Terms Of Fitness Research Articles

Coupled thermo–geophysical inversion for permafrost monitoring

Abstract. This study explores an alternative way of deriving soil thermal properties from surface geophysical measurements. We combined ground surface temperature time series with time lapse geoelectrical acquisitions measured from the ground surface in a fully coupled inversion scheme to calibrate a heat conduction model. The quantitative link between the thermal and geoelectrical parts of the modeling framework is the temperature-dependent unfrozen water content, which is also the main factor influencing electrical response of the ground. The apparent resistivity data were incorporated into the coupled framework without being inverted separately, thus reducing the uncertainty inevitably associated with inverted resistivity models. We show that geoelectrical time lapse data are useful as alternative calibration data and can provide as good results as borehole temperature measurements. The fully coupled modeling framework using field data achieved performance comparable to calibration on borehole temperature records in terms of model fit within 0.6 ∘C, inversion convergence metrics, as well as the predictive performance of the calibrated model.

Mapping Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) to Recovering Quality of Life (ReQoL) to estimate health utilities

BackgroundThe Short Warwick and Edinburgh Mental Wellbeing Scale (SWEMWBS) is a widely used non-preference-based measure of mental health in the UK. The primary aim of this paper is to construct an algorithm to translate the SWEMWBS scores to utilities using the Recovering Quality of Life Utility Index (ReQoL-UI) measure.MethodsService users experiencing mental health difficulties were recruited in two separate cross-sectional studies in the UK. The following direct mapping functions were used: Ordinary Least Square, Tobit, Generalised Linear Models. Indirect (response) mapping was performed using seemingly unrelated ordered probit to predict responses to each of the ReQoL-UI items and subsequently to predict using UK tariffs of the ReQoL-UI from SWEMWBS. The performance of all models was assessed by the mean absolute errors, root mean square errors between the predicted and observed utilities and graphical representations across the SWEMWBS score range.ResultsAnalyses were based on 2573 respondents who had complete data on the ReQoL-UI items, SWEMWBS items, age and sex. The direct mapping methods predicted ReQoL-UI scores across the range of SWEMWBS scores reasonably well. Very little differences were found among the three regression specifications in terms of model fit and visual inspection when comparing modelled and actual utility values across the score range of the SWEMWBS. However, when running simulations to consider uncertainty, it is clear that response mapping is superior.ConclusionsThis study presents mapping algorithms from SWEMWBS to ReQoL as an alternative way to generate utilities from SWEMWBS. The algorithm from the indirect mapping is recommended to predict utilities from the SWEMWBS.

Predicting the crack repair rate of self-healing concrete using soft-computing tools

To tackle the challenges of cracking and insufficient durability in traditional concrete, researchers are exploring self-healing concrete (SHC) as a potential alternative material. Nevertheless, doing laboratory studies can incur significant expenses and consume considerable time. Therefore, utilizing machine learning (ML) algorithms can contribute to advancing improved predictions for self-healing concrete. This study aimed to create ML models, specifically adaptive boosting (AB), artificial neural network (ANN), and gradient boosting (GB), to forecast and evaluate the repair rate of the cracked region in SHC that incorporates fibers and bacteria in their compositions. To further evaluate the importance of inputs, RReliefF analysis was performed. The findings indicated that the AB algorithm outperformed GB and ANN algorithms by achieving a coefficient of determination (R2) value of 0.987, a mean absolute error (MAE) value of 0.001 mm, and a root mean square error (RMSE) value of 0.026 mm. The GB approach yielded an R2 value of 0.962, an MAE value of 0.035 mm, and an RMSE value of 0.044 mm. Similarly, the ANN approach yielded an R2 value of 0.943, an MAE value of 0.040 mm, and an RMSE value of 0.054 mm. These results demonstrate that all AB, GB, and ANN algorithms outperformed in terms of prediction accuracy and model fit. Hence, the application of these models can be employed to construct and verify advanced SHC compositions that rely on polymeric fibers and bacteria, aiming to achieve superior performance. However, comparing the outcomes of developed models revealed that the AB model accuracy is higher than the GB and ANN models for predicting the area repair rate of cracks.

The physiological and physical benefits of two types of concurrent training: a randomized controlled trial

BackgroundIt is widely acknowledged that aerobic exercise and strength training are crucial components of most workout programs. However, there is no consensus as to whether the effectiveness of exercises is affected by the sequence in which they are performed. Therefore, the overarching aim of the study was to understand the optimal order of two types of concurrent training program for 13 weeks by comparing the effectiveness of the training on body composition, predicated maximal oxygen uptake (VO2max), dynamic respiratory parameters and muscle strength in healthy middle-aged people.MethodsThirty-three middle-aged individuals, who were categorized as moderately active based on their responses to International Physical Activity Questionnaires, underwent random allocation. The participants were randomly assigned into two groups: the Strength Training followed by Aerobic Training group (SAG, n = 16) and the Aerobic Training followed by Strength Training group (ASG, n = 17). Body composition, aerobic endurance, respiratory parameters, and upper and lower strength were assessed at baseline and after (post-test) a 13-week intervention. The chi-square test and the independent t-test were used to compare sociodemographic variables between the groups. A 2 × 2 analysis of variance (ANOVA) with repeated measures (group x measurement) was conducted. The study was retrospectively registered on clinicaltrials.gov in May of 2023 (clinicaltials.gov identifier: NCT05862415; in 04/25/2023).ResultsFindings showed no significant differences between the group in the VO2max, FVC or FEV1 (F = 1.122, 0.028, 0.06, 2.483; p > 0.05, respectively). Intragroup analysis revealed changes in PEF compared to baseline in the ASG (F = 5.895; p < 0.05). Increases were observed in all strength parameters for both training programs.ConclusionsThe concurrent training effect on muscle composition, oxygen consumption and muscle strength specifically 1RM, in middle-aged individuals are equivocal, regardless of the exercise order. The results indicate that both exercise sequences can elicit similar benefits in terms of cardiovascular fitness, muscular strength, and endurance. This lack of difference suggests that the order of exercise does not play a significant role in determining the effectiveness of the workout or the subsequent physiological adaptations.Clinicaltials.gov identifierNCT05862415. Date of registration: 04/25/2023

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it is a natural way to organise these data as tensors prior to performing automated analyses such as discrimination of neurological disorders like Parkinson's Disease (PD) and Attention Deficit and Hyperactivity Disorder (ADHD). However, the existing approaches are often subject to performance bottlenecks (e.g., conventional feature extraction and deep learning based feature construction), as these can lose the structural information that correlates multiple data dimensions or/and demands excessive empirical and application-specific settings. This study proposes a Deep Factor Learning model on a Hilbert Basis tensor (namely, HB-DFL) to automatically derive latent low-dimensional and concise factors of tensors. This is achieved through the application of multiple Convolutional Neural Networks (CNNs) in a non-linear manner along all possible dimensions with no assumed a priori knowledge. HB-DFL leverages the Hilbert basis tensor to enhance the stability of the solution by regularizing the core tensor to allow any component in a certain domain to interact with any component in the other dimensions. The final multi-domain features are handled through another multi-branch CNN to achieve reliable classification, exemplified here using MRI discrimination as a typical case. A case study of MRI discrimination has been performed on public MRI datasets for discrimination of PD and ADHD. Results indicate that 1) HB-DFL outperforms the counterparts in terms of FIT, mSIR and stability (mSC and umSC) of factor learning; 2) HB-DFL identifies PD and ADHD with an accuracy significantly higher than state-of-the-art methods do. Overall, HB-DFL has significant potentials for neuroimaging data analysis applications with its stability of automatic construction of structural features.

A155: Research on the Influence of Kinesthetic Intelligence Courses with Industrialized Education on Children's Physical Fitness

Purpose: Nowadays, the industrialization of education has led to the shortage of teachers and consequently hindered resource allocation and curriculum optimization. These problems are profound for educators when applying Kinesthetic Intelligence (KI) curriculum for children in early childhood. This paper examines the impact of the current KI course delivery on children’s physical fitness to provide reference for researchers and educators. Methods: Forty-six kids were randomly selected from two intermediate Kindergarten classes and were divided into an experimental group (EG) and a control group (CG). A pre-test of physical fitness was done on eight indices such as stature, weight, throwing, and running. Then, the EG had KI classes, twice a week, 40 minutes for each class, while the CG had traditional classes only. A post-test was done at the end of the experiment, and the data were analyzed through calculating descriptive statistics and performing t-tests and an ANCOVA. Results: The pre-test results showed no significant difference between the two groups in terms of body shape and physical fitness. The post-test results revealed that the CG did not have a significant increase in stature (F = 0.39, p = 0.70) but an increase in weight (F = 5.50, p &lt; 0.01). Regarding the other six indices, distance of throwing tennis ball and time used by walking on a balance beam improved (p &lt; 0.01), the rest had no significant differences (p &gt; 0.05). For the EG group, their statures and weights increased (p &lt; 0.05), and all other six indices showed an improvement (p &lt; 0.01). A comparison between the two groups showed no significant difference in stature and weight, standing long jump, or tennis long throw, while the other four indices were significantly different (p &lt; 0.01). Conclusion: The KI course under the industrialized education generated more positive results than the traditional course in promoting children’s physical fitness, especially fitness of speed, flexibility, balance, and coordination. Further endeavors can be directed to implementing the KI curriculum on a larger scale.

A novel family of distributions: Properties, inequality measures and applications to socio economic development indicators

In this paper, we focused on two families of distributions: the Topp–Leone Kumaraswamy family and a novel proposed family of distributions. Subsequently, we explore their composition, leading to a novel family of distributions exhibiting compelling features for data modeling. Specifically, we examine a special member of this novel family, employing the inverse exponential distribution as the cumulative density function. We establish the mathematical properties, investigate the moments and the stochastic properties, and propose a parameter estimation method based on the maximum likelihood of the new model. To assess the applicability of our model, we gather data related to development indicators in Benin Republic. Additionally, employing competing models, we analyze some real-life data and compare the results to the novel distribution. Model performance is evaluated in terms of fitting observed data, and we conduct an in-depth interpretation of the outcomes. This study makes a significant contribution by introducing a novel family of distributions tailored for modeling development indicators. The findings of this research may have substantial implications for statistical analysis and decision-making in the context of Benin’s economic and social development.

Tooth morphology, internal fit, occlusion and proximal contacts of dental crowns designed by deep learning-based dental software: A comparative study

ObjectivesThis study compared the tooth morphology, internal fit, occlusion, and proximal contacts of dental crowns automatically generated via two deep learning (DL)-based dental software systems with those manually designed by an experienced dental technician using conventional software. MethodsThirty partial arch scans of prepared posterior teeth were used. The crowns were designed using two DL-based methods (AA and AD) and a technician-based method (NC). The crown design outcomes were three-dimensionally compared, focusing on tooth morphology, internal fit, occlusion, and proximal contacts, by calculating the geometric relationship. Statistical analysis utilized the independent t-test, Mann–Whitney test, one-way ANOVA, and Kruskal–Wallis test with post hoc pairwise comparisons (α = 0.05). ResultsThe AA and AD groups, with the NC group as a reference, exhibited no significant tooth morphology discrepancies across entire external or occlusal surfaces. The AD group exhibited higher root mean square and positive average values on the axial surface (P < .05). The AD and NC groups exhibited a better internal fit than the AA group (P < .001). The cusp angles were similar across all groups (P = .065). The NC group yielded more occlusal contact points than the AD group (P = .006). Occlusal and proximal contact intensities varied among the groups (both P < .001). ConclusionsCrowns designed by using both DL-based software programs exhibited similar morphologies on the occlusal and axial surfaces; however, they differed in internal fit, occlusion, and proximal contacts. Their overall performance was clinically comparable to that of the technician-based method in terms of the internal fit and number of occlusal contact points. Clinical significanceDL-based dental software for crown design can streamline the digital workflow in restorative dentistry, ensuring clinically-acceptable outcomes on tooth morphology, internal fit, occlusion, and proximal contacts. It can minimize the necessity of additional design optimization by dental technician.

The role and importance of play in physiotherapy

Play is one of the human condition, as it accompanies us throughout our lives. The developmental role of play is used in many areas, education and training being no exception. In this article, the role and importance of toys in physical education sessions for pupils in the specialised teaching service who are in physical education are examined. The research included 120 students from schools in Hajdúböszörmény and Hajdúdorog, who are part of the Hajdúböszörmény branch of the Hajdú-Bihar County Pedagogical Service and are classified as students in physical education. We were looking for answers to questions such as what sports or movement games they play in PE sessions, how much they like them and how motivated they are by playing them. The results of the research confirm that the positive personality of the physical education teacher, the colourful toolbox and the methods he uses, such as the use of movement games, motivate pupils to actively participate in physical education lessons. By exploiting the motivational role of games, both children's attitudes towards physical education and their activity in class can be shaped. According to the pupils who participated in the sessions, it was found that they felt the positive benefits of the PE sessions in terms of performance, fitness and resilience and self-confidence.

An Emerging Leadership Model for Millennial Educators

The study unveiled perceptions of school leaders and their expectations of millennial educators in terms of Drivers, Fit, Leadership Style and Culture, and Unconscious Biases with the endview of discovering an emerging leadership model for millennial educators. This employed explanatory-sequential design with two-phased, mixed-method approach. Results revealed that Drivers, Fit, Leadership Style and Culture, and Unconscious Biases and other leadership principles, school heads’ consideration of these domains lead to better commitment and performance of their leadership roles, while millennial teachers’ expectations of their school head’s role can guide current and aspiring school heads in managing a growing millennial generation of educators. Hereafter, six key elements appeared include Professional Instigation, Personal Provocation, Leadership Direction, Cultural Condition, Self-Predilection, and Organizational Acclimation. These emerging leadership principles upheld by millennial educators may be considered priorities of Professional Development Programs across all levels.

PHYSICAL CAPACITY AS MARKER FOR RATE OF AGING IN MID LIFE

Abstract Biological age captures the heterogeneity of aging by providing an estimation for rate of aging. As an alternative to the widely investigated laboratory biomarkers of aging, behavioral functional markers of physical capacity may offer a feasible alternative for identifying individuals at risk for negative aging trajectory. We aimed to examine the associations of different functional tests both together and as a composite score of physical capacity with rate of aging. We conducted a cross-sectional study, with midlife adults (age ~45) reporting being able to perform leisure physical exercise. Biological age was estimated using the Klemera-Doubal method and a set of physiological biomarkers; rate of aging, ∆Age, was defined as the difference between age and estimated biological age. Physical capacity was measured using a testing battery of 15 functional tests linked with six physical capacity domains. 116 participants completed the testing battery. For women, better results in terms of strength, flexibility, cardiorespiratory fitness, and balance tests were negatively correlated with ∆Age (r=0.38-0.29, p&amp;lt;0.05); for men, beyond these tests, agility was also negatively correlated with ∆Age (r=0.27-0.59, p&amp;lt;0.05). A sex-standardized composite score of physical capacity was negatively associated with ∆Age after controlling for chronological age, smoking, and education (r=-0.437, p=0.007; r=-0.491, p&amp;lt;0.001 for women and men respectively). The suggested physical capacity battery offers a functional assessment for ∆Age. Higher physical capacity metrics correlate with smaller ∆Age, corresponding with younger biological age. Measuring physical capacity may help to assess aging trajectory and offer a suitable behavioral intervention goal.

Spatiotemporal Analysis and Prediction of Carbon Emissions from Energy Consumption in China through Nighttime Light Remote Sensing

With burgeoning economic development, a surging influx of greenhouse gases, notably carbon dioxide (CO2), has precipitated global warming, thus accentuating the critical imperatives of monitoring and predicting carbon emissions. Conventional approaches employed in the examination of carbon emissions predominantly rely on energy statistics procured from the National Bureau of Statistics and local statistical bureaus. However, these conventional data sources, often encapsulated in statistical yearbooks, exclusively furnish insights into energy consumption at the national and provincial levels, so the assessment at a more granular scale, such as the municipal and county levels, poses a formidable challenge. This study, using nighttime light data and statistics records spanning from 2000 to 2019, undertook a comparative analysis, scrutinizing various modeling methodologies, encompassing linear, exponential, and logarithmic models, with the aim of assessing carbon emissions across diverse spatial scales. A multifaceted analysis unfolded, delving into the key attributes of China’s carbon emissions, spanning total carbon emissions, per capita carbon emissions, and carbon emission intensity. Spatial considerations were also paramount, encompassing an examination of carbon emissions across provincial, municipal, and county scales, as well as an intricate exploration of spatial patterns, including the displacement of the center of gravity and the application of trend analyses. These multifaceted analyses collectively contributed to the endeavor of predicting China’s future carbon emission trajectory. The findings of the study revealed that at the national scale, total carbon emissions exhibited an annual increment throughout the period spanning 2000 to 2019. Secondly, upon an in-depth evaluation of model fitting, it was evident that the logarithmic model emerged as the most adept in terms of fitting, presenting a mean R2 value of 0.83. Thirdly, the gravity center of carbon emissions in China was situated within Henan Province, and there was a discernible overall shift towards the southwest. In 2025 and 2030, it is anticipated that the average quantum of China’s carbon emissions will reach 7.82 × 102 million and 25.61 × 102 million metric tons, with Shandong Province emerging as the foremost contributor. In summary, this research serves as a robust factual underpinning and an indispensable reference point for advancing the scientific underpinnings of China’s transition to a low-carbon economy and the judicious formulation of policies governing carbon emissions.

Research on the Application and Effect of Innovative Education Model in Youth Physical Fitness Training

Nowadays, students tend to pamper themselves at home and rarely participate in physical exercise. Not only do they have changes in their physical form, but they also have a high failure rate in physical functions such as lung capacity. In terms of physical fitness, they perform poorly in indicators such as sitting forward, running, sit-ups, and pull-ups. In response to the above situation, this article conducts further research on the application and effectiveness of innovative education models in adolescent physical fitness training. Adolescents use a tiered lifelong health fitness structure chart, moving from dependence to independence, providing high-quality services around the nine major systems of the human body, and maximizing the stimulation of happiness index. Through the correlation assessment of fitness items in sports, it can be seen that engaging in a certain sport improves the trainees' speed, strength, flexibility, coordination, balance, flexibility, and other indicators. Through innovative educational models, we aim to promote students' balanced and comprehensive development, cultivate their awareness and ability to engage in lifelong physical education, and enable them to recognize themselves, understand themselves, and enhance confidence and teamwork in sports activities.

A comparative evaluation of nature-inspired algorithms for feature selection problems

Feature selection is a critical component of machine learning and data mining which addresses challenges like irrelevance, noise, redundancy in large-scale data etc., which often result in the curse of dimensionality. This study employs a K-nearest neighbour wrapper to implement feature selection using six nature-inspired algorithms, derived from human behaviour and mammal-inspired techniques. Evaluated on six real-world datasets, the study aims to compare the performance of these algorithms in terms of accuracy, feature count, fitness, convergence and computational cost. The findings underscore the efficacy of the Human Learning Optimization, Poor and Rich Optimization and Grey Wolf Optimizer algorithms across multiple performance metrics. For instance, for mean fitness, Human Learning Optimization outperforms the others, followed by Poor and Rich Optimization and Harmony Search. The study suggests the potential of human-inspired algorithms, particularly Poor and Rich Optimization, in robust feature selection without compromising classification accuracy.

Measurement of transformational leadership in South Africa: Assessed from an etic perspective

Orientation: It is scientifically questionable whether a predominantly Western construct and instrument could be used in the culturally diverse South African context. The sound measurement of transformational leadership in a multicultural context with the Transformational Leadership Questionnaire (TLQ), developed by Podsakoff et al. (1990), is studied from an etic perspective.Research purpose: Our study aimed to investigate the TLQ in terms of its construct validity. The instrument was developed outside the South African and the broader African and context; our study thus adopted an etic approach.Motivation for the study: The psychometric properties of the TLQs are examined through a contextual lens (the South African context), building on the previously conducted work of Podsakoff et al. (1990) and Podsakoff et al. (2012).Research approach/design and method: This research employed a typical empirical paradigm using a cross-sectional design and quantitative analysis. The sample comprised 3805 respondents. A multi-factorial model was explored and confirmed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The results support the original conceptualisation of the overall construct, but with an adjusted factorisation.Main findings: Two factors of transformational leadership were confirmed, namely leadership focused on organisational, group and individual transformation and leadership focused on creativity and innovation. Good psychometric properties, in terms of good fit and acceptable levels of convergent and discriminant validity were reported for a two-factor model.Practical/managerial implications: The adapted TLQ was found to be valid on structural (factorial) as well as external levels, and it was found to be reliable. The results suggest that transformational leadership could be used with confidence within the South African context.Contribution/value-add: Our study established a valid measure of transformational leadership from an etic approach. The necessity for our study originated from the diverse nature of the South African workforce. Participants from both the private and public sectors were included in our study.

Health behavioural changes in older people post pandemic.

This article explores the lasting effects of the pandemic on physical and mental health among older people due to behavioural change. It discusses ways in which older people can change their health behaviours once again, and regain what has temporarily been lost in their lives in terms of strength, fitness and mental wellbeing.

Population size shapes trade-off dilution and adaptation to a marginal niche unconstrained by sympatric habitual conditions.

How does niche expansion occur when the habitual (high-productivity) and marginal (low-productivity) niches are simultaneously available? Without spatial structuring, such conditions should impose fitness maintenance in the former while adapting to the latter. Hence, adaptation to a given marginal niche should be influenced by the identity of the simultaneously available habitual niche. This hypothesis remains untested. Similarly, it is unknown if larger populations, which can access greater variation and undergo more efficient selection, are generally better at niche expansion. We tested these hypotheses using a large-scale evolution experiment with Escherichia coli. While we observed widespread niche expansion, larger populations consistently adapted to a greater extent to both marginal and habitual niches. Owing to diverse selection pressures in different habitual niches (constant vs. fluctuating environments; environmental fluctuations varying in both predictability and speed), fitness in habitual niches was significantly shaped by their identities. Surprisingly, despite this diversity in habitual selection pressures, adaptation to the marginal niche was unconstrained by the habitual niche's identity. We show that in terms of fitness, two negatively correlated habitual niches can still have positive correlations with the marginal niche. This allows the marginal niche to dilute fitness trade-offs across habitual niches, thereby allowing costless niche expansion. Our results provide fundamental insights into the sympatric niche expansion.

High Levels of Circulating Granulocytic Myeloid-Derived Suppressor Cells (G-MDSCs) Predict Failure of CD19-Targeting CAR-T Cell Therapy

Background The mechanisms of resistance to CAR-T cell therapy may depend on the physical and biological characteristics of the tumor itself, on CAR-T cells in terms of efficiency and fitness, or on the microenvironment. Myeloid-derived suppressor cells (MDSCs) play a crucial role in hematological tumors, promoting immunosuppression and tumor progression. Their presence hampers the anti-tumor immune response, posing challenges for effective therapeutic interventions. The predictive role of myeloid-derived suppressor cells (MDSCs) of monocytic and granulocytic origin (M-MDSCs and G-MDSCs, respectively) in the context of CAR-T cell therapy is largely unknown. Aim and methods Our aim was to quantify circulating M-MDSC and G-MDSC in patients (n=26) receiving approved CAR-T cell products for high-grade B cell lymphoma or acute lymphoblastic leukemia. Whole blood samples were collected at enrolment (time point 1), after leukapheresis (time point 2) and just after Cy/Flu lymphodepletion (time point 3). Stringent criteria for multicolour flow cytometry identification of M-MDSC (CD14/HLA-DR/CD124/CD11b/CD45 and light scattering) and G-MDSC (CD15/CD11b/CD66b/oxidized low-density lipoprotein receptor -1, LOX-1, /CD45 and light scattering) were used. Blood samples were examined immediately after collection to prevent artefactual G-MDSC changes. Results M-MDSC number, although very variable between patients, remained essentially constant from time point 1 to time point 3 in each individual (range 1.2-947 cells/ml at the latest time point) and was not associated with a higher risk of relapse. Conversely, G-MDSC number, which was extremely low at time points 1 and 2, markedly increased at time point 3 (range 1.5-85.5 cells/ml). According to our preliminary data, at time point 3, mean G-MDSC were 39.3, 44.5, and 12.3 cells/ml in tisacel, axi-cel and brexu-cel, respectively. Mean G-MDSC were significantly lower in patients treated to receive brexu-cel (Figure 1). Responses were evaluated one, three, and six months after receiving CAR-T cells infusion. As we aimed at finding if G-MDSC could predict prognosis, we set a cut-off value of 30 G-MDSC/ml at time-point 3 (immediately after the completion of lymphodepletion); overall, approximately 35% of the patients had G-MDSC &amp;gt;30/ml at that moment. Kaplan-Meier survival analysis revealed that G-MDSC &amp;gt;30/ml at time point 3 associated with a higher risk of relapse, with a PFS of 80% vs 10% six months after CAR-T infusion ( p= 0.0002) (Figure 1). The sudden appearance of G-MDSC following the lymphodepletion is likely to reflect emergency granulopoiesis that favors an increased release of G-MDSC to the bloodstream and may be ideally customized with a tailored lymphodepletion. Conclusion In conclusion, here we show that the high circulating levels of G-MDSC induced by the lymphodepletion are associated with a lack of response to CAR-T cell therapy, thus representing a promising predictive biomarker of CAR-T cell therapy efficacy and possibly a hypothetical point of intervention for ameliorating CAR-T efficacy.

Evaluation of photopolymer resins for dental prosthetics fabricated via the stereolithography process at different polymerization temperatures. Part II: Dimensional accuracy and fracture load of fixed dental prostheses

Prostheses printed on a 3-dimensional (3D) printer need to undergo the postpolymerization process, which can increase the working time. However, it has been not suggested for reducing workload and improving the properties of prostheses in dental clinical practice. The purpose of this in vitro study was to evaluate how the printing temperature impacts the dimensional accuracy and fracture load of 3D printed fixed dental prostheses (FDPs). Dental prostheses were printed at room temperature (RT), 50°C, and 70°C using a stereolithography 3D printer. Subsequently, after rinsing away residual monomer, the printed parts underwent the green condition (it was not subjected to any postprocessing) and postpolymerization. The mechanical properties of the printed FDPs were determined by loading to fracture (n=6). To evaluate their clinical applicability, the dimensional accuracy and fit of FDPs fabricated at various resin polymerization temperatures were measured (n=6). The 1-way analysis of variance was used to perform statistical comparisons, followed by the Tukey honestly significant difference test (α=.05). The specimens printed at RT and 50°C were better than those printed at 70°C in terms of dimensional accuracy and fit (P<.05). Nonetheless, the dimensional accuracy and fit of the specimens printed at 70°C were clinically acceptable. The fracture load of the 3-unit FDPs depended significantly on the printing temperature. The dimensional accuracy and fracture load of the 70°C group were acceptable for FDP fabrication. Thus, the temperature of 70°C without postprocessing may help make the procedure more efficient.

The effect of multimedia-assisted sport education model on teaching tennis lessons in colleges and universities

Abstract The development of information technology is accelerating the development of network multimedia-assisted teaching modes. In this regard, this paper studies the influence of multimedia-assisted sports education on the teaching of tennis classes in colleges and universities. The first step is to analyze the applicable fields and characteristics of network multimedia CAI. Then, the TLBO algorithm focuses on analyzing the “teaching” and “learning” phases of tennis teaching and finds that the TLBO algorithm has defects. To overcome the local optimal situation of the TLBO algorithm, a grouping mechanism is implemented to optimize the TLBO algorithm and maintain the diversity of the population. Finally, the influence of multimedia-assisted teaching was studied in terms of learning motivation, physical fitness, and learning effect. From the perspective of liking tennis sport, the number of experimental classes is 20, which is 15 more than the number of control classes. The mean score of the experimental class in the forehand hitting spinning ball test was 19 points higher than that of the control class.