Data Collection Protocol Research Articles

A Site-Wise Reliability Analysis of the ABCD Diffusion Fractional Anisotropy and Cortical Thickness: Impact of Scanner Platforms.

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce nonbiological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site-wise adaptive ICC (AICC) to evaluate the reliability of imaging-derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population-level age-related brain development trajectory using a weighted mixed model and updates age-corrected site-wise reliability until convergence. We evaluated the test-retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61 ± 0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76 ± 0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared with those using GE/Philips scanners for both FA (AICC = 0.71 ± 0.12 vs. 0.46 ± 0.17, p < 0.001) and CT (AICC = 0.80 ± 0.10 vs. 0.69 ± 0.11, p < 0.001). These findings demonstrate site-and-scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.

Texas Water Observatory: A Distributed Network for Monitoring Water, Energy, and Carbon Cycles under Variable Climate and Land Use on Gulf Coast Plains

Abstract In the Gulf Coastal Plains of Texas, a state-of-the-art distributed network of field observatories, known as the Texas Water Observatory (TWO), is developed to better understand the water, energy, and carbon cycles across the critical zone (encompassing aquifers, soils, plants, and atmosphere) at different spatiotemporal scales. Using more than 300 advanced real-time/near-real-time sensors, this observatory monitors high-frequency water, energy, and carbon storage and fluxes in the Brazos River corridor, which are critical for coupled hydrologic, biogeochemical, and land–atmosphere process understanding in the region. TWO provides a regional resource for better understanding and/or managing agriculture, water resources, ecosystems, biodiversity, disasters, health, energy, and weather/climate. TWO infrastructure spans common land uses in this region, including traditional/aspirational cultivated agriculture, rangelands, native prairie, bottomland hardwood forest, and coastal wetlands. Sites represent landforms from low-relief erosional uplands to depositional lowlands across climatic and geologic gradients of central Texas. We present the overarching vision of TWO and describe site design, instrumentation specifications, data collection, and quality control protocols. We also provide a comparison of water, energy, and carbon budget across sites, including evapotranspiration, carbon fluxes, radiation budget, weather, profile soil moisture and soil temperature, soil hydraulic properties, hydrogeophysical surveys, groundwater levels, and groundwater quality reported at TWO primary sites for 2018–20 (with certain data gaps). In conjunction with various Earth-observing remote sensing and legacy databases, TWO provides a master testbed to evaluate process-driven or data-driven critical zone science, leading to improved natural resource management and decision support at different spatiotemporal scales. Significance Statement We provide the vision, design setup, and data acquisition of a state-of-the-art network of field observatories across the Gulf Coastal Plains of Texas. This observatory provides a wealth of measurements of the water, energy, and carbon fluxes, thereby providing a critical testbed for improving the understanding of terrestrial hydrological, biogeochemical, and atmospheric processes across diverse land-use and climate conditions.

The impact of garbage codes on the quality of death coding in Belgrade in 2010, 2015, and 2020

Abstract Background Accurate data on the causes of death (CoDs) is the foundation for analyzing the health situation and developing health policy in various countries. The percentage of garbage codes (GCs) indicates the quality of the CoDs data. Within the Western Balkan Strategic Partnership Project (Work Package 4: Use of Population Health Metrics for Improved Surveillance), we evaluated the impact of the four levels of GCs on the quality of mortality data of Belgrade. Methods In a retrospective study, we examined the underlying CoDs of Belgrade residents by age and sex and the change in GCs’ share in the CoDs in 2010, 2015, and 2020. We used anonymous individual mortality data and population age and sex annual data from the electronic databases of the City Institute of Public Health of Belgrade, as well as the list of GS levels 1-4 from the 2019 Global Burden of Disease Study. Results Over the observed years, the percentage of all GCs in underlying CoDs was approximately 20%. The GCs with profound policy implications level 1 accounted for 52% of all GCs in 2020, higher than those recorded in previous years - 46.3% in 2010 and 45.1% in 2015. The total number of GCs per 100,000 inhabitants in Belgrade was the highest in 2020, with a rate of 325.2/100,000 (males: 374.4, females: 301.3). This rate has increased by about one-third since 2010 (both sexes: 245.4, males: 269.2, females: 221.1) and by about two-fifths since 2015 (both sexes: 235.8, males: 262.7, females: 211.8). Conclusions This research has revealed a significant decline in the quality of data related to the causes of death in Belgrade. One-fifth of the recorded causes of death are inaccurate or unreliable. The study highlights the importance of maintaining strict protocols for data collection, analysis, and management to ensure the accuracy and reliability of CoDs data in developing effective public health policies. Key messages • The rate of total garbage codes per 100,000 inhabitants of Belgrade has increased by about two-fifths since 2015. • Failure in quality, accuracy, and reliability of causes of death can lead to suboptimal health policymaking.

Evaluating the feasibility of upper arm ECG for cardiac monitoring

Abstract Background Ambulatory electrocardiogram (ECG) monitoring is essential for detecting paroxysmal cardiac events and tracking long-term physiological parameters. However, traditional Holter devices are cumbersome, restrict movement, and can cause skin irritation, resulting in sub-optimal patient compliance during extended monitoring periods. Wearable devices utilizing non-conventional form factors are emerging as promising non-intrusive alternatives for ECG monitoring. Purpose To determine the optimal location for single-lead ECG acquisition on the upper arm and assess the feasibility of using dry electrode armbands for cardiac monitoring when coupled with robust ECG processing software. Methods Stationary ECGs from 20 healthy adult participants were recorded across a two-phase data collection protocol and processed with clinically validated ECG software. In the first phase, the average signal amplitudes of wet-electrode ECGs obtained from 30 different configurations across the mid-brachial and proximal-brachial regions of the upper arm were assessed. In the second, the performance of two proprietary dry-electrode armbands, featuring either a single-point or three-point electrode strap, were evaluated on the optimal upper arm configuration using QRS detection and heart rate (HR) metrics. Comparative analysis was performed throughout using data simultaneously acquired on a gold standard Holter device in modified lead III (MLIII) configuration. Performance metrics were calculated relative to manually generated signal annotations. Results Wet-electrode-acquired ECGs from the proximal-brachial region exhibited significantly higher average signal amplitudes (0.12 ± 0.06 mV) compared to those from the mid-brachial region (0.05 ± 0.04 mV) of the upper arm (p &amp;lt; 0.05). The optimal configuration (A-F), positioned at the lateral and medial extents of the proximal-brachial region, demonstrated comparable average signal amplitudes (0.16 ± 0.07 mV, 0.16 ± 0.06 mV, 0.19 ± 0.08 mV) for single-point dry, three-point dry, and wet-electrode setups, respectively. Upon processing with ECG software, data from optimal dry-electrode configurations showed excellent average QRS sensitivity and specificity of 98.2% and 98.9% for the one-point strap, and 98.9% and 99.8% for the three-point strap, respectively. HR metrics from the optimized dry-electrode setups were comparable in accuracy to those derived from the reference Holter device, with the average HR error being similar across the three-point dry electrode strap (0.57% ± 0.28%), one-point dry electrode strap (1.16% ± 1.46%), and Holter device (0.74% ± 1.50%). Conclusion(s) Optimized upper arm single-lead ECG with dry electrodes can yield signals with sufficient amplitude and quality for automated ECG analysis. Coupled with robust ECG signal processing software, the cardiac metrics obtained from the optimized upper arm setups exhibited comparable performance to those derived from medical Holter data.

A Feasibility Study of Thermography for Detecting Pressure Injuries Across Diverse Skin Tones.

Pressure injury (PI) detection is challenging, especially in dark skin tones, due to the unreliability of visual inspection. Thermography may serve as a viable alternative as temperature differences in the skin can indicate impending tissue damage. Although deep learning models hold considerable promise toward reliably detecting PI, existing work fails to evaluate performance on diverse skin tones and varying data collection protocols. We collected a new dataset of 35 participants focused on darker skin tones where temperature differences are induced through cooling and cupping protocols. The dataset includes different cameras, lighting, patient pose, and camera distance. We compare the performance of three convolutional neural network (CNN) models trained on either the thermal or the optical images on all skin tones. Our results suggest thermography-based CNN is robust to data collection protocols. Moreover, the visual explanation often captures the region of interest without requiring explicit bounding box labels.

3D reconstruction and depth estimation method for local anomalies of rail surface based on multi-view stereo matching

Abstract Detecting rail surface anomalies has become crucial for ensuring the safety of train operations. However, manual detection methods are time-consuming and labor-intensive. Although traditional detection models based on one-dimensional signals or two-dimensional images can effectively identify the existence of defects, they are difficult to use to obtain local depth characteristic information, leading to difficulties in accurately assessing the extent of damage in abnormal regions. Rail track maintenance strategies are typically developed based on the detected different depth ranges of defects. To address this issue, this paper proposes a local depth estimation method based on the point cloud of the target rail surface reconstructed by PatchmatchNet. Firstly, according to the acquisition method of multi-view images and the sampling environment at the site, a practical data collection protocol is established for generating a multi-view dataset of rail surface. Next, dense point cloud of the target rail surface were reconstructed by PatchmatchNet. Subsequently, a method for estimating the local anomaly depth is developed based on the point cloud. Experimental results demonstrate that the proposed method achieves a minimum error of approximately 5.5% within a maximum depth range of 0.35mm, when compared to depth measurements obtained using a structured light camera. Finally, this paper presents a more comprehensive three-dimensional representation of the local abnormal physical structure, surpassing traditional one-dimensional and two-dimensional forms. This enhanced representation offers richer information for the effective determination of whether the anomalies constitute defects and aids in distinguishing true defects from other surface irregularities. Such advancement significantly improves the precision of defect assessment and supports the development of highly precise repair strategies.

Measuring Environmental and Behavioral Drivers of Chronic Diseases Using Smartphone-Based Digital Phenotyping: Intensive Longitudinal Observational mHealth Substudy Embedded in 2 Prospective Cohorts of Adults.

Previous studies investigating environmental and behavioral drivers of chronic disease have often had limited temporal and spatial data coverage. Smartphone-based digital phenotyping mitigates the limitations of these studies by using intensive data collection schemes that take advantage of the widespread use of smartphones while allowing for less burdensome data collection and longer follow-up periods. In addition, smartphone apps can be programmed to conduct daily or intraday surveys on health behaviors and psychological well-being. The aim of this study was to investigate the feasibility and scalability of embedding smartphone-based digital phenotyping in large epidemiological cohorts by examining participant adherence to a smartphone-based data collection protocol in 2 ongoing nationwide prospective cohort studies. Participants (N=2394) of the Beiwe Substudy of the Nurses' Health Study 3 and Growing Up Today Study were followed over 1 year. During this time, they completed questionnaires every 10 days delivered via the Beiwe smartphone app covering topics such as emotions, stress and enjoyment, physical activity, access to green spaces, pets, diet (vegetables, meats, beverages, nuts and dairy, and fruits), sleep, and sitting. These questionnaires aimed to measure participants' key health behaviors to combine them with objectively assessed high-resolution GPS and accelerometer data provided by participants during the same period. Between July 2021 and June 2023, we received 11.1 TB of GPS and accelerometer data from 2394 participants and 23,682 survey responses. The average follow-up time for each participant was 214 (SD 148) days. During this period, participants provided an average of 14.8 (SD 5.9) valid hours of GPS data and 13.2 (SD 4.8) valid hours of accelerometer data. Using a 10-hour cutoff, we found that 51.46% (1232/2394) and 53.23% (1274/2394) of participants had >50% of valid data collection days for GPS and accelerometer data, respectively. In addition, each participant submitted an average of 10 (SD 11) surveys during the same period, with a mean response rate of 36% across all surveys (SD 17%; median 41%). After initial processing of GPS and accelerometer data, we also found that participants spent an average of 14.6 (SD 7.5) hours per day at home and 1.6 (SD 1.6) hours per day on trips. We also recorded an average of 1046 (SD 1029) steps per day. In this study, smartphone-based digital phenotyping was used to collect intensive longitudinal data on lifestyle and behavioral factors in 2 well-established prospective cohorts. Our assessment of adherence to smartphone-based data collection protocols over 1 year suggests that adherence in our study was either higher or similar to most previous studies with shorter follow-up periods and smaller sample sizes. Our efforts resulted in a large dataset on health behaviors that can be linked to spatial datasets to examine environmental and behavioral drivers of chronic disease.

Exploring the use of wearable sensors for assessing risk factors during orthopedic surgeries: A protocol for data collection

During orthopedic surgeries, surgeons are generally exposed to prolonged periods of standing, awkward and sustained body postures, and forceful movements, which can increase the likelihood of developing work-related musculoskeletal disorders (WRMSD). Therefore, this study proposes a protocol to measure parameters related to physical risk factors contributing to lower limb WRMSD, during orthopedic surgery procedures. The protocol development was preceded by an initial phase of understanding and specifying the context of use, followed by pre-tests in laboratory environment. It integrates a motion capture system, using inertial measurement units (IMU) to collect posture data from hip, knee, and ankle, and electromyography system (EMG) to measure and record data from muscle activity of biceps femoris, rectus femoris, and gastrocnemius lateralis. Pre-tests provided insights for protocol optimization, estimating a 3-hour data collection session per surgery due to sensor battery limitations, streamlining the process by placing EMG sensors before IMU and refining thigh sensor placement strategies. The protocol presents an opportunity for a real-time and quantitative approach to monitor surgeon's exposure to risk factors contributing to lower limb WRMSD while performing surgical procedures. Two months after pre-tests, the protocol implementation began in a real work context. The study's final outcomes fall outside the paper's scope.

Developmental and epileptic encephalopathy 56 due to YWHAG variants: 12 new cases and review of the literature

Background and objectivesDevelopmental and epileptic encephalopathy 56 (DEE-56) is caused by pathogenic variants in YWHAG and is characterized by early-onset epilepsy and neurodevelopmental delay. This study reports on a cohort of DEE-56 individuals, correlating antiseizure medication usage and comorbidities, to aid in understanding disease evolution. MethodsWe analyzed data from thirty-nine individuals aged 3–40 years with YWHAG variants, including 12 previously unreported individuals (2 of these with recurrent distal 7q11.23 deletions) and 27 previously published cases (21 families, including 3 adult individuals reported in a family case). Our assessments encompassed clinical, radiological, and genetic evaluations. All procedures adhered to standardized protocols for patient approvals, registrations, and data collection. ResultsIndividuals with YWHAG variants exhibited variable psychomotor delay, with the majority experiencing mild intellectual disability. Early-onset seizures, particularly febrile seizures, were common, with various seizure types reported. Valproic acid has emerged as an effective antiseizure medication. Movement disorders were present in a subset of individuals, primarily manifesting as ataxia and tremor. Comorbidities such as autism spectrum disorders and attention deficit-hyperreactivity disorder were observed in a proportion of individuals. We identified a novel YWHAG variant (c.634_645del/p.Asn212_Ser215del) and expanded the genotypic spectrum of the disease. ConclusionsWe provide insights into the clinical, radiological, and genetic features of YWHAG-related epileptic encephalopathy. Despite mild clinical symptoms, affected individuals face challenges in daily functioning, underscoring the need for comprehensive care. Valproic acid has been used for seizure control with variable results.

The visual experience dataset: Over 200 recorded hours of integrated eye movement, odometry, and egocentric video.

We introduce the Visual Experience Dataset (VEDB), a compilation of more than 240hours of egocentric video combined with gaze- and head-tracking data that offer an unprecedented view of the visual world as experienced by human observers. The dataset consists of 717 sessions, recorded by 56 observers ranging from 7 to 46 years of age. This article outlines the data collection, processing, and labeling protocols undertaken to ensure a representative sample and discusses the potential sources of error or bias within the dataset. The VEDB's potential applications are vast, including improving gaze-tracking methodologies, assessing spatiotemporal image statistics, and refining deep neural networks for scene and activity recognition. The VEDB is accessible through established open science platforms and is intended to be a living dataset with plans for expansion and community contributions. It is released with an emphasis on ethical considerations, such as participant privacy and the mitigation of potential biases. By providing a dataset grounded in real-world experiences and accompanied by extensive metadata and supporting code, the authors invite the research community to use and contribute to the VEDB, facilitating a richer understanding of visual perception and behavior in naturalistic settings.

AI Methods and Algorithms for Diagnosis of Intestinal Parasites: Applications, Challenges and Future Opportunities

Artificial intelligence (AI) transforms intestinal parasite diagnosis, particularly through deep learning models like convolutional neural networks (CNNs). This paper reviews the application of AI, especially CNNs, in automating parasite detection and classification from microscopic images. Integrating AI into parasitology diagnostics speeds up the process, reduces human error, and enhances treatment and patient outcomes. However, there is a need for more datasets reflecting the African context to ensure accurate ground-truthing, particularly in low- and middle-income countries (LMICs) across Africa. Most AI models for medical diagnosis are trained on datasets from high-income countries, which may not capture the unique epidemiological, genetic, and environmental factors prevalent in African populations. This can lead to less accurate diagnoses and treatment recommendations in African LMICs. For example, intestinal parasitic infections are common in many African regions, yet the datasets used to train AI models often lack sufficient representation from these areas. Developing datasets that reflect the diverse African context is crucial for improving the accuracy and reliability of AI-based diagnostic tools. Issues like overfitting, data privacy, and cost also require attention. Collaboration between researchers, healthcare professionals, and technologists is essential to address these challenges. Standardized protocols for data collection, model training, and validation are necessary for reliable AI systems. Combining AI with traditional techniques holds promise for better parasite diagnosis, ultimately improving African healthcare outcomes

Digital data collection protocols and template design for an oral health survey of elite athletes in Ireland

AimTo design a digital template for form building, data collection and data management to assess the diet and oral health of elite athletes.Subject and methodsA web-based open-source platform, KoboToolbox (KBTB), was used to aid design and manufacture of a digital data collection form for use in oral health surveys. KBTB frontend form builder was used to create a customised questionnaire. Dental charting systems for the chosen indices were designed using XLSForms, converted to XForm and uploaded to the KBTB interface to create a single form for digital data input. This template was used to collect data for a cross-sectional study investigating the diet and oral health of a convenience sample of elite athletes in Ireland.ResultsA digital data collection (DDC) form was designed for use in oral health surveys. The customised form was then used to collect data for a survey of 88 elite athletes on the KBTB platform. Dietary behaviours were recorded by the participants using KBTB. Following data collection, the dataset was downloaded from KBTB and cleaned for subsequent analysis.ConclusionThe design of a versatile form and oral health questionnaire template using digital tools customised for elite athletes was useful for efficient, comprehensive field data collection. DDC tools are convenient for data form design, collection, and storage. This template can be easily modified for use by other researchers planning oral health surveys.

Evaluating User Experience and Data Quality in Gamified Data Collection for Appearance-Based Gaze Estimation

Appearance-based gaze estimation, which uses only a regular camera to estimate human gaze, is important in various application fields. While the technique faces data bias issues, data collection protocol is often demanding, and collecting data from a wide range of participants is difficult. It is an important challenge to design opportunities that allow a diverse range of people to participate while ensuring the quality of the training data. To tackle this challenge, we introduce a novel gamified approach for collecting training data. In this game, two players communicate words via eye gaze through a transparent letter board. Images captured during gameplay serve as valuable training data for gaze estimation models. The game is designed as a physical installation that involves communication between players, and it is expected to attract the interest of diverse participants. We assess the game’s significance on data quality and user experience through a comparative user study.

Remote data collection of infant activity and sleep patterns via wearable sensors in the HEALthy Brain and Child Development Study (HBCD)

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Wearable and remote sensing technologies have advanced data collection outside of laboratory settings to enable exploring, in more detail, the associations of early experiences with brain development and social and health outcomes. In the HBCD Study, the Novel Technology/Wearable Sensors Working Group (WG-NTW) identified two primary data types to be collected: infant activity (by measuring leg movements) and sleep (by measuring heart rate and leg movements). These wearable technologies allow for remote collection in the natural environment. This paper illustrates the collection of such data via wearable technologies and describes the decision-making framework, which led to the currently deployed study design, data collection protocol, and derivatives, which will be made publicly available. Moreover, considerations regarding actual and potential challenges to adoption and use, data management, privacy, and participant burden were examined. Lastly, the present limitations in the field of wearable sensor data collection and analysis will be discussed in terms of extant validation studies, the difficulties in comparing performance across different devices, and the impact of evolving hardware/software/firmware.

International Humanitarian Assistance for Health: Türkiye’s Health Diplomacy Practices during the Covid-19 Pandemic

COVID-19 has significantly impacted the health systems of developed countries, leading to a surge in the demand for medical supplies and equipment. This study is dedicated to shedding light on Türkiye's health diplomacy practices and its pivotal role as a facilitator in addressing the escalating global humanitarian and medical aid demand for medical equipment, supplies, medicines, and vaccines due to COVID-19. The study employs the PRISMA systematic analysis method as a data collection protocol to scrutinize Türkiye's international humanitarian and medical assistance during the COVID-19 pandemic in the context of health diplomacy practices. The study's findings, which are categorized under four headings: international humanitarian aid based on COVID-19, international medical equipment and supplies, international ventilator aid, and Türkiye's international COVID-19 vaccine aid, reveal that Türkiye has extended a broad spectrum of medical aid and support, including ventilators, vaccines, and medical equipment, to 161 countries across five continents, demonstrating its global reach and impact. Furthermore, as a testament to its “entrepreneurial and humanitarian foreign policy,” Türkiye has underscored regional and international cooperation through its medical and humanitarian endeavors. Türkiye's international humanitarian assistance has gone beyond material aid to encourage dialogue and has been a message of international solidarity for communities. In future research, establishing a connection between Türkiye's regional and international medical assistance and tangible political, social, and economic outcomes will validate the impact and benefits of its humanitarian efforts.

Validity and Reliability of Movesense HR+ ECG Measurements for High-Intensity Running and Cycling.

Low-cost, portable devices capable of accurate physiological measurements are attractive tools for coaches, athletes, and practitioners. The purpose of this study was primarily to establish the validity and reliability of Movesense HR+ ECG measurements compared to the criterion three-lead ECG, and secondarily, to test the industry leader Garmin HRM. Twenty-one healthy adults participated in running and cycling incremental test protocols to exhaustion, both with rest before and after. Movesense HR+ demonstrated consistent and accurate R-peak detection, with an overall sensitivity of 99.7% and precision of 99.6% compared to the criterion; Garmin HRM sensitivity and precision were 84.7% and 87.7%, respectively. Bland-Altman analysis compared to the criterion indicated mean differences (SD) in RR' intervals of 0.23 (22.3) ms for Movesense HR+ at rest and 0.38 (18.7) ms during the incremental test. The mean difference for Garmin HRM-Pro at rest was -8.5 (111.5) ms and 27.7 (128.7) ms for the incremental test. The incremental test correlation was very strong (r = 0.98) between Movesense HR+ and criterion, and moderate (r = 0.66) for Garmin HRM-Pro. This study developed a robust peak detection algorithm and data collection protocol for Movesense HR+ and established its validity and reliability for ECG measurement.

Toward Ensuring Data Quality in Multi-Site Cancer Imaging Repositories

Cancer remains a major global health challenge, affecting diverse populations across various demographics. Integrating Artificial Intelligence (AI) into clinical settings to enhance disease outcome prediction presents notable challenges. This study addresses the limitations of AI-driven cancer care due to low-quality datasets by proposing a comprehensive three-step methodology to ensure high data quality in large-scale cancer-imaging repositories. Our methodology encompasses (i) developing a Data Quality Conceptual Model with specific metrics for assessment, (ii) creating a detailed data-collection protocol and a rule set to ensure data homogeneity and proper integration of multi-source data, and (iii) implementing a Data Integration Quality Check Tool (DIQCT) to verify adherence to quality requirements and suggest corrective actions. These steps are designed to mitigate biases, enhance data integrity, and ensure that integrated data meets high-quality standards. We applied this methodology within the INCISIVE project, an EU-funded initiative aimed at a pan-European cancer-imaging repository. The use-case demonstrated the effectiveness of our approach in defining quality rules and assessing compliance, resulting in improved data integration and higher data quality. The proposed methodology can assist the deployment of big data centralized or distributed repositories with data from diverse data sources, thus facilitating the development of AI tools.

Ergonomic Considerations for Practicing Massage Therapists.

The physical demands and repetitive movements performed by massage therapists during a treatment increase the risk for work-related musculoskeletal disorders. While massage therapy meets the level of heavy physical demand, the ergonomic risk of performing a massage was previously unknown. The Massage Therapy Foundation undertook a job task analysis to be used by educators, employers, and clinicians to create an optimal work environment. Data collected via survey were used to create the protocol for an onsite data collection where massage therapists were evaluated on the ergonomic risk factors by trained ergonomists. The results of the job task analysis found the highest variability in movement when the massage therapist was treating the neck prone, neck supine, and leg prone. When the massage therapists did not change positions during a stroke, there was increased forward flexion of the trunk which increased postural stress on the extensors of the back and neck. Strokes such as effleurage which can span the entire length of the body part had the highest cumulative load, requiring postures that put mild biomechanical stress on the shoulders (90% of the time), neck (70% of the time), and trunk (60% of the time). The forward-bending posture of the massage therapist increases the eccentric demands of the therapist's neck and back musculature, thereby increasing fatigue and leading to an increase in the chance of overuse injury. The job task analysis recommends the massage therapist monitor the duration of application in an unsupported position, take more time between sessions, work in an optimum environment, including correct table height, and have a healthy, active lifestyle to maintain career longevity.

Microcrystal electron diffraction structure of Toll-like receptor 2 TIR-domain-nucleated MyD88 TIR-domain higher-order assembly.

Eukaryotic TIR (Toll/interleukin-1 receptor protein) domains signal via TIR-TIR interactions, either by self-association or by interaction with other TIR domains. In mammals, TIR domains are found in Toll-like receptors (TLRs) and cytoplasmic adaptor proteins involved in pro-inflammatory signaling. Previous work revealed that the MAL TIR domain (MALTIR) nucleates the assembly of MyD88TIR into crystalline arrays in vitro. A microcrystal electron diffraction (MicroED) structure of the MyD88TIR assembly has previously been solved, revealing a two-stranded higher-order assembly of TIR domains. In this work, it is demonstrated that the TIR domain of TLR2, which is reported to signal as a heterodimer with either TLR1 or TLR6, induces the formation of crystalline higher-order assemblies of MyD88TIR in vitro, whereas TLR1TIR and TLR6TIR do not. Using an improved data-collection protocol, the MicroED structure of TLR2TIR-induced MyD88TIR microcrystals was determined at a higher resolution (2.85 Å) and with higher completeness (89%) compared with the previous structure of the MALTIR-induced MyD88TIR assembly. Both assemblies exhibit conformational differences in several areas that are important for signaling (for example the BB loop and CD loop) compared with their monomeric structures. These data suggest that TLR2TIR and MALTIR interact with MyD88 in an analogous manner during signaling, nucleating MyD88TIR assemblies unidirectionally.

Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data

In this study, we address critical barriers hindering the widespread adoption of surgical navigation in orthopedic surgeries due to limitations such as time constraints, cost implications, radiation concerns, and integration within the surgical workflow. Recently, our work X23D showed an approach for generating 3D anatomical models of the spine from only a few intraoperative fluoroscopic images. This approach negates the need for conventional registration-based surgical navigation by creating a direct intraoperative 3D reconstruction of the anatomy. Despite these strides, the practical application of X23D has been limited by a significant domain gap between synthetic training data and real intraoperative images.In response, we devised a novel data collection protocol to assemble a paired dataset consisting of synthetic and real fluoroscopic images captured from identical perspectives. Leveraging this unique dataset, we refined our deep learning model through transfer learning, effectively bridging the domain gap between synthetic and real X-ray data. We introduce an innovative approach combining style transfer with the curated paired dataset. This method transforms real X-ray images into the synthetic domain, enabling the in-silico-trained X23D model to achieve high accuracy in real-world settings.Our results demonstrated that the refined model can rapidly generate accurate 3D reconstructions of the entire lumbar spine from as few as three intraoperative fluoroscopic shots. The enhanced model reached a sufficient accuracy, achieving an 84% F1 score, equating to the benchmark set solely by synthetic data in previous research. Moreover, with an impressive computational time of just 81.1 ms, our approach offers real-time capabilities, vital for successful integration into active surgical procedures.By investigating optimal imaging setups and view angle dependencies, we have further validated the practicality and reliability of our system in a clinical environment. Our research represents a promising advancement in intraoperative 3D reconstruction. This innovation has the potential to enhance intraoperative surgical planning, navigation, and surgical robotics.