Large-scale Collection Research Articles

Newborn cytomegalovirus screening: is this the new standard?

Congenital cytomegalovirus infection (cCMV) is a major cause of childhood hearing loss and neurodevelopmental delay. Early identification of cCMV allows for interventions that improve outcomes, particularly for cCMV-related hearing loss that develops in early childhood. Most cCMV is asymptomatic at birth and is rarely diagnosed without newborn screening. Therefore, various approaches to cCMV screening are increasingly being adopted. Both universal screening (testing all newborns) and targeted screening (testing triggered by failed hearing screening) for cCMV appear valuable, feasible and cost-effective, though universal screening is predicted to have greatest potential overall benefits. CMV PCR testing of newborn oral swabs is sensitive and practical and is therefore widely used in targeted screening programs. In contrast, PCR using dried-blood spots (DBS) is less sensitive but was adopted by current universal cCMV screening initiatives because DBS are already collected from all newborns in high-income countries, which circumvents large-scale oral swab collection. Targeted screening is widely recommended as standard of care, while universal screening is less common but is progressively considered as the optimal strategy for identification of children with cCMV. As with all screening programs, cCMV screening requires commitments to equitable and reliable testing, follow-up and services.

Provincial Financing During the First Regional Reform. The Arkhangelsk Version

Provincial institutions created by Peter I in 1710 were designed to ensure fiscal mobilisation, large-scale and maximum possible collection of material resources for the needs of warfare and the simultaneous reform of the armed forces. The first Russian governors had extensive administrative powers but, at the same time, they were under the strict financial control of the highest governing bodies: the Senate, its divisions, and the monarch himself. The governors were deprived of legal opportunities to spend at least some of the money collected through the provincial cash desks to ensure the functioning of their apparatus. It was not only about paying salaries to the ranks of provincial administrations, maintaining administrative buildings in working order, and purchasing consumables for office work. There was no money for more substantial expenditures: payment for the travel of numerous commissioners from the centre to the provinces, for the travel of their officials within the provinces, for the fees for the accommodation of these agents, for the expenses of their maintenance, for the payment of various kinds of state works, for the transportation of recruits and material supplies to the centre, i. e. everything that constituted the very essence of the functioning of local authorities. In this paradoxical situation, the main support could only be obtained from the zemstvo self-government bodies. The long-known practice of state bodies of local power being maintained by the population of the uyezd was replenished in the Petrine era with new elements. Lay fees covered the expenses of crown agents sent from the centre to the region and helped pay for some government work within the province. However, the peculiarities of the legislation of the era put the governors and their employees, who shifted the financial burden on the zemstvo, into a risky position. They could be accused of bribery and “unspecified fees” that undermined the solvency of the taxed population. Never previously published documents on Arkhangelsk province discovered by the author of the article reveal the complete picture of the financial support of the township communities of the Dvina uyezd for the activities of the provincial administration in 1711–1713 and the complex vicissitudes of relations between the central and local crown authorities and secular organisations. The documents were archived during the investigation of the case of the Arkhangelsk vice-governor A. A. Kurbatov and reflect one of the investigative episodes of 1716.

Reliability Evaluation of DC/DC Converter in Direct Current Collection System of Wind Farm Considering the Influence of Control Strategy

The DC collection system of wind farms can effectively solve the problems of harmonic resonance and reactive power transmission in large-scale wind power AC collection systems, and has broad development prospects. The DC/DC converter is critical equipment in the wind farm DC collection system, and its reliability is related to the entire collection system’s safe and stable operation. The reliability of DC/DC converters is affected by factors such as system operating conditions and control strategies. Based on this, a reliability evaluation model for boost full-bridge isolated (BFBIC) DC/DC converters is proposed from the device level to the submodule level and then to the equipment level, incorporating operating conditions and control strategies into reliability analysis. Firstly, in device-level reliability modeling, the impact of DC/DC converter operating conditions and control strategies on insulated gate bipolar transistor (IGBT) module losses and junction temperature fluctuations are considered. Secondly, fault tree analysis (FTA) is used to establish the reliability model of the BFBIC submodule and DC/DC converter. In the end, we verify the effectiveness of the proposed model through a numerical example, and provide solid data support for analyzing the reliability of the wind farm DC collection system.

Digital methods in memory studies: A beginner’s guide to scalable reading of Twitter data

This article makes a methodological contribution to the growing subfield of digital memory studies. It demonstrates a possible way forward for memory studies scholars who want to try out digital methods but also remain in conversation with the kinds of research traditionally produced within the field. The article revolves around a showcase of an analytical workflow for conducting a scalable reading of large quantities of tweets through access to the Twitter API. The article argues that using only basic computational approaches to social media data in combination with API access can drastically improve data collection practices and enrich analytical practices, producing results recognizable and compatible with existing research in memory studies. As a case study, the article uses a dataset of nearly 200,000 tweets collected around two events that prompted Twitter users to discuss the history of the American children’s television program Sesame Street. It does so to demonstrate: first, how a visualization focusing on chronology can help underpin arguments about heightened activity around certain events. Second, a close reading of selected tweets from these events can support claims of shared activity, even if no hashtags were used. And third, how using simple tools for distant reading makes it possible to converse with questions and issues about gatekeepers and connectivity already central within memory studies. Furthermore, the article demonstrates how the Twitter API supports a more systematical, large-scale collection of tweets than usually seen in memory studies, making researchers less dependent on the algorithmic bias that rules the search in the platform’s regular interface.

Data donation of individual shopping data to help predict the occurrence of disease: A pilot study linking individual loyalty card and health survey data to investigate COVID-19

Introduction & BackgroundPrevious studies have found shopping data could increase the predictive accuracy of disease surveillance systems and illuminate behavioural responses in the self-management of symptoms of disease. Yet, accessing individual sales datasets for linkage to health datasets is challenging, and the recruitment of appropriate sample sizes for medical research has been limited.
 Objectives & ApproachObjectivesCollect and link individual health data to individual shopping data to investigate COVID-19. Assess the feasibility of scaling-up this method, and use the collected data to investigate using loyalty card data in machine learning (ML) models for disease.
 MethodsBased on recommendations on the public’s preferences for data donation a new protocol was designed for collecting, linking and analysing shopping and health data. Participants were requested to use the Tesco Clubcard website data portability function to share their loyalty card data and complete an online health survey. An exploratory data analysis was conducted on the linked dataset. Participants were recruited online (18/01/2022 to 04/02/2022) with a recruitment target of 200.
 Relevance to Digital FootprintsThe collection and analysis of individual transactional sales data for health research.
 Results197 participants shared their Tesco Clubcard and health survey data. Tesco Clubcard data contained 893,414 transactions of 65,310 uniquely named items purchased from 2015 to 2022. Average transactions per participant were 4,653 (SD 5256) and average timeframe recorded was five years 6 months and 30 days (SD 836 days). A total of 6,993 medication sales were recorded accounting for 1% of sales, 81% (159/197) of participants bought medications and the average was 44 (STD 68) medications per individual. Most participants (196/197) shared their health status in the survey, and 94% (81/86) of those on medication shared the medication names. Participants reported donating their data to do good (79%, 155/197), help the NHS (77%, 152/197), be socially responsible (74%, 144/197) and because data was secure and anonymised (78%, 153/197).
 Conclusions & ImplicationsUsing this new protocol which enables convenient data sharing with transparent data safeguards, the public were willing to share both their shopping and health data for research into COVID-19. To apply robust ML analysis, particularly to explore self-medication at an individual level, recruitment must be significantly scaled to collect data from enough individuals with high sales and regular shopping frequency, or new ML techniques developed to address sparseness in loyalty card data of key purchasing events related to health. The study suggests public readiness to share shopping data for health research, but investment is needed for large-scale data collection and AI application.

A paradigm shift in the prevention and diagnosis of oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is a widespread disease with only 50%-60% 5-year survival. Individuals with potentially malignant precursor lesions are at high risk. Survival could be increased by effective, affordable, and simple screening methods, along with a shift from incisional tissue biopsies to non-invasive brush biopsies for cytology diagnosis, which are easy to perform in primary care. Along with the explainable, fast, and objective artificial intelligence characterisation of cells through deep learning, an easy-to-use, rapid, and cost-effective methodology for finding high-risk lesions is achievable. The collection of cytology samples offers the further opportunity of explorative genomic analysis. Our prospective multicentre study of patients with leukoplakia yields a vast number of oral keratinocytes. In addition to cytopathological analysis, whole-slide imaging and the training of deep neural networks, samples are analysed according to a single-cell RNA sequencing protocol, enabling mapping of the entire keratinocyte transcriptome. Mapping the changes in the genetic profile, based on mRNA expression, facilitates the identification of biomarkers that predict cancer transformation. This position paper highlights non-invasive methods for identifying patients with oral mucosal lesions at risk of malignant transformation. Reliable non-invasive methods for screening at-risk individuals bring the early diagnosis of OSCC within reach. The use of biomarkers to decide on a targeted therapy is most likely to improve the outcome. With the large-scale collection of samples following patients over time, combined with genomic analysis and modern machine-learning-based approaches for finding patterns in data, this path holds great promise.

Litter Selfie: A Citizen Science Guide for Photorecording Macroplastic Deposition along Mountain Rivers Using a Smartphone

Macroplastic pollution in mountain rivers can threaten water resources, biodiversity, and the recreational values provided by them. The first step towards evaluating and then mitigating these risks is the systematic collection of reliable and spatially uniform data on the amount and type of macroplastics deposited in different land covers occurring in a mountain river channel. To maximise the opportunity for the large-scale collection of such data using the citizen science approach, we propose in this study an illustrated step-by-step guide to sample the macroplastic deposited along mountain rivers and to record the collected information using a photo taken by smartphone and a simple online form. Our guide includes three steps: (i) the location of sampling plots across 3–4 predefined surface covers occurring in mountain rivers of temperate climate, (ii) the hand collection of macroplastic deposited in them, and (iii) the photorecording and archiving of information on macroplastics collected using a smartphone and an online form. The proposed guide can allow for the low-cost collection of data on macroplastic deposition in mountain rivers on regional and global scales. The collected data can be further analysed by environmental scientists to quantify the amount and types of macroplastic deposited and to evaluate the resulting risks. They can be also used as illustrative materials to increase the awareness of local communities about the plastic pollution problem.

Personality trait analysis during the COVID-19 pandemic: a comparative study on social media

The COVID-19 pandemic, a global contagion of coronavirus infection caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has triggered severe social and economic disruption around the world and provoked changes in people’s behavior. Given the extreme societal impact of COVID-19, it becomes crucial to understand the emotional response of the people and the impact of COVID-19 on personality traits and psychological dimensions. In this study, we contribute to this goal by thoroughly analyzing the evolution of personality and psychological aspects in a large-scale collection of tweets extracted during the COVID-19 pandemic. The objectives of this research are: i) to provide evidence that helps to understand the estimated impact of the pandemic on people’s temperament, ii) to find associations and trends between specific events (e.g., stages of harsh confinement) and people’s reactions, and iii) to study the evolution of multiple personality aspects, such as the degree of introversion or the level of neuroticism. We also examine the development of emotions, as a natural complement to the automatic analysis of the personality dimensions. To achieve our goals, we have created two large collections of tweets (geotagged in the United States and Spain, respectively), collected during the pandemic. Our work reveals interesting trends in personality dimensions, emotions, and events. For example, during the pandemic period, we found increasing traces of introversion and neuroticism. Another interesting insight from our study is that the most frequent signs of personality disorders are those related to depression, schizophrenia, and narcissism. We also found some peaks of negative/positive emotions related to specific events.

Feasibility of large-scale systematic data collection in a quality improvement registry of deep brain stimulation in Parkinson’s disease

Feasibility of large-scale systematic data collection in a quality improvement registry of deep brain stimulation in Parkinson’s disease

Smartphone Diary Application in Household Surveys: Integration of High Frequency Temporal Data in Large-scale Data Collection

Household expenditure data is at the core of efforts to measure living standards, inequality and financial protection against illness. Currently it is mainly derived from recall-based surveys that are time consuming and prone to measurement errors. Diaries are often used as an alternative approach, however this results in fatigue and low compliance. Smartphone- based diaries could overcome many of these challenges, but it is yet unknown how this approach could be used and its relationship with data from the household surveys. This study evaluated a smartphone-based diary application for collecting daily household expenditures and compared data with that of household surveys. Results show that smartphone-based diary applications could be used to optimize or complement the information from conventional survey-based indicators.

An evaluation of DNA extraction methods on historical and roadkill mammalian specimen

Guidelines identifying appropriate DNA extraction methods for both museum and modern biological samples are scarce or non-existent for mammalian species. Yet, obtaining large-scale genetic material collections are vital for conservation and management purposes. In this study, we evaluated five protocols making use of either spin-column, organic solvents, or magnetic bead-based methods for DNA extraction on skin samples from both modern, traffic-killed (n = 10) and museum (n = 10) samples of European hedgehogs, Ericaneus europaeus. We showed that phenol–chloroform or silica column (NucleoSpin Tissue) protocols yielded the highest amount of DNA with satisfactory purity compared with magnetic bead-based protocols, especially for museum samples. Furthermore, extractions using the silica column protocol appeared to produce longer DNA fragments on average than the other methods tested. Our investigation demonstrates that both commercial extraction kits and phenol–chloroform protocol retrieve acceptable DNA concentrations for downstream processes, from degraded remnants of traffic-killed and museum samples of mammalian specimens. Although all the tested methods could be applied depending on the research questions and laboratory conditions, commercial extraction kits may be preferred due to their effectiveness, safety and the higher quality of the DNA extractions.

From a potentially powerful to a polarised court: the emergence of dissenting opinions on the Croatian Constitutional Court

ABSTRACT One of the most important aspects of making the 1990 Croatian Constitutional Court was the ‘establishment of an independent and potentially powerful’ court. For the first time since 1963, the Court obtained full powers of judicial review. Yet, despite its newly gained powers, the Croatian Constitutional Court refrained from an activist approach adopted by other constitutional courts in the region and was not fond of annulling laws adopted by the legislator. One possible explanation of such a conformist approach was the ideological consonance between the centre-right legislative and the centre-right Court. Most of the constitutional judges were appointed by the centre-right party (HDZ), which held the majority in the Parliament during the first decade of the country’s independence. However, recent years have witnessed an increase in the dissenting opinions, following the nomination of five judges from the centre-left opposition (SDP) to a predominantly centre-right Court. Based on the large-scale data collection efforts, this paper looks into the origin of these nominations, as well as their effect on the emergance of dissenting opinion and the growing ideological polarisation on the Court, which changed the relationship between judicial and legislative powers in Croatia.

Imagining machine vision: Four visual registers from the Chinese AI industry

Machine vision is one of the main applications of artificial intelligence. In China, the machine vision industry makes up more than a third of the national AI market, and technologies like face recognition, object tracking and automated driving play a central role in surveillance systems and social governance projects relying on the large-scale collection and processing of sensor data. Like other novel articulations of technology and society, machine vision is defined, developed and explained by different actors through the work of imagination. In this article, we draw on the concept of sociotechnical imaginaries to understand how Chinese companies represent machine vision. Through a qualitative multimodal analysis of the corporate websites of leading industry players, we identify a cohesive sociotechnical imaginary of machine vision, and explain how four distinct visual registers contribute to its articulation. These four registers, which we call computational abstraction, human–machine coordination, smooth everyday, and dashboard realism, allow Chinese tech companies to articulate their global ambitions and competitiveness through narrow and opaque representations of machine vision technologies.

Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available.

Open-source, publicly available neuroimaging datasets - whether from large-scale data collection efforts or pooled from multiple smaller studies - offer unprecedented sample sizes and promote generalization efforts. Releasing data can democratize science, increase the replicability of findings, and lead to discoveries. Partly due to patient privacy, computational, and data storage concerns, researchers typically release preprocessed data with the voxelwise time series parcellated into a map of predefined regions, known as an atlas. However, releasing preprocessed data also limits the choices available to the end-user. This is especially true for connectomics, as connectomes created from different atlases are not directly comparable. Since there exist several atlases with no gold standards, it is unrealistic to have processed, open-source data available from all atlases. Together, these limitations directly inhibit the potential benefits of open-source neuroimaging data. To address these limitations, we introduce Cross Atlas Remapping via Optimal Transport (CAROT) to find a mapping between two atlases. This approach allows data processed from one atlas to be directly transformed into a connectome based on another atlas without the need for raw data access. To validate CAROT, we compare reconstructed connectomes against their original counterparts (i.e., connectomes generated directly from an atlas), demonstrate the utility of transformed connectomes in downstream analyses, and show how a connectome-based predictive model can generalize to publicly available data that was processed with different atlases. Overall, CAROT can reconstruct connectomes from an extensive set of atlases - without needing the raw data - allowing already processed connectomes to be easily reused in a wide range of analyses while eliminating redundant processing efforts. We share this tool as both source code and as a stand-alone web application (http://carotproject.com/).

Blockchain-Based Two-Stage Federated Learning With Non-IID Data in IoMT System

The Internet of Medical Things (IoMT) has a bright future with the development of smart mobile devices. Information technology is also leading changes in the healthcare industry. IoMT devices can detect patient signs and provide treatment guidance and even instant diagnoses through technologies, such as artificial intelligence (AI) and wireless communication. However, conventional centralized machine learning approaches are often difficult to apply within IoMT devices because of the difficulty of large-scale collection of patient data and the potential risk of privacy breaches. Therefore, we propose a blockchain-based two-stage federated learning approach that allows IoMT devices to train a global model collaboratively without gathering the data to a central server. Specifically, to address the problem of poor training performance on non-independent identically distributed (non-IID) data, we design a blockchain-based data-sharing scheme that can significantly improve the model’s accuracy without threatening user privacy. We also design a client selection mechanism to further improve the system’s efficiency. Finally, we validate the feasibility and effectiveness of our system through simulation experiments on three popular datasets (i.e., MNIST, Fashion-MNIST, and CIFAR-10).

Results and Methodological Implications of the Digital Epidemiology of Prescription Drug References Among Twitter Users: Latent Dirichlet Allocation (LDA) Analyses.

Social media is an important information source for a growing subset of the population and can likely be leveraged to provide insight into the evolving drug overdose epidemic. Twitter can provide valuable insight into trends, colloquial information available to potential users, and how networks and interactivity might influence what people are exposed to and how they engage in communication around drug use. This exploratory study was designed to investigate the ways in which unsupervised machine learning analyses using natural language processing could identify coherent themes for tweets containing substance names. This study involved harnessing data from Twitter, including large-scale collection of brand name (N=262,607) and street name (N=204,068) prescription drug-related tweets and use of unsupervised machine learning analyses (ie, natural language processing) of collected data with data visualization to identify pertinent tweet themes. Latent Dirichlet allocation (LDA) with coherence score calculations was performed to compare brand (eg, OxyContin) and street (eg, oxys) name tweets. We found people discussed drug use differently depending on whether a brand name or street name was used. Brand name categories often contained political talking points (eg, border, crime, and political handling of ongoing drug mitigation strategies). In contrast, categories containing street names occasionally referenced drug misuse, though multiple social uses for a term (eg, Sonata) muddled topic clarity. Content in the brand name corpus reflected discussion about the drug itself and less often reflected personal use. However, content in the street name corpus was notably more diverse and resisted simple LDA categorization. We speculate this may reflect effective use of slang terminology to clandestinely discuss drug-related activity. If so, straightforward analyses of digital drug-related communication may be more difficult than previously assumed. This work has the potential to be used for surveillance and detection of harmful drug use information. It also might be used for appropriate education and dissemination of information to persons engaged in drug use content on Twitter.

Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment

This paper explores the concept of smart cities and the role of the Internet of Things (IoT) and machine learning (ML) in realizing a data-centric smart environment. Smart cities leverage technology and data to improve the quality of life for citizens and enhance the efficiency of urban services. IoT and machine learning have emerged as key technologies for enabling smart city solutions that rely on large-scale data collection, analysis, and decision-making. This paper presents an overview of smart cities’ various applications and discusses the challenges associated with implementing IoT and machine learning in urban environments. The paper also compares different case studies of successful smart city implementations utilizing IoT and machine learning technologies. The findings suggest that these technologies have the potential to transform urban environments and enable the creation of more livable, sustainable, and efficient cities. However, significant challenges remain regarding data privacy, security, and ethical considerations, which must be addressed to realize the full potential of smart cities.

Adapting Participatory Rank Methodology: From Public Health to Educational Research

ABSTRACT Within educational research, qualitative data offers unique opportunities to contextualize findings that arise from large-scale quantitative data collection processes. Still, to date, most educational research is based on researchers’ determination of what is important, not the participants’. Participatory Rank Methodology (PRM) is a mixed-methods, public health methodology applied during crises to generate rich, contextualized data that can be counted, ranked, and compared across groups or individuals. This paper invites scholars to explore and consider enacting PRM to address issues of educational inequity. Thus, I (1) define PRM, (2) name reasons why PRM may be beneficial for educational researchers working with marginalized communities, (3) present rationales for choosing PRM as opposed to more familiar participatory research approaches, (4) explicate how PRM is enacted in the research process: across research design, collection, analysis, and publication, and (5) briefly share the promise and possibilities of taking up PRM in educational research.

SARS-CoV-2 entry into and evolution within a skilled nursing facility

SARS-CoV-2 belongs to the family Coronaviridae which includes multiple human pathogens that have an outsized impact on aging populations. As a novel human pathogen, SARS-CoV-2 is undergoing continuous adaptation to this new host species and there is evidence of this throughout the scientific and public literature. However, most investigations of SARS-CoV-2 evolution have focused on large-scale collections of data across diverse populations and/or living environments. Here we investigate SARS-CoV-2 evolution in epidemiologically linked individuals within a single outbreak at a skilled nursing facility beginning with initial introduction of the pathogen. The data demonstrate that SARS-CoV-2 was introduced to the facility multiple times without establishing an interfacility transmission chain, followed by a single introduction that infected many individuals within a week. This large-scale introduction by a single genotype then persisted in the facility. SARS-CoV-2 sequences were investigated at both the consensus and intra-host variation levels. Understanding the variability in SARS-CoV-2 during transmission chains will assist in understanding the spread of this disease and can ultimately inform best practices for mitigation strategies.

LAD: an application design model to support the analysis of large-scale personal data collections generated by lifelogging

AbstractLifelogging is a form of personal data collection which seeks to capture the totality of one’s experience through intelligent technology and sensors. Yet despite notable advancement in such technologies, there remain persistent challenges to developing interactive systems to analyse the types of large-scale personal collections often generated by lifelogging. In response to this, we present the Lifelog Application Design (LAD) model which is intended to address these challenges and support the design of more novel interactive systems that may target a broader range of application use cases. The model is deliberately structured to remain impartial to the specific personal data, technology platform, or application criterion, to provide maximum utility across the domain. We demonstrate this utility by exploring two case studies and a retrospective analysis of VRLE, a real-world application prototype developed to examine the potential of large-scale personal data retrieval in virtual reality. This work is based on the accumulation of insights garnered from involvement in a number of collaborative lifelogging projects over the past decade. It is our goal to encourage future researchers to utilise the LAD model to support the design and development of their own application prototypes and further solidify the model’s contribution to the domain as a whole.