Positive Errors Research Articles

Enhancing Clinical Applications by Evaluation of Sensitivity and Specificity in Whole Exome Sequencing

The cost-effectiveness of whole exome sequencing (WES) remains controversial due to variant call variability, necessitating sensitivity and specificity evaluation. WES was performed by three companies (AA, BB, and CC) using reference standards composed of DNA from hydatidiform mole and individual blood at various ratios. Sensitivity was assessed by the detection rate of null–homozygote (N–H) alleles at expected variant allelic fractions, while false positive (FP) errors were counted for unexpected alleles. Sensitivity was approximately 20% for in-house results from BB and CC and around 5% for AA. Dynamic Read Analysis for GENomics (DRAGEN) analyses identified 1.34 to 1.71 times more variants, detecting over 96% of in-house variants, with sensitivity for common variants increasing to 5%. In-house FP errors varied significantly among companies (up to 13.97 times), while DRAGEN minimized this variation. Despite DRAGEN showing higher FP errors for BB and CC, the increased sensitivity highlights the importance of effective bioinformatic conditions. We also assessed the potential effects of target enrichment and proposed optimal cutoff values for the read depth and variant allele fraction in WES. Optimizing bioinformatic analysis based on sensitivity and specificity from reference standards can enhance variant detection and improve the clinical utility of WES.

Development of a Symptom Validity Index for the Beck Anxiety Inventory

Objective: Anxiety disorders are the most prevalent psychiatric disorders experienced by individuals in the United States, and anxiety is often assessed with anxiety symptom inventories. At present, though, there are no anxiety symptom inventories that include symptom validity indices. The present study sought to develop a symptom validity index for the most commonly used anxiety symptom inventory in neuropsychological practice, the Beck Anxiety Inventory (BAI). Method: A sample of 244 veterans seeking outpatient neuropsychological assessments were included in the study. Participants were divided into valid and invalid groups based on external criterion symptom validity tests. The valid participants were then divided into clinical subgroups (Current Anxiety, Lifetime Anxiety, and No Anxiety). A validity index derived from the BAI total score was examined for the total sample and all subgroups. Results: A cutoff of ≥ 29 was identified when utilizing the Valid Full Sample, which resulted in a 0.91 specificity rate and 0.54 sensitivity rate. However, cutoffs had to be adjusted when applied to the clinical subgroups. The Valid Current Anxiety subgroup required the largest cutoff increase (i.e. ≥ 36), which resulted in a 0.91 specificity rate and a 0.42 sensitivity rate. Conclusions: This is the first published article to develop a symptom validity index for the BAI. To minimize false positive errors, a BAI total score of ≥ 36 is recommended.

Diffusion Augmented Complex Inverse Square Root for Adaptive Frequency Estimation over Distributed Networks

Using adaptive filtering to estimate the frequency of power systems has become a popular trend. In recent years, however, few studies have been performed on adaptive frequency estimations in non-stationary noise environments. In this paper, we propose the distributed complex inverse square root algorithm and distributed augmented complex inverse square root algorithm for the frequency estimation of power systems based on the widely linear model and the inverse square root cost function, where the function can restrain both positive and negative large errors, based on its symmetry. Moreover, the wireless sensor networks support monitoring and adaptation for the frequency estimation in the distributed networks, and the proposed approach can ensure good robustness of the balanced or unbalanced three-phase power system with the help of a local complex-value voltage signal generated by Clark’s transformation. In addition, the bound of step size is driven by the global vectors, and that low computation complexity do not hinder those performances. The results of several experiments demonstrate that our algorithms can effectively estimate the frequency in impulsive noise environments.

Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery

Abstract Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High‐resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double‐counted plants) error in counts from high‐resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post‐wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high‐resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting (p) adult plants (0.25 m tall) varied between sites within 0.52 < < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness. Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.

Circuit-noise-resilient virtual distillation

Quantum error mitigation (QEM) is vital for improving quantum algorithms’ accuracy on noisy near-term devices. A typical QEM method, called Virtual Distillation (VD), can suffer from imperfect implementation, potentially leading to worse outcomes than without mitigation. To address this, we introduce Circuit-Noise-Resilient Virtual Distillation (CNR-VD), which includes a calibration process using simple input states to enhance VD’s performance despite circuit noise, aiming to recover the results of an ideally conducted VD circuit. Simulations show that CNR-VD significantly mitigates noise-induced errors in VD circuits, boosting accuracy by up to tenfold over standard VD. It provides positive error mitigation even under high noise, where standard VD fails. Furthermore, our estimator’s versatility extends its utility beyond VD, enhancing outcomes in general Hadamard-Test circuits. The proposed CNR-VD significantly enhances the noise-resilience of VD, and thus is anticipated to elevate the performance of quantum algorithm implementations on near-term quantum devices.

Comparison of the effectiveness of 0.5% and 0.25% proparacaine hydrochloride eye drops as topical anesthetics in routine ocular investigations and procedures.

To compare the effectiveness of 0.5% and 0.25% proparacaine eye drops in providing topical anesthesia for routine ocular procedures. 137 patients (274 eyes) were included in this study. They were categorized into two groups. Group A patients received 0.5% and Group B received 0.25% proparacaine drops. A single surgeon performed all the procedures. Intraprocedural and postprocedural pain scores, surgeon comfort, supplemental anesthesia, and vital parameters, were noted. The mean age of the participants was 69.42 years (±12.05). An equal number of procedures were performed (n = 30) for applanation tonometry, lacrimal sac syringing, and A-scan biometry. The other procedures performed were removal of the conjunctiva (n = 5) and corneal foreign bodies (n = 16 per group), corneal scraping (n = 8 per group), and corneal suture removal (n = 18 per group).The mean visual analog pain score during the procedure was 6.9663 in Group A and 8.0803 in Group B (P = 0.66). The mean postprocedural pain score was not significant (P = 0.21). None of the patients required any additional anesthesia during or after the procedure. The average surgeon's experience was 0.152±0.507 in Group A and 0.111±0.402 in Group B (P = 0.07).In Group A, 133 of 137 patients (97.08%), and in Group B, 132 out of 137 patients (96.35%) preferred to use the same anesthetic for future procedures. None of the patients experienced a vasovagal attack or any change in vital parameters. The present study aimed to establish equivalence between two concentrations of proparacaine hydrochloride in which false positive and false negative errors were expected. This is the inherent challenge in the equivalence and inferiority trials. To mitigate these risks, the present study was carefully designed with specific statistical methods, sample size calculation, and rigorous methodology. Nevertheless, it is crucial to recognize that the risk of false positive errors remains, and the results should be interpreted with this in mind. These findings indicated that 0.25% proparacaine is a viable alternative to the standard 0.5% concentration in routine ophthalmic procedures, with the potential for improved patient comfort.

Suspected Pseudobulbar Affect in Neurodegenerative Disease.

To investigate the association between suspected pseudobulbar affect (PBA), clinical diagnosis, cognitive testing, and self-reported mood in older adults presenting for evaluation of dementia. Patients presenting to an outpatient memory disorders clinic (N=311). We used traditional and novel network modeling approaches to examine associations between neuropsychological (NP) tests, patient and clinician rating scales, and the Center for Neurological Study-Lability Scale (CNS-LS) among patients with suspected AD (n=133) and other neurocognitive diagnosis (n=178). We then examined differences in test performance between patients with and without suspected PBA (CNS-LS cut-off of ≥ 13), while accounting for demographic and psychiatric covariates with propensity score matching. Group differences were assessed with Bayesian models. Prevalence of suspected PBA in AD was slightly less than half (44.4%) and at a similar rate in other dementias (e.g., 46.9% in CVD and 45.5% in LBD). In network models, the CNS-LS was associated with higher anxiety and better word list recall. After accounting for covariates, AD patients with suspected PBA performed better on word list recall βM=0.40, 95% CI [0.15, 0.66], and committed fewer false positive errors on recognition βM=-1.51, 95% CI [-2.34, -0.59] than AD patients without suspected PBA. There were no differences in patients with any other diagnostic impression, nor group differences on other NP measures. Patients with suspected PBA and AD diagnosis had better memory recall and recognition than those without suspected PBA, suggesting that impaired emotional regulation may be an early sign of AD in patients with less prominent memory decline. Better understanding PBA in neurodegenerative diseases, including prevalence and comorbidity with psychiatric conditions, could help with early identification, education, and initiation of treatment.

Development of a web-based ecological momentary assessment tool to measure day-to-day variability of the symptoms in patients with Sjögren’s disease

ObjectivesTo develop and validate a web-based ecological momentary assessment (EMA) tool to enhance symptoms monitoring among patients with Sjögren’s disease (SjD).MethodsConsecutive adults with SjD were enrolled in this pilot observational...

Analysis of Public Interest in Smartfren SIM Cards using the K-Nearest Neighbors Method

The use of Smartfren SIM cards is increasing along with the public's need for fast and stable internet services. However, a deep understanding of public interest in the SIM card is necessary to optimize marketing strategies and increase sales. Proper analysis can help companies identify potential target markets and develop effective marketing strategies. We chose the K-Nearest Neighbors method to analyze public interest in using Smartfren SIM cards. This study aims to develop and evaluate the K-Nearest Neighbors model in predicting public interest in using Smartfren SIM cards. This study uses a dataset containing information about Smartfren SIM card users. We divide the data into two sets: a training set for model building and a test set for evaluating model performance. We apply the K-Nearest Neighbors method to classify the data into two categories: interested and not interested. We evaluate the model performance using accuracy, precision, recall, and F1-score metrics. We present the evaluation results as a confusion matrix. The developed K-Nearest Neighbors model showed excellent performance with an accuracy of 94.29%, a precision of 94.20%, a recall of 100%, and an F1-score of 97.01%. These results indicate that the K-Nearest Neighbors model is effective in predicting people's interest in Smartfren SIM cards. The high recall value indicates that the model is able to identify all interested individuals without missing any, while the high precision value indicates that the model rarely makes false positive prediction errors. This study concludes that the K-Nearest Neighbors method is very effective for use in analyzing people's interest in using Smartfren SIM cards. We can rely on the developed model's strong performance for real-world applications in marketing strategies.

Analysis Of Public Interest In Smartfren SIM Cards Using The K-Nearest Neighbors Method

The use of Smartfren SIM cards is increasing along with the public's need for fast and stable internet services. However, a deep understanding of public interest in the SIM card is necessary to optimize marketing strategies and increase sales. Proper analysis can help companies identify potential target markets and develop effective marketing strategies. We chose the K-Nearest Neighbors method to analyze public interest in using Smartfren SIM cards. This study aims to develop and evaluate the K-Nearest Neighbors model in predicting public interest in using Smartfren SIM cards. This study uses a dataset containing information about Smartfren SIM card users. We divide the data into two sets: a training set for model building and a test set for evaluating model performance. We apply the K-Nearest Neighbors method to classify the data into two categories: interested and not interested. We evaluate the model performance using accuracy, precision, recall, and F1-score metrics. We present the evaluation results as a confusion matrix. The developed K-Nearest Neighbors model showed excellent performance with an accuracy of 94.29%, a precision of 94.20%, a recall of 100%, and an F1-score of 97.01%. These results indicate that the K-Nearest Neighbors model is effective in predicting people's interest in Smartfren SIM cards. The high recall value indicates that the model is able to identify all interested individuals without missing any, while the high precision value indicates that the model rarely makes false positive prediction errors. This study concludes that the K-Nearest Neighbors method is very effective for use in analyzing people's interest in using Smartfren SIM cards. We can rely on the developed model's strong performance for real-world applications in marketing strategies.

The expectation-updating mechanism in gratitude: A predictive coding perspective.

The fluctuations in emotions during constant help are unexplained by traditional emotion theories but may align with the predictive coding theory. This theory suggests that individuals tend to form expectations of others' help during social interactions. When outcomes exceed expectations, positive prediction errors are generated, potentially increasing gratitude. Conversely, constant help may build up expectations that surpass outcomes, resulting in negative prediction errors and reduced gratitude. Nevertheless, there is a lack of studies to examine the relationship between prediction errors and gratitude and its underlying mechanism. Here, we conducted two studies. Study 1 consistently found that higher expectations were associated with lower gratitude, when benefactors refused to help, in both reward-gaining and punishment-avoiding tasks. Moreover, prediction errors were positively and reliably linked to gratitude. Study 2 further identified that gratitude dynamically changed through an expectation-updating mechanism. A computational model incorporating predictive coding outperformed traditional theories in predicting the dynamics of gratitude. The findings support predictive coding theory, providing a temporal perspective and a mechanistic understanding of the fluctuations in gratitude, thus having implications for new interventions to improve mental health and well-being. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Trajectory tracking control of a four-bar linkage prosthetic knee with magnetorheological damper

In order to improve biomimicry, a four-bar linkage prosthetic knee with magnetorheological (MR) damper was developed in this paper. The principle and structure of four-bar linkage MR prosthetic knee was introduced and its kinetic model was established. A MR damper matching the proposed prosthetic knee was designed, fabricated and tested, and the forward and inverse mechanical model were conducted. In addition, to solve the problems of complex model, poor bionics and low trajectory tracking accuracy in the MR prosthetic knee, a PD-type iterative learning control (ILC) controller was developed. The simulated and experimental test results show that the positive maximum error of the knee joint swing trajectory based on PD-type ILC controller are 2.2°(speed of 0.5 m/s), 4.1°(speed of 1.0 m/s), and 1.9°(speed of 1.5 m/s), respectively, and the negative maximum error of that are −1.4°(speed of 0.5 m/s), −6.7°(speed of 1.0 m/s), and −5.5°(speed of 1.5 m/s), respectively, which effectively tracks the reference swing trajectory.

Neural dynamics underlying the illusion of control during reward processing.

The illusion of control refers to a behavioral bias in which people believe they have greater control over completely stochastic events than they actually do, leading to an inflated estimate of reward probability than objective probability warrants. In this study, we examined how reward system is modulated by the illusion of control through the lens of neural dynamics. Participants in a behavioral task exhibited a classical illusion of control, assigning a higher value to the gambling wheels they picked themselves than to those given randomly. An event-related potential study of the same task revealed that this behavioral bias is associated with reduced reward anticipation, as indexed by the stimulus-preceding negativity, diminished positive prediction error signals, as reflected by the reward positivity, and enhanced motivational salience, as revealed by the P300. Our findings offer a mechanistic understanding of the illusion of control in terms of reward dynamics.

Hierarchical collaboration for referring image segmentation

In the field of referring segmentation, top-down methods and bottom-up methods are the two prevailing approaches. Both of these methods inevitably exhibit certain drawbacks. Top-down methods are susceptible to Polar Negative (PN) errors due to their limited understanding of multi-modal fine-grained features. Bottom-up methods lack macro-level object positional information, making them susceptible to Inferior Positive (IP) errors. However, we find that the two approaches are highly complementary in addressing their respective weaknesses, but combining them directly through a simple average does not yield complementary advantages. Therefore, we proposed a hierarchical collaboration approach to explore the complementary characteristics of the existing two methods from the perspectives of fusion and interaction, aiming to achieve more precise segmentation results. We proposed the Complementary Feature Interaction (CFI) module, which enables top-down methods to access fine-grained information and allows bottom-up approaches to obtain object positional information interactively. Regarding integration, Gaussian Scoring Integration (GSI) models the Gaussian performance distributions of two branches and performs weighted integration by sampling confidence scores from these distributions. We integrate various top-down and bottom-up methods within the proposed architecture and conduct experiments on three standard datasets. The experimental results demonstrate that our method outperforms the state-of-the-art independent segmentation algorithms. On the RefCOCO validation, test A and test B datasets, our proposed method achieved IoU scores of 77.51, 79.12, and 72.79, respectively. Extensive experiments demonstrate that our method can significantly improve segmentation accuracy when fusing different sub-methods.

A Behavior-Based Model to Validate Electronic Systems Designed to Collect Patient-Reported Outcomes: Model Development and Application.

The merits of technology have been adopted in capturing patient-reported outcomes (PROs) by incorporating PROs into electronic systems. Following the development of an electronic system, evaluation of system performance is crucial to ensuring the collection of meaningful data. In contemporary PRO literature, electronic system validation is overlooked, and evidence on validation methods is lacking. This study aims to introduce a generalized concept to guide electronic patient-reported outcome (ePRO) providers in planning for system-specific validation methods. Since electronic systems are essentially products of software engineering endeavors, electronic systems used to collect PRO should be viewed from a computer science perspective with consideration to the health care environment. On this basis, a testing model was blueprinted and applied to a newly developed ePRO system designed for clinical use in pediatric dentistry (electronic Personal Assessment Questionnaire-Paediatric Dentistry) to investigate its thoroughness. A behavior-based model of ePRO system validation was developed based on the principles of user acceptance testing and patient-centered care. The model allows systematic inspection of system specifications and identification of technical errors through simulated positive and negative usage pathways in open and closed environments. The model was able to detect 15 positive errors with 1 unfavorable response when applied to electronic Personal Assessment Questionnaire-Paediatric Dentistry system testing. The application of the behavior-based model to a newly developed ePRO system showed a high ability for technical error detection in a systematic fashion. The proposed model will increase confidence in the validity of ePRO systems as data collection tools in future research and clinical practice.

Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set.

Aqueous solubility is one of the most important physicochemical properties of drug molecules and a major driving force for oral drug absorption. To date, the performance of in silico models for the estimation of solubility for novel chemical space is limited. To investigate possible reasons and remedies for this, the Johnson and Johnson in-house aqueous solubility data with over 40,000 compounds was leveraged. All data were generated through the same high-throughput assay, providing a unique opportunity to explore the relationship between data quality, quantity, and model estimations. Six intrinsic solubility data sets with different sizes and noise levels were generated by making use of three different approaches: (i) inclusion or exclusion of amorphous solid residue, (ii) measured or experimental log D to identify the intrinsic solubility, and (iii) adopting or omitting a quality check process in the data processing workflow. A random forest regressor was trained on the data sets with three different sets of descriptors calculated from RDKit, ADMET predictor, or Mordred, and the performances were evaluated with nested cross-validation as well as ten refined test sets. The models confirm, as expected, that with the same data set size, high-quality data leads to better model performance; however, also, models trained with larger data sets containing analytical variability can give equally accurate estimations compared to models trained with small, clean, and diverse data sets. However, noise introduced by including the presence of amorphous solid postsolubility measurement in the training data set cannot be overcome by increasing data size, as they are introducing a biased systematic positive error in the data set, confirming the importance of critical data review. Finally, two top-performing models were tested on the first test set from the second solubility challenge, achieving RMSE values of 0.74 and 0.72 and log S ± 0.5 of 46 and 48%, respectively. These results demonstrated improved performance compared to those reported in the findings of the competition, highlighting that a single-source curated data set can enhance the prediction of intrinsic solubility.

On the effects of impulsivity and compulsivity on neural correlates of model-based performance

Impaired goal-directed behavior is associated with a range of mental disorders, implicating underlying transdiagnostic factors. While compulsivity has been linked to reduced model-based (MB) control, impulsivity has rarely been studied in the context of reinforcement learning despite its links to reward processing and cognitive control. This study investigated the neural mechanisms underlying MB control and the influence of impulsivity and compulsivity, using EEG data from 238 individuals during a two-step decision making task. Single-trial analyses revealed a modulation of the feedback-related negativity (FRN), where amplitudes were higher after common transitions and positive reward prediction error (RPE), indicating a valence effect. Meanwhile, enhanced P3 amplitudes after rare transitions and both positive and negative RPE possibly reflect surprise. In a second step, we regressed the mean b values of the effect of RPE on the EEG signals onto self-reported impulsivity and compulsivity and behavioral MB control (w). The effect of RPE on FRN-related activity was mainly associated with higher w scores, linking the FRN to MB control. Crucially, the modulation of the P3 by RPE was negatively associated with compulsivity, pointing to a deficient mental model in highly compulsive individuals.

Why stock analysts may make wrong predictions?

Investors and financial analysts employ various mathematical and statistical methods to forecast stock prices. An investor or a stock analyst with a superior forecasting method can harvest huge and almost riskless profits. This is the reason why so many people are investing time and money enthusiastically in developing methods to correctly predict future stock prices. In this note, we demonstrate that even if a sophisticated investor or a stock analyst has superior (but not perfect) forecasting methods, she/he can suffer from a large false positive error. We provide a simple formula for the probability of the rate of False Positives, and analyze its properties.

Safeguards-related event detection in surveillance video using semi-supervised learning approach

We develop a deep learning model employing a semi-supervised learning approach, which can detect automatically safeguards-related events in nuclear facility from surveillance video. Our model is designed after a comprehensive analysis of the trends in artificial intelligence-based models to identify abnormal events in video. Our model incorporates a reconstruction module and a prediction module independently. The reconstruction module is trained to generate video frames within a sliding window, while the prediction module is trained to predict future motion feature based on the motion features within the video frames in a sliding window. Each module utilizes an autoencoder with a memory module positioned between an encoder and an decoder of the autoencoder. We evaluate the model's performance using a benchmark dataset and a self-produced dataset obtained from facility related to pyroprocessing. Our model's performanace is comparable to or superior to that of the prevous models from the benchmark dataset analysis, and all the abnormal events can be detected without false positive error from the self-produced dataset analysis.

Distributed midbrain responses signal the content of positive identity prediction errors

Recent work across species has shown that midbrain dopamine neurons signal not only errors in the prediction of reward value but also in the prediction of value-neutral sensory features. To support learning of associative structures in downstream areas, identity prediction errors (iPEs) should signal specific information about the mis-predicted outcome. Here, we used pattern-based analysis of functional magnetic resonance imaging (fMRI) data acquired during reversal learning to characterize the information content of iPE responses in the human midbrain. We find that fMRI responses to value-neutral identity errors contain information about the identity of the unexpectedly received reward (positive iPE+) but not about the identity of theomitted reward (negative iPE-). Exploratory analyses revealed representations of iPE- in the dorsomedial prefrontal cortex. These results demonstrate that ensemble midbrain responses to value-neutral identity errors convey information about the identity of unexpectedly received outcomes, which could shape the formation of novel stimulus-outcome associations that constitute cognitive maps.