chao , Sun Jie , Chen Wei , Shen Huahao , Ying Songmin 
Published: August 2022

AbstractSevere eosinophilic asthma (SEA) is a therapy-resistant respiratory condition with poor clinical control. Treatment efficacy and patient compliance of current therapies remain unsatisfactory. Here, inspired by the remarkable success of chimeric antigen receptor-based cellular adoptive immunotherapies demonstrated for the treatment of a variety of malignant tumors, we engineered a cytokine-anchored chimeric antigen receptor T (CCAR-T) cell system using a chimeric IL-5-CD28-CD3ζ receptor to trigger T-cell-mediated killing of eosinophils that are elevated during severe asthma attacks. IL-5-anchored CCAR-T cells exhibited selective and effective killing capacity in vitro and restricted eosinophil differentiation with apparent protection against allergic airway inflammation in two mouse models of asthma. Notably, a single dose of IL-5-anchored CCAR-T cells resulted in persistent protection against asthma-related conditions over three months, significantly exceeding the typical therapeutic window of current mAb-based treatments in the clinics. This study presents a cell-based treatment strategy for SEA and could set the stage for a new era of precision therapies against a variety of intractable allergic diseases in the future.
Symmetric or asymmetric glide resistance to twinning disconnection?
Authors: Gong Mingyu , Ma Houyu , Yang Kunming , Liu Yue , Nie Jian-Feng , Wang Jian 
Published: August 2022

AbstractSuccessive gliding of twinning disconnections (TDs) creates three-dimensional twins in parent crystal and accommodates shear deformation. It is generally recognized that TD is subject to the same Peierls stress as it glides forward or backward because of its dislocation character and the twofold rotation symmetry of the twin plane. Based on atomistic simulations, we demonstrate that the glide of TDs may be subject to a symmetric or asymmetric resistance corresponding to step character, symmetric resistance for A/A type steps but asymmetric resistance for A/B type steps, where A and B represent crystallographic planes in twin and matrix. Furthermore, we experimentally demonstrate that the asymmetric resistance results in asymmetric propagation and growth of twins in Mg alloys.
Molecular data storage with zero synthetic effort and simple read-out
Authors: Bohn Philipp , Weisel Maximilian P. , Wolfs Jonas , Meier Michael A. R. 
Published: August 2022

AbstractCompound mixtures represent an alternative, additional approach to DNA and synthetic sequence-defined macromolecules in the field of non-conventional molecular data storage, which may be useful depending on the target application. Here, we report a fast and efficient method for information storage in molecular mixtures by the direct use of commercially available chemicals and thus, zero synthetic steps need to be performed. As a proof of principle, a binary coding language is used for encoding words in ASCII or black and white pixels of a bitmap. This way, we stored a 25 × 25-pixel QR code (625 bits) and a picture of the same size. Decoding of the written information is achieved via spectroscopic (1H NMR) or chromatographic (gas chromatography) analysis. In addition, for a faster and automated read-out of the data, we developed a decoding software, which also orders the data sets according to an internal “ordering” standard. Molecular keys or anticounterfeiting are possible areas of application for information-containing compound mixtures.
A machine learning framework supporting prospective clinical decisions applied to risk prediction in oncology
Authors: Coombs Lorinda , Orlando Abigail , Wang Xiaoliang , Shaw Pooja , Rich Alexander S. , Lakhtakia Shreyas , Titchener Karen , Adamson Blythe , Miksad Rebecca A. , Mooney Kathi 
Published: August 2022

AbstractWe present a general framework for developing a machine learning (ML) tool that supports clinician assessment of patient risk using electronic health record-derived real-world data and apply the framework to a quality improvement use case in an oncology setting to identify patients at risk for a near-term (60 day) emergency department (ED) visit who could potentially be eligible for a home-based acute care program. Framework steps include defining clinical quality improvement goals, model development and validation, bias assessment, retrospective and prospective validation, and deployment in clinical workflow. In the retrospective analysis for the use case, 8% of patient encounters were associated with a high risk (pre-defined as predicted probability ≥20%) for a near-term ED visit by the patient. Positive predictive value (PPV) and negative predictive value (NPV) for future ED events was 26% and 91%, respectively. Odds ratio (OR) of ED visit (high- vs. low-risk) was 3.5 (95% CI: 3.4–3.5). The model appeared to be calibrated across racial, gender, and ethnic groups. In the prospective analysis, 10% of patients were classified as high risk, 76% of whom were confirmed by clinicians as eligible for home-based acute care. PPV and NPV for future ED events was 22% and 95%, respectively. OR of ED visit (high- vs. low-risk) was 5.4 (95% CI: 2.6–11.0). The proposed framework for an ML-based tool that supports clinician assessment of patient risk is a stepwise development approach; we successfully applied the framework to an ED visit risk prediction use case.
Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition
Authors: Nurputra Dian Kesumapramudya , Kusumaatmaja Ahmad , Hakim Mohamad Saifudin , Hidayat Shidiq Nur , Julian Trisna , Sumanto Budi , Mahendradhata Yodi , Saktiawati Antonia Morita Iswari , Wasisto Hutomo Suryo , Triyana Kuwat 
Published: August 2022

AbstractThe reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88–95%), sensitivity (86–94%), and specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.
Supporting Access to Mental Health Services for Patients Who Use Augmentative and Alternative Communication: A Proposed Framework and Suggestions for Future Directions
Authors: Noyes Adrianna M. , Wilkinson Krista M. 
Published: August 2022

Purpose:
The purpose of this review article is to examine interacting factors that affect the psychotherapeutic process for patients who use augmentative and alternative communication (AAC). This is examined from two perspectives: (a) how the skills and needs brought by the individual with communication disabilities influence the process of therapy and (b) how the individual's ecological systems may affect an individual's mental health and their access to mental health supports. Understanding these factors will guide future efforts aimed at ensuring that individuals with communication disorders have adequate access to services and supports for mental health.

Method:
A narrative synthesis approach was used to explore the interaction of psychotherapy and the use of AAC. Existing research in the fields of psychotherapy and speech-language pathology was examined for its relevance to issues of communication using AAC. Specifically, information was synthesized concerning mental health, prevalence rates of mental health diagnoses, the provision of traditional psychotherapy, and communicating via AAC in order to describe the intersection of psychotherapy and AAC. This narrative synthesis approach sought to initiate discussion of relevant factors that may affect the psychotherapeutic process for patients who use AAC.

Results:
A variety of factors may affect the psychotherapeutic process for patients who use AAC. Bronfenbrenner's (1977) Ecological Systems Theory was used to explore access to mental health services for individuals who use AAC at the levels of microsystem, mesosystem, exosystem, and macrosystem. Such factors include alterations to communication between patient and psychotherapist, skills brought by the patient, involvement of a communication assistant and/or speech-language pathologist, education and skill level of the psychotherapist, appropriateness of billing codes, and system and policy factors that may affect the provision of mental health services to patients who use AAC.

Conclusion:
It is necessary to explore the factors that affect the provision of psychotherapy for patients who use AAC in order to more fully address the mental health needs of individuals with communication disorders. Further research is needed to determine the impacts of these factors and ways to address those impacts.