A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma: A Study Based on SEER Database

Background: Primary liver cancer is a common malignant tumor primarily represented by hepatocellular carcinoma (HCC). The number of elderly patients with early HCC is increasing, and older age is related to a worse prognosis. However, an accurate predictive model for the prognosis of these patients is still lacking.Methods: Data of eligible elderly patients with early HCC in Surveillance, Epidemiology, and End Results database from 2010 to 2016 were downloaded. Patients from 2010 to 2015 were randomly assigned to the training cohort (n = 1093) and validation cohort (n = 461). Patients' data in 2016 (n = 431) was used for external validation. Independent prognostic factors were obtained using univariate and multivariate analyses. Based on these factors, a cancer-specific survival (CSS) nomogram was constructed. The predictive performance and clinical practicability of our nomogram were validated. According to the risk scores of our nomogram, patients were divided into low-, intermediate-, and high-risk groups. A survival analysis was performed using Kaplan–Meier curves and log-rank tests.Results: Age, race, T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent predictors for CSS and thus were included in our nomogram. In the training cohort and validation cohort, the concordance indices (C-indices) of our nomogram were 0.739 (95% CI: 0.714–0.764) and 0.756 (95% CI: 0.719–0.793), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves (AUCs) showed similar results. Calibration curves revealed high consistency between observations and predictions. In external validation cohort, C-index (0.802, 95%CI: 0.778–0.826) and calibration curves also revealed high consistency between observations and predictions. Compared with the TNM stage, nomogram-related decision curve analysis (DCA) curves indicated better clinical practicability. Kaplan–Meier curves revealed that CSS significantly differed among the three different risk groups. In addition, an online prediction tool for CSS was developed.Conclusions: A web-based prediction model for CSS of elderly patients with early HCC was constructed and validated, and it may be helpful for the prognostic evaluation, therapeutic strategy selection, and follow-up management of these patients.

Cognitive predictors of vaccine hesitancy and COVID-19 mitigation behaviors in a population representative sample

With the continued threat of COVID-19, predictors of vaccination hesitancy and mitigation behaviors are critical to identify. Prior studies have found that cognitive factors are associated with some COVID-19 mitigation behaviors, but few studies employ representative samples and to our knowledge no prior studies have examined cognitive predictors of vaccine hesitancy. The purpose of the present study, conducted among a large national sample of Canadian adults, was to examine associations between cognitive variables (executive function, delay discounting, and temporal orientation) and COVID-19 mitigation behaviors (vaccination, mask wearing, social distancing, and hand hygiene). Findings revealed that individuals with few executive function deficits, limited delay discounting and who adopted a generally future-orientation mindset were more likely to be double-vaccinated and to report performing COVID-19 mitigation behaviors with high consistency. The most reliable findings were for delay discounting and future orientation, with executive function deficits predicting mask wearing and hand hygiene behaviors but not distancing and vaccination. These findings identify candidate mediators and moderators for health communication messages targeting COVID-19 mitigation behaviors and vaccine hesitancy.

Acculturation Scale to Russia for Chinese Students: Development and Psychometric Verification

The topic on diagnosing intercultural adaptation and psychological acculturation to a new socio-cultural environment is relevant in connection with the processes of globalization and, in particular, with the growth of academic mobility in education. Research on this problem are especially relevant for Russian science due to the lack of acculturation scales, which are widely represented in Western and international psychology. In 2019, a research team led by Professor A. Ardila developed the Russian-language Acculturation Scale for Russia (ASR), which was validated on a sample of international university students from 71 countries of the world. The aim of the present study was to adapt the ASR for the Chinese students studying in Russian universities. Chinese students are the largest group of international students from far abroad studying at Russian universities, while many of them experience significant difficulties in adapting to a new socio-cultural environment, as well as in learning the Russian language. In total, the study involved 213 Chinese students (59% - female) studying at Russian universities, of which 93 students (58% - female) completed the Russian-language version of the ASR, and 118 students (61% - female) completed the Chinese version of the ASR, which was designed using the back translation. Both ASR versions were psychometrically tested using Cronbachs and MacDonalds coefficients, as well as bifactorial analysis. The methods of descriptive statistics and the Wilcoxon W-test were used to compare the Russian-language and Chinese versions of the ASR. The normalization was carried out by means of non-linear percentile normalization (stanines). As a result, a high consistency of the Russian-language and Chinese versions of the ASR and their equivalence with each other were shown. Both versions can be used to assess the level of acculturation of Chinese students to Russia, while the Chinese version is recommended for the Chinese students who have been living in Russia for less than 1-1.5 years and/or have a poor Russian proficiency.

Electrospray Deposition of Cellulose Nanofibers on Paper: Overcoming the Limitations of Conventional Coating

While the potential of cellulose nanofibers to enhance the mechanical and barrier properties of paper is well-known, there are many uncertainties with respect to how to apply them. In this study, we use not only bulk addition of micro-/nanofibers and bar coating with oxidized nanofibers, but also a combination of these and, as a novel element, electrospray deposition of nanofiber dispersions. Characterization involved testing the strength of uncoated and coated paper sheets, their resistance to air flow, their Bendtsen roughness, and their apparent density, plus visualization of their surface and cross-sections by scanning electron microscopy. As expected, bulk addition to the unrefined pulp was sufficient to attain substantial strengthening, but this enhancement was limited to approximately 124%. Following this, surface addition by bar coating improved air resistance, but not strength, since, as applying nanocellulose at high consistency was technically unfeasible, this was performed several times with detrimental drying stages in between. However, replacing bar coating with electrospraying helped us overcome these apparent limitations, producing enhancements in both barrier and tensile properties. It is concluded that electrosprayed nanofibers, owing to their uniform deposition and favorable interactions, operate as an effective binder between fibers (and/or fines).

Examining the Association of Grit Profiles With Big Five Personality and Achievement Among Iranian Foreign Language Learners

Grit—trait-level perseverance and prolonged passion for primary goals—is related to several indicators of educational success such as academic achievement, engagement, and motivation. Although there is new evidence showing the beneficial effects of grit, most research has taken a variable-focused approach and consequently has failed to indicate how individuals with different grit profiles might have different academic results. The present study aimed to build on the existing literature on grit by investigating the relationship between grit, big five personality and L2 achievement. The participants of this study were 384 English as a Foreign Language (EFL) students from different academic majors studying at Yazd University. They had enrolled in General English (GE) courses. The results of the cluster analyses showed that there were three natural grit profiles (Cluster 1 = High Perseverance and High Consistency; Cluster 2 = High Perseverance and Low Consistency; Cluster = Low Perseverance and High Consistency) in the current study. The results of the multivariate analysis of variance (ANOVA) showed that students belonging to cluster 1 had the highest scores on big five personality traits and L2 achievement. The results also showed that when taking academic exams, high perseverance of effort and low consistency of interest were related to higher level of neuroticism and lower level of consciousness. Our study has some theoretical and practical implications. Regarding the theory, this study is related to the existing grit literature by elaborating the relationship between grit profiles, big five personality traits, and L2A. Regarding the practice, our findings affirm the significance of developing and implementing the big five personality traits interventions in grit prediction.

Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury

This study aimed to explore the application value of magnetic resonance imaging (MRI) images based on deep learning algorithms in the diagnosis of tibial plateau fractures combined with meniscus injuries. The original MRI image was input into the deep learning convolutional neural network (CNN), and the knee joint undersampled and fully sampled MRI image data were used for training to obtain a neural network model that can effectively remove the noise and blur of the undersampled image. Then, the image was reconstructed by the Regridding model to obtain an image with less noise and clearer structure. At the same time, all subjects underwent knee MRI examinations, and algorithms were used to analyze the sensitivity, specificity, and accuracy of their images. It was found that of 160 menisci from 80 cases of tibial plateau fractures, 64 were normal meniscus and 88 were injured menisci. The sensitivity, specificity, and accuracy of optimized MRI in diagnosing fracture of tibial plateau combined with meniscus injury were 96.9%, 93.2%, and 95.3%, respectively. In conclusion, the restored MRI images have high sensitivity in the diagnosis of meniscus injury and high consistency with the intraoperative results. It suggests that the optimized MRI image is effective in the diagnosis of meniscus injury.

Municipal Solid Waste Disposal Site Selection Using Remote Sensing Technology and AHP Process (Case Study: Khesht city, Fars Province, Iran)

The design and implementation of the municipal waste management system aim to solve the urban issues and ultimately contribute to the citizens' health and welfare. The first step in this system is to select a site for the safe disposal of wastes. This survey aimed to select the most suitable landfill site for municipal solid waste using the geographic information system (GIS) and the analytic hierarchy process (AHP) model following the environmental, economic, and engineering criteria. Therefore, by reviewing the literature and backgrounds, the parameters required for locating the landfill site were extracted, and the most important ones (15 sub-criteria) were selected and divided into four groups. The results showed that the sub-criterion of distance to the river with the weight of 0.218 was identified as the most critical parameter, followed by groundwater depth. In this way, the flooding parameter with the weight of 0.010 was considered the least important variable. In addition, it was found that the moderate suitability class with 50% of the area had the highest area, and the very high suitability with 0.8% had the lowest area. The inconsistency rate was equal to 0.04, which illustrates the high consistency of matrices. Then, the overlaying was carried out, and three sites were determined as the best place for a landfill in Khesht county.

Selecting Drought-Tolerance Markers: An Exploratory Analysis in Contrasting Soybeans

Identifying high-yield genotypes under low water availability is an important goal for soybean breeding. However, a major bottleneck lies in phenotyping, particularly in selecting cost-efficient markers associated with stress tolerance and yield stabilization. Here, we conducted in-depth phenotyping experiments using two soybean genotypes with contrasting drought tolerance, MUNASQA (tolerant) and TJ2049 (susceptible), aiming to identify and statistically validate stress tolerance and yield stabilization markers. Firstly, at the R5 critical reproductive stage, the molecular differences between the genotypes responses to mild water deficit were explored through massive analysis of cDNA ends (MACE)-transcriptomic and gene ontology. MUNASQA transcriptional profile, compared to TJ2049, revealed significant differences when responding to drought. Next, both genotypes were phenotyped under mild water deficit, imposed in vegetative (V3) and R5 stages, by evaluating 22 stress-response, growth and water-use markers, which were subsequently correlated between phenological stages and with yield. Several markers showed high consistency, independent of the phenological stage, demonstrating the effectiveness of the phenotyping methodology and its possible use for early selection. Finally, these markers were classified and selected according to their cost-feasibility, statistical weight and correlation with yield. Here, pubescence, stomatal density and canopy temperature depression emerged as promising markers for early selection of drought-tolerant soybeans.

High-performance HgCdTe avalanche photodetector enabled with suppression of band-to-band tunneling effect in mid-wavelength infrared

HgCdTe avalanche photodiodes promise various fascinating applications due to the outstanding capability of detecting weak signals or even single photon. However, the underlying transport mechanisms of diverse dark current components are still unresolved at high reverse bias, thus limiting the development of high-performance devices. Here, we establish an accurate model to demonstrate the competitive mechanism between band-to-band and avalanche dark currents in positive-intrinsic-negative structures. Based on the high consistency between the simulated and measured results, we find that both components jointly dominate overall dark current but with a larger avalanche current. This breaks the conventional cognition that band-to-band dark current contributes the majority. With the guidance, we reconstruct an optimized device and achieve gain 1876 (6153) and dark current 10−10 (10−9) A at bias −10 (−10.5) V, respectively. Comparisons of dark current and gain with reported single-element devices further confirm the outstanding performance of our device.

Study on Competency Elements of Excellent Front-Line Health Inspector Based on BEI

Background: In order to find out the competency elements of excellent front-line health inspector, and to improve the comprehensive quality and law enforcement level, we conducted this study. Methods: From May to September in 2020, seven health inspectors engaged in front-line inspection and law enforcement work in the Pudong New Area Health Inspection agency were interviewed by using the method of key Behavior Event Interview (BEI). The competency differences of front-line personnel with different performance levels were compared. Combined with the results of expert consultation, the prominent competency elements of excellent front-line health inspector were found out. Results: The total 6 prominent competency elements were investigation and evidence collection ability, on-site control ability, legal awareness, career love, communication and coordination ability, teamwork ability. Cohen's Kappa coefficient of the two coders was more than 0.6, which had high consistency. Conclusion: The key Behavior Event Interview (BEI) is scientific and reasonable method to find out prominent competency elements of excellent front-line health inspector, and the prominent competency elements found out will be helpful in the human resources planning and management of health inspection in the future.