Although polyp size dictates surveillance intervals, endoscopists often estimate polyp size inaccurately. We hypothesized that an intervention providing didactic instruction and real-time feedback could significantly improve polyp size classification. We conducted a multicenter randomized controlled trial to evaluate the impact of different components of an online educational module on polyp sizing. Participants were randomized to control (no video, no feedback), video only, feedback only, or video + feedback. The primary outcome was accuracy of polyp size classification into clinically relevant categories (diminutive [1-5mm], small [6-9mm], large [≥10mm]). Secondary outcomes included accuracy of exact polyp size (inmm), learning curves, and directionality of inaccuracy (over- vs. underestimation). 36 trainees from five training programs provided 1360 polyp size assessments. The feedback only (80.1%, P=0.01) and video + feedback (78.9%, P=0.02) groups had higher accuracy of polyp size classification compared with controls (71.6%). There was no significant difference in accuracy between the video only group (74.4%) and controls (P=0.42). Groups receiving feedback had higher accuracy of exact polyp size (inmm) and higher peak learning curves. Polyps were more likely to be overestimated than underestimated, and 29.3% of size inaccuracies impacted recommended surveillance intervals. Our online educational module significantly improved polyp size classification. Real-time feedback appeared to be a critical component in improving accuracy. This scalable and no-cost educational module could significantly decrease under- and overutilization of colonoscopy, improving patient outcomes while increasing colonoscopy access.

We regularly form impressions of who a person is from their voice, such that we can readily categorise people as being female or male, child or adult, trustworthy or not, and can furthermore recognise who specifically is speaking. How we establish mental representations for such categories of person characteristics has, however, only been explored in detail for voice identity learning. In a series of experiments, we therefore set out to examine whether and how listeners can learn to recognise a novel person characteristic. We specifically asked how diagnostic acoustic properties underpinning category distinctions inform perceptual judgements. We manipulated recordings of voices to create acoustic signatures for a person's handedness (left-handed vs. right-handed) in their voice. After training, we found that listeners were able to successfully learn to recognise handedness from voices with above-chance accuracy, although no significant differences in accuracy between the different types of manipulation emerged. Listeners were, furthermore, sensitive to the specific distributions of acoustic properties that underpinned the category distinctions. We, however, also find evidence for perceptual biases that may reflect long-term prior exposure to how voices vary in naturalistic settings. These biases shape how listeners use acoustic information in the voices when forming representations for distinguishing handedness from voices. This study is thus a first step to examine how representations for novel person characteristics are established, outside of voice identity perception. We discuss our findings in light of theoretical accounts of voice perception and speculate about potential mechanisms that may underpin our results.

Developing, validating, and comparing an analytical method to simultaneously detect z-drugs in urine samples using the QuEChERS approach with both liquid chromatography and gas chromatography-tandem mass spectrometry

To compare the performance of the ACS NSQIP "universal" risk calculator (N-RC) to operation-specific RCs. Resources have been directed toward building operation-specific RCs because of an implicit belief that they would provide more accurate risk estimates than the N-RC. However, operation-specific calculators may not provide sufficient improvements in accuracy to justify the costs in development, maintenance, and access. For the N-RC, a cohort of 5,020,713 NSQIP patient records were randomly divided into 80% for machine learning algorithm training and 20% for validation. Operation-specific risk calculators (OS-RC) and OS-RCs with operation-specific predictors (OSP-RC) were independently developed for each of 6 operative groups (colectomy, whipple pancreatectomy, thyroidectomy, abdominal aortic aneurysm (open), hysterectomy/myomectomy, and total knee arthroplasty) and 14 outcomes using the same 80%/20% rule applied to the appropriate subsets of the 5M records. Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), and Hosmer-Lemeshow (H-L) P values, for 13 binary outcomes, and mean squared error for the length of stay outcome. The N-RC was found to have greater AUROC (P = 0.002) and greater AUPRC (P < 0.001) compared to the OS-RC. No other statistically significant differences in accuracy, across the 3 risk calculator types, were found. There was an inverse relationship between the operation group sample size and magnitude of the difference in AUROC (r = -0.278; P = 0.014) and in AUPRC (r = -0.425; P < 0.001) between N-RC and OS-RC. The smaller the sample size, the greater the superiority of the N-RC. While operation-specific RCs might be assumed to have advantages over a universal RC, their reliance on smaller datasets may reduce their ability to accurately estimate predictor effects. In the present study, this tradeoff between operation specificity and accuracy, in estimating the effects of predictor variables, favors the N-R, though the clinical impact is likely to be negligible.

IntroductionPrevious studies have shown that music training modulates adults’ categorical perception of Mandarin tones. However, the effect of music training on tone categorical perception ability in individuals in Chinese dialect areas remains unclear.MethodsWe recruited three groups of college students majoring in music in Chongqing, a dialect area in southwestern China. These groups included an instrumental music group (n = 31), a vocal music group (n = 32), and an instrumental-vocalist music group (n = 30). Additionally, we recruited a group of college students who did not receive any music training (n = 32). The accuracy and reaction time of the four groups were measured using the oddball task and compared to explore the differences in their tone categorical perception ability.ResultsConsidering both between-tone category and within-tone category, the three music groups showed both greater accuracy and shorter reaction times than the non-music group. Regarding the three music groups, no significant differences in accuracy and reaction time were detected between the instrumental and vocal groups. However, the instrumental vocalist group outperformed both the instrumental and vocal groups in terms of accuracy and reaction time.DiscussionResults showed that music training seems to have a positive effect on the categorical perception of Mandarin tone categories in Chinese dialect areas, and demonstrated that the combination of instrumental and vocal music training can further enhance tone categorical perception. To some extent, these findings provide a theoretical basis for the improvement of individual tone perception ability in dialect areas, and provided theoretical support for music and language education.

Gemcitabine is a chemotherapeutic agent used to treat various malignancies, including breast and bladder cancer. In the current study, three innovative selective gemcitabine hydrochloride sensors are developed using 4-tert-butylcalix-[8]-arene (sensor 1), β-cyclodextrin (sensor 2), and γ-cyclodextrin (sensor 3) as ionophores. The three sensors were prepared by incorporating the ionophores with o-nitrophenyl octyl ether as plasticizer and potassium tetrakis(4-chlorophenyl) borate as ionic additive into a polyvinyl chloride polymer matrix. These sensors are considered environmentally friendly systems in the analytical research. The linear responses of gemcitabine hydrochloride were in the concentration range of 6.0 × 10-6 to 1.0 × 10-2 mol L-1 and 9.0 × 10-6 to 1.0 × 10-2 mol L-1 and 8.0 × 10-6 to 1.0 × 10-2 mol L-1 for sensors 1, 2, and 3, respectively. Over the pH range of 6-9, fast-Nernst slopes of 52 ± 0.6, 56 ± 0.3, and 55 ± 0.8 mV/decade were found in the same order with correlation regressions of 0.998, 0.999, and 0.998, respectively. The lower limits of detection for the prepared sensors were 2.5 × 10-6, 2.2 × 10-6, and 2.7 × 10-6 mol L-1. The sensors showed high selectivity and sensitivity for gemcitabine. Validation of the sensors was carried out in accordance with the requirements established by the IUPAC, while being inexpensive and easy to use in drug formulation. A statistical analysis of the methods in comparison with the official method showed that there was no significant difference in accuracy or precision between them. It was shown that the new sensors could selectively and accurately find gemcitabine hydrochloride in bulk powder, pharmaceutical formulations, and quality control tests. The ionophore-based sensor shows several advantages over conventional PVC membrane sensor sensors regrading the lower limit of detection, and higher selectivity towards the target ion.

Effects of short-term exposure to moderate pure carbon dioxide levels on cognitive performance, health symptoms and perceived indoor environment quality

A - 175 EEG Phase Can be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and SNR.

ObjectivesThe study aimed to comparatively assess the accuracy and retention of molding plates made of polyethylene terephthalate modified with glycol (PET-G) and polymethyl methacrylate (PMMA) materials, while also investigating the effect of adhesive addition on retention.DesignA cross-sectional clinical study.Patients/settingThe study included 30 infants diagnosed with non-syndromic cleft lip and palate (16 unilateral, 14 bilateral). Two molding plates were fabricated for each infant, and their accuracy and retention were evaluated. The data were analyzed using independent t-tests, Mann-Whitney U tests, and Wilcoxon rank tests.Main outcome measuresAccuracy: The accuracy of the molding plates was assessed by measuring the virtual gap between the inner surface of the plates and their working cast using Exocad software. The accuracy was evaluated in different regions (anterior, middle, and posterior) and compared between PET-G and PMMA materials. Retention: The retention of the molding plates was measured using a digital force gauge, which recorded the force required to dislodge the plates from the infant's mouth. The retention was compared between PET-G and PMMA materials, as well as the effect of adhesive addition on retention. Overall adaptation; PET-G plates showed a significantly smaller gap (mean= 0.264 ±0.106) compared to PMMA (mean= 0.362 ±0.130). Region-specific adaptation: PET-G plates demonstrated better accuracy in all regions (means of anterior =0.246, middle =0.262, posterior =0.282 µm). Significant differences in accuracy were observed in the middle and posterior regions compared to PMMA. Retention assessment revealed that the PET-G groups (with or without adhesive) exhibited significantly higher retention compared to the PMMA groups (P<0.01).ConclusionPET-G plates demonstrated superior accuracy and retention compared to PMMA, with a significant difference observed in both accuracy and retention. Furthermore, the addition of denture adhesive had a positive effect on retention for both materials.

Perception of environmental sounds (PES), encompassing informationally and/or aesthetically salient nonspeech, nonmusical sounds, is crucial for safety, independence, and quality-of-life among listeners with hearing loss. The aims of this study were (1) to systematically review methodologies used to assess PES among cochlear implant (CI) users and identify performance trends in this population, and (2) to present preliminary data using a novel PES task requiring inference of materials and actions generating environmental sounds. For aim 1, PES in quiet using open- or closed-set response formats were most commonly used. PES accuracy in pediatric (3 studies) and adult (16 studies) CI users was highly variable but generally mediocre (mean correct: 31–87%). Most studies were cross-sectional; only two evaluated PES prospectively before and after CI. No significant differences in accuracy were reported between CI candidates and CI users. PES correlated in with measures of speech perception and spectro-temporal processing. For aim 2, we present preliminary findings on identification of materials and actions responsible for generating environmental sounds among normal hearing listeners and listeners with hearing loss using hearing aids or CIs. We discuss the types of materials and actions that are most difficult for listeners with hearing loss and common sources of confusion.

Food recognition plays a crucial role in various fields, including healthcare, nutrition, and the food industry. It involves identifying different types of foods or dishes from images, videos, or other data sources. In healthcare, food recognition aids individuals in monitoring their daily food intake and managing their diet. It also assists dietitians and nutritionists in creating personalized meal plans based on patients' nutritional requirements and preferences. This article focuses on the development of software that can recognize food products and predict their nutritional facts. The software extracts essential nutritional facts such as fat, carbohydrates, protein, and energy from the food products and compiles them into a comprehensive list. For each of the 20 food products, 36 food images were obtained, resulting in a total of 720 food images. To validate the accuracy of the trained models, six different images of each food product were set aside for external validation purposes. The rest of the images were then trained using deep learning algorithms, namely, GoogleNet, ResNet-50, and Inception-v3, in the MATLAB software. The training and validation processes yielded over 98% correct predictions for each of the deep learning algorithms. Although there were no significant differences in accuracy among the algorithms, GoogleNet stood out when considering both prediction accuracy and prediction time. The validated deep learning algorithms were employed in developing the software for food recognition and nutritional value determination. The results indicate that the developed software can reliably identify foods and provide their corresponding nutritional facts. This software holds significant potential for application in the nutrition and dietetic field and can be particularly useful in healthcare settings for monitoring the dietary intake of patients with chronic diseases such as diabetes, heart disease, or obesity. The system can track the types and quantities of foods consumed, offering personalized feedback to patients and healthcare providers.

BackgroundGenomic data-based machine learning tools are promising for real-time surveillance activities performing source attribution of foodborne bacteria such as Listeria monocytogenes. Given the heterogeneity of machine learning practices, our aim was to identify those influencing the source prediction performance of the usual holdout method combined with the repeated k-fold cross-validation method.MethodsA large collection of 1 100 L. monocytogenes genomes with known sources was built according to several genomic metrics to ensure authenticity and completeness of genomic profiles. Based on these genomic profiles (i.e. 7-locus alleles, core alleles, accessory genes, core SNPs and pan kmers), we developed a versatile workflow assessing prediction performance of different combinations of training dataset splitting (i.e. 50, 60, 70, 80 and 90%), data preprocessing (i.e. with or without near-zero variance removal), and learning models (i.e. BLR, ERT, RF, SGB, SVM and XGB). The performance metrics included accuracy, Cohen’s kappa, F1-score, area under the curves from receiver operating characteristic curve, precision recall curve or precision recall gain curve, and execution time.ResultsThe testing average accuracies from accessory genes and pan kmers were significantly higher than accuracies from core alleles or SNPs. While the accuracies from 70 and 80% of training dataset splitting were not significantly different, those from 80% were significantly higher than the other tested proportions. The near-zero variance removal did not allow to produce results for 7-locus alleles, did not impact significantly the accuracy for core alleles, accessory genes and pan kmers, and decreased significantly accuracy for core SNPs. The SVM and XGB models did not present significant differences in accuracy between each other and reached significantly higher accuracies than BLR, SGB, ERT and RF, in this order of magnitude. However, the SVM model required more computing power than the XGB model, especially for high amount of descriptors such like core SNPs and pan kmers.ConclusionsIn addition to recommendations about machine learning practices for L. monocytogenes source attribution based on genomic data, the present study also provides a freely available workflow to solve other balanced or unbalanced multiclass phenotypes from binary and categorical genomic profiles of other microorganisms without source code modifications.

The efficacy of isolated and relative performance indicators (PIs) has been compared in rugby union; the latter more effective at discerning match outcomes. However, this methodology has not been applied in women's rugby. The aim of this study was to identify PIs that maximize prediction accuracy of match outcome, from isolated and relative data sets, in women's rugby union. Twenty-six PIs were selected from 110 women's international rugby matches between 2017 and 2022 to form an isolated data set, with relative data sets determined by subtracting corresponding opposition PIs. Random forest classification was completed on both data sets, and feature selection and importance were used to simplify models and interpret key PIs. Models were used in prediction on the 2021 World Cup to evaluate performance on unseen data. The isolated full model correctly classified 75% of outcomes (CI, 65%-82%), whereas the relative full model correctly classified 78% (CI, 69%-86%). Reduced respective models correctly classified 74% (CI, 65%-82%) and 76% (CI, 67%-84%). Reduced models correctly predicted 100% and 96% of outcomes for isolated and relative test data sets, respectively. No significant difference in accuracy was found between data sets. In the relative reduced model, meters made, clean breaks, missed tackles, lineouts lost, carries, and kicks from hand were significant. Increased relative meters made, clean breaks, carries, and kicks from hand and decreased relative missed tackles and lineouts lost were associated with success. This information can be utilized to inform physical and tactical preparation and direct physiological studies in women's rugby.

Zinc oxide nanoparticles (ZnONPs) are used to modify carbon paste electrodes for a fast, stable, and sensitive electrochemical determination of difloxacin HCl in pharmaceutical formulation and their photodegradation rate. The small particle size and large surface area of ZnONPs enhance the diffusion rates of the chosen target analyte to many accessible binding sites, increasing the range of the linear structure with a low detection limit and improving the stability and lifetime of the electrode. An initial optimization study included sixteen modified carbon paste electrodes to achieve optimal electrode composition in carbon paste composition, plasticizer type, and the amount of ZnO nanoparticles added as a modifier. We studied the performance parameter of two electrodes, one with ZnONPs (E11) and the other without (E5) to evaluate using ZnONPs as a modifier. The optimized electrode is electrode number eleven (E11), with a paste composition of 39% graphite powder, 39% dibutyl phthalate (DBP) as a plasticizer, 15% difloxacin HCl-sodium tetraphenylborate (DIF-TPB) ion-pair as an ion exchanger, and 7% zinc oxide nanoparticles (ZnONPs) as a modifier, it maintained a Nernstian response over 4 months, with a slope of (57.0 ± 0.4) over the linearity concentration range 6.3 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 10−6–10−2 M with a correlation coefficient (r2) ≥ 0.998 and detection limit 6.3 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 10−6 M within pH range 2–5. The applications of the proposed electrodes are the determination of difloxcine HCl pharmaceuticals formulation and their photocatalytic degradation rate in water samples. The statistical comparison shows no significant difference in accuracy or precision between the reported and the proposed methods.Graphical

SYNOPSIS Using a sample of S&amp;P 500 companies, this study constructs a measure of CEO narcissism and examines whether and how it impacts the accuracy and dispersion of analysts’ forecasts. Empirical evidence suggests that firms with narcissistic CEOs have higher accuracy and lower dispersion of such forecasts. In investigating the mechanism through which CEO narcissism impacts these properties, we find that firms with narcissistic CEOs are more likely to issue management earnings guidance, albeit less accurate, which results in significant differences in accuracy and dispersion of analysts’ forecasts. This study concludes that through more management voluntary disclosure, CEO narcissism has a positive impact on the accuracy and a negative impact on the dispersion of financial analysts’ forecasts. Although several studies have explored the effect of CEO narcissism on corporate financial reporting, this is the first study to expand such inquiry into the sector of financial analysts. JEL Classifications: G17; M12; M41.

Considering the green chemistry perspective and improving the environmental impact of quality control labs; two direct techniques with less hazardous solvents, less waste production and less energy consumption were developed for simultaneous analysis of Aspirin and Metoclopramide in bulk powder and pharmaceutical formulation. The ratio between the two drugs in their co-formulated preparation is very challenging; (90: 1, Aspirin: Metoclopramide). The first technique is spectrophotometry using simple mathematical operations; ratio difference and derivative ratio–zero crossing. The second technique is high-performance thin-layer chromatography (HPTLC) -densitometry which used a mobile phase consisting of cyclo-hexane: methanol: methylene chloride in a ratio of (1:4:1, v/v/v). The greenest solvents which give acceptable resolution were chosen. Following the International Conference on Harmonization (ICH) guidelines, the methods were found to be accurate, precise, and selective. Those methods were statistically compared to the reported spectrophotometric method and the results proved that there is no significant difference in accuracy and precision. Furthermore, the developed methods were assessed using the Analytical Eco-scale, Green Analytical Procedure Index (GAPI) and the Analytical Greenness calculator (AGREE), which gave a full image about their greenness profile. The spectrophotometry was found to be an excellent green technique compared to HPTLC with was considered an acceptable green one. The developed HPTLC-densitometric method was used for the first time for the analysis of this binary mixture. The two proposed spectrophotometric methos have advantages over the published methods as they used easy manipulation steps and are applied on the market pharmaceutical formulation. Owing to the advantages of the developed techniques; being green, do not require expensive sophisticated equipment or large volume of solvents; they could be used for routine analysis in quality control aspects.

Based on Kellogg's writing model, Skehan's Limited Attentional Capacity Model (LACM), and Robinson's Cognition Hypothesis, our study investigated the effects of cognitive task complexity on syntactic complexity, lexical complexity, accuracy, fluency, and functional adequacy in Chinese L2 students' argumentative writing, when students were under an online planning condition. Sixty-eight participants from a Chinese university were recruited to complete two writing tasks with task complexity varied in terms of [+ argument elements]. The findings showed that increasing task complexity led to decreased subordination in terms of clauses per T-unit and dependent clauses per clause, increased phrasal elaboration in terms of coordinate phrases per clause, and no changes in mean length of T-unit, T-units per sentence, mean length of clause, and complex nominals per clause. Neither significant differences in accuracy nor fluency were found as a function of increasing task complexity. Detrimental effects on functional adequacy in content, organization, and overall scores were identified with the increases in task complexity. The trade-offs between syntactic and lexical complexity and between syntactic complexity and functional adequacy support the basic principle of Skehan's LACM that human's information processing capacity is limited and Kellogg's claim that learners have a limited central executive capacity in writing. Implications of the results of this research are discussed.

Previous literature has indicated conflicting results regarding a response time bias favouring words indicating large real-world objects (RWO) over words indicating small RWO during a lexical decision task. This study aimed to replicate an original experiment and, expanding on it, disentangle possible alternatives for why this effect is sometimes observed and sometimes not. The same methods as the original study were followed, and the results were inconsistent with all previously published findings. Although no significant difference was observed for response time, the findings indicated a significant difference in accuracy and inverse efficiency scores such that "large" words were recognised significantly more accurately than "small" words. After examining several linguistic dimensions that may also contribute to response time, statistical models accounting for these dimensions yielded a significant and increased effect size for the response time size rating of words in our sample from the United States. Our findings indicate that there is a cognitive bias favouring words representing large RWO over small ones but suggest several additional linguistic factors need to be controlled for it to be detected consistently in response time.

A selective, sensitive and low sample consumption GC method for determination of residual solvents in PSMA samples was developed. Selectivity with respect to solvents such as ethanol, diethyl ether, 2-propanole, acetonitrile, dichloromethane, used in manufacturing of PSMA-11, was obtained on two different GC systems and columns. In all these cases, good separation and other system suitability parameters were obtained. The values of detection limit (LOD) and limit of quantitation (LOQ) were estimated and verified experimentally for ethanol, diethyl ether, 2-propanol (IPA), acetonitrile (ACN) and dichloromethane. Full validation parameters i.e.: selectivity, limit of detection (LOD), limit of quantitation (LOQ), linearity, precision, accuracy, robustness and system suitability parameters are presented with respect to ACN and IPA. Accuracy and precision were measured on two GC systems. Statistical tests exhibit, that there are no significant differences in accuracy and precision between two GC systems

Immersive Virtual Reality (IVR) has gained popularity in neurorehabilitation for its potential to increase patients’ motivation and engagement. A crucial yet relatively unexplored aspect of IVR interfaces is the patients’ representation in the virtual world, such as with an avatar. A higher level of embodiment over avatars has been shown to enhance motor performance during upper limb training and has the potential to be employed to enhance neurorehabilitation. However, the relationship between avatar embodiment and gait performance remains unexplored. In this work, we present the results of a pilot study with 12 healthy young participants that evaluates the effect of different virtual lower limb representations on foot placement accuracy while stepping over a trail of 16 virtual targets. We compared three levels of virtual representation: i) a full-body avatar, ii) only feet, and iii) no representation. Full-body tracking is computed using standard VR trackers to synchronize the avatar with the participants’ motions. Foot placement accuracy is measured as the distance between the foot’s center of mass and the center of the selected virtual target. Additionally, we evaluated the level of embodiment over each virtual representation through a questionnaire. Our findings indicate that foot placement accuracy increases with some form of virtual representation, either full-body or foot, compared to having no virtual representation. However, the foot and full-body representations do not show significant differences in accuracy. Importantly, we found a negative correlation between the level of embodiment of the foot representation and the distance between the placed foot and the target. However, no such correlation was found for the full-body representation. Our results highlight the importance of embodying a virtual representation of the foot when performing a task that requires accurate foot placement. However, showing a full-body avatar does not appear to further enhance accuracy. Moreover, our results suggest that the level of embodiment of the virtual feet might modulate motor performance in this stepping task. This work motivates future research on the effect of embodiment over virtual representations on motor control to be exploited for IVR gait rehabilitation.