Hi-C/3C-seq Data Analysis for Prokaryotic Genomes with HiC-Pro.

Hi-C and 3C-seq are powerful tools to study the 3D genomes of bacteria and archaea, whose small cell sizes and growth conditions are often intractable to detailed microscopic analysis. However, the circularity of prokaryotic genomes requires a number of tricks for Hi-C/3C-seq data analysis. Here, I provide a practical guide to use the HiC-Pro pipeline for Hi-C/3C-seq data obtained from prokaryotes.

Accurate identification of locally aneuploid cells by incorporating cytogenetic information in single cell data analysis.

Single-cell RNA sequencing is a powerful tool to investigate the cellular makeup of tumor samples. However, due to the sparse data and the complex tumor microenvironment, it can be challenging to identify neoplastic cells that play important roles in tumor growth and disease progression. This is especially relevant for blood cancers, where neoplastic cells may be highly similar to normal cells. To address this challenge, we have developed partCNV and partCNVH, two methods for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. PartCNV uses an expectation-maximization (EM) algorithm with mixtures of Poisson distributions and incorporates cytogenetic information to guide the classification. PartCNVH further improves partCNV by integrating a hidden Markov model for feature selection. We have thoroughly evaluated the performance of partCNV and partCNVH through simulation studies and real data analysis using three scRNA-seq datasets from blood cancer patients. Our results show that partCNV and partCNVH have favorable accuracy and provide more interpretable results compared to existing methods. In the real data analysis, we have identified multiple biological processes involved in the oncogenesis of myelodysplastic syndromes and acute myeloid leukemia.

Host genetics and microbiota data analysis in colorectal cancer research.

Colorectal cancer (CRC) is a heterogeneous disease with a complex aetiology influenced by a myriad of genetic and environmental factors. Despite advances in CRC research, it is a major burden of disease, with the second highest incidence and third leading cause of cancer deaths worldwide. To individualise diagnosis, prognosis, and treatment of CRC, developing new strategies combining precision medicine and bioinformatic procedures is promising. Precision medicine is based on omics technologies and aims to individualise the management of CRC based on patient host genetic characteristics and microbiota. Bioinformatics is central to the application of personalised medicine because it enables the analysis of large datasets generated by these technologies. At the level of host genetics, bioinformatics allows the identification of mutations, genes, molecular pathways, biomarkers and drugs relevant to colorectal carcinogenesis. At the microbiota level, bioinformatics is fundamental to analysing microbial communities' composition and functionality and developing biomarkers and personalised microbiota-based therapies. This paper explores the host and microbiota genetic data analysis in CRC research.

Phenolic content discrimination in Thai holy basil using hyperspectral data analysis and machine learning techniques.

Hyperspectral imaging has emerged as a powerful tool for the non-destructive assessment of plant properties, including the quantification of phytochemical contents. Traditional methods for antioxidant analysis in holy basil (Ocimum tenuiflorum L.) are time-consuming, while hyperspectral imaging has the potential to rapidly observe holy basil. In this study, we employed hyperspectral imaging combined with machine learning techniques to determine the levels of total phenolic contents in Thai holy basil. Spectral data were acquired from 26 holy basil cultivars at different growth stages, and the total phenolic contents of the samples were measured. To extract the characteristics of the spectral data, we used 22 statistical features in both time and frequency domains. Relevant features were selected and combined with the corresponding total phenolic content values to develop a neural network model for classifying the phenolic content levels into 'low' and 'normal-to-high' categories. The neural network model demonstrated high performance, achieving an area under the receiver operating characteristic curve of 0.8113, highlighting its effectiveness in predicting phenolic content levels based on the spectral data. Comparative analysis with other machine learning techniques confirmed the superior performance of the neural network approach. Further investigation revealed that the model exhibited increased confidence in predicting the phenolic content levels of older holy basil samples. This study exhibits the potential of integrating hyperspectral imaging, feature extraction, and machine learning techniques for the rapid and non-destructive assessment of phenolic content levels in holy basil. The demonstrated effectiveness of this approach opens new possibilities for screening antioxidant properties in plants, facilitating efficient decision-making processes for researchers based on comprehensive spectral data.

Non-communicable diseases in the world over the past century: a secondary data analysis.

Introduction: We analyzed the changes in the top 10 non-communicable diseases (NCDs) over the past century across the World Health Organization (WHO) regions. Materials and methods: The data were extracted from the Global Burden of Disease (GBD) studies. After we accessed this source, all NCDs were sorted according to their prevalence in 2019, and the 10 most common NCDs were selected. Then, the incidence, prevalence, and mortality rates of these 10 NCDs were compared to the rates in 2000. Results: Diabetes and kidney disease had the highest increase in incidence (49.4%) and prevalence (28%) in the Eastern Mediterranean region. Substance use disorders had a huge increase (138%) in the mortality rates among women in the Americas region. On the other hand, women in Southeast Asia experienced the greatest decrease in incidence (-19.8%), prevalence (-15.8%), and mortality rates (-66%). Conclusion: In recent years, nearly all NCDs have shown an increase, yet mortality rates have declined across all regions. Lifestyle can be a major cause of this increase, but advancements in health and medical services, such as screening and treatment, have played a crucial role in improving survival rates.

Smartphone Screen Time Characteristics in People With Suicidal Thoughts: Retrospective Observational Data Analysis Study.

Background: Smartphone-based monitoring in natural settings provides opportunities to monitor mental health behaviors, including suicidal thoughts and behaviors. To date, most suicidal thoughts and behaviors research using smartphones has primarily relied on collecting so-called "active" data, requiring participants to engage by completing surveys. Data collected passively from smartphone sensors and logs may offer an objectively measured representation of an individual's behavior, including smartphone screen time. Objective: This study aims to present methods for identifying screen-on bouts and deriving screen time characteristics from passively collected smartphone state logs and to estimate daily smartphone screen time in people with suicidal thinking, providing a more reliable alternative to traditional self-report. Methods: Participants (N=126; median age 22, IQR 16-33 years) installed the Beiwe app (Harvard University) on their smartphones, which passively collected phone state logs for up to 6 months after discharge from an inpatient psychiatric unit (adolescents) or emergency department visit (adults). We derived daily screen time measures from these logs, including screen-on time, screen-on bout duration, screen-off bout duration, and screen-on bout count. We estimated the mean of these measures across age subgroups (adults and adolescents), phone operating systems (Android and iOS), and monitoring stages after the discharge (first 4 weeks vs subsequent weeks). We evaluated the sensitivity of daily screen time measures to changes in the parameters of the screen-on bout identification method. Additionally, we estimated the impact of a daylight time change on minute-level screen time using function-on-scalar generalized linear mixed-effects regression. Results: The median monitoring period was 169 (IQR 42-169) days. For adolescents and adults, mean daily screen-on time was 254.6 (95% CI 231.4-277.7) and 271.0 (95% CI 252.2-289.8) minutes, mean daily screen-on bout duration was 4.233 (95% CI 3.565-4.902) and 4.998 (95% CI 4.455-5.541) minutes, mean daily screen-off bout duration was 25.90 (95% CI 20.09-31.71) and 26.90 (95% CI 22.18-31.66) minutes, and mean daily screen-on bout count (natural logarithm transformed) was 4.192 (95% CI 4.041-4.343) and 4.090 (95% CI 3.968-4.213), respectively; there were no significant differences between smartphone operating systems (all P values were >.05). The daily measures were not significantly different for the first 4 weeks compared to the fifth week onward (all P values were >.05), except average screen-on bout in adults (P value = .018). Our sensitivity analysis indicated that in the screen-on bout identification method, the cap on an individual screen-on bout duration has a substantial effect on the resulting daily screen time measures. We observed time windows with a statistically significant effect of daylight time change on screen-on time (based on 95% joint confidence intervals bands), plausibly attributable to sleep time adjustments related to clock changes. Conclusions: Passively collected phone logs offer an alternative to self-report measures for studying smartphone screen time characteristics in people with suicidal thinking. Our work demonstrates the feasibility of this approach, opening doors for further research on the associations between daily screen time, mental health, and other factors.

ELISA-R: an R-based method for robust ELISA data analysis.

Enzyme-linked immunosorbent assay (ELISA) is a technique to detect the presence of an antigen or antibody in a sample. ELISA is a simple and cost-effective method that has been used for evaluating vaccine efficacy by detecting the presence of antibodies against viral/bacterial antigens and diagnosis of disease stages. Traditional ELISA data analysis utilizes a standard curve of known analyte, and the concentration of the unknown sample is determined by comparing its observed optical density against the standard curve. However, in the case of vaccine research for complicated bacteria such as Mycobacterium tuberculosis (Mtb), there is no prior information regarding the antigen against which high-affinity antibodies are generated and therefore plotting a standard curve is not feasible. Consequently, the analysis of ELISA data in this instance is based on a comparison between vaccinated and unvaccinated groups. However, to the best of our knowledge, no robust data analysis method exists for "non-standard curve" ELISA. In this paper, we provide a straightforward R-based ELISA data analysis method with open access that incorporates end-point titer determination and curve-fitting models. Our modified method allows for direct measurement data input from the instrument, cleaning and arranging the dataset in the required format, and preparing the final report with calculations while leaving the raw data file unchanged. As an illustration of our method, we provide an example from our published data in which we successfully used our method to compare anti-Mtb antibodies in vaccinated vs non-vaccinated mice.

Data-driven batch detection enhances single-cell omics data analysis.

In single-cell omics studies, data are typically collected across multiple batches, resulting in batch effects: technical confounders that introduce noise and distort data distribution. Correcting these effects is challenging due to their unknown sources, nonlinear distortions, and the difficulty of accurately assigning data to batches that are optimal for integration methods.

A new approach to three-dimensional monitoring of surface changes in lakes: application of three-way data analysis model in Lake Burdur, Turkey.

Lakes are areas of ecological significance that host a wide range of living species. Therefore, monitoring changes in lake surfaces is critical for ecosystem preservation. Since conventional methods based on one-dimensional data analysis have limited capacity to analyze complex systems, they may not always produce the desired results in examining surface change. In this regard, a novel approach based on a three-way analysis model of a multi-temporal dataset was improved for the first time to monitor surface changes in Lake Burdur between 2014 and 2023 in terms of latitude, longitude, and year modes. The newly proposed approach, based on the deconvolution of a multi-temporal data series, provided a new way and an alternative approach with a three-dimensional perspective for the simultaneous prediction of changes in latitude, longitude, and relative quantity profiles of Lake Burdur between 2014 and 2023. The proposed approach is based on the parallel factor analysis (PARAFAC) modeling of the multi-temporal datasets. The change in the lake surface as water levels were monitored from latitudinal and longitudinal profiles was obtained by applying the PARAFAC model to the multi-temporal data array. In the same model, the change in lake water surface over the years was determined from the relative quantity profile. The results obtained from the three-way analysis model were compared with the results provided by the conventional method. It was concluded that the findings of this study could provide valuable information to researchers and policymakers in developing effective strategies to analyze changes in lake surfaces in future studies.

Reallocation of time to moderate-to-vigorous physical activity and estimated changes in physical fitness among preschoolers: a compositional data analysis.

BACKGROUND: Previous research has examined the associations of preschoolers' 24-h movement behaviours, including light and moderate-to-vigorous physical activity (LPA and MVPA), sedentary behaviour (SB), sleep, with physical fitness in isolation, ignoring intrinsically compositional nature of movement data while increasing the risk of collinearity. Thus, this study investigated the associations of preschoolers' 24-h Movement behaviours composition with physical fitness, estimated changes in physical fitness when time was reallocated between movement behaviours composition, and determined whether associations differ between different genders, using compositional data analysis. METHODS: In the cross-sectional study, a total of 275 preschoolers (3 ~ 6 y) from China were included. SB, LPA and MVPA times were objectively monitored with an ActiGraph GT9X accelerometer for 7 consecutive days. Sleep duration was obtained using parental reports. Physical fitness parameters, including upper and lower limb strength, static balance, speed-agility, and cardiorespiratory fitness (CRF), were determined with the PREFIT battery. The associations of 24-h movement behaviours composition with each physical fitness parameter were examined employing compositional multivariable linear regression models. The changes following time reallocation among behaviours were estimated employing compositional isotemporal substitution analyses. RESULTS: Greater MVPA, but not LPA, was significantly related to better upper and lower limb strength, speed-agility, and CRF. Reallocating time from LPA or SB to MVPA was related to better physical fitness. The associations were non-symmetrical: the estimated detriments to physical fitness from replacing MVPA with LPA or SB were larger than the estimated benefits associated with adding MVPA of the same magnitude. The aforementioned associations with lower limb strength, CRF, and speed-agility were observed in boys, while associations with upper and lower limb strength were noted in girls. CONCLUSION: Our findings reinforce the importance of physical activity (PA) intensity for the development of physical fitness in preschoolers. Replacing LPA or SB time with MVPA may be an appropriate strategy for enhancing preschoolers' physical fitness.

Identifying extreme events in the stock market: A topological data analysis.

This paper employs Topological Data Analysis (TDA) to detect extreme events (EEs) in the stock market at a continental level. Previous approaches, which analyzed stock indices separately, could not detect EEs for multiple time series in one go. TDA provides a robust framework for such analysis and identifies the EEs during the crashes for different indices. The TDA analysis shows that L1, L2 norms and Wasserstein distance (WD) of the world leading indices rise abruptly during the crashes, surpassing a threshold of µ+4∗σ, where µ and σ are the mean and the standard deviation of norm or WD, respectively. Our study identified the stock index crashes of the 2008 financial crisis and the COVID-19 pandemic across continents as EEs. Given that different sectors in an index behave differently, a sector-wise analysis was conducted during the COVID-19 pandemic for the Indian stock market. The sector-wise results show that after the occurrence of EE, we have observed strong crashes surpassing µ+2∗σ for an extended period for the banking, automobile, IT, realty, energy, and metal sectors. While for the pharmaceutical and FMCG sectors, no significant spikes were noted. Hence, TDA also proves successful in identifying the duration of shocks after the occurrence of EEs. This also indicates that the banking sector continued to face stress and remained volatile even after the crash. This study gives us the applicability of TDA as a powerful analytical tool to study EEs in various fields.

A model proposal for qualitative data analysis, interpretation, and reporting: contextuality, reflectivity, and narrativity.

RATIONALE: From education to healthcare and management processes, it is important to address the experience in health within its own complexity, context, and uniqueness. At this point, qualitative studies come to the fore and this increases the need for practical guides and models for qualitative studies. Qualitative studies have a paradigm that is different from quantitative research and its paradigm ontologically, epistemologically, and methodologically. These differences are reflected in the design of the research as well as the analysis, interpretation, and reporting of qualitative data. From such a point of view, this paper first briefly outlines the design process of qualitative studies and then proposes a model for the analysis, interpretation, and reporting of qualitative data. CONCEPTUAL/THEORETICAL FRAMEWORK: The three core concepts of the model are 'contextuality', 'reflectivity', and 'narrativity'. Such a conceptual/theoretical framework transforms qualitative data analysis, interpretation, and reporting processes into processes that are carried out with a reflective approach within their specific contexts. MODEL: Taking this into account, by considering a contextual, reflective, and narrative approach, two frameworks, namely, the 'Contextual (Multiple) Reading and Analysis Framework' consisting of three stages and seven steps, and the 'Contextual Understanding, Interpretation and Reporting Framework' consisting of four stages, were developed. This provides a practical guide to contextual and reflective data analysis, interpretation, and reporting for the use of those conducting qualitative studies.

Functional Data Analysis: An Introduction and Recent Developments.

Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub.

Examining changes in pain interference via pandemic-induced isolation among patients receiving medication for opioid use disorder: a secondary data analysis.

BACKGROUND: Early in the pandemic, the United States population experienced a sharp rise in the prevalence rates of opioid use, social isolation, and pain interference. Given the high rates of pain reported by patients on medication for opioid use disorder (MOUD), the pandemic presented a unique opportunity to disentangle the relationship between opioid use, pain, and social isolation in this high-risk population. We tested the hypothesis that pandemic-induced isolation would partially mediate change in pain interference levels experienced by patients on MOUD, even when controlling for baseline opioid use. Such work can inform the development of targeted interventions for a vulnerable, underserved population. METHODS: Analyses used data from a cluster randomized trial (N = 188) of patients on MOUD across eight opioid treatment programs. As part of the parent trial, participants provided pre-pandemic data on pain interference, opioid use, and socio-demographic variables. Research staff re-contacted participants between May and June 2020 and 133 participants (71% response rate) consented to complete a supplemental survey that assessed pandemic-induced isolation. Participants then completed a follow-up interview during the pandemic that again assessed pain interference and opioid use. A path model assessed whether pre-pandemic pain interference had an indirect effect on pain interference during the pandemic via pandemic-induced isolation. RESULTS: Consistent with hypotheses, we found evidence that pandemic-induced isolation partially mediated change in pain interference levels among MOUD patients during the pandemic. Higher levels of pre-pandemic pain interference and opioid use were both significantly associated with higher levels of pandemic-induced isolation. In addition, pre-pandemic pain interference was significantly related to levels of pain interference during the pandemic, and these pain levels were partially explained by the level of pandemic-induced isolation reported. CONCLUSIONS: Patients on MOUD with higher use of opioids and higher rates of pain pre-pandemic were more likely to report feeling isolated during COVID-related social distancing and this, in turn, partially explained changes in levels of pain interference. These results highlight social isolation as a key risk factor for patients on MOUD and suggest that interventions promoting social connection could be associated with reduced pain interference, which in turn could improve patient quality of life. TRIAL REGISTRATION: NCT03931174 (Registered 04/30/2019).

Incidence and prevalence of idiopathic inflammatory myopathies in Thailand from the Ministry of Public Health data analysis.

The epidemiology of idiopathic inflammatory myopathies (IIMs) varies by country. Investigating the epidemiological profile among Thai IIMs could help to inform public health policy, potentially leading to cost-reducing strategies. We aimed to assess the prevalence and incidence of IIM in the Thai population between 2017 and 2020. A descriptive epidemiological study was conducted on patients 18 or older, using data from the Information and Communication Technology Center, Ministry of Public Health, with a primary diagnosis of dermatopolymyositis, as indicated by the ICD-10 codes M33. The prevalence and incidence of IIMs were analyzed with their 95% confidence intervals (CIs) and then categorized by sex and region. In 2017, the IIM cases numbered 9,074 among 65,204,797 Thais, resulting in a prevalence of 13.9 per 100,000 population (95% CI 13.6-14.2). IIMs were slightly more prevalent among women than men (16.8 vs 10.9 per 100,000). Between 2018 and 2020, the incidence of IIMs slightly declined from 5.09 (95% CI 4.92-5.27) in 2017 and 4.92 (95% CI 4.76-5.10) in 2019 to 4.43 (95% CI 4.27-4.60) per 100,000 person-years in 2020. The peak age group was 50-69 years. Between 2018 and 2020, the majority of cases occurred in southern Thailand, with incidence rates of 7.60, 8.34, and 8.74 per 100,000 person-years. IIMs are uncommon among Thais, with a peak incidence in individuals between 60 and 69, especially in southern Thailand. The incidence of IIMs decreased between 2019 and 2020, most likely due to the COVID-19 pandemic, which reduced reports and investigations.

The effect of air pollution quality on lung cancer rates in middle-income and high-income countries: a panel data analysis approach.

Background: Air pollution is one of the biggest problems in societies today. The intensity of indoor and outdoor air pollutants and the urbanization rate can cause or trigger many different diseases, especially lung cancer. In this context, this study's aim is to reveal the effects of the indoor and outdoor air pollutants, and urbanization rate on the lung cancer cases. Methods: Panel data analysis method is applied in this study. The research includes the period between 1990 and 2019 as a time series and the data type of the variables is annual. The dependent variable in the research model is lung cancer cases per 100,000 people. The independent variables are the level of outdoor air pollution, air pollution level indoor environment and urbanization rate of countries. Results: In the modeling developed for the developed country group, it is seen that the variable with the highest level of effect on lung cancer is the outdoor air pollution level. Conclusions: In parallel with the development of countries, it has been determined that the increase in industrial production wastes, in other words, worsening the air quality, may potentially cause an increase in lung cancer cases. Indoor air quality is also essential for human health; negative changes in this variable may negatively impact individuals' health, especially lung cancer.

Exploring the Birthday Week Effect on Hand, Foot, and Mouth Disease in Yunnan Province, China, From 2008 to 2022: Surveillance Data Analysis.

Background: Hand, foot, and mouth disease (HFMD) is a notable infectious disease predominantly affecting infants and children worldwide. Previous studies on HFMD have primarily focused on natural patterns, such as seasonality, but research on the influence of important social time points is lacking. Several studies have indicated correlations between birthdays and certain disease outcomes. Objective: This study aimed to explore the association between birthdays and HFMD. Methods: Surveillance data on HFMD from 2008 to 2022 in Yunnan Province, China, were collected. We defined the period from 6 days before the birthday to the exact birthday as the "birthday week." The effect of the birthday week was measured by the proportion of cases occurring during this period, termed the "birthday week proportion." We conducted subgroup analyses to present the birthday week proportions across sexes, age groups, months of birth, and reporting years. Additionally, we used a modified Poisson regression model to identify conditional subgroups more likely to contract HFMD during the birthday week. Results: Among the 973,410 cases in total, 116,976 (12.02%) occurred during the birthday week, which is 6.27 times the average weekly proportion (7/365, 1.92%). While the birthday week proportions were similar between male and female individuals (68,849/564,725, 12.19% vs 48,127/408,685, 11.78%; χ21=153.25, P<.001), significant differences were observed among different age groups (χ23=47,145, P<.001) and months of birth (χ211=16,942, P<.001). Compared to other age groups, infants aged 0-1 year had the highest birthday week proportion (30,539/90,709, 33.67%), which is 17.57 times the average weekly proportion. Compared to other months, patients born from April to July and from October to December, the peak months of the HFMD epidemic, had higher birthday week proportions. Additionally, a decreasing trend in birthday week proportions from 2008 to 2022 was observed, dropping from 33.74% (3914/11,600) to 2.77% (2254/81,372; Cochran-Armitage trend test: Z=-102.53, P<.001). The results of the modified Poisson regression model further supported the subgroup analyses findings. Compared with children aged >7 years, infants aged 0-1 year were more likely to contract HFMD during the birthday week (relative risk 1.182, 95% CI 1.177-1.185; P<.001). Those born during peak epidemic months exhibited a higher propensity for contracting HFMD during their birthday week. Compared with January, the highest relative risk was observed in May (1.087, 95% CI 1.084-1.090; P<.001). Conclusions: This study identified a novel "birthday week effect" of HFMD, particularly notable for infants approaching their first birthday and those born during peak epidemic months. Improvements in surveillance quality may explain the declining trend of the birthday week effect over the years. Higher exposure risk during the birthday period and potential biological mechanisms might also account for this phenomenon. Raising public awareness of the heightened risk during the birthday week could benefit HFMD prevention and control.

The irtQ R package: a user-friendly tool for item response theory-based test data analysis and calibration.

Computerized adaptive testing (CAT) has become a widely adopted test design for high-stakes licensing and certification exams, particularly in the health professions in the United States, due to its ability to tailor test difficulty in real time, reducing testing time while providing precise ability estimates. A key component of CAT is item response theory (IRT), which facilitates the dynamic selection of items based on examinees' ability levels during a test. Accurate estimation of item and ability parameters is essential for successful CAT implementation, necessitating convenient and reliable software to ensure precise parameter estimation. This paper introduces the irtQ R package, which simplifies IRT-based analysis and item calibration under unidimensional IRT models. While it does not directly simulate CAT, it provides essential tools to support CAT development, including parameter estimation using marginal maximum likelihood estimation via the expectation-maximization algorithm, pretest item calibration through fixed item parameter calibration and fixed ability parameter calibration methods, and examinee ability estimation. The package also enables users to compute item and test characteristic curves and information functions necessary for evaluating the psychometric properties of a test. This paper illustrates the key features of the irtQ package through examples using simulated datasets, demonstrating its utility in IRT applications such as test data analysis and ability scoring. By providing a user-friendly environment for IRT analysis, irtQ significantly enhances the capacity for efficient adaptive testing research and operations. Finally, the paper highlights additional core functionalities of irtQ, emphasizing its broader applicability to the development and operation of IRT-based assessments.

Efficient diagnostic classification of diverse pathologies through contextual eye movement data analysis with a novel hybrid architecture.

The analysis of eye movements has proven valuable for understanding brain function and the neuropathology of various disorders. This research aims to utilize eye movement data analysis as a screening tool for differentiation between eight different groups of pathologies, including scholar, neurologic, and postural disorders. Leveraging a dataset from 20 clinical centers, all employing AIDEAL and REMOBI eye movement technologies this study extends prior research by considering a multi-annotation setting, incorporating information from recordings from saccade and vergence eye movement tests, and using contextual information (e.g. target signals and latency of the eye movement relative to the target and confidence level of the quality of eye movement recording) to improve accuracy while reducing noise interference. Additionally, we introduce a novel hybrid architecture that combines the weight-sharing feature of convolution layers with the long-range capabilities of the transformer architecture to improve model efficiency and reduce the computation cost by a factor of 3.36, while still being competitive in terms of macro F1 score. Evaluated on two diverse datasets, our method demonstrates promising results, the most powerful discrimination being Attention & Neurologic; with a macro F1 score of up to 78.8%; disorder. The results indicate the effectiveness of our approach in classifying eye movement data from different pathologies and different clinical centers accurately, thus enabling the creation of an assistant tool in the future.