Error Correction Rate Research Articles

Analysis of eye movement features in patients with Alzheimer's disease based on intelligent eye movement analysis and evaluation system.

The early identification of Alzheimer's disease (AD) benefits patients, so creating a simple and convenient method is crucial for diagnosing early symptoms. To offer a potential approach for the early detection of both AD and mild cognitive impairment (MCI). Eye movement data from 66 patients were divided into three groups, including healthy control group (HC), MCI group, and AD group. We searched for parameters that can detect MCI at an early stage and drew receiver operating characteristic (ROC) curves. The correlation between eye movement parameters and cognitive scores was analyzed. The MCI group differed from the HC group in error correction rate of antisaccade (p = 0.008) and total offset degrees (>4°) (p = 0.011) of lateral fixation. The AD group had different overlap prosaccade accuracy (p = 0.025), latency (p = 0.009) and average completion time (p = 0.015), gap prosaccade latency (p = 0.005) and average completion time (p = 0.005), antisaccade accuracy (p = 0.006), error correction rate (p < 0.001) and average saccade velocity (p = 0.035), and lateral fixation accuracy (p = 0.018), total offset degrees (>4°) (p = 0.041) compared to the HC group. The AD group differed significantly from the MCI group in accuracy (p = 0.001) and error correction rate (p = 0.044) of antisaccades, the latency (p = 0.009) and average completion time (p = 0.025) of overlap prosaccade and the latency (p = 0.038) of gap prosaccade, these parameters can serve as indicators to monitor the progress of the disease. Lateral fixation combined with antisaccade was more conducive to identifying MCI patients with the area under the ROC curve of 0.837. Most eye movement parameters had a light to moderate correlation with cognitive scores. Eye movements can be used for early identification of MCI/AD patients and to monitor disease progression.

Longitudinal Evolution of the Brain Microstructure in Cirrhotic Patients on Diffusion Kurtosis Imaging.

Although improvement of cognitive function after liver transplantation has been demonstrated in several neuropsychological studies, there is limited research on longitudinal changes in the cirrhotic patients' brain structure before and after transplantation. To investigate longitudinal changes of brain microstructure in cirrhotic patients using diffusion kurtosis imaging (DKI). Prospective. A total of 153 cirrhosis patients, comprising 60 hepatic encephalopathy (HE) patients (16 females/44 males) and 93 no-HE patients (35 females/58 males), along with 93 healthy controls (HCs) (53 females/40 males) were enrolled. Subsequently, 58 recipients completed 1-month postoperative follow-up, 29 patients completed 1-, 3-months, and 17 patients completed 1-, 3-, 6-month follow-up. Spin-echo single-shot echo-planar sequence using a 3.0 T scanner. Diffusion kurtosis estimator software was used to estimate the DKI parameter maps by a MR imaging physicist (Y.-Y.C. with 12 years of experience). The diffusion metrics (eg, radial kurtosis [RK], mean kurtosis, fractional anisotropy, mean diffusivity) of the patients before transplantation were compared with those of the HCs using voxel-wise analysis of variance (ANOVA), along with t tests for post hoc analysis. Linear mixed-effects models were applied to the longitudinal data. We imposed a cluster level Family Wise Error (FWE) correction rate of PFWE = 0.05 with voxel-wise cutoff of P = 0.001 together with a cluster-size cutoff of N ≥ 56, and generated smoothness estimates from the preprocessed data using the mixed-model autocorrelation function. The RK metrics of the patients decreased significantly in the anterior cingulate cortex (HE/no-HE < HC, ANOVA-F = 21.91). After transplantation, the RK of the pallidum showed a continuous upward trend (time effect T = 11.26); whereas the RK of the right postcentral gyrus showed a continuous downward trend (time effect T = -9.56). In addition, the RK in superior longitudinal fasciculus showed new-onset decrease after transplantation. Longitudinal changes in DKI metrics reveal the course of brain microstructural changes before and after transplantation in cirrhotic patients, potentially associated with cognitive alterations after surgery. 1 TECHNICAL EFFICACY: Stage 4.

Diagnosis of schizophrenia by integrated saccade scores and associations with psychiatric symptoms, and functioning.

Eye movement as a neurobiological biomarker of schizophrenia. We aim to estimate diagnostic accuracy of integrated pro/antisaccade eye movement measurements to discriminate between healthy individuals and schizophrenic patients. We compared the eye movement performance of 85 healthy individuals and 116 schizophrenia-stable patients during prosaccade and antisaccade tasks. The difference eye movement measurements were accumulated by stepwise discriminant analysis to produce an integrated score. Finally, the diagnostic value of the integrated score was calculated by the receiver operating characteristic (ROC) area under the curve (AUC), and the best sensitivity and specificity were calculated based on the given cutoff values. Using discriminant analysis, an integrated score included the residual gain and latency (step) during the prosaccade test, the error rate, and the corrected error rate during the antisaccade test. We found that the integrated score could well classify schizophrenia patients and healthy individuals with an accuracy of 80.6%. In the ROC, Youden's index was 0.634 (sensitivity = 81.0%, specificity = 82.4%) and AUC was 0.871. There were significant difference patterns of correlation between the severity of psychiatric symptoms and daily functioning and diagnostic eye movement measurements. Using only 2 saccade tasks to discriminate well between schizophrenia patients and healthy controls, suggesting that abnormalities in saccade behavior is a potential biomarker and efficient diagnostic tool for identifying schizophrenia. The underlying neuropathologic mechanisms associated with abnormal saccades may provide insights into the intervention and diagnosis of schizophrenia.

Ultra-high-cardinality geometric shaping in the finite SNR regime.

Four-dimensional (4D) constellations with up to 131 072 points (17 bit/4D-sym) are designed for the first time using geometric shaping. The constellations are optimized in terms of mutual information (MI) and generalized MI (GMI) for the additive white Gaussian noise (AWGN) channel, targeting a forward error correction (FEC) rate of 0.8 at finite signal-to-noise ratios. The presented 15-17 bit constellations are currently the highest-performing constellations in the literature, having a gap to the AWGN capacity as low as 0.17 dB (MI) and 0.45 dB (GMI) at 17 bit/4D-sym. For lower cardinalities, our constellations match or closely approach the performance of previously published optimized constellations. We also show that (GMI-)optimized constellations with a symmetry constraint, optimized for a FEC rate of 0.8, perform nearly identical to their unconstrained counterparts for cardinalities above 8 bit/4D-sym. A symmetry constraint for MI-optimized constellations is shown to have a negative impact in general. The proposed procedure relies on a Monte-Carlo-based approach for evaluating performance and is extendable to other (nonlinear) channels. Stochastic gradient descent is used for the optimization algorithm for which the gradients are computed using automatic differentiation. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'.

IVF laboratory management through workflow-based RFID tag witnessing and real-time information entry

BackgroundDual-person inspection in IVF laboratories cannot fully avoid mix-ups or embryo transfer errors, and data transcription or entry is time-consuming and redundant, often leading to delays in completing medical records.MethodsThis study introduced a workflow-based RFID tag witnessing and real-time information entry platform for addressing these challenges. To assess its potential in reducing mix-ups, we conducted a simulation experiment in semen preparation to analyze its error correction rate. Additionally, we evaluated its impact on work efficiency, specifically in operation and data entry. Furthermore, we compared the cycle costs between paper labels and RFID tags. Finally, we retrospectively analyzed clinical outcomes of 20,424 oocyte retrieval cycles and 15,785 frozen embryo transfer cycles, which were divided into paper label and RFID tag groups.ResultsThe study revealed that comparing to paper labels, RFID tag witnessing corrected 100% of tag errors, didn’t affect gamete/embryo operations, and notably shorten the time of entering data, but the cycle cost of RFID tags was significantly higher. However, no significant differences were observed in fertilization, embryo quality, blastocyst rates, clinical pregnancy, and live birth rates between two groups.ConclusionsRFID tag witnessing doesn’t negatively impact gamete/embryo operation, embryo quality and pregnancy outcomes, but it potentially reduces the risk of mix-ups or errors. Despite highly increased cost, integrating RFID tag witnessing with real-time information entry can remarkably decrease the data entry time, substantially improving the work efficiency. This workflow-based management platform also enhances operational safety, ensures medical informational integrity, and boosts embryologist’s confidence.

Detection and Quantification of Arnica montana L. Inflorescences in Grassland Ecosystems Using Convolutional Neural Networks and Drone-Based Remote Sensing

Arnica montana L. is a medicinal plant with significant conservation importance. It is crucial to monitor this species, ensuring its sustainable harvesting and management. The aim of this study is to develop a practical system that can effectively detect A. montana inflorescences utilizing unmanned aerial vehicles (UAVs) with RGB sensors (red–green–blue, visible light) to improve the monitoring of A. montana habitats during the harvest season. From a methodological point of view, a model was developed based on a convolutional neural network (CNN) ResNet101 architecture. The trained model offers quantitative and qualitative assessments of A. montana inflorescences detected in semi-natural grasslands using low-resolution imagery, with a correctable error rate. The developed prototype is applicable in monitoring a larger area in a short time by flying at a higher altitude, implicitly capturing lower-resolution images. Despite the challenges posed by shadow effects, fluctuating ground sampling distance (GSD), and overlapping vegetation, this approach revealed encouraging outcomes, particularly when the GSD value was less than 0.45 cm. This research highlights the importance of low-resolution image clarity, on the training data by the phenophase, and of the need for training across different photoperiods to enhance model flexibility. This innovative approach provides guidelines for mission planning in support of reaching sustainable management goals. The robustness of the model can be attributed to the fact that it has been trained with real-world imagery of semi-natural grassland, making it practical for fieldwork with accessible portable devices. This study confirms the potential of ResNet CNN models to transfer learning to new plant communities, contributing to the broader effort of using high-resolution RGB sensors, UAVs, and machine-learning technologies for sustainable management and biodiversity conservation.

VSD: A Novel Method for Video Segmentation and Storage in DNA Using RS Code

As data continue to grow in complexity and size, there is an imperative need for more efficient and robust storage solutions. DNA storage has emerged as a promising avenue to solve this problem, but existing approaches do not perform efficiently enough on video data, particularly for information density and time efficiency. This paper introduces VSD, a pioneering encoding method for video segmentation and storage in DNA, leveraging the Reed–Solomon (RS) error correction code. This method addresses these limitations through an innovative combination of segmentation and encoding, accompanied by RS coding to bolster error resilience. Additionally, the method ensures that the GC-content of the resultant DNA sequences remains around 50%, which further enhances the storage robustness. The experimental results demonstrate the method has commendable encoding efficiency and offers a solution to the prevailing issue of time inefficiency and error correction rates in DNA storage. This groundbreaking approach paves the way for the practical and reliable storage of large-scale video data in DNA, heralding a new era in the domain of information storage.

Two-Dimensional Protection Code for Virtual Page Information in Translation Lookaside Buffers

Severe conditions such as high-energy particle strikes may induce soft errors in on-chip memory, like cache and translation lookaside buffers (TLBs). As the key component of virtual-to-physical address translation, TLB directly affects processor performance. To protect the virtual page information stored in TLB, several studies have introduced error detection or correction codes. However, most schemes proposed for data TLB cannot support the detection of multi-bit upsets, which is reported as a serious issue in modern fault-tolerant processors due to the downscaling of CMOS process technology. In this paper, we propose a new two-dimensional protection technique, called the matrix protection tag (MaP-Tag), to provide stronger error detection and correction capability to TLB virtual page information. Our proposal reorganizes virtual page information into a matrix and employs adjacent check bits and interleaved check bits for error detection. The two-dimensional design offers one-bit error detection, burst multi-bit error detection, and even multi-bit error correction in some cases. The simulation results show that our proposal can detect almost all error patterns when injected with up to eight bit-flips. Furthermore, the technique provides a better error correction rate than conventional single error correction (SEC) codes. The reliability calculation shows that our proposal is powerful in both error detection and correction with affordable storage overhead.

An Encoding Table Corresponding to ASCII Codes for DNA Data Storage and a New Error Correction Method HMSA.

DNA storage stands out from other storage media due to its high capacity, eco-friendliness, long lifespan, high stability, low energy consumption, and low data maintenance costs. To standardize the DNA encoding system, maintain consistency in character representation and transmission, and link binary, base, and character together, this paper combines the encoding method with ASCII code to construct an ASCII-DNA encoding table. The encoding method can encode not only pure text information but also audio and video information and satisfies the GC content constraint and the homopolymer constraint, with the encoding density reaching 1.4 bits/nt. In particular, when encoding textual information, it directly skips the binary conversion process, which reduces the complexity of encoding, and increasing the encoding density to 1.6 bits/nt. In order to solve the problem of errors in sequences, under the influence of heuristic algorithms, this paper proposes a new error correction method (HMSA) by combining minimum Hamming distance, multiple sequence alignment, and encoding scheme. It can correct not only substitution, insertion, and deletion errors in Reads but also consecutive errors in Reads. It greatly improves the utilization of the Reads and avoids the waste of resources. Simulation results show that the recovery rate of Reads increases with the increasing number of sequencing times. When the number of erroneous bases in a 150nt sequence reaches 5nt, the error correction rate can exceed 96% by sequencing the base sequence only 10 times regardless of whether the errors are consecutive or not. Additionally, the HMSA error correction method is applicable to all coding schemes for lookup code table types.

Digital RNA sequencing using unique molecular identifiers enables ultrasensitive RNA mutation analysis

Mutation analysis is typically performed at the DNA level since most technical approaches are developed for DNA analysis. However, some applications, like transcriptional mutagenesis, RNA editing and gene expression analysis, require RNA analysis. Here, we combine reverse transcription and digital DNA sequencing to enable low error digital RNA sequencing. We evaluate yield, reproducibility, dynamic range and error correction rate for seven different reverse transcription conditions using multiplexed assays. The yield, reproducibility and error rate vary substantially between the specific conditions, where the yield differs 9.9-fold between the best and worst performing condition. Next, we show that error rates similar to DNA sequencing can be achieved for RNA using appropriate reverse transcription conditions, enabling detection of mutant allele frequencies <0.1% at RNA level. We also detect mutations at both DNA and RNA levels in tumor tissue using a breast cancer panel. Finally, we demonstrate that digital RNA sequencing can be applied to liquid biopsies, analyzing cell-free gene transcripts. In conclusion, we demonstrate that digital RNA sequencing is suitable for ultrasensitive RNA mutation analysis, enabling several basic research and clinical applications.

Error Correction Method of Platform Displacement for Large Load Static Test

The test platform is an indispensable supporting bearing structure in static tests, which is mainly used to fix test products, simulate the real loading boundary, and provide better operation space for measurement. With the large-scale and complicated spacecraft structure, the static test platform tends to be large-scale, and the displacement measurement error of the static test platform under large load conditions cannot be ignored. The conventional error correction method only uses the displacement value of the monitoring point at the corresponding position of the platform to compensate, ignoring the comprehensive influence of the six degrees of freedom displacement for the test platform on the displacement measurement of the specimen. When it is used for the error correction of the static test of large spacecraft structures, there is a large measurement error affected by the structural size. In view of this, according to the static test requirements of a large spacecraft, a large bearing static test platform with a lateral load of 22 T and a longitudinal load of 52 T is designed, and a displacement measurement error correction method based on six degrees of freedom is proposed. Through the static test of a large spacecraft structure, the method can effectively reduce the displacement measurement error caused by the displacement of the test platform, and the error correction rate can reach 20%.

Tap to Sign: Towards using American Sign Language for Text Entry on Smartphones

Soon, smartphones may be capable of allowing American Sign Language (ASL) signing and/or fingerspelling for text entry. To explore the usefulness of this approach, we compared emulated fingerspelling recognition with a virtual keyboard for 12 Deaf participants. With practice, fingerspelling is faster (42.5 wpm), potentially has fewer errors (4.02% corrected error rate) and higher throughput (14.2 bits/second), and is as desired as virtual keyboard texting (31.9 wpm; 6.46% corrected error rate; 10.9 bits/second throughput). Our second study recruits another 12 Deaf users at the 2022 National Association for the Deaf conference to compare the walk-up usability of fingerspelling alone, signing, and virtual keyboard text entry for interacting with an emulated mobile assistant. Both signing and virtual keyboard text entry were preferred over fingerspelling.

The application of saccades to assess cognitive impairment among older adults: a systematic review and meta-analysis.

Saccade is a novel and feasible method for cognition assessment and has potential to screen older people with cognitive impairment. To systematically summarize the evidence and determine whether different saccade parameters can effectively identify patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). English and Chinese databases were searched until 19 April 2022. Studies analyzing saccade parameters in older adults with normal cognition, MCI, or AD were included. Two researchers independently performed the screening, data extraction, and quality appraisal. Meta-analyses were conducted and standard mean differences and 95% confidence intervals were estimated with a random effects model. Thirty-five studies were included, and 26 studies were pooled for the meta-analysis. The results demonstrated that patients with cognitive impairment exhibited longer latency and lower accuracy rates in the prosaccade and antisaccade tasks, along with lower corrected error rates in the antisaccade tasks. However, the pooled results for antisaccades were more stable, providing the ability to distinguish patients with cognitive impairment among older adults. The results of the subgroup analyses revealed that only the accuracy rates of the antisaccades differed significantly between people with MCI and AD. Regarding the differences between older adults with normal cognition and those with MCI, the effect sizes of latency and the accuracy rates of saccades as well as the corrected error rates of antisaccades were significant. Saccades, especially antisaccades, are a potential screening and assessment tool for distinguishing older adults with MCI or AD from those with normal cognition.

The Effect of Using Autocorrection on Improving Typing Skills for Female Students with Mild Intellectual Disabilities

This study aimed to identify the effect of the using autocorrection to improve typing skill for students with intellectual disabilities in the transition program. The sample of the study include four students with intellectual disabilities, who enrolled in the after-school day care program, whose ages range from (21-25) years, where they taught typing skills by applying autocorrection option on the (Microsoft Word) program. Using single subject design (multiple baseline design across participants), the results showed that all students improve their typing skills on three measures: total number of correct words, accuracy, and error rates. Keywords: autocorrection, computer printing, students with intellectual disabilities

A Monte Carlo permutation procedure for testing variance components in generalized linear regression models

Testing zero variance components is of utmost importance in various applications empowered by the use of mixed-effects models. Focusing on generalized linear models, this article proposes a permutation test using an analogue of the ANOVA test statistic that merely requires fitting the null model with independent observations. Monte Carlo simulations reveal that the new test has correct Type-I error rate and that its power compares favorably to an existing bootstrap score test. A real data application illustrates the advantageous capability of the proposed test in detecting the need for random effects.

Familial Risk for Schizophrenia vs Bipolar Disorder and Task-Based Neural Activation: A functional Magnetic Resonance Imaging Meta-Analysis.

Individuals at familial risk for developing schizophrenia (FRSZ) or bipolar disorder (FRBD) have shared and unique genetic risks. Few studies have compared neural activation between these two groups. Therefore, the present meta-analysis investigated functional brain similarities and differences between FRSZ and FRBD individuals. A systematic literature review was conducted of articles that compared FRSZ or FRBD individuals to healthy controls (31 FRSZ and 22 FRBD). Seed-based d mapping was used to conduct the meta-analysis. Analyses included comparisons of FRSZ to controls, FRBD to controls, and both relative groups to each other. Using a highly conservative family-wise error rate correction, there were no significant findings. Using a less conservative threshold, FRSZ compared to controls had lower activation in the left precuneus (Puncorrected = .02) across all studies and in the left middle frontal gyrus (Puncorrected = .03) in nonsocial cognition studies. FRBD compared to controls had lower activation in the left superior parietal gyrus (Puncorrected = .03) and right angular gyrus (Puncorrected = .03) in nonsocial cognition studies, and higher activation in the left superior frontal gyrus (Puncorrected = .01) in social tasks. Differences between FRSZ and FRBD were not significant. There were few robust differences between FRSZ or FRBD compared to controls. This suggests only weak support for neural activation differences between individuals at genetic risk for schizophrenia or bipolar disorder and controls. The tentative findings observed were in different brain regions for FRSZ and FRBD, with no strong evidence for shared effects between schizophrenia and bipolar genetic risk on neural activation.

Improving FEC layer frame for DVB-S2 link system based on 5G NR polar coding

Within the scope of this investigation, the MATLAB simulation code for digital video broadcasting–for satellite–second generation (DVB-S2) has been constructed. Forward error correction (FEC) rates as 3/5, 2/3, and 3/4 across additive white Gaussian noise (AWGN) and Rayleigh fading channels are used to evaluate the system's performance, mainly while working on quadrature phase-shift keying (QPSK), 8-ary phase-shift keying (8PSK), 16-ary amplitude phase-shift keying (16APSK), and 32-ary amplitude phase-shift keying (32APSK) official modulation types. The system's redesign has been achieved to investigate high performance and reliability based on a cascade of new radio (NR) fifth generation (5G) Polar coding with low-density parity-check (LDPC). Some signal-to-noise ratio (SNR) levels were changed when evaluated in contrast to the conventional model. It has been determined that five iterations of the LDPC decoder were performed. In comparison to the traditional model. The proposed design's performance accomplished the highest possible value for reducing the bit error rate (BER) value and investigated better-transmitted power gain for most testing cases.

Chronic cannabis use and error awareness: The effect on learning from errors.

Cannabis is the third most commonly used drug worldwide, with studies suggesting a deleterious effect on some aspects of performance monitoring. It is unknown, however, whether diminished error awareness influences adaptive behaviour in cannabis users. Therefore, this study examined the effect of error awareness on learning from errors in cannabis users. Thirty-six chronic cannabis users (Mage = 23.81 years; female, 36%) and 34 controls (Mage = 21.53 years; female, 76%) completed a Go/No-Go task that allowed participants to learn from errors and adapt their behaviour. Multilevel models were specified to determine whether the effect of error awareness on learning from errors differs between cannabis users and controls, and whether cannabis-use measures predict error correction while accounting for error awareness. While error awareness and correction rates did not differ between the groups, there was a significant effect of age of use onset on error correction in cannabis users. Further, the effect of error awareness was dependent on age of onset, and cannabis use-related frequency and harm. That is, cannabis users reporting an earlier age of regular use or scoring higher on the cannabis use index were less likely to perform correctly following an aware error. It appears overall cannabis use might not be tightly coupled to behavioural indices of performance monitoring. There is evidence, however, that aspects of cannabis use predict impairments in learning from errors that may be associated with treatment outcomes.

Single-station quad-constellation GNSS regional ionospheric modelling for navigation

Conventionally, regional ionospheric models (RIMs) are established using GNSS at multiple stations. However, this will severely rely on the availability of the local GNSS observation network. In this study, a single-station quad-constellation GNSS regional ionospheric modelling (SQG-RIM) approach is proposed by establishing the RIM with the GPS/GLONASS/BDS/Galileo observations at a single station. To facilitate its application, an algorithm is further proposed to divide the entire modelling region into the core area and the non-core area. Compared with the global ionospheric map (GIM), the SQG-RIM in the core area can increase the ionospheric error correction rate by about 9%. Further, the SQG-RIM is applied to single point positioning (SPP) using a differentiated weighting scheme for different signal-passing areas. Test results indicate that the three-dimensional quad-constellation SPP accuracies are improved by 57% and 52% after applying the SQG-RIM as compared to the Klobuchar and GIM models, respectively.

Research on Algorithm of Video Analysis System Based on Text Error Correction

When making a video, if the video has a language organization error, it needs to be re-recorded. It is not possible to remove inappropriate or unnatural pronunciation parts of the recording more effectively. In response to this problem, this paper studies the speech extraction, error correction and synthesis of video, which is divided into three parts: (1) Speech segmentation and speech-to-text of video; (2) Text recognition error correction; (3) Text-to-speech and video speech synthesis. For the first part, we applied the staged and efficient algorithm based on (Bayesian Information Criterion) BIC &amp; (Statistical Mean Euclidean Distance) MEdist to segment the video voice, and then, the segmented audio is subtracted to reduce noise, and finally converted to text using the iFLYTEK interface. For the second part, we apply the (Double Automatic Error Correction) DAEC algorithm to text error correction. For the third part, we use the (Improved Chinese Realtime Voice Cloning) I-Zhrtvc for text-to-speech. Then merge the voice into the video. The simulation result shows that the staged and efficient algorithm based on BIC &amp; MEdist, which accurately segmented by sentences, can identify audio with dialect accents, and has high accuracy in translating to text, up to an average of 95.8%. DAEC algorithm has a high error correction rate. The audio prosody accuracy after synthesis is high. ZVTOW text-to-speech (Mean Opinion Score) MOS up to 4.5.