Development of a monoclonal antibody sandwich ELISA for the quality control of human and animal tetanus vaccines.

Antigen identity, quantity and integrity are key factors to be evaluated as part of consistency testing of tetanus vaccines. Here we have developed a mAb sandwich ELISA to measure the relative amount and quality of tetanus toxoid (TTxd) in human and animal tetanus vaccines. The ELISA is highly specific, has good dilutional linearity and is suitable for detecting TTxd in a range of different products. We have demonstrated the ability of the assay to discriminate between batches of different content, using vaccine batches that had been prepared to contain differing amounts of TTxd, and of different quality, using samples of non-adjuvanted TTxd that had been exposed to sonication and final lot vaccines that had been exposed to heat or oxidative stress. We have also demonstrated successful transfer of the method to other laboratories and have shown that different tetanus antigen materials may be able to serve as a reference antigen for standardisation of the method. The results show this test has the potential to play a key role in a control strategy no longer including an in vivo potency test.

Tetanus vaccines help to protect against tetanus infection. Currently, animal tests are used to ensure the potency of such vaccines. Since these tests were first introduced, there have been improvements in non-animal technologies that can be used to ensure consistent production of potent vaccine batches. To demonstrate that a new batch of tetanus vaccine is consistent with a previous batch of known potency, the quality and amount of the component that stimulates the immune response upon vaccination must be assessed in comparison. We have developed an assay that can measure the quality of a range of different tetanus vaccine product types. The assay is very specific and reliable, and different laboratories obtained comparable results, showing that the assay is suited for routine use. Once validated by manufacturers and accepted by regulators, this assay will greatly reduce the number of animals needed for batch release of tetanus vaccines.

Consistency and stability of individualized cortical functional networks parcellation at 3.0 T and 5.0 T MRI.

Background: Individualized cortical functional networks parcellation has been reported as highly reproducible at 3.0 T. However, in view of the complexity of cortical networks and the greatly increased sensitivity provided by ultra-high field 5.0 T MRI, the parcellation consistency between different magnetic fields is unclear. Purpose: To explore the consistency and stability of individualized cortical functional networks parcellation at 3.0 T and 5.0 T MRI based on spatial and functional connectivity analysis. Materials and methods: Thirty healthy young participants were enrolled. Each subject underwent resting-state fMRI at both 3.0 T and 5.0 T in a random order in less than 48 h. The individualized cortical functional networks was parcellated for each subject using a previously proposed iteration algorithm. Dice coefficient was used to evaluate the spatial consistency of parcellated networks between 3.0 T and 5.0 T. Functional connectivity (FC) consistency was evaluated using the Euclidian distance and Graph-theory metrics. Results: A functional cortical atlas consisting of 18 networks was individually parcellated at 3.0 T and 5.0 T. The spatial consistency of these networks at 3.0 T and 5.0 T for the same subject was significantly higher than that of inter-individuals. The FC between the 18 networks acquired at 3.0 T and 5.0 T were highly consistent for the same subject. Positive cross-subject correlations in Graph-theory metrics were found between 3.0 T and 5.0 T. Conclusion: Individualized cortical functional networks at 3.0 T and 5.0 T showed consistent and stable parcellation results both spatially and functionally. The 5.0 T MR provides finer functional sub-network characteristics than that of 3.0 T.

The prediction of occupational health risks of n-Hexane in small and micro enterprises within China's printing industry using five occupational health risk assessment models.

Background: Chronic n-Hexane poisoning is prevalent among workers in small and micro printing industries in China. Despite this, there is limited research on occupational health risk assessment in these sectors. Conducting comprehensive risk assessments at key positions and proposing effective countermeasures are essential. Methods: Data were collected from 84 key positions across 32 small and micro-sized printing enterprises. Air samples were tested for n-Hexane exposure levels in accordance with Chinese standards. Five risk assessment models were employed: COSHH, EPA, MOM, ICMM, and Technical Guide GBZ/T 289-2017 of China. The consistency of results across these models was analyzed. Results: Workers in 84 job positions were categorized into four exposure groups, with exposure to n-Hexane for 8-10 h daily, 5-6 days weekly. Most positions operated with low automation levels (96.9% in printing, 5.9% in oil blending, and 42.9% in pasting), while others were manual. Localized ventilation rates were notably low in oil blending (23.5%), cleaning (14.3%), and pasting (9.5%) groups. n-Hexane concentrations exceeded Chinese occupational limits in 15.6% of printing, 17.7% of oil blending, and 21.4% of cleaning groups. Risk assessment models identified over 60% of work groups as high risk. Significant differences (p < 0.05) were found among the seven risk assessment methods. Consistency analysis revealed moderate agreement between the Chinese synthesis index and exposure index methods (k = 0.571, p < 0.01). Conclusion: The Chinese synthesis and exposure index methods from Technical Guide GBZ/T 289-2017 are practical and reliable for assessing n-Hexane exposure risks in small and micro printing enterprises. Cleaning and printing roles were found to be at the highest risk for n-Hexane exposure. These findings provide valuable insights for targeted risk management strategies to protect workers' health in the industry.

Validation of the basic need satisfaction for sport scale in Ethiopian athletes.

By anchoring on the self-determination theory in an Ethiopian context, this study tried to establish the basic need satisfaction sport scales (BNSSS) reliability and validity. Despite the scale's usefulness in measuring athletes' psychological need fulfillment during a sporting event, no study has proven the scale's validity in a setting of Ethiopian sports. To validate the BNSSS scale, confirmatory factor analysis was used in the study. The 20 items of the BNSSS questionnaire's English translation are divided into five categories: relatedness, competence, autonomy-perceived locus of internal causality, autonomy-choice, and volition. Senior language experts translated the BNSSS questionnaire into Amharic. The Amharic version of the instrument was used to gather data from 321 athletes, 174 men, and 147 women, with a mean age of 23.34 22.59 and a standard deviation of 5.08 and mean age 5.32; a standard deviation of 2.33 year of experience in their sports from four baseball games. With a Cronbach's alpha value ranging from 0.848 to 0.882 (IPLOC to Volition respectively) across the five subscales and, the results confirm the reliability of the BNSSS for evaluating satisfaction with basic needs and motivation among Ethiopian athletes." The result demonstrated an acceptable fit with the data (CFI, = 0.958, GFI, = 0.933, RMR, = 0.76, RMSEA, = 0.39) as well as internal consistency. All of the components' Cronbach's alpha values met expectations. The instrument's Amharic translation was thus valid and reliable for determining the extent to which Ethiopian athletes' basic needs were met.

Individual differences in the consistency of neural and behavioural responses to speech sounds.

There are documented individual differences among adults in the consistency of speech sound processing, both at neural and behavioural levels. Some adults show more consistent neural responses to speech sounds than others, as measured by an event-related potential called the frequency-following response (FFR); similarly, some adults show more consistent behavioural responses to native speech sounds than others, as measured by two-alternative forced choice (2AFC) and visual analog scaling (VAS) tasks. Adults also differ in how successfully they can perceive non-native speech sounds. Interestingly, it remains unclear whether these differences are related within individuals. In the current study, native English-speaking adults completed native phonetic perception tasks (2AFC and VAS), a non-native (German) phonetic perception task, and an FFR recording session. From these tasks, we derived measures of the consistency of participants' neural and behavioural responses to native speech as well as their non-native perception ability. We then examined the relationships among individual differences in these measures. Analysis of the behavioural measures revealed that more consistent responses to native sounds predicted more successful perception of unfamiliar German sounds. Analysis of neural and behavioural data did not reveal clear relationships between FFR consistency and our phonetic perception measures. This multimodal work furthers our understanding of individual differences in speech processing among adults, and may eventually lead to individualized approaches for enhancing non-native language acquisition in adulthood.

Consistency of CSCO AI with Multidisciplinary Clinical Decision-Making Teams in Breast Cancer: A Retrospective Study.

Background: The Chinese Society of Clinical Oncology Artificial Intelligence System (CSCO AI) serves as a clinical decision support system developed utilizing Chinese breast cancer data. Our study delved into the congruence between breast cancer treatment recommendations provided by CSCO AI and their practical application in clinical settings. Methods: A retrospective analysis encompassed 537 breast cancer patients treated at the Second Affiliated Hospital of Anhui Medical University between January 2017 and December 2022. Proficient senior oncology researchers manually input patient data into the CSCO AI system. "Consistent" and "Inconsistent" treatment categories were defined by aligning our treatment protocols with the classification system in the CSCO AI recommendations. Cases that initially showed inconsistency underwent a second evaluation by the Multi-Disciplinary Treatment (MDT) team at the hospital. Concordance was achieved when MDTs' treatment suggestions were in the 'Consistent' categories. Results: An impressive 80.4% concurrence was observed between actual treatment protocols and CSCO AI recommendations across all breast cancer patients. Notably, the alignment was markedly higher for stage I (85.02%) and stage III (88.46%) patients in contrast to stage II patients (76.06%, P=0.023). Moreover, there was a significant concordance between invasive ductal carcinoma and lobular carcinoma (88.46%). Interestingly, triple-negative breast cancer (TNBC) exhibited a high concordance rate (87.50%) compared to other molecular subtypes. When contrasting MDT-recommended treatments with CSCO AI decisions, an overall 92.4% agreement was established. Furthermore, a logistic multivariate analysis highlighted the statistical significance of age, menstrual status, tumor type, molecular subtype, tumor size, and TNM stage in influencing consistency. Conclusion: In the realm of breast cancer treatment, the alignment between recommendations offered by CSCO AI and those from MDT is predominant. CSCO AI can be a useful tool for breast cancer treatment decisions.

[Development and psychometric properties of the Japanese version of Child and Youth Resilience Measure-Revised (CYRM-R) among Japanese youth].

Objectives　Resilience is the ability to navigate adversity and recover from challenging situations. Developing resilience at a young age promotes mental health and provides benefits throughout one's lifespan. Validated measures for assessing resilience at a young age are required in both clinical practice and epidemiological studies. The Child and Youth Resilience Measure-Revised (CYRM-R) is a tool used worldwide to assess resilience in children and young people. The objective of this study was to develop a Japanese version of the CYRM-R and investigate its psychometric properties.Methods　Children in grades 5-9 were recruited via a two-stage cluster random sampling taken from the Japan Adolescent and Youth (JAY) Longitudinal Cohort Study. The participants completed the Japanese version of the CYRM-R . The CYRM-R consists of two subscales, personal resilience and caregiver resilience, with a total of 17 items. The linguistic validity of the Japanese version of the CYRM-R was ensured through translation and back-translation.Cronbach's alpha coefficients were examined for the total score and the two subscales to assess the internal consistency of the Japanese version of the CYRM-R scale. In addition, correlation coefficients of the subscales were calculated. For factorial validity, a confirmatory factor analysis was conducted on the same two-factor structure as in the original version to assess the model's goodness of fit. Results　A total of 2,266 children (50.0% male) were included in the study. The overall Cronbach's alpha of the Japanese version of the CYRM-R obtained from this sample was 0.956; For the personal resilience subscale, Cronbach's alpha was 0.932; for the caregiver resilience subscale, it was 0.919. Significant positive correlations were also found between subscales (r = 0.827, p < 0.001). A confirmatory factor analysis was conducted using a two-factor structure for validity. The model fit was good (RMSEA = 0.085, SRMR = 0.041, CFI = 0.934).Conclusion　The Japanese version of the CYRM-R maintained the same two-factor structure as the original version. The study findings showed that the Japanese version of the CYRM-R had adequate internal consistency and factorial validity for assessing resilience in children and youth. Therefore, this scale is a valuable tool for identifying individuals or groups at risk in terms of children's resilience and for evaluating the effectiveness of support and interventions.

Query-Based Multiview Detection for Multiple Visual Sensor Networks.

In IoT systems, the goal of multiview detection for multiple visual sensor networks is to use multiple camera perspectives to address occlusion challenges with multiview aggregation being a crucial component. In these applications, data from various interconnected cameras are combined to create a detailed ground plane feature. This feature is formed by projecting convolutional feature maps from multiple viewpoints and fusing them using uniform weighting. However, simply aggregating data from all cameras is not ideal due to different levels of occlusion depending on object positions and camera angles. To overcome this, we introduce QMVDet, a new query-based learning multiview detector, which incorporates an innovative camera-aware attention mechanism for aggregating multiview information. This mechanism selects the most reliable information from various camera views, thus minimizing the confusion caused by occlusions. Our method simultaneously utilizes both 2D and 3D data while maintaining 2D-3D multiview consistency to guide the multiview detection network's training. The proposed approach achieves state-of-the-art accuracy on two leading multiview detection benchmarks, highlighting its effectiveness for IoT-based multiview detection scenarios.

ABDGAN: Arbitrary Time Blur Decomposition Using Critic-Guided TripleGAN.

Recent studies have proposed methods for extracting latent sharp frames from a single blurred image. However, these methods still suffer from limitations in restoring satisfactory images. In addition, most existing methods are limited to decomposing a blurred image into sharp frames with a fixed frame rate. To address these problems, we present an Arbitrary Time Blur Decomposition Triple Generative Adversarial Network (ABDGAN) that restores sharp frames with flexible frame rates. Our framework plays a min-max game consisting of a generator, a discriminator, and a time-code predictor. The generator serves as a time-conditional deblurring network, while the discriminator and the label predictor provide feedback to the generator on producing realistic and sharp image depending on given time code. To provide adequate feedback for the generator, we propose a critic-guided (CG) loss by collaboration of the discriminator and time-code predictor. We also propose a pairwise order-consistency (POC) loss to ensure that each pixel in a predicted image consistently corresponds to the same ground-truth frame. Extensive experiments show that our method outperforms previously reported methods in both qualitative and quantitative evaluations. Compared to the best competitor, the proposed ABDGAN improves PSNR, SSIM, and LPIPS on the GoPro test set by 16.67%, 9.16%, and 36.61%, respectively. For the B-Aist++ test set, our method shows improvements of 6.99%, 2.38%, and 17.05% in PSNR, SSIM, and LPIPS, respectively, compared to the best competitive method.

Adaptation and validation of the evidence-based practice profile (EBP2) questionnaire in a Norwegian primary healthcare setting.

BACKGROUND: Access to valid and reliable instruments is essential in the field of implementation science, where the measurement of factors associated with healthcare professionals' uptake of EBP is central. The Norwegian version of the Evidence-based practice profile questionnaire (EBP2-N) measures EBP constructs, such as EBP knowledge, confidence, attitudes, and behavior. Despite its potential utility, the EBP2-N requires further validation before being used in a cross-sectional survey targeting different healthcare professionals in Norwegian primary healthcare. This study assessed the content validity, construct validity, and internal consistency of the EBP2-N among Norwegian primary healthcare professionals. METHODS: To evaluate the content validity of the EBP2-N, we conducted qualitative individual interviews with eight healthcare professionals in primary healthcare from different disciplines. Qualitative data was analyzed using the "text summary" model, followed by panel group discussions, minor linguistic changes, and a pilot test of the revised version. To evaluate construct validity (structural validity) and internal consistency, we used data from a web-based cross-sectional survey among nurses, assistant nurses, physical therapists, occupational therapists, medical doctors, and other professionals (n = 313). Structural validity was tested using a confirmatory factor analysis (CFA) on the original five-factor structure, and Cronbach's alpha was calculated to assess internal consistency. RESULTS: The qualitative interviews with primary healthcare professionals indicated that the content of the EBP2-N was perceived to reflect the constructs intended to be measured by the instrument. However, interviews revealed concerns regarding the formulation of some items, leading to minor linguistic revisions. In addition, several participants expressed that some of the most specific research terms in the terminology domain felt less relevant to them in clinical practice. CFA results exposed partial alignment with the original five-factor model, with the following model fit indices: CFI = 0.749, RMSEA = 0.074, and SRMR = 0.075. Cronbach's alphas ranged between 0.82 and 0.95 for all domains except for the Sympathy domain (0.69), indicating good internal consistency in four out of five domains. CONCLUSION: The EBP2-N is a suitable instrument for measuring Norwegian primary healthcare professionals' EBP knowledge, attitudes, confidence, and behavior. Although EBP2-N seems to be an adequate instrument in its current form, we recommend that future research focuses on further assessing the factor structure, evaluating the relevance of the items, and the number of items needed. REGISTRATION: Retrospectively registered (prior to data analysis) in OSF Preregistration. Registration DOI: https://doi.org/10.17605/OSF.IO/428RP .

RegionSizeR- A Novel App for Regional Sample Size Planning in MRCTs.

In multi-regional clinical trials, planning the sample size for participating regions is essential for the evaluation of the treatment effect consistency across regions. Based on the MRCT design and sample size allocation to regions, consistency probability is usually used to predict the consistent trend between regions and the overall population, while preserving a certain proportion of the overall treatment effect. Specific enrollment characteristics in a region of interest should also be considered during the time of the sample size planning. To facilitate efficient and harmonized regional sample size planning, we have developed RegionSizeR, a comprehensive and user-friendly interactive web-based R shiny application that can be obtained from https://github.com/rsr-ss/RegionSizeR . This simulation-based app can serve as an initial point for discussions on sample size allocation plans, following preservation of treatment effect method in ICH E17. The app accommodates various types of endpoints and designs, including continuous, binary, and time-to-event endpoints, for superiority, non-inferiority, and MCP-Mod designs. To ensure the validity of this app, independent testing is conducted allowing a discrepancy of no more than 1% across all results considering various scenarios.

Assessing Consistency of Respondent-driven Sampling Estimators by Using Repeated Surveys among People Who Inject Drugs (PWID) in New Jersey.

Respondent-driven sampling (RDS) is widely used to sample populations with higher risk of HIV infection for whom no sampling frames exist. However, few studies have been done to assess the consistency of RDS estimators in real world settings. We conducted an assessment study on the consistency of RDS estimators using data from the National HIV Behavioral Surveillance - People Who Inject Drugs surveys in Newark, New Jersey from 2005 to 2018. Population parameter estimates based on RDS-I, RDS-II, Gile's SS, and HCG were compared longitudinally and cross-sectionally. Population homophily statistics and differential recruitment statistics were estimated and compared. Convergence plots were used for RDS diagnosis. Sensitivity analyses were conducted on population size estimates and seeds biases. By comparing time-insensitive population parameters and population homophily statistics estimated by four RDS estimators, the study found that RDS-II and Gile's SS could provide longitudinally and cross-sectionally consistent estimates and population homophily statistics on gender and sexual orientation. Cross-sectional comparison of time-sensitive population parameter estimates also supported the consistency of RDS-II and Gile's SS. However, RDS-I and HCG did not perform well in those comparisons. In conclusion, RDS estimators may not address all inconsistencies, but RDS-II and Gile's SS are recommended to weight RDS samples given enough consistency was observed in them.

Reproducibility study of intravoxel incoherent motion and apparent diffusion coefficient parameters in normal pancreas.

BACKGROUND: The consistency of pancreatic apparent diffusion coefficient (ADC) values and intravoxel incoherent motion (IVIM) parameter values across different magnetic resonance imaging (MRI) devices significantly impacts the patient's diagnosis and treatment. AIM: To explore consistency in image quality, ADC values, and IVIM parameter values among different MRI devices in pancreatic examinations. METHODS: This retrospective study was approved by the local ethics committee, and informed consent was obtained from all participants. In total, 22 healthy volunteers (10 males and 12 females) aged 24-61 years (mean, 28.9 ± 2.3 years) underwent pancreatic diffusion-weighted imaging using 3.0T MRI equipment from three vendors. Two independent observers subjectively scored image quality and measured the pancreas's overall ADC values and signal-to-noise ratios (SNRs). Subsequently, regions of interest (ROIs) were delineated for the IVIM parameters (true diffusion coefficient, pseudo-diffusion coefficient, and perfusion fraction) using post-processing software. These ROIs were on the head, body, and tail of the pancrease. The subjective image ratings were assessed using the kappa consistency test. Intraclass correlation coefficients (ICCs) and mixed linear models were used to evaluate each device's quantitative parameter values. Finally, a pairwise analysis of IVIM parameter values across each device was performed using Bland-Altman plots. RESULTS: The Kappa value for the subjective ratings of the different observers was 0.776 (P < 0.05). The ICC values for inter-observer and intra-observer agreements for the quantitative parameters were 0.803 [95% confidence interval (CI): 0.684-0.880] and 0.883 (95%CI: 0.760-0.945), respectively (P < 0.05). The ICCs for the SNR between different devices was comparable (P > 0.05), and the ICCs for the ADC values from different devices were 0.870, 0.707, and 0.808, respectively (P < 0.05). Notably, only a few statistically significant inter-device agreements were observed for different IVIM parameters, and among those, the ICC values were generally low. The mixed linear model results indicated differences (P < 0.05) in the f-value for the pancreas head, D-value for the pancreas body, and D-value for the pancreas tail obtained using different MRI machines. The Bland-Altman plots showed significant variability at some data points. CONCLUSION: ADC values are consistent among different devices, but the IVIM parameters' repeatability is moderate. Therefore, the variability in the IVIM parameter values may be associated with using different MRI machines. Thus, caution should be exercised when using IVIM parameter values to assess the pancreas.

Assessing the inter-observer and intra-observer reliability of radiographic measurements for size-specific dose estimates.

BACKGROUND: Calculating size-specific dose estimates (SSDEs) requires measurement of the patient's anteroposterior (AP) and lateral thickness based on computed tomography (CT) images. However, these measurements can be subject to variation due to inter-observer and intra-observer differences. This study aimed to investigate the impact of these variations on the accuracy of the calculated SSDE. METHODS: Four radiographers with 1-10 years of experience were invited to measure the AP and lateral thickness on 30 chest, abdomen, and pelvic CT images. The images were sourced from an internet-based database and anonymized for analysis. The observers were trained to perform the measurements using MicroDicom software and asked to repeat the measurements 1 week later. The study was approved by the institutional review board at Taibah University, and written informed consent was obtained from the observers. Statistical analyses were performed using Python libraries Pingouin (version 0.5.3), Seaborn (version 0.12.2), and Matplotlib (version 3.7.1). RESULTS: The study revealed excellent inter-observer agreement for the calculated effective diameter and AP thickness measurements, with Intraclass correlation coefficients (ICC) values of 0.95 and 0.96, respectively. The agreement for lateral thickness measurements was lower, with an ICC value of 0.89. The second round of measurements yielded nearly the same levels of inter-observer agreement, with ICC values of 0.97 for the effective diameter, 1.0 for AP thickness, and 0.88 for lateral thickness. When the consistency of the observer was examined, excellent consistency was found for the calculated effective diameter, with ICC values ranging from 0.91 to 1.0 for all observers. This was observed despite the lower consistency in the lateral thickness measurements, which had ICC values ranging from 0.78 to 1.0. CONCLUSIONS: The study's findings suggest that the measurements required for calculating SSDEs are robust to inter-observer and intra-observer differences. This is important for the clinical use of SSDEs to set diagnostic reference levels for CT scans.

Standardizing Protein Corona Characterization in Nanomedicine: A Multicenter Study to Enhance Reproducibility and Data Homogeneity.

We recently revealed significant variability in protein corona characterization across various proteomics facilities, indicating that data sets are not comparable between independent studies. This heterogeneity mainly arises from differences in sample preparation protocols, mass spectrometry workflows, and raw data processing. To address this issue, we developed standardized protocols and unified sample preparation workflows, distributing uniform protein corona digests to several top-performing proteomics centers from our previous study. We also examined the influence of using similar mass spectrometry instruments on data homogeneity and standardized database search parameters and data processing workflows. Our findings reveal a remarkable stepwise improvement in protein corona data uniformity, increasing overlaps in protein identification from 11% to 40% across facilities using similar instruments and through a uniform database search. We identify the key parameters behind data heterogeneity and provide recommendations for designing experiments. Our findings should significantly advance the robustness of protein corona analysis for diagnostic and therapeutics applications.

Testing the Psychometric Properties of the Readiness for Hospital Discharge Scale Among Residents in Skilled Nursing Facilities: A Pilot Study.

Background and Purpose: The Readiness for Hospital Discharge Scale (RHDS) was created to determine the patient's readiness for discharge to home from the hospital. The purpose of this study is to determine the scale's internal consistency and predictive validity in a skilled nursing facility (SNF) setting. Methods: Participants (N = 30) over the age of 65 were conveniently selected from 10 different SNFs in the Midwest to complete the RHDS prior to discharge. Results: Cronbach's alpha for internal consistency was 0.917. Participants with higher RHDS scores, those who were male, respondents with less education, and participants with Medicare Advantage insurance were more likely to be rehospitalized within 30 days of discharge to home. Conclusions: The results indicate that the RHDS has a good internal consistency in the SNF setting.

Evaluating the two-item measure of engagement at work for Japan Self-Defense Forces: A cross-sectional study.

Aim: Work engagement is critical in both occupational and mental health contexts. However, no studies have compared the usefulness of the nine-, three-, and two-item measures from the Utrecht Work Engagement Scale (UWES). Therefore, this study aimed to examine the internal consistency and convergent validity of the two-item measure and compare its usefulness with the nine-item and three-item versions for assessing engagement at work among Japan Self-Defense Forces (JSDF) personnel. Methods: This cross-sectional study included 229,383 participants who underwent an annual mental health check between October 19 and December 17, 2021. To test the internal consistency of the scales, Cronbach's alpha was used. To test the convergent validity, Pearson's correlation coefficients were examined for each item corresponding to job resources, job satisfaction, stress reactions, and job demands assessed by the Brief Job Stress Questionnaire. Results: Most participants were men (89.8%). Cronbach's alpha coefficients for the nine-, three-, and two-item scales were 0.95, 0.85, and 0.80, respectively. All three versions showed significant and positive correlations with each of the items corresponding to job resources and job satisfaction. The correlation coefficients of the two-item scale were not inferior to those of the nine-item and three-item scales for job resources and job satisfaction items. Conclusion: Our results showed the internal consistency and convergent validity for the two-item measure of engagement at work among JSDF personnel. The two-item measure may be useful for briefly and efficiently assessing the actual state of workers' engagement at work.

Swedish Translation and Cultural Adaptation of the Head and Neck Patient Symptom Checklist: An Instrument to Screen for Nutrition Impact Symptoms in Clinical Practice and Research.

BACKGROUND: The Head and Neck Patient Symptom Checklist (HNSC) is a validated 2-part instrument used to ask patients with head and neck cancer about the nutrition impact symptoms they experience (part 1) and how these interfere with their eating (part 2). PURPOSE: The purpose of this work was to translate and culturally adapt the HNSC into Swedish in accordance with the guidelines of the International Society for Health Economics and Outcomes Research (ISPOR). METHODS: The ISPOR guidelines include 10 steps, and these were thoroughly followed. In step 7, 9 health care professionals from the field of head and neck cancer assessed the perceived relevance (content validity) of each item in the HNSC, as well as the full HNSC. A total of 522 participants with head and neck cancer were included and followed up on 7 occasions using the HNSC to assess internal consistency. RESULTS: The HNSC was translated from English into Swedish, ensuring accuracy through forward and backward translation and harmonization in the research team. Content validity for each part of the HNSC was rated excellent (scale content validity index 0.96). Internal consistency demonstrated a good Cronbach's alpha score (>0.8) across the 7 follow-up time points (from baseline [before the start of treatment] and up to 24 months posttreatment). CONCLUSIONS: The HNSC has been successfully translated and culturally adapted into Swedish. The HNSC can be used in both clinical practice and research to screen for nutrition impact symptoms and symptoms that interfere with eating in patients with head and neck cancer. TRIAL REGISTRATION: ClinicalTrials.gov NCT03343236 (date of registration: November 17, 2017).

A veracity dissemination consistency-based few-shot fake news detection framework by synergizing adversarial and contrastive self-supervised learning.

With the rapid growth of social media, fake news (rumors) are rampant online, seriously endangering the health of mainstream social consciousness. Fake news detection (FEND), as a machine learning solution for automatically identifying fake news on Internet, is increasingly gaining the attentions of academic community and researchers. Recently, the mainstream FEND approaches relying on deep learning primarily involves fully supervised fine-tuning paradigms based on pre-trained language models (PLMs), relying on large annotated datasets. In many real scenarios, obtaining high-quality annotated corpora are time-consuming, expertise-required, labor-intensive, and expensive, which presents challenges in obtaining a competitive automatic rumor detection system. Therefore, developing and enhancing FEND towards data-scarce scenarios is becoming increasingly essential. In this work, inspired by the superiority of semi-/self- supervised learning, we propose a novel few-shot rumor detection framework based on semi-supervised adversarial learning and self-supervised contrastive learning, named Detection Yet See Few (DetectYSF). DetectYSF synergizes contrastive self-supervised learning and adversarial semi-supervised learning to achieve accurate and efficient FEND capabilities with limited supervised data. DetectYSF uses Transformer-based PLMs (e.g., BERT, RoBERTa) as its backbone and employs a Masked LM-based pseudo prompt learning paradigm for model tuning (prompt-tuning). Specifically, during DetectYSF training, the enhancement measures for DetectYSF are as follows: (1) We design a simple but efficient self-supervised contrastive learning strategy to optimize sentence-level semantic embedding representations obtained from PLMs; (2) We construct a Generation Adversarial Network (GAN), utilizing random noises and negative fake news samples as inputs, and employing Multi-Layer Perceptrons (MLPs) and an extra independent PLM encoder to generate abundant adversarial embeddings. Then, incorporated with the adversarial embeddings, we utilize semi-supervised adversarial learning to further optimize the output embeddings of DetectYSF during its prompt-tuning procedure. From the news veracity dissemination perspective, we found that the authenticity of the news shared by these collectives tends to remain consistent, either mostly genuine or predominantly fake, a theory we refer to as "news veracity dissemination consistency". By employing an adjacent sub-graph feature aggregation algorithm, we infuse the authenticity characteristics from neighboring news nodes of the constructed veracity dissemination network during DetectYSF inference. It integrates the external supervisory signals from "news veracity dissemination consistency" to further refine the news authenticity detection results of PLM prompt-tuning, thereby enhancing the accuracy of fake news detection. Furthermore, extensive baseline comparisons and ablated experiments on three widely-used benchmarks demonstrate the effectiveness and superiority of DetectYSF for few-shot fake new detection under low-resource scenarios.

Over-specification of small, borderline cardinalities and color in referential communication: the role of visual context, modifier position, and consistency.

This paper reports on two flash-mode experiments that test redundant descriptions of small (2-4) cardinalities, borderline (5-8) cardinalities, and color in referential communication. It provides further support for the idea that small cardinalities are more salient (due to subitizing), less sensitive to visual context, and therefore give rise to higher over-specification rates than color. Because of greater salience, Russian speakers more often use prenominal positions for numerals than for color adjectives. The paper also investigates borderline cardinalities and argues for the order factor that affects their salience, since ordered items can be perceived in small subitized parts. The ordered mode of presentation of the borderline cardinalities leads to higher over-specification rates and to higher percentages of prenominal positions than the unordered one. The paper provides further evidence for the consistency of small, borderline cardinalities, and color in people's choices to minimally specify or over-specify given objects in referential communication.