Standard Deviation Of Time Research Articles

Neurocognitive Profiles of Early Adulthood Attention-Deficit/Hyperactivity Disorder.

This study examined the neurocognitive profiles of early adulthood attention-deficit/hyperactivity disorder (ADHD) patients using the Korean version of the Wechsler Adult Intelligence Scale, 4th Edition (K-WAIS-IV) and Continuous Performance Test 3rd Edition (CPT-3) assessment results. A total of 105 individuals underwent the K-WAIS-IV assessment, and 68 participants completed the CPT-3. We examined the differences between intelligence subindex scores using paired t-tests and applied Pearson's correlation analysis to determine the correlation between the K-WAIS-IV and CPT-3 scores. Working Memory Index scores were significantly lower than Verbal Comprehension Index scores, whereas Processing Speed Index (PSI) scores were significantly lower than all three other subindex scores. Significant negative correlations were found between all four K-WAIS-IV subindex scores and the CPT-3 scores for Detectability, Omissions, Commissions, Perseverations, Hit Reaction Time, Hit Reaction Time Standard Deviation, and Variability. The PSI of the K-WAIS-IV can be considered a useful predictor in early adulthood ADHD patients combined with the CPT-3 examination.

Modernizing USP Melatonin Liquid Chromatographic Analyses for Increased Throughput and Reduced Environmental Impact.

Melatonin supplements are often used to alleviate jetlag and other sleep-depletion related disorders. Recent studies found large inconsistencies between labeled values and actual contents of melatonin within products, which has led to concerns over the quality of melatonin supplements. In order to facilitate the quality control testing of melatonin supplements, an improved and more modern approach to the liquid chromatographic analysis of melatonin is required. In addition, growing public concern over the environmental footprint of analytical laboratories exacerbates the need to modernize legacy analytical procedures with more eco-friendly or greener approaches. This study aims to optimize the routine liquid chromatographic analyses that are prescribed in the US Pharmacopeia (USP) melatonin monograph on a High Performance Liquid Chromatography (HPLC) system without fundamentally modifying the methods. The melatonin assay and the melatonin related compounds (impurities) test were optimized on a C18 column packed with 2.5 µm particles. The column length and the gradient elution parameters were adjusted following the guidelines on Adjustment of Chromatographic Conditions in USP General Chapter <621>. The mobile phase compositions were optimized to meet the system suitability requirements that were specified in the USP melatonin monograph. The flow rate was optimized for better separation efficiency. The optimized HPLC methods not only met the USP system suitability requirements in relative retention time (RRT), resolution, and relative standard deviation (RSD), but also demonstrated excellent linearity, sensitivity, accuracy, precision (repeatability), and would have a lower environmental impact. The optimized HPLC methods for assay of melatonin and test of its related compounds achieved significantly increased throughput and a reduced environmental impact, without fundamentally modifying the methods. The optimized HPLC methods are significantly faster and more eco-friendly. These methods can be implemented on HPLC systems without a full re-validation.

A time-varying EWMA-CV control chart based on Upper Triangular Minimax ranked set sampling method

When the process mean fluctuates with time and process standard deviation is a linear function of process mean, the coefficient of variation is usually used to measure the process variation in industry. On the other hand, the use of Ranked Set Sampling as a smart sampling technique enables the ranking of samples before measurement. The purpose of this research is to introduce a new sampling method referred to as Upper Triangular Minimax Ranked Set Sampling and design a time-varying Exponentially Weighted Moving Average control chart to monitor the coefficient of variation. Also, in this research, average run length, standard deviation of run length, median of run length, extra quadratic loss, relative average run length and performance comparison index criteria under two states including zero-state and steady-state have been used to compare the performance of the designed control chart with the ones provided in the literature. The results show the superiority of the proposed control chart based on the novel sampling method rather than the control charts available in the literature. The application of the proposed control chart is also shown through a real case and compared with available competitors in the literature.

Comprehensive comparative analysis of reconstructed sea level datasets in the China Seas: insights from tide gauge and satellite altimetry

At present, there are many reconstructed datasets at the global scale. To test the applicability of these datasets in the China seas, the study comprehensively analyzes the reliability and accuracy of reconstructed sea level datasets in capturing nuanced temporal patterns of sea level changes in the China Seas. This study applied analysis methods or indicators such as time series, Taylor plots, correlation coefficients, growth rates, and standard deviations. Ocean Data Assimilations (ODAs) outperform Tide Gauge Reconstructions (TGRs) in terms of correlation with measured data in the nearshore, while TGRs exhibit superior capability in capturing oceanic sea level variability. Although the ODAs and TGRs both suffer from the underestimation of sea level variability in China as well as in neighboring seas, the TGRs perform better than the former. ODAs show inconsistency in reflecting the rate of sea level rise, but they, particularly the China Ocean Reanalysis (CORA), demonstrate a better correlation with satellite altimetry datasets. Meanwhile, both of them can reflect the Pacific Decadal Oscillation (PDO) well. TGRs, relying on oceanic tide gauge stations, suffer from poor correlation with tide gauge stations due to limited coverage. Reconstruction discrepancies are attributed to methodological differences and data assimilation techniques. Future studies should explore alternative variables like sea surface temperature and so on to enhance sea-level reconstruction, especially in regions with sparse tide gauge coverage.

Validity of specific CPT indices in differentiating school-aged children previously diagnosed with attention deficit/hyperactivity disorder from school-aged children with non-attention deficit/hyperactivity disorder in general education classrooms: a case control study

BackgroundContinuous performance tests (CPTs) are a popular tool for evaluating the symptoms of attention-deficit/hyperactivity disorder (ADHD). Performance measurements are typically linked to the biological features and cognitive functions of individuals. To determine the validity of specific CPT indices in differentiating between school-aged children with ADHD from with non-ADHD, each student’s sex, chronological age, and cognitive abilities should be considered.MethodsIn this prospective case–control study, a total of 30 non-ADHD students and 26 with ADHD who were aged 6 to 12 years were from general education classrooms. All students completed the Continuous Performance Tests (CPTs) and the Peabody Picture Vocabulary Test-revised (Mandarin-Chinese version). Demographic data were collected from the students’ parents.ResultsDetectability, Omissions, Commissions, and Hit Reaction Time Standard Deviation (HRT SD) yielded higher T-scores in children with ADHD than those without. Compared with non-ADHD students, those with ADHD had higher classification scores for Detectability, Omissions, Perseverations, and HRT SD. For each CPT index, after individual factors were controlled for, logistic regression revealed that only students with positive scores for Detectability, Omission, and HRT SD (adjusted odds ratios = 4.627, 9.977, and 3.908, Ps < 0.05) were likely to receive a diagnosis of ADHD. Furthermore, after individual characteristics were controlled for, Logistic regression also revealed that the cumulative positive scores of the Detectability, Omission, or and HRT SD remained associated with an increased risk of ADHD (adjusted odds ratio = 3.116, P < 0.01).ConclusionsCompared with school-aged children with non-ADHD in general education classrooms, those with ADHD exhibited significantly lower performance in inattention-related CPT indices. To reach an accurate diagnosis through CPTs, clinicians should pay attention to Detectability, Omission, and HRT SD. Compared with other CPT indices, Detectability, Omission, and HRT SD may function as more suitable indicators for distinguishing between school-aged children with and non-ADHD in general education classrooms. These indicators are robust and unobscured by individual characteristics.

Anti Wind‐Up and Robust Data‐Driven Model‐Free Adaptive Control for MIMO Nonlinear Discrete‐Time Systems

ABSTRACTThis article addresses a solution to one of the main challenges of online data‐driven control (DDC) methods: reducing the sensitivity of the model‐free adaptive control (MFAC) method to initial conditions and control parameters with the new control cost function and added the output error rate and integral along with a new anti‐wind up strategy for multi‐input multi‐output (MIMO) systems. The parameters introduced to the new control law have been validated using the boundary‐input boundary‐output (BIBO) approach to design and converge the controller. The simulation findings on a nonlinear auto‐regressive moving average model with exogenous inputs (NARMAX) system with triangular control input demonstrate that the proposed control rule will outperform to prototype MFAC. Furthermore, to analyze the sensitivity of the controller to the initial conditions and the uncertainties of the control parameters, 30 Monte Carlo simulations were performed with random initial conditions in the presence of disturbance in the control input, and output noise, and the results were compared with the prototype MFAC and conventional PID controller using standard criteria such as integral time absolute error, standard deviation, steady‐state error, and mean maximum error, which shows a noticeable superiority of proposed controller relative to the prototype MFAC.

Unraveling the mechanisms of high-level gait control in functional gait disorders

Functional gait disorders (FGDs) are a disabling subset of Functional Neurological Disorders in which presenting symptoms arise from altered high-level motor control. The dual-task paradigm can be used to investigate mechanisms of high-level gait control. The study aimed to determine the objective measures of gait that best discriminate between individuals with FGDs and healthy controls and the relationship with disease severity and duration. High-level spatiotemporal gait outcomes were analyzed in 87 patients with FGDs (79.3% women, average age 41.9±14.7 years) and 48 healthy controls (60.4% women, average age 41.9±15.7 years) on single and motor, cognitive, and visual-fixation dual tasks. The area under the curve (AUC) from the receiver operator characteristic plot and the dual-task effect (DTE) were calculated for each measure. Dual-task interference on the top single-task gait characteristics was determined by two-way repeated measures ANOVA. Stride time variability and its standard deviation (SD) failed to discriminate between the two groups in single and dual-task conditions (AUC<0.80 for all). Significant group x task interactions were observed for swing time SD and stride time on the cognitive dual tasks (p<0.035 for all). Longer disease duration was associated with poor gait performance and unsteadiness in motor and cognitive DTE (p<0.003) but improvement in stride length and swing time on the visual dual tasks (p<0.041). Our preliminary findings shed light on measures of gait automaticity as a diagnostic and prognostic gait biomarker and underline the importance of early diagnosis and management in individuals with FGDs.

Enhancing pilot vigilance assessment: The role of flight data and continuous performance test in detecting random attention loss in short IFR flights

Situational awareness (SA) and fatigue management are crucial aspects of aviation safety, particularly during demanding flight phases. This study introduces an innovative approach employing flight data, machine learning, and Continuous Performance Test (CPT) metrics to predict pilot performance and SA during instrument approaches under Instrument Meteorological Conditions (IMC). Data were collected from over 10 pilots across more than 40 flights in a high-fidelity Cessna 172 analog flight simulator.Significant correlations were observed between dynamic cognitive performance parameters and the exceedance shape factor, a novel measure of pilot sustained attention introduced in this research. Key variables identified through correlation analysis included variability, interstimulus change, and reaction time standard deviation.Importantly, commission scores and reaction time standard deviation emerged as key predictors in the machine learning model, specifically the Optimizable Gaussian Process Regression (GPR) model with a radial basis function kernel. The model achieved a validation R-squared of 0.90 and a test R-squared of 0.70. These systems could incorporate additional data sources, such as eye-tracking and scan pattern analysis, for a better assessment of pilot SA and fatigue levels. While post-flight measurements are inherently reactive, they are effective for monitoring the degradation of pilot CPT scores after each leg of high-frequency, short-duration flights.Notable limitations include the need to understand individual cognitive differences among pilots, such as age, experience, and cognitive style. The predictive model also requires validation in actual flight conditions to determine its ecological validity. Future research should aim to address these limitations, generalize the findings, and integrate CPT data with other sensor inputs to provide a more comprehensive understanding of pilot performance.

Deep Q learning cloud task scheduling algorithm based on improved exploration strategy

In cloud computing, task scheduling is a critical process that involves efficiently allocating computing resources to fulfill diverse task requirements. To address issues such as unstable response times, extensive computations, and challenges in parameter adjustment faced by traditional task scheduling methods, an enhanced deep Q-learning cloud-task-scheduling algorithm was proposed. This algorithm utilizes deep reinforcement learning and introduces an improved strategy. The optimization of the objective function was achieved by defining the state space, action space, and reward function. The agent’s exploration capability was enhanced through the utilization of a UCB exploration strategy and Boltzmann action exploration. Simulation experiments were conducted using Pycloudsim. The average instruction response time ratio and standard deviation of CPU utilization were compared to measure the advantages and disadvantages of the algorithm. The results indicate that the proposed algorithm surpasses the random, earliest, and RR algorithms in terms of the instruction-to-response time ratio and CPU utilization, demonstrating enhanced efficiency and performance in cloud-task scheduling.

Lectin microarray based glycan profiling of exosomes for dynamic monitoring of colorectal cancer progression

BackgroundExosomes, as emerging biomarkers in liquid biopsies in recent years, offer profound insights into cancer diagnostics due to their unique molecular signatures. The glycosylation profiles of exosomes have emerged as potential biomarkers, offering a novel and less invasive method for cancer diagnosis and monitoring. Colorectal cancer (CRC) represents a substantial global health challenge and burden. Thus there is a great need for the aberrant glycosylation patterns on the surface of CRC cell-derived exosomes, proposing them as potential biomarkers for tumor characterization. ResultsThe interactions of 27 lectins with exosomes from three CRC cell lines (SW480, SW620, HCT116) and one normal colon epithelial cell line (NCM460) have been analyzed by the lectin microarray. The result indicates that Ulex Europaeus Agglutinin I (UEA-I) exhibits high affinity and specificity towards exosomes derived from SW480 cells. The expression of glycosylation related genes within cells has been analyzed by high-throughput quantitative polymerase chain reaction (HT-qPCR). The experimental result of HT-qPCR is consistent with that of lectin microarray. Moreover, the limit of detection (LOD) of UEA-I microarray is calculated to be as low as 2.7 × 105 extracellular vehicles (EVs) mL−1 (three times standard deviation (3σ) of blank sample). The UEA-I microarray has been successfully utilized to dynamically monitor the progression of tumors in mice-bearing SW480 CRC subtype, applicable in tumor sizes ranging from 2 mm to 20 mm in diameter. SignificanceThe results reveal that glycan expression pattern of exosome is linked to specific CRC subtypes, and regulated by glycosyltransferase and glycosidase genes of mother cells. Our findings illuminate the potential of glycosylation molecules on the surface of exosomes as reliable biomarkers for diagnosis of tumor at early stage and monitoring of cancer progression.

Quantifying the effects of sleep on sensor-derived variables from upper limb accelerometry in people with and without upper limb impairment

BackgroundDespite the promise of wearable sensors for both rehabilitation research and clinical care, these technologies pose significant burden on data collectors and analysts. Investigations of factors that may influence the wearable sensor data processing pipeline are needed to support continued use of these technologies in rehabilitation research and integration into clinical care settings. The purpose of this study was to investigate the effect of one such factor, sleep, on sensor-derived variables from upper limb accelerometry in people with and without upper limb impairment and across a two-day wearing period.MethodsThis was a secondary analysis of data collected during a prospective, longitudinal cohort study (n = 127 individuals, 62 with upper limb impairment and 65 without). Participants wore a wearable sensor on each wrist for 48 h. Five upper limb sensor variables were calculated over the full wear period (sleep included) and with sleep time removed (sleep excluded): preferred time, non-preferred time, use ratio, non-preferred magnitude and its standard deviation. Linear mixed effects regression was used to quantify the effect of sleep on each sensor variable and determine if the effect differed between people with and without upper limb impairment and across a two-day wearing period.ResultsThere were significant differences between sleep included and excluded for the variables preferred time (p < 0.001), non-preferred time (p < 0.001), and non-preferred magnitude standard deviation (p = 0.001). The effect of sleep was significantly different between people with and without upper limb impairment for one variable, non-preferred magnitude (p = 0.02). The effect of sleep was not substantially different across wearing days for any of the variables.ConclusionsOverall, the effects of sleep on sensor-derived variables of upper limb accelerometry are small, similar between people with and without upper limb impairment and across a two-day wearing period, and can likely be ignored in most contexts. Ignoring the effect of sleep would simplify the data processing pipeline, facilitating the use of wearable sensors in both research and clinical practice.

Association between intra-individual variability and prefrontal cortex activity measured by functional Near Infrared Spectroscopy (fNIRS) in children with ADHD

ABSTRACT This study uses fNIRS to determine whether there is a difference in the relationship between intra-individual variability and frontal lobe activity between ADHD patients and typically developing children. A total of 28 subjects (14 in ADHD patient group and 14 in control group) participated in this study. The subjects were tested for K-SADS and intelligence, and then the frontal lobe activity of the subjects was measured by continuous performance test, using functional near-infrared spectroscopy (NIRSIT). Processing speed index was significantly lower in the ADHD patient group than in the control group (p = .04). The CPT test results showed a positive correlation in the activity of the right dorsolateral prefrontal region in the patient group, but not at a statistically significant level. In the control group, activity showed a significant level of negative correlation with commission and hit reaction time standard deviation (p = .023; p = .063 respectively). In contrary to ADHD patient group, activation of the right dorsolateral prefrontal area was significantly correlated with reduction of intra-individual variability. This result showing that the relationship between activation of the right dorsolateral prefrontal area of the ADHD patient group and intra-individual variability shows a different pattern from typically developing children.

Hyperkalemia Recurrence Following Medical Nutrition Therapy in Patients with Stage3-4 Chronic Kidney Disease: The REVOLUTIONIZEI Real-World Study.

The REVOLUTIONIZEI study aimed to characterize the relationships between medical nutrition therapy (MNT) and hyperkalemia recurrence in patients with stage3-4 chronic kidney disease (CKD) and hyperkalemia who received MNT in real-world clinical practice. This observational cohort study used de-identified electronic health record data from patients aged ≥18years with stage3-4 CKD who received MNT between January 2019 and October 2022 and had hyperkalemia (serum potassium >5.0mmol/L) within 30days before MNT. Patients were followed for 6months or until the first censoring event (death, prescription of outpatient potassium binder, or study end). The primary outcome was the percentage of patients with ≥1hyperkalemia recurrence during follow-up. Secondary outcomes included the number of hyperkalemia recurrences per patient, time to each recurrence, and hyperkalemia-related healthcare resource utilization. Exploratory outcomes included all-cause healthcare resource utilization and mortality. The final cohort comprised 2048 patients; 1503 (73.4%) patients remained uncensored after 6months. During the 6-month follow-up period, 56.0% of patients had ≥1hyperkalemia recurrence and 37.4% had ≥1 recurrence within the first month. Patients with ≥1hyperkalemia recurrence during follow-up had a mean ± standard deviation (SD) of 2.6 ± 2.2 recurrences. The mean ± SD time to first hyperkalemia recurrence was 45 ± 46days; the time between recurrences decreased with subsequent episodes. Hyperkalemia-related hospitalizations and emergency department visits were recorded for 13.7% and 1.5% of patients, respectively. Sensitivity analyses showed that results were consistent across patient subgroups, including those with comorbid heart failure and patients receiving renin-angiotensin-aldosterone system inhibitor therapy at baseline. Most patients with stage3-4 CKD had hyperkalemia recurrence, and MNT alone was inadequate to prevent recurrence. These patients may require additional long-term treatment, such as novel potassium binders, to maintain normokalemia and prevent hyperkalemia recurrence following MNT. Infographic available for this article. INFOGRAPHIC.

Gait variability, fractal dynamics, and statistical regularity of treadmill and overground walking recorded with a smartphone

BackgroundThe nonlinear variability present during steady-state gait may provide a signature of health and showcase one’s walking adaptability. Although treadmills can capture vast amounts of walking data required for estimating variability within a small space, gait patterns may be misrepresented compared to an overground setting. Smartphones may provide a low-cost and user-friendly estimate of gait patterns among a variety of walking settings. However, no study has investigated differences in gait patterns derived from a smartphone between treadmill walking (TW) and overground walking (OW). Research questionThis study implemented a smartphone accelerometer to compare differences in temporal gait variability and gait dynamics between TW and OW. MethodsSixteen healthy adults (8F; 24.7 ± 3.8 years) visited the laboratory on three separate days and completed three 8-minute OW and three TW trials, at their preferred speed, during each visit. The inter-stride interval was calculated as the time difference between right heel contact events located within the vertical accelerometery signals recorded from a smartphone while placed in participants front right pant pocket during walking trials. The inter-stride interval series was used to calculate stride time standard deviation (SD) and coefficient of variation (COV), statistical persistence (fractal scaling index), and statistical regularity (sample entropy). Two-way analysis of variance compared walking condition and laboratory visits for each measure. ResultsCompared to TW, OW displayed significantly (p < 0.01) greater stride time SD (0.014 s, 0.020 s), COV (1.26 %, 1.82 %), fractal scaling index (0.70, 0.79) and sample entropy (1.43, 1.63). No differences were found between visits for all measures. SignificanceSmartphone-based assessment of gait provides the ability to distinguish between OW and TW conditions, similar to previously established methodologies. Furthermore, smartphones may be a low-cost and user-friendly tool to estimate gait patterns outside the laboratory to improve ecological validity, with implications for free-living monitoring of gait among various populations.

The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study: feasibility of long-term monitoring with Fitbit smartwatches in central disorders of hypersomnolence and extraction of digital biomarkers in narcolepsy.

The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a multicenter research initiative to identify new biomarkers in central disorders of hypersomnolence (CDH). Whereas narcolepsy type 1 (NT1) is well characterized, other CDH disorders lack precise biomarkers. In SPHYNCS, we utilized Fitbit smartwatches to monitor physical activity, heart rate, and sleep parameters over 1 year. We examined the feasibility of long-term ambulatory monitoring using the wearable device. We then explored digital biomarkers differentiating patients with NT1 from healthy controls (HC). A total of 115 participants received a Fitbit smartwatch. Using a adherence metric to evaluate the usability of the wearable device, we found an overall adherence rate of 80% over 1 year. We calculated daily physical activity, heart rate, and sleep parameters from 2 weeks of greatest adherence to compare NT1 (n = 20) and HC (n = 9) participants. Compared to controls, NT1 patients demonstrated findings consistent with increased sleep fragmentation, including significantly greater wake-after-sleep onset (p = .007) and awakening index (p = .025), as well as standard deviation of time in bed (p = .044). Moreover, NT1 patients exhibited a significantly shorter REM latency (p = .019), and sleep latency (p = .001), as well as a lower peak heart rate (p = .008), heart rate standard deviation (p = .039) and high-intensity activity (p = .009) compared to HC. This ongoing study demonstrates the feasibility of long-term monitoring with wearable technology in patients with CDH and potentially identifies a digital biomarker profile for NT1. While further validation is needed in larger datasets, these data suggest that long-term wearable technology may play a future role in diagnosing and managing narcolepsy.

Surface Electromyography Data Analysis for Evaluation of Physical Exercise Habits between Athletes and Non-Athletes during Indoor Rowing.

In this paper, surface electromyography (sEMG) is used to gather the activation neural signal from muscles during an indoor rowing exercise. The exercise was performed by professional athletes and amateur non-athletes. The data acquisition and processing are described to obtain a set of parameters: number of cycles, average cycle time, cycle time standard deviation, fatigue time, muscle activation time, and muscle energy. These parameters are used to draw conclusions on common non-athletes' mistakes during exercise for better training advice and a way of statistically distinguishing an athlete from a non-athlete.

Comparison of Four Different Scoring Methods for Ki-67 Percentage in Breast Carcinoma: A Cross-sectional Study

Introduction: In breast cancer, the prognostic role of Ki-67 has been comprehensively studied, and its usefulness has been proven. Ki-67 expression has a prognostic and predictive value in both adjuvant therapy response and neoadjuvant settings. However, the inter and the intraobserver variability in manual counting limits the accuracy of scoring Ki-67 and consequently its application in treatment. Aim: To examine four different methods of Ki-67 estimation to find the most reliable, reproducible and time-efficient scoring methods for Ki-67. Materials and Methods: The present cross-sectional study was conducted in the Department of Pathology, Sree Mookambika Institute of Medical Sciences, Nagercoil, Tamil Nadu, India, from May 2023 to August 2023. Ki-67 immunostained slides of 30 trucut biopsies of invasive breast carcinoma were retrieved and analysed by two observers in a blinded manner. The four different methods of analysis of Ki-67 expression carried out were the global method, the hotspot method, the Eye-10 method and the stepwise counting strategy. The parameters included in the present study were the mean age of the study population, the pre/postmenopausal status, the histopathological type of invasive breast carcinoma, Estrogen receptor (ER), Progesterone Receptor (PR), and Human Epidermal growth factor Receptor 2 (HER2)/neu status, as well as, the interobserver agreement and the mean time taken by the four methods to analyse the Ki-67 expression. Data entry was performed using Microsoft Office Excel 2013, and statistical analysis was conducted using Statistical Package for the Social Sciences (SPSS) software version 20.0. Descriptive statistics for qualitative variables and mean and Standard Deviation (SD) for quantitative variables were used for data analysis. Chi-square tests were used for bivariate analysis with a determined statistical significance of 5% (p-value&lt;0.05). Results: The mean age of the study population was 52.9±9.1 years. The highest interobserver concordance was observed among the observers using the weighted global scoring method, with an Interclass Correlation Coefficient (ICC) of 0.967. This method was time consuming, with the first observer taking a mean±SD time of 5.5±0.8 minutes and the second observer taking 4.6±0.9 minutes. The least time was consumed for performing the Eye-10 scoring, with mean±SD time was 1±0.87 minutes. The stepwise counting and hotspot method demonstrated excellent inter-rater agreement, with a kappa of 0.8304 (p-value&lt;0.001) between both observers. Conclusion: Although the Eye-10 method and stepwise counting took the least time, they are limited by a gray/intermediate zone for scoring compared to the global scoring method and the hotspot method. As a result, the global and/or hotspot method after proper training is a robust and reliable method of assessing Ki-67, with the hotspot method being the most reliable, as the global method is limited by the use of an online tool.

Validation of Linear and Nonlinear Gait Variability Measures Derived From a Smartphone System Compared to a Gold-Standard Footswitch System During Overground Walking.

Smartphones, with embedded accelerometers, may be a viable method to monitor gait variability in the free-living environment. However, measurements estimated using smartphones must first be compared to known quantities to ensure validity. This study assessed the validity and reliability of smartphone-derived gait measures compared to a gold-standard footswitch system during overground walking. Seventeen adults completed three 8-minute overground walking trials during 3 separate visits. The stride time series was calculated as the time difference between consecutive right heel contact events within the footswitch and smartphone-accelerometry signals. Linear (average stride time, stride time standard deviation, and stride time coefficient of variation) and nonlinear (fractal scaling index, approximate entropy, and sample entropy) measures were calculated for each stride time series. Bland-Altman plots with 95% limits of agreement assessed agreement between systems. Intraclass correlation coefficients assessed reliability across visits. Bland-Altman plots revealed acceptable limits of agreement for all measures. Intraclass correlation coefficients revealed good-to-excellent reliability for both systems, except for fractal scaling index, which was moderate. The smartphone system is a valid method and performs similarly to gold-standard research equipment. These findings suggest the development and implementation of an inexpensive, easy-to-use, and ubiquitous telehealth instrument that may replace traditional laboratory equipment for use in the free-living environment.

Design and experimentation of soil organic matter content detection system based on high-temperature excitation principle

With the continuous development of precision agriculture, the integration of sensor technology, control technology, and agricultural production activities has become increasingly tight. Soil organic matter (SOM) content is an important parameter in precision agriculture, closely related to the growth status of crops. Therefore, in-situ detection of SOM is an important part of variable seeding. Currently, most researchers use spectroscopy to detect SOM. However, the accuracy of spectroscopy is affected by factors such as soil texture, soil moisture content, soil iron oxide content, and soil particle size. Additionally, spectroscopic instruments are expensive and complex. To address these issues, this study designed and developed a real-time detection system for soil organic matter content based on the high-temperature excitation principle. The SOM detection system includes the selection and application of carbon dioxide sensor, the design of detection devices, the design of control systems, and the design of human–machine interface. Benchtop tests showed that the system's response time standard deviation was 0.252 s, indicating good stability. The filtering and cooling effects of the system met the requirements of the detection system. Five different natural soils were collected, and two of them, along with organic soil, were used to create artificially graded soil samples. Multiple linear regression was used for modeling, selecting six models with an R2 greater than 0.8 for accurate prediction of the remaining three different soil textures. The modeling results showed that a heating depth of 10 mm was susceptible to external interference, and the accuracy of the model with a heating depth of 15 mm was similar to that of the model with a heating depth of 20 mm. When the heating time exceeded 10 s, both models with different heating depths had an R2 greater than 0.8. The accuracy of the models increased with increasing heating time. Among them, the 15 mm-20 s model had the highest accuracy, with an average prediction accuracy of 91.4 %, a maximum accuracy of 94.1 %, and a minimum accuracy of 86.7 %. The experimental results showed that the SOM detection system designed in this study had good predictive capabilities for SOM in different soil textures and could achieve in-situ detection of SOM.

Assessment of cognitive function and sleep-wake rhythms in community-dwelling older adults.

Disruption of the circadian rhythm and sleep-wake cycles is a consequence of aging and is associated with the cognitive decline and many neurodegenerative conditions. We investigated the bedtime, wake-up time, sleep timing (midpoint between bedtime and wake-up time), and sleep timing standard deviation (SD) using the actigraphy among 80 consecutive volunteers aged ≥ 60years. Global cognitive function and executive function of detailed cognitive domains were evaluated using the mini-mental state examination (MMSE) and Wisconsin card sorting test (WCST) and subjective daytime sleepiness was assessed using the Epworth Sleepiness Scale (ESS). The category achievement (CA), total errors (TE), perseverative errors of Nelson (PEN), non-perseverative errors (NPE), and difficulties in maintaining set (DMS) on the WCST were significantly correlated with sleep timing SD (CA: r = - 0.276, p = 0.013, TE: r = 0.311, p = 0.005, PEN: r = 0.241, p = 0.032, NPE: r = 0.250, p = 0.025, DMS: r = 0.235, p = 0.036), but not with the MMSE score. Multiple regression analyses with the stepwise forward selection method including age, ESS score, bedtime, sleep timing, and sleep timing SD, revealed that the ESS score, and sleep timing SD were significant factors related to CA on the WCST (ESS score: β = - 0.322, p = 0.004; sleep timing SD: β = - 0.250, p = 0.022). Assessment of sleep-wake rhythms, daytime sleepiness, and cognitive function using the MMSE and WCST is valuable for the prediction of cognitive decline in the geriatric population.