General Data Research Articles

Psychometric Properties of the Spanish Version of the Cognitive Problems and Strategies Assessment in Patients with Bipolar Disorder.

Cognitive deficits play an important role in Bipolar Disorder (BPD). The Cognitive Problems and Strategies Assessment (CPSA) is a measure that evaluates the patient's perception of cognitive difficulties, and the spontaneous use of compensatory strategies and could thus have potential utility for clinical practice in patients with BPD. Our aim was to determine the validity and reliability of the Cognitive Problems and Strategies Assessment (CPSA) in Bipolar Disorder (BPD). Ninety-three BPD outpatients and 90 controls completed the Assessment of Problems with Thinking and Memory (APTM) questionnaire and the Assessment of Memory and Thinking Strategies (AMTS) questionnaire which constitute the CPSA, the Cognitive Complaints in Bipolar Disorder Rating Assessment (COBRA), as a measure of convergent validity, and general sociodemographic data. Cronbach's alpha coefficient, Spearman's correlation coefficient and independent sample t tests were used for Internal consistency, Convergent validity and Discriminant validity. The APTM had a Cronbach's alpha coefficient of 0.93 and the AMTS 0.90. The COBRA score and the APTM were significantly correlated. BPD patients exhibited higher scores on the APTM and lower scores on the AMTS than controls. The present instrument enriches the clinician's repertoire for rapid and inexpensive cognitive evaluation in BPD.

Risk Factors for Delayed Atrioventricular Block after Aortic Valve Surgery: A Retrospective Study

Objective: This study aimed to examine the potential factors that contribute to the occurrence of delayed high-grade atrioventricular block (DHAVB) following transcatheter aortic valve replacement (TAVR). Methods: A retrospective analysis was conducted on the clinical data of 115 patients who underwent TAVR at Jiaozhou Central Hospital of Qingdao Hospital between January 2018, and June 2023. A follow-up period of 30 days post-operation was observed for all patients. The patients were categorized into two groups on the basis of the occurrence of DHAVB: DHAVB group (n = 35) and control group (n = 80). The general clinical data preoperative and postoperative heart disease characteristics of the groups were compared. The risk factors associated with DHAVB after TAVR were analyzed. Results: The mean systolic blood pressure (SBP) level of the DHAVB group significantly increased compared with that of the control group, whereas the heart rate (HR) level significantly reduced (p < 0.05). The average preoperative left ventricular ejection fraction (LVEF) was significantly lower in the DHAVB group than in the control group (p < 0.05). The control group exhibited a significantly higher prevalence of preoperative QRS wave broadening, severe calcification of the aortic valve, and right bundle branch block than the control group (p < 0.05). Spearman's correlation and logistic regression analyses identified increased SBP, decreased HR, diminished LVEF, the presence of preoperative and postoperative right bundle branch block, and thickened interventricular septum were as risk factors for DHAVB in patients undergoing TAVR (p < 0.05). Conclusion: Close surveillance of blood pressure, heart rate, and cardiac function is recommended for individuals undergoing TAVR. Pre-operative and post-operative electrocardiography and echocardiography are valuable tools in identifying potential risk factors for DHAVB, offering a solid foundation for effective patient prognostic management.

“Collecting Loose Change:” Conceptual Clarifications on Bias, Subjectivity, Positionality, Reflexivity, & Autoethnography in Leisure Research

With the rise of the acceptance, normalization, and increasing sophistication of qualitative research across the social sciences, there is a proliferation of paradigmatic approaches, methodologies, data generation methods, analytical techniques, and forms of representation. As qualitative methods continue to develop, expand, and hybridize, there is a need to conceptually clarify some of the “loose change” that has come with the rise of its legitimacy. In this essay, we build upon previous work advocating for a greater need for the use and conceptual clarification of trustworthiness in qualitative research to make an additional necessary call to encourage nuance when engaging with concepts that focus on the role of the self. We try to nuance the role(s) of articulating one’s place through declarations of bias, subjectivity, and positionality and the mechanisms for managing and/or understanding one’s place in the research vis-a-vis reflexivity as a process and/or autoethnography (methodology). In doing so, we call for the appropriate deployment of these techniques to align with the paradigmatic approach used in the research endeavor.

Opportunities for incorporating intersectionality into biomedical informatics

Many approaches in biomedical informatics (BMI) rely on the ability to define, gather, and manipulate biomedical data to support health through a cyclical research-practice lifecycle. Researchers within this field are often fortunate to work closely with healthcare and public health systems to influence data generation and capture and have access to a vast amount of biomedical data. Many informaticists also have the expertise to engage with stakeholders, develop new methods and applications, and influence policy. However, research and policy that explicitly seeks to address the systemic drivers of health would more effectively support health. Intersectionality is a theoretical framework that can facilitate such research. It holds that individual human experiences reflect larger socio-structural level systems of privilege and oppression, and cannot be truly understood if these systems are examined in isolation. Intersectionality explicitly accounts for the interrelated nature of systems of privilege and oppression, providing a lens through which to examine and challenge inequities. In this paper, we propose intersectionality as an intervention into how we conduct BMI research. We begin by discussing intersectionality’s history and core principles as they apply to BMI. We then elaborate on the potential for intersectionality to stimulate BMI research. Specifically, we posit that our efforts in BMI to improve health should address intersectionality’s five key considerations: (1) systems of privilege and oppression that shape health; (2) the interrelated nature of upstream health drivers; (3) the nuances of health outcomes within groups; (4) the problematic and power-laden nature of categories that we assign to people in research and in society; and (5) research to inform and support social change.

GDPR compliance through standard security controls: An automated approach

Since 2018, the enactment of the General Data Protection Regulation (GDPR) has bestowed distinct privileges upon each person while imposing protocols to safeguard personal information. The GDPR effectively tackles an evident requirement within our interconnected, social media-driven society. However, its compliance poses a considerable challenge, particularly for small and medium-sized businesses. This work aims to identify and select the proper countermeasures in order to comply with GDPR, by using standard security controls. Thus, we designed a tool to handle some phases of the compliance process in an almost semi-automated way. The proposed approach relies on standard security control frameworks (namely NIST SP-800-53) and can be easily adapted to different frameworks. The proposed technique was validated using our university as a case study, through a simple demonstrator, although the solution can be transparently applied to different contexts.

Suture passer combined with two-hole laparoscopic peritoneal dialysis catheterization in patients undergoing peritoneal dialysis

Background Laparoscopic techniques are being widely applied for peritoneal dialysis (PD) catheter (PDC) placement. The suture passer is a novel fixation tool that aims to reduce catheter migration. We compared the clinical value of the suture passer combined with two-hole laparoscopic PDC placement to open surgical placement by evaluating preoperative and postoperative conditions, as well as the onset of complications in both groups. Methods A retrospective study was conducted including 169 patients who underwent PDC placement surgery from January 2021 to May 2023. Based on the method employed, patients were divided into two groups: the suture passer combined with a two-hole laparoscopy group (SLG) and the open surgical group (SG). Comprehensive patient information, including general data, preoperative and postoperative indicators, peritoneal function after surgery, and the incidence rate of complications, were collected and analyzed. Results The SLG showed a statistically significant decrease in operative time, intraoperative blood loss, and 6-month postoperative drift rate compared to the SG (p < 0.05). No statistically significant differences were observed between the two groups in terms of sex, age, primary disease, hospitalization time, hospitalization costs, preoperative and postoperative examination indicators, peritonitis, and omental wrapping. Conclusions Suture passer combined with two-hole laparoscopic PDC placement, characterized by simplicity and facilitating secure catheter fixation, was deemed safe and effective for patients undergoing PD. It reduces the catheter migration rate and improved surgical comfort. Overall, this technique demonstrates favorable outcomes in clinical practice.

Cybersecurity of Internet of Things in the health sector: Understanding the applicable legal framework

Although the digitalisation of healthcare is an ongoing process that dates back to more than two decades ago, it has gained more momentum with the COVID-19 pandemic. Recent developments in this sector include the adoption of wearable devices based on Internet of Things technology. The possibility of connecting devices that can work outside the physical boundaries of a hospital and follow patients at home, i.e. during their day-to-day life, has several obvious advantages. However, the digitalisation of the health sector through increased adoption of connected devices does not exclude vulnerabilities, particularly risks concerning the protection of patients’ data and the security of networks and information systems. Connected devices can gather, process, and store personal patient health data. Failure to safeguard the integrity and security of these data may affect the patients’ identity and finances and put their lives at risk. The presence of an IoT device in a healthcare setting may affect and reduce the level of network security of the overall system as it may provide an access point for an unlawful hacking attack. Although IoT technologies in the health sector are becoming increasingly pervasive, the European legal framework applicable to them is not clearly defined. This is extremely relevant in the case of cybersecurity, where the legal point of reference is the General Data Protection Regulation, addressing the measures and requirements applicable in case of data breaches, and the Medical Device Regulation, providing provisions focused on the security of data relevant to IoT defined as medical devices. The most recent interventions that address health data processing and cybersecurity are the proposed Cyber Resilience Act and the Health Data Space Regulation. The two acts provide measures and requirements applicable to IoT from two perspectives. Yet, they add complexities and some inconsistencies that may hamper the effectiveness of the overall cybersecurity framework.

Global trends of dioxin and dioxin-like PCBs in animal-origin foods: a systematic review and gap areas

Global trends of dioxin and dioxin-like PCBs in animal-origin foods: a systematic review and gap areas

Predicting unplanned hospitalisations in older adults using routinely recorded general practice data.

Unplanned hospitalisations represent a hazardous event for older persons. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions. To develop and validate an easy-to-use prediction model for unplanned hospitalisations in community-dwelling older adults using readily available data to allow rapid bedside assessment by general practitioners. Retrospective study using general practice electronic health records of 243,129 community-dwelling adults aged ≥65 years linked with national administrative data. The dataset was geographically split into a development (58.7%) and validation (41.3%) sample to predict unplanned hospitalisations within 6 months. We evaluated the performance of three different models with increasingly smaller selections of candidate predictors (i.e. optimal, readily-available and easy-to-use model, respectively). We used logistic regression with backward selection for model development. The models were validated internally and externally. We assessed predictive performance by area under the curve (AUC) and calibration plots. In both samples, 7.6% had at least one unplanned hospitalisation within 6 months. The discriminative ability of the three models was comparable and remained stable after geographic validation. The easy-to-use model included age, sex, prior hospitalisations, pulmonary emphysema, heart failure and polypharmacy. Its discriminative ability after validation was AUC 0.72 [95% confidence interval: 0.72-0.71]. Calibration plots showed good calibration. Our models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not impact predictive performance, demonstrating the robustness of the model. We developed an easy-to-use tool that may assist general practitioners in decision-making and targeted preventive interventions.

Bio2Py: An API for integrating Python with BioWin for enhanced data acquisition in wastewater treatment simulations

Commercial simulators for modeling wastewater treatment processes (WWTPs), such as BioWin, play a crucial role for optimizing the design, operation, and control of treatment facilities. These simulators offer fast and user-friendly interfaces and can be employed to enhance the efficiency of wastewater treatment plants. However, a notable limitation of BioWin and similar platforms is the lack of direct integration with Python®, a programming language renowned for its versatility and extensive data analysis and machine learning libraries. Bridging this gap constitutes an opportunity to significantly improve the simulation process by leveraging Python's capabilities. The Python package Bio2Py (BioWin to Python) is presented as a compelling solution, reconciling BioWin's specialized functionality and Python's automation and data processing strengths. By utilizing Python's PyAutoGUI package, Bio2Py automates various aspects of the simulation process, such as loading influent data, running simulations, and saving simulation results without manual intervention. The integration of BioWin with Python not only speeds up the workflow by automating repetitive tasks but also opens new possibilities, such as parameter calibration for specific wastewaters and using BioWin as a data generator for machine learning applications. This brief communication presents the Bio2Py API, illustrating its use and applicability through a simple implementation example. Bio2Py's ability to automate and optimize various aspects of wastewater treatment process simulation, including data acquisition, and its potential use for process variable optimization and parameter tuning, positions it as a valuable tool for engineers and researchers who work in this field.

Sustainable edge computing: Challenges and future directions

AbstractThe advent of edge computing holds immense promise for advancing the digitization of society, ushering in critical applications that elevate the overall quality of life. Yet, the practical implementation of the edge paradigm proves more challenging than anticipated, encountering disruptions primarily due to the constraints of applying conventional cloud‐based strategies at the network's periphery. Increasingly influenced by sustainability commitments, industry regulations currently view edge computing as a potential threat, primarily due to the energy inefficiency of solutions situated in close proximity to data generation sources and the rising density of computing. This paper presents a proactive strategy to transform the perceived threat into an opportunity, steering the sustainable evolution of future edge infrastructures to make them both environmentally and economically competitive for accelerated adoption. The vision outlined addresses key challenges associated with edge deployment and operation, emphasizing energy efficiency, fault‐tolerant automation, and collaborative orchestration. The proposed approach integrates two‐phase immersion cooling, formal modeling, machine learning, and federated management to effectively harness heterogeneity, propelling the sustainability of edge computing. To substantiate the efficacy of this approach, the paper details initial efforts towards establishing the sustainability of an edge infrastructure designed for an Advanced Driver Assistance Systems application.

Synthetic Data for Deep Learning in Computer Vision &amp; Medical Imaging: A Means to Reduce Data Bias

Deep-learning (DL) performs well in computer-vision and medical-imaging automated decision-making applications. A bottleneck of DL stems from the large amount of labelled data required to train accurate models that generalise well. Data scarcity and imbalance are common problems in imaging applications that can lead DL models towards biased decision making. A solution to this problem is synthetic data. Synthetic data is an inexpensive substitute to real data for improved accuracy and generalisability of DL models. This survey reviews the recent methods published in relation to the creation and use of synthetic data for computer-vision and medical-imaging DL applications. The focus will be on applications that utilised synthetic data to improve DL models by either incorporating an increased diversity of data that is difficult to obtain in real life, or by reducing a bias caused by class imbalance. Computer-graphics software and generative networks are the most popular data generation techniques encountered in the literature. We highlight their suitability for typical computer-vision and medical-imaging applications, and present promising avenues for research to overcome their computational and theoretical limitations.

Modelling the developmental path in the acquisition of /l/-final irregular plurals by Portuguese children

ABSTRACT Previous empirical research has shown that Portuguese children aged 4;0 to 6;0 are sensitive to the quality of stem-final vowels when acquiring the irregular plural forms of /l/-final words (acquisition order: plurals of /al, ɛl, ɔl, ul/ > plurals of /il/). This study presents a formal account of this emergent pattern. We first construct an initial-state grammar that arguably instantiates Portuguese children’s grammatical knowledge at the onset of morpho-phonological acquisition. We then simulate morpho-phonological learning using two constraint-based models (Stochastic Optimality Theory and Noisy Harmonic Grammar) and their associated Gradual Learning Algorithm. The results of our learning simulations corroborate the experimental data, showing that the plural form of /il/-final words takes longer to master than that of other /l/-final words. In addition to replicating the empirical results, the learning simulation reveals two important implications. First, the raw frequency of individual forms cannot account for the attested patterns, suggesting that the input frequency is mediated by some principles of grammatical learning. Second, Harmonic Grammar outperforms Optimality Theory in simulating real acquisition data. Through the additive constraint interaction, Harmonic-Grammar learning is more restrictive, avoiding the generation of unobserved data in the course of morpho-phonological acquisition.

FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled Global Navigation Satellite Systems (GNSS)/on-board sensor fusion localization framework is established to estimate vehicle states, including position, velocity, and attitude. To address the large drift rate of the Inertial Measurement Unit (IMU), this study introduces a novel IMU/Dynamics pre-integration method based on the vehicle dynamics model. We establish a two-degree-of-freedom vehicle dynamics model utilizing measurements from the wheel speed sensor and steering wheel angle sensor. The IMU/Dynamics factor is devised through a close integration of the model output and IMU pre-integration, enabling the construction of precise odometry with low-cost on-board sensors. Then, to address the issue of the non-Gaussian distribution of GNSS pseudorange error, this paper employs a Gaussian Mixture Model (GMM) to characterize the pseudorange noise, which is then applied to further sensor fusion. Given the time-varying nature of pseudorange noise, the expectation maximization algorithm is utilized to estimate GMM parameters online, leveraging the pseudorange residuals within a sliding window. Comprehensive experiments, inclusive of challenging scenarios such as urban canyons, tunnels, and wooded areas, have been carried out. They affirm the superior performance of the proposed method. Experiments have shown that our method demonstrates reliable localization across different statuses of GNSS signal, exhibiting a 39.4% improvement in the root mean square error of position error when compared to the state-of-the-art. Additionally, this FGO-MFI is a general sensor data fusion framework and is able to incorporate diverse sensor measurements, for example, from cameras and LiDARs to provide more reliable and accurate localization information.

Association between emergency department attendances, sociodemographic factors and long-term health conditions in the population of Norfolk and Waveney, England: Cross sectional study.

Demand for urgent and emergency health care in England has grown over the last decade, for reasons that are not clear. Changes in population demographics may be a cause. This study investigated associations between individuals' characteristics (including socioeconomic deprivation and long term health conditions (LTC)) and the frequency of emergency department (ED) attendances, in the Norfolk and Waveney subregion of the East of England. The study population was people who were registered with 91 of 106 Norfolk and Waveney general practices during one year from 1 April 2022 to 31 March 2023. Linked primary and secondary care and geographical data included each individual's sociodemographic characteristics, and number of ED attendances during the same year and, for some individuals, LTCs and number of general practice (GP) appointments. Associations between these factors and ED attendances were estimated using Poisson regression models. 1,027,422 individuals were included of whom 57.4% had GP data on the presence or absence of LTC, and 43.1% had both LTC and general practitioner appointment data. In the total population ED attendances were more frequent in individuals aged under five years, (adjusted Incidence Rate Ratio (IRR) 1.25, 95% confidence interval 1.23 to 1.28) compared to 15-35 years); living in more socioeconomically deprived areas (IRR 0.61 (0.60 to 0.63)) for least deprived compared to most deprived,and living closer to the nearest ED. Among individuals with LTC data, each additional LTC was also associated with increased ED attendances (IRR 1.16 (1.15 to 1.16)). Among individuals with LTC and GP appointment data, each additional GP appointment was also associated with increased ED attendances (IRR 1.03 (1.026 to 1.027)). In the Norfolk and Waveney population, ED attendance rates were higher for young children and individuals living in more deprived areas and closer to EDs. In individuals with LTC and GP appointment data, both factors were also associated with higher ED attendance.

Development and initial testing of a brief, generic self-reported disability questionnaire: The Universal Disability Index.

Disability is an important multifaceted construct. A brief, generic self-reported disability questionnaire that promises a broader and more comparable measure of disability than disease-specific instruments does not currently exist. The aim of this study was to develop and evaluate such a questionnaire: the Universal Disability Index (UDI). An online survey was used to collect general population data. Data were randomly divided into training and validation subsets. The dimensionality and structure of eight UDI questionnaire items were evaluated using exploratory factor analysis (EFA, training subset) followed by confirmatory factor analysis (CFA, validation subset). To assess concurrent validity, the UDI summed score from the full dataset was compared to the Groningen Activity Restriction Scale (GARS) and the Graded Chronic Pain Scale (GCPS) disability scores. Internal consistency and discriminant validity were also assessed. Bootstrapping was used to evaluate model stability and generalisability. 403 participants enrolled; 364 completed at least one UDI item. Three single-factor versions of the UDI were assessed (8-item, 7-item, and 6-item). All versions performed well during EFA and CFA (182 cases assigned to each), but none met the RMSEA (Root Mean Square Error of Approximation) criterion (≤ 0.08). All versions of the UDI had high internal consistency (Cronbach's α > 0.90), were strongly correlated (Pearson's r > 0.7) with both GARS and GCPS disability scores, indicating concurrent validity, and could accurately discriminate between upper and lower quartiles of these comparators. Confidence intervals of estimates were narrow, suggesting model stability and generalisability. A brief, generic self-reported disability questionnaire was found to be valid and to possess good psychometric properties. The UDI has a single factor structure and either a 6-item, 7-item or 8-item version can be used to measure disability. For brevity and parsimony, the 6-item UDI is recommended, but further testing of all versions is warranted.

Time series diffusion method: A denoising diffusion probabilistic model for vibration signal generation

Diffusion models have demonstrated powerful data generation capabilities in various research fields such as image generation. However, in the field of vibration signal generation, the criteria for evaluating the quality of the generated signal are different from that of image generation and there is a fundamental difference between them. At present, there is no research on the ability of diffusion model to generate vibration signal. In this paper, a Time Series Diffusion Method (TSDM) is proposed for vibration signal generation, leveraging the foundational principles of diffusion models. The TSDM uses an improved U-net architecture with attention block, ResBlock and TimeEmbedding to effectively segment and extract features from one-dimensional time series data. It operates based on forward diffusion and reverse denoising processes for time-series generation. Experimental validation is conducted using single-frequency, multi-frequency datasets, and bearing fault datasets. The results show that TSDM can accurately generate the single-frequency and multi-frequency features in the time series and retain the basic frequency features for the diffusion generation results of the bearing fault series. It is also found that the original DDPM could not generate high quality vibration signals, but the improved U-net in TSDM, which applied the combination of attention block and ResBlock, could effectively improve the quality of vibration signal generation. Finally, TSDM is applied to the small sample fault diagnosis of three public bearing fault datasets, and the results show that the accuracy of small sample fault diagnosis of the three datasets is improved by 32.380%, 18.355% and 9.298% at most, respectively.

Data-Driven Prediction Model for Analysis of Sensor Data

In view of Industry 4.0, data generation and analysis are challenges. For example, machine health monitoring and remaining useful life prediction use sensor signals, which are difficult to analyze using traditional methods and mathematical techniques. Machine and deep learning algorithms have been used extensively in Industry 4.0 to process sensor signals and improve the accuracy of predictions. Therefore, this paper proposes and validates the data-driven prediction model to analyze sensor data, including in the data transformation phase Principal Component Analysis tested by Fourier Transformation and Wavelet Transformation, and the modeling phase based on machine and deep learning algorithms. The machine learning algorithms used for tests in this research are Random Forest Regression (RFR), Multiple Linear Regression (MLR), and Decision Tree Regression (DTR). For the deep learning comparison, the algorithms are Deep Learning Regression and Convolutional network with LeNet-5 Architecture. The experimental results indicate that the models show promising results in predicting wear values and open the problem to further research, reaching peak values of 92.3% accuracy for the first dataset and 62.4% accuracy for the second dataset.

Epileptogenesis and neuroinflammation

Progress in the treatment of epilepsy in recent years has been determined mainly by the development and use of new anticonvulsants, as well as the search for new alternative methods for reducing epileptic seizures. The issue of pharmacoresistance remains relevant. This paper presents an analysis of information about neuroinflammation as a pathophysiological phenomenon, its possible role in epileptogenesis and the prospects for influencing the pathological process in epilepsy by influencing its elements. General data on epileptogenesis, the role of neuroinflammation in its formation and maintenance, the main cellular and humoral effectors of the neuroinflammatory process and the prospects for the development of new therapeutic approaches are presented.

The Causes and Consequences of Refugee Flows: A Contemporary Reanalysis

The world faces a forced displacement crisis. Tens of millions of individuals have been forced across international boundaries worldwide. Therefore, the causes and consequences of refugee flows are the subjects of significant social science inquiry. Unfortunately, the historical lack of reliable data on actual refugee flows, country-specific data reporting timelines, and more general pre-2000 data quality issues have significantly limited empirical inferences on these topics. We replicate 28 articles on these topics using data newly released after a multiyear collaboration with the United Nations on annual dyadic flows. We observe major inconsistencies between the newly released flow numbers and the stock-based flow estimates upon which decades of research are based; we also find widespread inappropriate treatment of missing historical values. When we replicate the existing literature using the newly introduced flow data, correcting the treatment of missing historical values, and temporally extending/restricting the study periods, we produce significantly different results.