Correlation Plots Research Articles

Free-Breathing Ungated Radial Simultaneous Multi-Slice Cardiac T1 Mapping.

Modified Look-Locker imaging (MOLLI) T1 mapping sequences are acquired during breath-holding and require ECG gating with consistent R-R intervals, which is problematic for patients with atrial fibrillation (AF). Consequently, there is a need for a free-breathing and ungated framework for cardiac T1 mapping. To develop and evaluate a free-breathing ungated radial simultaneous multi-slice (SMS) cardiac T1 mapping (FURST) framework. Retrospective, nonconsecutive cohort study. Twenty-four datasets from 17 canine and 7 human subjects (4 males, years; 3 females, years). Canines were from studies involving AF induction and ablation treatment. The human population included separate subjects with suspected microvascular disease, acute coronary syndrome with persistent AF, and transthyretin amyloidosis with persistent AF. The remaining human subjects were healthy volunteers. Pre- and post-contrast T1 mapping with the free-breathing and ungated SMS inversion recovery sequence with gradient echo readout and with conventional MOLLI sequences at 1.5 T and 3.0 T. MOLLI and FURST were acquired in all subjects, and American Heart Association (AHA) segmentation was used for segment-wise analysis. Pre-contrast T1, post-contrast T1, and ECV were analyzed using correlation and Bland-Altman plots in 13 canines and 7 human subjects. T1 difference box plots for repeated acquisitions in four canine subjects were used to assess reproducibility. The PIQUE image quality metric was used to evaluate the perceptual quality of T1 maps. Paired t-tests were used for all comparisons between FURST and MOLLI, with indicating statistical significance. There were no significant differences between FURST and MOLLI pre-contrast T1 reproducibility ( and , ), FURST and MOLLI ECV ( and , ), or FURST and MOLLI PIQUE scores ( and , ). The ECV mean difference was with . FURST had similar quality pre-contrast T1, post-contrast T1, and ECV maps and similar reproducibility compared to MOLLI. 3 TECHNICAL EFFICACY: 1.

Detecting departures from the conditional independence assumption in diagnostic latent class models: a simulation study

BackgroundLatent class models can be used to estimate diagnostic accuracy without a gold standard test. Early studies often assumed independence between tests given the true disease state, however this can lead to biased estimates when there are inter-test dependencies. Residual correlation plots and chi-squared statistics have been commonly utilized to assess the validity of the conditional independence assumption and, when it does not hold, identify which test pairs are conditionally dependent. We aimed to assess the performance of these tools with a simulation study covering a wide range of scenarios.MethodsWe generated data sets from a model with four tests and a dependence between tests 1 and 2 within the diseased group. We varied sample size, prevalence, covariance, sensitivity and specificity, with 504 combinations of these in total, and 1000 data sets for each combination. We fitted the conditional independence model in a Bayesian framework, and reported absolute bias, coverage, and how often the residual correlation plots, G2 and χ2 statistics indicated lack-of-fit globally or for each test pair.ResultsAcross all settings, residual correlation plots, pairwise G2 and χ2 detected the correct correlated pair of tests only 12.1%, 10.3%, and 10.3% of the time, respectively, but incorrectly suggested dependence between tests 3 and 4 64.9%, 49.7%, and 49.5% of the time. We observed some variation in this across parameter settings, with these tools appearing to perform more as intended when tests 3 and 4 were both much more accurate than tests 1 and 2. Residual correlation plots, G2 and χ2 statistics identified a lack of overall fit in 74.3%, 64.5% and 67.5% of models, respectively. The conditional independence model tended to overestimate the sensitivities of the correlated tests (median bias across all scenarios 0.094, 2.5th and 97.5th percentiles -0.003, 0.397) and underestimate prevalence and the specificities of the uncorrelated tests.ConclusionsResidual correlation plots and chi-squared statistics cannot be relied upon to identify which tests are conditionally dependent, and also have relatively low power to detect lack of overall fit. This is important since failure to account for conditional dependence can lead to highly biased parameter estimates.

CryptoGAN: a new frontier in generative adversarial network-driven image encryption

<span lang="EN-IN">There is a growing need for an image encryption scheme, for huge amount of social media data or even the medical data to secure the privacy of the patients or the user. This study introduces a ground-breaking deep learning architecture named crypto generative adversarial networks (CryptoGAN), a novel architecture for generating cipher images. This architecture has the ability to generate both encrypted and decrypted images. The CryptoGAN system consists of an initial encryption network, a generative network that verifies the output against the desired domain, and a subsequent decryption phase. The generative adversarial networks (GAN) are utilised as the learning network to generate cipher images. This is achieved by training the neural network using images encrypted from a conventional image encryption scheme such as advanced encryption standards (AES), and learning from the resulting losses. This enhances security measures when dealing with a large dataset of photos. The assessment of the performance metrics of the encrypted image, including entropy, histogram, correlation plot, and vulnerability to assaults, demonstrates that the suggested generative network may get a higher level of security.</span>

Screening for obstructive sleep apnea in the memory clinic: A comparison of questionnaires, pulse oximetry, and polysomnography.

Obstructive sleep apnea (OSA) is highly prevalent among older adults and has been associated with cognitive decline and dementia risk. The suitability of screening tools for detecting OSA in memory clinic settings is unclear. To evaluate the utility and validity of the STOP-Bang questionnaire (SBQ) and pulse oximeter as a screening tool, compared to gold-standard polysomnography (PSG) in older adults attending a memory clinic. Participants aged over 50 with new onset cognitive/mood concerns attended a memory clinic, then completed the SBQ, oximetry, and PSG. The SBQ and oximetry's accuracy in detecting moderate-severe and severe OSA was evaluated using receiver operating curves. Intraclass correlation and Bland-Altman plots compared the oximeter's adjusted oxygen desaturation index (ODI-Ox) and PSG's apnea-hypopnea index (AHI-PSG). Of 194 participants (mean age = 65.6, 64 males) who completed PSG, 184 completed the SBQ, and 138 completed oximetry. SBQ demonstrated limited performance for moderate-severe OSA (sensitivity = 52%, specificity = 62%, AUC = 0.600) and severe OSA (sensitivity = 18%, specificity = 87%, AUC = 0.577). Oximetry was satisfactory for moderate-severe OSA (sensitivity = 67%, specificity = 73%, AUC = 0.769) and severe OSA (sensitivity = 50%, specificity = 88%, AUC = 0.730). The diagnostic performance was improved with new cut-offs at ODI-Ox ≥ 11 for AHI-PSG ≥ 15 and ODI-Ox ≥ 20 for AHI-PSG ≥ 30. Bland-Altman plots and intraclass correlation indicated acceptable agreement for oximetry. The findings suggest that while the SBQ may be unsuitable to detect moderate or severe OSA for older adults with cognitive impairment, oximetry may be a viable screening tool. Given OSA treatment can optimize sleep and may slow cognitive decline, routine screening for OSA should be part of memory clinic assessments.

Accelerometry for sleep assessment in children: Criterium validity of different algorithms in wrist- and ankle-worn devices.

Polysomnography, the gold-standard for measuring sleep, is costly, intrusive and usually limited to 1 night. Actigraphy offers a more affordable, less intrusive method over multiple nights. However, little research validates ActiGraph accelerometers against polysomnography, especially in children. This study evaluated the validity of different algorithms and compared wrist versus ankle accelerometer placements for estimating sleep in children aged 1-12 years. Twenty-nine children undergoing overnight type 1 polysomnography wore ActiGraph accelerometers. Six algorithms were evaluated against polysomnography using Pearson correlations, intraclass correlation, paired t-tests and Bland-Altman plots. Agreement was classified as poor (intraclass correlation coefficient < 0.4), fair (0.4 < intraclass correlation coefficient < 0.6), good (0.6 < intraclass correlation coefficient < 0.75) or excellent (intraclass correlation coefficient > 0.75). Total sleep time was the primary outcome. For wrist-worn devices, the Sadeh (Actilife) and Cole-Kripke (Actilife and GGIR) algorithms showed excellent agreement with polysomnography (intraclass correlation coefficient = 0.80-0.85), while vanHees showed good agreement (intraclass correlation coefficient = 0.67) and Galland showed fair agreement (intraclass correlation coefficient = 0.46). The Cole-Kripke algorithm did not significantly differ from polysomnography total sleep time, whereas others underestimated total sleep time. For ankle-worn devices, Sadeh (Actilife), Cole-Kripke (Actilife) and vanHees algorithms demonstrated excellent agreement (intraclass correlation coefficient = 0.75-0.82). No significant differences were found between wrist and ankle placements for certain algorithms. The findings support accelerometry as a valid tool for sleep assessment in children, recommending that algorithm selection be tailored to specific study requirements.

Evolution of X-ray and optical rapid variability during the low/hard state in the 2018 outburst of MAXI J1820+070 = ASASSN-18ey

Abstract We performed shot analyses of X-ray and optical subsecond flares observed during the low/hard state of the 2018 outburst in MAXI J1820+070. Optical shots were less spread than X-ray shots. The amplitude of X-ray shots was highest at the onset of the outburst, and they faded at the transition to the intermediate state. The timescale of shots was ~0.2 s, and we detected abrupt spectral hardening synchronized with this steep flaring event. The time evolution of optical shots was not similar to that of X-ray shots. These results suggest that accreting gas blobs triggered a series of magnetic reconnections at the hot inner accretion flow in the vicinity of the black hole, which enhanced X-ray emission and generated flaring events. Rapid X-ray spectral hardening would be caused by this kind of magnetic activity. Also, synchrotron emission not only at the hot flow but also at the jet plasma would contribute to the optical rapid variability. We also found that the low/hard state exhibited six different phases in the hardness–intensity diagram and the correlation plot between the optical flux and the X-ray hardness. The amplitude and duration of X-ray shots varied in synchrony with these phases. This time variation may provide key information about the evolution of the hot flow, the low-temperature outer disk, and the jet-emitting plasma.

Technical Analysis of the Indonesian Stock Market with Gated Recurrent Unit and Temporal Convolutional Network

Big data is essential in the age of 4.0 industry as it becomes the basis of decision making. Deep learning research in the last few years has been proven effective in understanding complex big data patterns, especially in the finance sector. The rapid growth of the Indonesian stock market in the last 20 years, which was driven by globalization, prompted fluctuation in the Bursa Efek Jakarta (JKSE) which was influenced by stock prices, commodity prices, and exchange rate. This study identifies the main indicators of Indonesian stock market crisis, applies and compares deep learning models, particularly Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN), in predicting stock prices. This study identified 20 JKSE crisis points between the 2002-2023 period with average return value at around -6%. All variables correlated positively with JKSE, with SET.BK as the highest correlated variable in lag 0. The American and European stock market, commodity price, and exchange rate tend to show a pattern opposite to the JKSE crisis. Predictor variables such as STI, HIS, KLSE, KS11, SET.BK, PSEI.PS, RUT, and USDIDR are chosen based on significant cross correlation and average return plot. Hyperparameter tuning and cross validation within a 3 years window concluded that the GRU model is accurate and efficient, with RMSE value at 43.35568 and MAE value at 33.66909 in the validation data.

Physicochemical characterization and deterioration condition evaluation of three primary documents of Costa Rica's independence process

This research reports the results of the physicochemical characterization and deterioration condition evaluation of three nineteenth-century manuscripts, which are essential components of the Costa Rican documentary heritage. The documents were characterized by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), X-ray fluorescence (XRF), and multispectral photography. The documents' condition was thoroughly evaluated, and the results were transformed into an index to correlate their deterioration state with the document's composition; this was achieved by using correlation plots and heatmaps. The study revealed that the Independence Act of Costa Rica was the most deteriorated document among the three manuscripts from the year 1821. This was attributed to its acidic pH level, high zinc content, and greater exposure to light compared to the other two documents.

Use of ST/HR hysteresis decreases false positive rate in exercise electrocardiography test of middle-aged asymptomatic women

BackgroundExercise electrocardiography (ECG) is a common diagnostic and prognostic method for the detection of coronary artery disease (CAD). However, its accuracy in asymptomatic women has not been comprehensively investigated and the diagnostic criteria may require refinements. This study evaluated the performance of ECG-derived ST/HR-index, ST/HR hysteresis and ST-segment depression parameters among asymptomatic middle-aged women. Methods108 women (mean age 56 ± 4 years) performed exercise ECG test on treadmill until exhaustion three times within a nine-month period. False-positive rates of maximum ST/HR-index, ST/HR hysteresis, and ST-segment depression values measured from 12 leads at peak exercise and after one-minute recovery were evaluated with recommended diagnostic partition values. Repeatability was assessed with intraclass correlation (ICC) and Bland-Altman plot analysis. ResultsFalse-positive rate was lower for all variables when a two‑lead configuration was used instead of a single‑lead configuration. Using a two‑lead configuration, ST/HR hysteresis (0–1.9 %) and ST-segment depression after one-minute recovery (0–2.8 %) had lower false-positive rates compared to ST/HR index (3.7–20.4 %) and ST-segment depression at peak exercise (9.3–27.8 %). ICC values indicated moderate repeatability for ST/HR hysteresis while ST/HR index, ST-segment at peak exercise, and ST-segment after one-minute recovery had moderate-to-good repeatability. Bland-Altman analysis indicated poor repeatability for all evaluated ECG variables. ConclusionIn asymptomatic middle-aged women, practitioners should prefer the use of ST/HR hysteresis and ST-segment after one-minute recovery over the conventional ST-segment depression at peak exercise or ST/HR index, and evaluate the ECG data from a two‑lead configuration instead of single‑lead.

The impact of temperature on the growth of Pseudomonas aeruginosa in mineral waters originated from different wells: A predictive approach

This study aimed to evaluate the behavior of Pseudomonas aeruginosa (PSA) in natural mineral water sourced from three different extraction wells and stored at various temperatures (10, 12, 20, 23, and 30 °C) to calculate the kinetic growth parameters of this microorganism through predictive modeling. The physicochemical characterization of waters was also evaluated at the time of collection, and included the analysis of 40 different minerals, and quality parameters such as pH, conductivity, oxidation-reduction potential (ORP), total dissolved solids (TDS), salinity (PSU), and temperature (T). PSA survived in raw mineral water incubated at 12, 20, 23, and 30 °C; however, no growth was observed at 10 °C. Growth curves started with an initial population of ~ 2.5–3 log CFU/mL, and final PSA populations ranged from 3.5 to 4.9 log CFU/mL. The maximum specific growth rate (μmax) at 30 °C varied among the wells, with Well P-07 showing the highest growth rate (0.2 h−1), followed by Well P-08 (0.195 h−1) and well P-01 (0.133 h−1). At 12 °C, well P-01 exhibited the highest growth rate (μmax = 0.22 h−1), indicating a influence of mineral composition in the growth of PSA. The lag time (λ) also varied, with minimum values of 2.4 ± 0.1 h at 30 °C and maximum values of 41.6 ± 0.2 h at 12 °C. From these primary estimated parameters, it was possible to obtain five robust secondary models to describe the influence of temperature on the maximum growth rates and lag phase of PSA in the well. The estimated PSA growth parameters at 20 and 23 °C were subjected to a hierarchical cluster analysis and correlation plots to verify the influence of the physicochemical composition of the waters on the PSA behavior at each well's specific annual average temperature. This analysis confirmed a positive relationship (p < 0.05) between the presence of minerals (Ca, Fe, Sr, Mn, Na) and ions (SO4−3, Cl−) and the PSA lag phase time. These results underscore the need for tailored water quality management strategies that consider chemical composition and temperature to address specific microbial contamination risks.

Web-based vs. conventional: a comprehensive analysis of visual acuity assessment using the PocDoc tool in a tertiary eye care centre.

Visual acuity (VA) represents a fundamental measure of visual function. The significant prevalence of underdiagnosed ocular disorders underscores the importance of effective VA assessment. This study evaluates the efficacy of a web-based VA assessment tool ("PocDoc") versus conventional VA testing. Prospective observational study including 353 participants recruited from various eye clinics in a tertiary referral centre. Age, diagnosis, and VA related information (i.e. VA measurements from PocDoc and conventional VA test [Snellen chart], test type, etc) were collected. Spearman's rank correlation, Intraclass Correlation, and Bland-Altman plot compared outcomes of both tests. One-way ANOVA and paired-T test were used to compare means. Most patients were males (59.2%) with a mean age of 52.2 ± 20.6 years. PocDoc had moderate positive correlation to conventional testing (rho = 0.50, p < 0.001). PocDoc led to higher logMAR scores compared to conventional testing (mean logMAR 0.19 and 0.13 respectively, p < 0.01). Moreover, PocDoc demonstrated a sensitivity of 82.8% and specificity of 79% for detecting visual impairment. The discrepancy between PocDoc and conventional VA testing increased with higher logMAR values, indicating greater inconsistency between the tests for patients with poorer VA. Age, test type, and disease type contributed to this variability. The concordance between PocDoc and conventional testing for VA measurement across various ages and conditions makes it a suitable screening tool. Future technological inventions should consider age, test type, and disease type as critical factors related to the level of agreement and correlation between digital and conventional VA testing methods.

A-sloth reveals the nature of the first stars

ABSTRACT The first generation of stars (Pop III) are too dim to be observed directly and probably too short-lived to have survived for local observations. Hence, we rely on simulations and indirect observations to constrain the nature of the first stars. In this study, we calibrate the semi-analytical model a-sloth (Ancient Stars and Local Observables by Tracing Haloes), designed for simulating star formation in the early Universe, using a likelihood function based on nine independent observables. These observables span Milky Way-specific and cosmologically representative variables, ensuring a comprehensive calibration process. This calibration methodology ensures that a-sloth provides a robust representation of the early Universe’s star formation processes, aligning simulated values with observed benchmarks across a diverse set of parameters. The outcome of this calibration process is best-fitting values and their uncertainties for 11 important parameters that describe star formation in the early Universe, such as the shape of the initial mass function (IMF) of Pop III stars or escape fractions of ionizing photons. Our best-fitting model has a Pop III IMF with a steeper slope, dN/d$M \propto M^{-1.77}$, than the log-flat models often proposed in the literature, and also relatively high minimum and maximum masses, $M_{\rm min} = 13.6~\, \mathrm{M}_\odot$ and $M_{\rm max} = 197~\, \mathrm{M}_\odot$. However, we emphasize that the IMF-generating parameters are poorly constrained and, e.g. the IMF slope could vary from log-flat to Salpeter. We also provide data products, such as delay time distribution, bubble size distributions for ionizing and metal-enriched bubbles at high redshift, and correlation plots between all 11 input parameters. Our study contributes to understanding the formation of early stars through a-sloth, providing valuable insights into the nature of Pop III stars and the intricate processes involved in the early Universe’s star formation.

Validation of steps countering recorded with a smartwatch.

Activity trackers are becoming increasingly popular in everyday life, sport and research. Accurate measurement of step counts is essential for understanding an individual's daily activity levels and promoting healthier lifestyles. As the sensor technology has become very sophisticated, such a device can be worn on the body for several hours or days without restricting the subject's daily activities. The aim of this study was to evaluate the accuracy of a commercially available wrist-worn pedometer in accurately recording step counts. A total of twenty-one subjects participated in this comparative study by running at various speeds in a laboratory setting while wearing a Garmin Forerunner 245 Music watch (Garmin Ltd, Schaffhausen, Switzerland) on each wrist. Simultaneously, their steps were recorded by a camera system. The step counts recorded by the pedometer were compared with the counts obtained by the camera system. Descriptive statistical analyses such as Pearson correlation and Bland-Altman plots were used to assess the accuracy and reliability of the pedometer. For most settings the results on the treadmills showed a high correlation between the pedometer and the camera system. On the step machine the results showed a low correlation between the pedometer and the camera system, the pedometer showed a systematic bias by consistently overestimating the number of steps. Bland-Altman plots showed that the results were within the limits of agreement. Although pedometers provide a convenient tool for monitoring step count, this study highlights some limitations and potential inaccuracies of a commercially available pedometer. Our study indicates that the Garmin watch is sufficiently accurate to be used as a measurement tool in a treadmill running environment.

Development of land use regression, dispersion, and hybrid models for prediction of outdoor air pollution exposure in Barcelona

BackgroundAir pollution is the leading environmental risk factor for health. Assessing outdoor air pollution exposure with detailed spatial and temporal variability in urban areas is crucial for evaluating its health effects. Aim: We developed and compared Land Use Regression (LUR), dispersion (DM), and hybrid (HM) models to estimate outdoor concentrations for NO2, PM2.5, black carbon (BC), and PM2.5-constituents (Fe, Cu, Zn) in Barcelona. MethodsTwo monitoring campaigns were conducted. In the first, NO2 concentrations were measured twice at 984 home addresses and in the second, NO2, PM2.5, and BC were measured four times at 34 points across Barcelona. LUR and DM were constructed using conventional techniques, while HM was developed using Random Forest (RF). Model performance was evaluated using leave-one-out cross-validation (LOOCV) and 10-fold cross-validation (10-CV) for LUR and HM, and by comparing DM and LUR estimates with routine monitoring stations. NO2 levels estimated by all models were externally validated using the home monitoring campaign. Agreement between models was assessed using Spearman correlation (rs) and Bland-Altman (BA) plots. ResultsModels showed moderate to good performance. LUR exhibited R2LOOCV of 0.62 (NO2), 0.45 (PM2.5), 0.83 (BC), and 0.85 to 0.89 (PM2.5-constituents). DM model comparison showed R2 values of 0.39 (NO2), 0.26 (PM2.5), and 0.65 (BC). HM models had higher R210-CV 0.64 (NO2), 0.66 (PM2.5), 0.86 (BC), and 0.44 to 0.70 (PM2.5-constituents). Validation for NO2 showed R2 values of 0.56 (LUR), 0.44 (DM), and 0.64 (HM). Correlations between models varied from −0.38 to 0.92 for long-term exposure, and − 0.23 to 0.94 for short-term exposure. BA plots showed good agreement between models, especially for NO2 and BC. ConclusionsOur models varied substantially, with some models performing better in validation samples (NO2 and BC). Future health studies should use the most accurate methods to minimize bias from exposure measurement error.

Design, development and optimization of blockchain-based secure data management for nanotechnology in healthcare

The integration of nanotechnology in healthcare has brought about revolutionary advancements, particularly in diagnostics and therapeutics. However, managing the vast and sensitive data generated by these applications poses significant challenges, particularly concerning security, privacy, and regulatory compliance. This study explores the design, development, and optimization of a blockchain-based secure data management system tailored for nanotechnology applications in the United States healthcare sector. The proposed system leverages blockchain's decentralized and immutable ledger technology to enhance data security and privacy, ensure regulatory compliance, and improve overall system performance. Our implementation showed substantial improvements in data security, with an 86.67% reduction in data breaches and an 85% decrease in unauthorized access cases. Additionally, compliance with major data privacy standards like HIPAA, GDPR, and CCPA increased by over 20%. Performance metrics demonstrated robust scalability, maintaining high transaction speeds and manageable latency across various load conditions. Cost analysis revealed significant reductions in data storage, security, and compliance costs, highlighting the economic benefits of adopting blockchain technology in healthcare. Moreover, the system exhibited lower energy consumption compared to traditional data management systems, addressing environmental concerns. Correlation and scatter plot analyses provided insights into the scalability and performance dynamics of the blockchain system. These findings underscore the transformative potential of blockchain technology in addressing the data management challenges of nanotechnology in healthcare. The study concludes with recommendations for adopting blockchain to enhance data security, regulatory compliance, and economic efficiency in the U.S. healthcare sector.

A comparison of data visualization tools: A case study in health-related research

Data visualization tools are important tools for making health decisions based on large amounts of data. However, very few comparisons of data visualization tools have been done in the context of health-related research. This paper compares commonly available visualization tools from the perspective of a dashboard developer in the context of scientific health research using real-world data and to provide general suggestions for dashboard developers’ future visualization efforts in health-related scientific discoveries. We evaluated four commonly available visualization tools (Tableau, Spotfire, PowerBI, and R Shiny). Each tool was evaluated on visualization outcomes (comparing the generated outputs of tables, scatterplots, correlation plots, treemaps, heatmaps, and geographic maps), usability, Cloud compatibility, analytic integration, and a few other factors. All tools generated comparable visualization outcomes in our tests and were equally usable with the exception of R Shiny which is not recommended for users with limited coding experience. Every tool except R Shiny supports connections to PostgreSQL, gSheets, and Athena without additional configurations. All tools support integration with R and Python; however, PowerBI requires some additional configuration. Every tool is capable of data pre-processing and supports data transformation, null detection and outlier detection. Each tool has its own unique advantages and limitations.

In Silico Decoding of Parkinson's: Speech & Writing Analysis.

Background: Parkinson's disease (PD) has transitioned from a rare condition in 1817 to the fastest-growing neurological disorder globally. The significant increase in cases from 2.5 million in 1990 to 6.1 million in 2016, coupled with predictions of a further doubling by 2040, underscores an impending healthcare challenge. This escalation aligns with global demographic shifts, including rising life expectancy and a growing global population. The economic impact, notably in the U.S., reached $51.9 billion in 2017, with projections suggesting a 46% increase by 2037, emphasizing the substantial socio-economic implications for both patients and caregivers. Coupled with a worldwide demand for health workers that is expected to rise to 80 million by 2030, we have fertile ground for a pandemic. Methods: Our transdisciplinary research focused on early PD detection through running speech and continuous handwriting analysis, incorporating medical, biomedical engineering, AI, and linguistic expertise. The cohort comprised 30 participants, including 20 PD patients at stages 1-4 on the Hoehn and Yahr scale and 10 healthy controls. We employed advanced AI techniques to analyze correlation plots generated from speech and handwriting features, aiming to identify prodromal PD biomarkers. Results: The study revealed distinct speech and handwriting patterns in PD patients compared to controls. Our ParkinsonNet model demonstrated high predictive accuracy, with F1 scores of 95.74% for speech and 96.72% for handwriting analyses. These findings highlight the potential of speech and handwriting as effective early biomarkers for PD. Conclusions: The integration of AI as a decision support system in analyzing speech and handwriting presents a promising approach for early PD detection. This methodology not only offers a novel diagnostic tool but also contributes to the broader understanding of PD's early manifestations. Further research is required to validate these findings in larger, diverse cohorts and to integrate these tools into clinical practice for timely PD pre-diagnosis and management.

Application of machine learning and fuzzy AHP for identification of suitable groundwater potential zones using field based hydrogeophysical and soil hydraulic factors in a complex hydrogeological terrain

The eastern section of West Bengal grapples with limited surface water availability in its hard rock terrain, compounded by a semi-arid climate, variable rainfall, and a plateau topography, prompting communities to adapt groundwater water-use practices, leading to unsustainable extraction and misuse. Thus, the novel objective of the present research was to produce groundwater potential maps by comparing machine learning techniques with a Fuzzy MCDM model using specific field-based conditioning factors. In the first step, 285 wells were identified, of which 70 percent were used for training and 30 percent for the validation of the models. Secondly, field-based conditioning factors including, longitudinal conductance (SC), longitudinal resistance (ρl), transverse resistance (TR), coefficient of electrical anisotropy (λ), resistivity of formation (ρm), fracture porosity (φf), reflection coefficients (r), hydraulic conductivity (K), transmissivity(Tr), bulk density, porosity, permeability, soil moisture content and water holding capacity were used to analyze the association between these conditioning factors and groundwater occurrences. In the following steps, the XGBoost, Random Forest, and Naïve Bayes models were executed using the training dataset, and factor weights were calculated using Fuzzy Analytical Hierarchy Process of Extent analysis method. To validate and compare the performance of four models, ROC curves, AUCs, MCAs, and correlation plots were used. In general, all four models were successful in evaluating the potential of groundwater occurrences. The predictive capability of the XGBoost techniques with the highest AUC values (0.79) and the highest correlation value (0.78) is superior to those of other machine learning and MCDM models. Geophysical survey revealed that transmissivity and hydraulic conductivity of the aquifer of the river basin range from 1.55 to 440.11 m/day and 10.15–2253 m2/day, indicating a moderate to good hydrodynamic potential. Planners and engineers can use such groundwater potential maps to manage water resources effectively.

Septal Perforation Quality of Life questionnaire (SEPEQOL): validation of a new instrument to assess patients undergoing endoscopic repair of a nasal septal perforation

PurposeNasoseptal perforations (NSP) are a clinically heterogeneous group of disorders with a wide range of available treatments. Patient-reported outcome measures (PROMs) can provide valuable insights for assessing clinical and surgical outcomes. This study aims to develop and validate a novel-specific questionnaire for patients with NSP.MethodsA multi-centre prospective observational study was conducted at two tertiary referral hospitals. “Septal Perforation Quality of Life” (SEPEQOL) was developed by a committee of experts. The psychometric properties, including reproducibility, reliability, validity, and responsiveness, were assessed.ResultsThe study included 96 symptomatic NSP patients and 30 healthy controls. SEPEQOL internal consistency was satisfactory [Cronbach´s α = 0.7843; 95% confidence interval (CI), 0.702–0.856]. Test-retest reliability was excellent, demonstrated by the absolute intraclass correlation (ICC = 0.974; 95% CI, 0.935–0.989, P-value < 0.001) and Bland-Altman plot (line bias = 1.6 ± 4.57; 95% CI -0.54–3.74, P-value < 0.001). The mean total SEPEQOL score was higher before surgery (25.16 ± 1.65) compared to 6-months after the procedure (13.72 ± 11.39), with a mean difference of 12.19 [standard deviation (SD) 10.76], P-value < 0.001.ConclusionsSEPEQOL is reliable, consistent, valid, and sensitive to change over time. SEPEQOL assesses the impact of health-related quality of life on NSP and its management in clinical practice. Moreover, it is easy to apply in clinical settings with minimal burden.

Self-Reported COVID-19 Vaccine and Booster Acceptance and Hesitancy Among Autistic Adults in Pennsylvania: Cross-Sectional Analysis of Survey Data.

The autistic population is rapidly increasing; meanwhile, autistic adults face disproportionate risks for adverse COVID-19 outcomes. Limited research indicates that autistic individuals have been accepting of initial vaccination, but research has yet to document this population's perceptions and acceptance of COVID-19 boosters. This study aims to identify person-level and community characteristics associated with COVID-19 vaccination and booster acceptance among autistic adults, along with self-reported reasons for their stated preferences. Understanding this information is crucial in supporting this vulnerable population given evolving booster guidelines and the ending of the public health emergency for the COVID-19 pandemic. Data are from a survey conducted in Pennsylvania from April 11 to September 12, 2022. Demographic characteristics, COVID-19 experiences, and COVID-19 vaccine decisions were compared across vaccination status groups. Chi-square analyses and 1-way ANOVA were conducted to test for significant differences. Vaccination reasons were ranked by frequency; co-occurrence was identified using phi coefficient correlation plots. Most autistic adults (193/266, 72.6%) intended to receive or received the vaccine and booster, 15% (40/266) did not receive or intend to receive any vaccine, and 12.4% (33/266) received or intended to receive the initial dose but were hesitant to accept booster doses. Reasons for vaccine acceptance or hesitancy varied by demographic factors and COVID-19 experiences. The most significant were previously contracting COVID-19, desire to access information about COVID-19, and discomfort with others not wearing a mask (all P=.001). County-level factors, including population density (P=.02) and percentage of the county that voted for President Biden (P=.001) were also significantly associated with differing vaccination acceptance levels. Reasons for accepting the initial COVID-19 vaccine differed among those who were or were not hesitant to accept a booster. Those who accepted a booster were more likely to endorse protecting others and trusting the vaccine as the basis for their acceptance, whereas those who were hesitant about the booster indicated that their initial vaccine acceptance came from encouragement from someone they trusted. Among the minority of those hesitant to any vaccination, believing that the vaccine was unsafe and would make them feel unwell were the most often reported reasons. Intention to receive or receiving the COVID-19 vaccination and booster was higher among autistic adults than the population that received vaccines in Pennsylvania. Autistic individuals who accepted vaccines prioritized protecting others, while autistic individuals who were vaccine hesitant had safety concerns about vaccines. These findings inform public health opportunities and strategies to further increase vaccination and booster rates among generally accepting autistic adults, to better support the already strained autism services and support system landscape. Vaccination uptake could be improved by leveraging passive information diffusion to combat vaccination misinformation among those not actively seeking COVID-19 information to better alleviate safety concerns.