ResearchPad - statistical-methods https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Fruit and vegetable consumption in Europe according to gender, educational attainment and regional affiliation—A cross-sectional study in 21 European countries]]> https://www.researchpad.co/article/elastic_article_13846 The purpose of the present study was to examine fruit and vegetable consumption according to gender, educational attainment and regional affiliation in Europe.DesignCross-sectional study.Setting21 European countries.Participants37 672 adults participating in the 7th round of the European Social Survey.Main outcome measuresFruit and vegetable consumption was measured using two single frequency questions. Responses were dichotomized into low (<once a day) and high (≥once a day) consumption. The association between consumption of fruit and vegetables and gender, educational level, regional affiliation was examined using logistic regression analyses.ResultsOverall, females showed increased odds of consuming fruit (OR 1.71 (95%CI:1.62, 1.79) and vegetable (1.59 (1.51, 1.67)) compared to males and high educated participants showed increased odds of consuming fruit (1.53 (1.43, 1.63)) and vegetables (1.86 (1.74, 2.00)) compared to low educated participants. Our results also showed that participants living in Eastern Europe had the lowest odds of consuming fruit and vegetables, whereas participants from Southern- and Northern Europe had the highest odds of consuming fruit and vegetables, respectively. Results from interaction analyses confirmed the positive association between fruit and vegetable consumption and educational level, although for some European regions, decreased odds of fruit and vegetables was observed among medium educated participants compared to those with low education.ConclusionsOverall, the present study showed that being female and having a high education were associated with increased consumption of fruit and vegetables. However, the direction and strength of these relationships depends on regional affiliations. ]]> <![CDATA[Relationship between maximal incremental and high-intensity interval exercise performance in elite athletes]]> https://www.researchpad.co/article/elastic_article_13822 This descriptive study aimed to explore the physiological factors that determine tolerance to exertion during high-intensity interval effort. Forty-seven young women (15–28 years old) were enrolled: 23 athletes from Taiwan national or national reserve teams and 24 moderately active females. Each participant underwent a maximal incremental INC (modified Bruce protocol) cardiopulmonary exercise test on the first day and high-intensity interval testing (HIIT) on the second day, both performed on a treadmill. The HIIT protocol involved alternation between 1-min effort at 120% of the maximal speed, at the same slope reached at the end of the INC, and 1-min rest until volitional exhaustion. Gas exchange, heart rate (HR), and muscle oxygenation at the right vastus lateralis, measured by near-infrared spectroscopy, were continuously recorded. The number of repetitions completed (Rlim) by each participant was considered the HIIT tolerance index. The results showed a large difference in the Rlim (range, 2.6–12.0 repetitions) among the participants. Stepwise linear regression revealed that the variance in the Rlim within the cohort was related to the recovery rates of oxygen consumption (V˙O2), HR at the second minute after INC, and muscle tissue saturation index at exhaustion (R = 0.644). In addition, age was linearly correlated with Rlim (adjusted R = −0.518, p < 0.0001). In conclusion, the recovery rates for V˙O2 and HR after the incremental test, and muscle saturation index at exhaustion, were the major physiological factors related to HIIT performance. These findings provide insights into the role of the recovery phase after maximal INC exercise testing. Future research investigating a combination of INC and HIIT testing to determine training-induced performance improvement is warranted.

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<![CDATA[<i>In silico</i> analyses identify lncRNAs: WDFY3-AS2, BDNF-AS and AFAP1-AS1 as potential prognostic factors for patients with triple-negative breast tumors]]> https://www.researchpad.co/article/elastic_article_13870 Long non-coding RNAs (lncRNAs) are characterized as having 200 nucleotides or more and not coding any protein, and several been identified as differentially expressed in several human malignancies, including breast cancer.MethodsHere, we evaluated lncRNAs differentially expressed in triple-negative breast cancer (TNBC) from a cDNA microarray data set obtained in a previous study from our group. Using in silico analyses in combination with a review of the current literature, we identify three lncRNAs as potential prognostic factors for TNBC patients.ResultsWe found that the expression of WDFY3-AS2, BDNF-AS, and AFAP1-AS1 was associated with poor survival in patients with TNBCs. WDFY3-AS2 and BDNF-AS are lncRNAs known to play an important role in tumor suppression of different types of cancer, while AFAP1-AS1 exerts oncogenic activity.ConclusionOur findings provided evidence that WDFY3-AS2, BDNF-AS, and AFAP1-AS1 may be potential prognostic factors in TNBC development. ]]> <![CDATA[Estimating the potential impact of behavioral public health interventions nationally while maintaining agreement with global patterns on relative risks]]> https://www.researchpad.co/article/elastic_article_13880 This paper introduces a novel method to evaluate the local impact of behavioral scenarios on disease prevalence and burden with representative individual level data while ensuring that the model is in agreement with the qualitative patterns of global relative risk (RR) estimates. The method is used to estimate the impact of behavioral scenarios on the burden of disease due to ischemic heart disease (IHD) and diabetes in the Turkish adult population.MethodsDisease specific Hierarchical Bayes (HB) models estimate the individual disease probability as a function of behaviors, demographics, socio-economics and other controls, where constraints are specified based on the global RR estimates. The simulator combines the counterfactual disease probability estimates with disability adjusted life year (DALY)-per-prevalent-case estimates and rolls up to the targeted population level, thus reflecting the local joint distribution of exposures. The Global Burden of Disease (GBD) 2016 study meta-analysis results guide the analysis of the Turkish National Health Surveys (2008 to 2016) that contain more than 90 thousand observations.FindingsThe proposed Qualitative Informative HB models do not sacrifice predictive accuracy versus benchmarks (logistic regression and HB models with non-informative and numerical informative priors) while agreeing with the global patterns. In the Turkish adult population, Increasing Physical Activity reduces the DALYs substantially for both IHD by 8.6% (6.4% 11.2%), and Diabetes by 8.1% (5.8% 10.6%), (90% uncertainty intervals). Eliminating Smoking and Second-hand Smoke predominantly decreases the IHD burden 13.1% (10.4% 15.8%) versus Diabetes 2.8% (1.1% 4.6%). Increasing Fruit and Vegetable Consumption, on the other hand, reduces IHD DALYs by 4.1% (2.8% 5.4%) while not improving the Diabetes burden 0.1% (0% 0.1%).ConclusionWhile the national RR estimates are in qualitative agreement with the global patterns, the scenario impact estimates are markedly different than the attributable risk estimates from the GBD analysis and allow evaluation of practical scenarios with multiple behaviors. ]]> <![CDATA[Low LEF1 expression is a biomarker of early T-cell precursor, an aggressive subtype of T-cell lymphoblastic leukemia]]> https://www.researchpad.co/article/elastic_article_13868 Early T-cell precursor (ETP) is the only subtype of acute T-cell lymphoblastic leukemia (T-ALL) listed in the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia. Patients with ETP tend to have worse disease outcomes. ETP is defined by a series of immune markers. The diagnosis of ETP status can be vague due to the limitation of the current measurement. In this study, we performed unsupervised clustering and supervised prediction to investigate whether a molecular biomarker can be used to identify the ETP status in order to stratify risk groups. We found that the ETP status can be predicted by the expression level of Lymphoid enhancer binding factor 1 (LEF1) with high accuracy (AUC of ROC = 0.957 and 0.933 in two T-ALL cohorts). The patients with ETP subtype have a lower level of LEF1 comparing to the those without ETP. We suggest that incorporating the biomarker LEF1 with traditional immune-phenotyping will improve the diagnosis of ETP.

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<![CDATA[Crystal structure of <i>Thermus thermophilus</i> methylenetetrahydrofolate dehydrogenase and determinants of thermostability]]> https://www.researchpad.co/article/elastic_article_13865 The elucidation of mechanisms behind the thermostability of proteins is extremely important both from the theoretical and applied perspective. Here we report the crystal structure of methylenetetrahydrofolate dehydrogenase (MTHFD) from Thermus thermophilus HB8, a thermophilic model organism. Molecular dynamics trajectory analysis of this protein at different temperatures (303 K, 333 K and 363 K) was compared with homologous proteins from the less temperature resistant organism Thermoplasma acidophilum and the mesophilic organism Acinetobacter baumannii using several data reduction techniques like principal component analysis (PCA), residue interaction network (RIN) analysis and rotamer analysis. These methods enabled the determination of important residues for the thermostability of this enzyme. The description of rotamer distributions by Gini coefficients and Kullback–Leibler (KL) divergence both revealed significant correlations with temperature. The emerging view seems to indicate that a static salt bridge/charged residue network plays a fundamental role in the temperature resistance of Thermus thermophilus MTHFD by enhancing both electrostatic interactions and entropic energy dispersion. Furthermore, this analysis uncovered a relationship between residue mutations and evolutionary pressure acting on thermophilic organisms and thus could be of use for the design of future thermostable enzymes.

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<![CDATA[Operational method of reliability and content-validity analysis: Taking “trait-symptoms” screening of individuals at high-risk for OCD as an example]]> https://www.researchpad.co/article/elastic_article_13806 A well-designed self-reported scale is highly applicable to current clinical and research practices. However, the problems with the scale method, such as quantitative analysis of content validity and test-retest reliability analysis of state-like variables are yet to be resolved. The main purpose of this paper is to propose an operational method for solving these problems. Additionally, it aims to enhance understanding of the research paradigm for the scale method (excluding criterion-related validity). This paper used a study that involved screening of high-risk groups for OCD (Obsessive-Compulsive Disorder), conducted 5 rounds of tests, and developed scales, reliability, and validity analysis (using sample sizes of 496, 610, 600, 600 and 990). The operational method we propose is practical, feasible, and can be used to develop and validate a scale.

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<![CDATA[The early experiences of Physician Associate students in the UK: A regional cross-sectional study investigating factors associated with engagement]]> https://www.researchpad.co/article/elastic_article_13815 The number of physician associates (PAs) training and working in the UK has increased over the last few years following the proliferation of postgraduate courses. Understanding early experiences and what impacts on engagement is important if we are to appropriately support this relatively new professional group.MethodsThis paper reports on a cross-sectional analysis of the first year of data from a prospective 10-year longitudinal cohort study. First year PA students (n = 89) were enrolled from five universities in one UK region where the training programmes were less than 2 years old. Data collected were: demographic information, wellbeing, burnout and engagement, expectations, placement experience, performance and caring responsibilities. Pearson’s correlations were used to examine relationships between variables and to select variables for a hierarchical regression analysis to understand which factors were associated with engagement. Descriptive statistics were calculated for questions relating to experience.ResultsThe experiences of PA students during their first 3–6 months were mixed. For example, 78.7% of students felt that there were staff on placement they could go to for support, however, 44.8% reported that staff did not know about the role and 61.3% reported that staff did not know what clinical work they should undertake. Regression analysis found that their level of engagement was associated with their perceived career satisfaction, overall well-being, and caring responsibilities.ConclusionsThe support systems required for PAs may need to be examined as results showed that the PA student demographic is different to that of medical students and caring responsibilities are highly associated with engagement. A lack of understanding around the PA role in clinical settings may also need to be addressed in order to better support and develop this workforce. ]]> <![CDATA[Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019]]> https://www.researchpad.co/article/elastic_article_13811 The identification of statistical models for the accurate forecast and timely determination of the outbreak of infectious diseases is very important for the healthcare system. Thus, this study was conducted to assess and compare the performance of four machine-learning methods in modeling and forecasting brucellosis time series data based on climatic parameters.MethodsIn this cohort study, human brucellosis cases and climatic parameters were analyzed on a monthly basis for the Qazvin province–located in northwestern Iran- over a period of 9 years (2010–2018). The data were classified into two subsets of education (80%) and testing (20%). Artificial neural network methods (radial basis function and multilayer perceptron), support vector machine and random forest were fitted to each set. Performance analysis of the models were done using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Root Error (MARE), and R2 criteria.ResultsThe incidence rate of the brucellosis in Qazvin province was 27.43 per 100,000 during 2010–2019. Based on our results, the values of the RMSE (0.22), MAE (0.175), MARE (0.007) criteria were smaller for the multilayer perceptron neural network than their values in the other three models. Moreover, the R2 (0.99) value was bigger in this model. Therefore, the multilayer perceptron neural network exhibited better performance in forecasting the studied data. The average wind speed and mean temperature were the most effective climatic parameters in the incidence of this disease.ConclusionsThe multilayer perceptron neural network can be used as an effective method in detecting the behavioral trend of brucellosis over time. Nevertheless, further studies focusing on the application and comparison of these methods are needed to detect the most appropriate forecast method for this disease. ]]> <![CDATA[Using case-level context to classify cancer pathology reports]]> https://www.researchpad.co/article/elastic_article_7869 Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence—for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks—site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks.

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<![CDATA[Pooling individual participant data from randomized controlled trials: Exploring potential loss of information]]> https://www.researchpad.co/article/elastic_article_7838 Pooling individual participant data to enable pooled analyses is often complicated by diversity in variables across available datasets. Therefore, recoding original variables is often necessary to build a pooled dataset. We aimed to quantify how much information is lost in this process and to what extent this jeopardizes validity of analyses results.MethodsData were derived from a platform that was developed to pool data from three randomized controlled trials on the effect of treatment of cardiovascular risk factors on cognitive decline or dementia. We quantified loss of information using the R-squared of linear regression models with pooled variables as a function of their original variable(s). In case the R-squared was below 0.8, we additionally explored the potential impact of loss of information for future analyses. We did this second step by comparing whether the Beta coefficient of the predictor differed more than 10% when adding original or recoded variables as a confounder in a linear regression model. In a simulation we randomly sampled numbers, recoded those < = 1000 to 0 and those >1000 to 1 and varied the range of the continuous variable, the ratio of recoded zeroes to recoded ones, or both, and again extracted the R-squared from linear models to quantify information loss.ResultsThe R-squared was below 0.8 for 8 out of 91 recoded variables. In 4 cases this had a substantial impact on the regression models, particularly when a continuous variable was recoded into a discrete variable. Our simulation showed that the least information is lost when the ratio of recoded zeroes to ones is 1:1.ConclusionsLarge, pooled datasets provide great opportunities, justifying the efforts for data harmonization. Still, caution is warranted when using recoded variables which variance is explained limitedly by their original variables as this may jeopardize the validity of study results. ]]> <![CDATA[A descriptive cross sectional study comparing barriers and determinants of physical activity of Sri Lankan middle aged and older adults]]> https://www.researchpad.co/article/elastic_article_7830 Benefits of physical activities are numerous. Barriers for physical exercise may differ among middle aged and older adults. Therefore, identifying and comparing the barriers for participating in regular physical exercises among middle aged and older adults will be useful in designing age specific physical exercise programmes.MethodsThis descriptive cross sectional study was carried out among 206 Sri Lankan adults in the age range of 40–84 years in the Colombo North region of Sri Lanka using culturally validated questionnaires to determine and compare the barriers and factors associated with regular physical activity participation. Majority were males (56%) and 54% were < 60 years. People in the age range of 40–59 years were considered as middle age and ≥ 60 years as older adults. Bivariate analysis and multivariate analysis was carried out to determine the significant factors that are associated with regular physical activity participation.ResultsLack of free time (52%), feeling too lazy (26%) and bad weather (29%) were the main barriers for the participants. In < 60 years, high level of income (p = 0.008) and in ≥ 60 years, being a male (p = 0.016), having a high level of education (P = 0.002) and a high BMI (p = 0.002) had a significant negative association with the level of physical activities.ConclusionsContrary to findings from surveys in several developed countries, this study showed that having a high level of education and being a male were strongly related with lack of physical activity participation. ]]> <![CDATA[The qualitative assessment of optical coherence tomography and the central retinal sensitivity in patients with retinitis pigmentosa]]> https://www.researchpad.co/article/elastic_article_7697 To analyze the relationships between qualitative and quantitative parameters of spectral-domain optical coherence tomography (SD-OCT) and the central retinal sensitivity in patients with retinitis pigmentosa (RP).Materials and methodsNinety-three eyes of 93 patients were finally enrolled, with a median age (quartile) of 58 (24.5) years. We assessed the patients using SD-OCT and the 10–2 program of a Humphry Field Analyzer (HFA). As a qualitative parameter, two graders independently classified the patients’ SD-OCT images into five severity grades (grades 1–5) based on the severity of damage to the photoreceptor inner and outer segments (IS/OS) layer. As quantitative parameters, we measured the IS-ellipsoid zone (IS-EZ) width, IS/OS thickness, outer nuclear layer (ONL) thickness, central macular thickness (CMT, 1 and 3 mm) and macular cube (6 × 6 mm) volume and thickness. The central retinal sensitivity was defined by the best-corrected visual acuity (BCVA; logMAR), average sensitivities of the central 4 (foveal sensitivity [FS]) and 12 (macular sensitivity [MS]) points of the HFA 10–2 program and the mean deviation (MD) of the 10–2 program. Spearman’s correlation was used to assess the association between both qualitative and quantitative parameters and variables of the central retinal sensitivity. In addition, we performed a multiple regression analysis using these parameters to identify the parameters most strongly influencing the central retinal sensitivity.ResultsThe IS/OS severity grade was significantly correlated with the BCVA (ρ = 0.741, P < 0.001), FS (ρ = −0.844, P < 0.001), MS (ρ = −0.820, P < 0.001) and MD (ρ = −0.681, P < 0.001) and showed stronger correlations to them than any other quantitative parameters including the IS-EZ width, IS/OS thickness, ONL thickness, CMTs and macular cube volume/thickness. Furthermore, a step-wise multiple regression analysis indicated that the IS/OS severity grade was more strongly associated with the BCVA (β = 0.659, P < 0.001), FS (β = −0.820, P < 0.001), MS (β = −0.820, P < 0.001) and MD (β = −0.674, P < 0.001) than any other quantitative parameters. The intraclass correlation coefficient between two graders indicated substantial correlation (κ = 0.70).DiscussionThe qualitative grading of OCT based on the severity of the IS/OS layer was simple and strongly correlated with the central retinal sensitivity in patients with RP. It may be useful to assess the central visual function in patients with RP, although there is some variation in severity within the same severity grade. ]]> <![CDATA[Prevalence, Severity and Mortality associated with COPD and Smoking in patients with COVID-19: A Rapid Systematic Review and Meta-Analysis]]> https://www.researchpad.co/article/elastic_article_7662 Coronavirus disease 2019 (COVID-19) is an evolving infectious disease that dramatically spread all over the world in the early part of 2020. No studies have yet summarized the potential severity and mortality risks caused by COVID-19 in patients with chronic obstructive pulmonary disease (COPD), and we update information in smokers.MethodsWe systematically searched electronic databases from inception to March 24, 2020. Data were extracted by two independent authors in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Study quality was assessed using a modified version of the Newcastle-Ottawa Scale. We synthesized a narrative from eligible studies and conducted a meta-analysis using a random-effects model to calculate pooled prevalence rates and 95% confidence intervals (95%CI).ResultsIn total, 123 abstracts were screened and 61 full-text manuscripts were reviewed. A total of 15 studies met the inclusion criteria, which included a total of 2473 confirmed COVID-19 patients. All studies were included in the meta-analysis. The crude case fatality rate of COVID-19 was 7.4%. The pooled prevalence rates of COPD patients and smokers in COVID-19 cases were 2% (95% CI, 1%–3%) and 9% (95% CI, 4%–14%) respectively. COPD patients were at a higher risk of more severe disease (risk of severity = 63%, (22/35) compared to patients without COPD 33.4% (409/1224) [calculated RR, 1.88 (95% CI, 1.4–2.4)]. This was associated with higher mortality (60%). Our results showed that 22% (31/139) of current smokers and 46% (13/28) of ex-smokers had severe complications. The calculated RR showed that current smokers were 1.45 times more likely [95% CI: 1.03–2.04] to have severe complications compared to former and never smokers. Current smokers also had a higher mortality rate of 38.5%.ConclusionAlthough COPD prevalence in COVID-19 cases was low in current reports, COVID-19 infection was associated with substantial severity and mortality rates in COPD. Compared to former and never smokers, current smokers were at greater risk of severe complications and higher mortality rate. Effective preventive measures are required to reduce COVID-19 risk in COPD patients and current smokers. ]]> <![CDATA[Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens]]> https://www.researchpad.co/article/5989db51ab0ee8fa60bdc4ab

Feed represents the major cost of chicken production. Selection for improving feed utilization is a feasible way to reduce feed cost and greenhouse gas emissions. The objectives of this study were to investigate the efficiency of genomic prediction for feed conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG) and average daily feed intake (ADFI) and to assess the impact of selection for feed efficiency traits FCR and RFI on eviscerating percentage (EP), breast muscle percentage (BMP) and leg muscle percentage (LMP) in meat-type chickens. Genomic prediction was assessed using a 4-fold cross-validation for two validation scenarios. The first scenario was a random family sampling validation (CVF), and the second scenario was a random individual sampling validation (CVR). Variance components were estimated based on the genomic relationship built with single nucleotide polymorphism markers. Genomic estimated breeding values (GEBV) were predicted using a genomic best linear unbiased prediction model. The accuracies of GEBV were evaluated in two ways: the correlation between GEBV and corrected phenotypic value divided by the square root of heritability, i.e., the correlation-based accuracy, and model-based theoretical accuracy. Breeding values were also predicted using a conventional pedigree-based best linear unbiased prediction model in order to compare accuracies of genomic and conventional predictions. The heritability estimates of FCR and RFI were 0.29 and 0.50, respectively. The heritability estimates of ADG, ADFI, EP, BMP and LMP ranged from 0.34 to 0.53. In the CVF scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR were slightly higher than those for RFI. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.360, 0.284, 0.574 and 0.520, respectively, and the model-based theoretical accuracies were 0.420, 0.414, 0.401 and 0.382, respectively. In the CVR scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR was lower than RFI, which was different from the CVF scenario. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.449, 0.593, 0.581 and 0.627, respectively, and the model-based theoretical accuracies were 0.577, 0.629, 0.631 and 0.638, respectively. The accuracies of genomic predictions were 0.371 and 0.322 higher than the conventional pedigree-based predictions for the CVF and CVR scenarios, respectively. The genetic correlations of FCR with EP, BMP and LMP were -0.427, -0.156 and -0.338, respectively. The correlations between RFI and the three carcass traits were -0.320, -0.404 and -0.353, respectively. These results indicate that RFI and FCR have a moderate accuracy of genomic prediction. Improving RFI and FCR could be favourable for EP, BMP and LMP. Compared with FCR, which can be improved by selection for ADG in typical meat-type chicken breeding programs, selection for RFI could lead to extra improvement in feed efficiency.

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<![CDATA[Time-lapse imaging of HeLa spheroids in soft agar culture provides virtual inner proliferative activity]]> https://www.researchpad.co/article/Nceafa1bd-f75c-4e08-9c15-587118f668b1

Cancer is a complex disease caused by multiple types of interactions. To simplify and normalize the assessment of drug effects, spheroid microenvironments have been utilized. Research models that involve agent measurement with the examination of clonogenic survival by monitoring culture process with image analysis have been developed for spheroid-based screening. Meanwhile, computer simulations using various models have enabled better predictions for phenomena in cancer. However, user-based parameters that are specific to a researcher’s own experimental conditions must be inputted. In order to bridge the gap between experimental and simulated conditions, we have developed an in silico analysis method with virtual three-dimensional embodiment computed using the researcher’s own samples. The present work focused on HeLa spheroid growth in soft agar culture, with spheroids being modeled in silico based on time-lapse images capturing spheroid growth. The spheroids in silico were optimized by adjusting the growth curves to those obtained from time-lapse images of spheroids and were then assigned virtual inner proliferative activity by using generations assigned to each cellular particle. The ratio and distribution of the virtual inner proliferative activities were confirmed to be similar to the proliferation zone ratio and histochemical profiles of HeLa spheroids, which were also consistent with those identified in an earlier study. We validated that time-lapse images of HeLa spheroids provided virtual inner proliferative activity for spheroids in vitro. The present work has achieved the first step toward an in silico analysis method using computational simulation based on a researcher’s own samples, helping to bridge the gap between experiment and simulation.

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<![CDATA[Analysis and modeling of coolants and coolers for specimen transportation]]> https://www.researchpad.co/article/N4e3aeb5c-7b13-42da-a06e-637c738940f8

Maintaining cold chain while transporting medical supplies and samples is difficult in remote settings. Failure to maintain temperature requirements can lead to degraded sample quality and inaccuracies in sample analysis. We performed a systematic analysis on different types of transport coolers (polystyrene foam, injection-molded, and rotational molded) and transport coolants (ice, cold packs, frozen water bottles) frequently in use in many countries. Polystyrene foam coolers stayed below our temperature threshold (6°C) longer than almost all other types of coolers, but were not durable. Injection-molded coolers were durable, but warmed to 6°C the quickest. Rotational molded coolers were able to keep temperatures below our threshold for 24 hours longer than injection molded coolers and were highly durable. Coolant systems were evaluated in terms of cost and their ability to maintain cold temperatures. Long lasting commercial cold packs were found to be less cost effective and were below freezing for the majority of the testing period. Frozen plastic water bottles were found to be a reusable and economical choice for coolant and were only below freezing briefly. Finally, we modeled the coolers performance at maintaining internal temperatures below 6°C and built a highly accurate linear model to predict how long a cooler will remain below 6°C. We believe this data may be useful in the planning and design of specimen transportation systems in the field, particularly in remote or resource limited settings.

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<![CDATA[‘In search of lost time’: Identifying the causative role of cumulative competition load and competition time-loss in professional tennis using a structural nested mean model]]> https://www.researchpad.co/article/N4f3da08e-598e-44d5-a4f3-a2c64fcebd1f

Injury prevention is critical to the achievement of peak performance in elite sport. For professional tennis players, the topic of injury prevention has gained even greater importance in recent years as multiple of the best male players have been sidelined owing to injury. Identifying potential causative factors of injury is essential for the development of effective prevention strategies, yet such research is hampered by incomplete data, the complexity of injury etiology, and observational study biases. The present study attempts to address these challenges by focusing on competition load and time-loss to competition—a completely observable risk factor and outcome—and using a structural nested mean model (SNMM) to identify the potential causal role of cumulative competition load on the risk of time-loss. Using inverse probability of treatment weights to balance exposure histories with respect to player ability, past injury, and consecutive competition weeks at each time point; the SNMM analysis of 389 professional male players and 55,773 weeks of competition found that total load significantly increases the risk of time-loss (HR = 1.05 per 1,000 games of additional load 95% CI 1.01-1.10) and this effect becomes magnified with age. Standard regression showed a protective effect of load, highlighting the value of more robust causal methods in the study of dynamic exposures and injury in sport and the need for further applications of these methods for understanding how time-loss and injuries of elite athletes might be prevented in the future.

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<![CDATA[Effects of sea-level rise on physiological ecology of populations of a ground-dwelling ant]]> https://www.researchpad.co/article/N7f89605c-5421-4b76-a019-ba0e7ddd5b34

Introduction

Sea-level rise is a consequence of climate change that can impact the ecological and physiological changes of coastal, ground-dwelling species. Sea-level rise has a potential to inundate birds, rodents, spiders, and insects that live on the ground in coastal areas. Yet, there is still much to be learned concerning the specifics of these impacts. The red imported fire ant Solenopsis invicta (Buren) excavates soil for its home and is capable of surviving flooding. Because of their ground-dwelling life history and rapid reproduction, fire ants make an ideal model for discovery and prediction of changes that may be due to sea-level rise. There are up to 500,000 individuals in a colony, and these invasive ants naturally have a painful sting. However, observations suggest that colonies of fire ants that dwell in tidally-influenced areas are more aggressive with more frequent stings and more venom injected per sting (behavioral and physiological changes) than those located inland. This may be an adaption to sea-level rise. Therefore, the objective of this study is to elucidate differences in inland and coastal defensiveness via micro-dissection and comparison of head width, head length, stinger length, and venom sac volume. But first because fire ants’ ability to raft on brackish tidal water is unknown, it had to be determined if fire ants could indeed raft in brackish water and examine the behavior differences between those flooded with freshwater vs. saltwater.

Methods

To test the coastal-aggression hypothesis, inland colonies and coastal colonies, which experience relatively greater amounts of flooding, specifically regular tidal and windblown water and oscillations (i.e. El Nińo Southern Oscillation) from the Gulf of Mexico, were collected. To mimic sea-level rise, the colonies were flooded in salinities that correspond to both their collection site and conditions found in a variety of locales and situations (such as storm surge from a tropical storm). Individual ants were immediately taken from each colony for dissection before flooding, 1-hour into flooding, and 24-hours into flooding.

Results and discussion

Fire ants use their venom to defend themselves and to communicate alarm or aggression. Dissections and measurement of heads, venom sacs, and stingers revealed both coastal and inland colonies experience an increase in venom sac volume after 24 hours; in fact coastal colonies increased their venom volume by 75% after 24 h of flooding Whether this venom sac enlargement is due to diffusion of water or venom sac production is unknown. These ground-dwelling ants exhibit physiological and behavioral adaptations to ongoing sea-level rise possibly indicating that they are responding to increased flooding. Fire ants will raft on high-salinity water; and sea-level rise may cause stings by flooded ants to be more severe because of increased venom volume.

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<![CDATA[Class enumeration false positive in skew-t family of continuous growth mixture models]]> https://www.researchpad.co/article/Ne0623f60-4058-4fc0-9606-ac0f597752dc

Growth Mixture Modeling (GMM) has gained great popularity in the last decades as a methodology for longitudinal data analysis. The usual assumption of normally distributed repeated measures has been shown as problematic in real-life data applications. Namely, performing normal GMM on data that is even slightly skewed can lead to an over selection of the number of latent classes. In order to ameliorate this unwanted result, GMM based on the skew t family of continuous distributions has been proposed. This family of distributions includes the normal, skew normal, t, and skew t. This simulation study aims to determine the efficiency of selecting the “true” number of latent groups in GMM based on the skew t family of continuous distributions, using fit indices and likelihood ratio tests. Results show that the skew t GMM was the only model considered that showed fit indices and LRT false positive rates under the 0.05 cutoff value across sample sizes and for normal, and skewed and kurtic data. Simulation results are corroborated by a real educational data application example. These findings favor the development of practical guides of the benefits and risks of using the GMM based on this family of distributions.

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