Search

A longstanding debate concerns the function of carinated elements in both, the Levantine, and European Aurignacian. The present study aims to contribute to this topic with the evaluation of the carinated assemblage from layer D in Hayonim Cave, Western Galilee, Israel, one of the type sites of the Levantine Aurignacian. An operational chain reconstruction with an attribute analysis is paired with a typological approach to the preparation and maintenance products based on artefacts defined as West European Aurignacian. The results of this study are investigated with multivariate statistics offering a methodological contribution. The data is subjected to a transformation into a distance matrix using the Gower distance and tested with the adonis-algorithm for significance. The results clearly indicate that the carinated items in Hayonim Cave did fully or partially function as cores. They are accompanied by diagnostic preparation- and- maintenance products known from the literature e.g. Thèmes bladelets. The statistical analysis indicates only a minor correlation with stratigraphy yet supports the techno-typological criteria applied for defining artefact categories (cores, debitage, tools), as well as the proposed differentiation of carinated 'core' types. The non-carinated cores in Hayonim Cave are characterised by a high variability in typology and reduction concepts. A curious similarity to the Levallois-concept is observed on some of the flake cores. It is therefore suggested that the frequent recycling of Middle Palaeolithic artefacts in the Levantine Aurignacian might have given the Aurignacian flint-knappers the opportunity to study the Levallois concept and apply an approximation of it in their own core reduction strategies. The notion that Palaeolithic flint-knappers actively observed former technological systems through the discarded artefacts directly opens up a new trajectory for the understanding of lithic reduction concept permanence. The conceptual diversification and variability in Hayonim Cave D indicate a highly dynamic period in the Levantine Upper Palaeolithic which increased the adaptive potential and promoted a rapid cultural change. [ABSTRACT FROM AUTHOR]

This study explains how the gap between theory and empirical research hinders scientific progress in the area of international political economy. To demonstrate this point, I use Chen's Extended Dependence Theory, which challenges liberal peace theory but fails to provide supporting empirical evidence. Chen contends that it is not trade dependence between two states that fosters peace but a challenger's trade relations with the defense-pact partners of the target. Although Chen criticizes liberal peace proponents whose primary concern is how to deter war, his empirical analysis is confined to how to decrease (fatal) militarized disputes short of war. I argue that for his theory to succeed, it must be validated against the most severe and intense form of conflict. Using statistical tests and substantive significance, I uncover no peace-building effect, with regards to war, attributable to Extended Dependence. It appears that the Extended Dependence variable exhibits a ceiling effect. Future research should explain why economic ties and security institutions fail to work together to lower the risk of the most destructive form of conflict. [ABSTRACT FROM AUTHOR]

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments. [ABSTRACT FROM AUTHOR]

Objective: This study explores the contribution of nutrition knowledge to the health status of pre-school children's mothers in Niger State, North-Central, Nigeria. Design: The study is a descriptive cross-sectional design using a quantitative data collection method. Setting/participants: A multi-stage sampling technique was used to recruit 450 mothers of pre-school children across Niger state, Nigeria. Chi-square and linear regression were used to test the level of statistical significance (at p < 0.05). Socioeconomic and demographic information, anthropometric indices and nutrition knowledge were obtained using semi-structured questionnaires. Feeding patterns were also assessed using a qualitative 7-day dietary recall. Result: The results showed that the majority (63.8%) of the mothers were within 26–35 years, and more than half (51.6%) of the mothers lacked knowledge of a "balanced diet". Fruits, vegetables, and dairy products were the least consumed food group among the mothers (7.1% and 9.1%, respectively). Place of residence, occupation, and method of waste disposal were significantly associated with maternal minimum dietary diversity adequacy (p<0.05). Over half (57.6%) of the mothers were within the normal BMI range, and the mean waist/hip ratio was 0.82± 0.08. Social media/online was the most (36.4%) explored source of nutrition information among the mothers. This study shows no significant association between nutrition knowledge and adequacy of minimum dietary diversity among the mothers of preschool children (p = 0.09, χ2 = 13.682). Conclusion: Dietary diversity among mothers was associated with the socioeconomic status and BMI of the mothers, which were strong determinants of meal quality and health outcomes in Nigeria and other developing countries experiencing food insecurity. [ABSTRACT FROM AUTHOR]

Hikers and hillwalkers typically use the gradient in the direction of travel (walking slope) as the main variable in established methods for predicting walking time (via the walking speed) along a route. Research into fell-running has suggested further variables which may improve speed algorithms in this context; the gradient of the terrain (hill slope) and the level of terrain obstruction. Recent improvements in data availability, as well as widespread use of GPS tracking now make it possible to explore these variables in a walking speed model at a sufficient scale to test statistical significance. We tested various established models used to predict walking speed against public GPS data from almost 88,000 km of UK walking / hiking tracks. Tracks were filtered to remove breaks and non-walking sections. A new generalised linear model (GLM) was then used to predict walking speeds. Key differences between the GLM and established rules were that the GLM considered the gradient of the terrain (hill slope) irrespective of walking slope, as well as the terrain type and level of terrain obstruction in off-road travel. All of these factors were shown to be highly significant, and this is supported by a lower root-mean-square-error compared to existing functions. We also observed an increase in RMSE between the GLM and established methods as hill slope increases, further supporting the importance of this variable. [ABSTRACT FROM AUTHOR]

In order to develop a statistical model for complex interactions among factors affecting early childhood caries development (ECC), 234 children from the "XXXX oral growth longitudinal study" were analysed at ages 3, 4 and 5. A questionnaire for children's parents (clinical history, nutritional and oral hygiene habits) and clinical oral examinations were recorded each year. The probabilistic dependence structure on the most significant factors was modelled with an Undirected Graphical Model (UGM or Markov random fields) which provides a probabilistic reasoning approach for the establishment of multi-way associations. The best fitting UGM was estimated through the maximum likelihood principle and two-way factor associations were verified through Fisher's exact statistical hypothesis tests for count data. The effect of sugar intake on caries incidence is mediated by oral hygiene; for caries incidence, oral hygiene quality is more relevant than toothbrushing time; the effect of pacifier on caries incidence is statistically significant only when considered in combination with breastfeeding time. Among behavioural ECC risk factors, the quality of oral hygiene, and not the toothbrushing frequency, is a primary factor that modulates the sugar intake in his primary role of the ECC developer. School-based oral health programs for ECC prevention should be improved with supervised toothbrushing program. UGM analysis could improve the school-based oral health programs with more effective and efficient prevention strategies based on the hierarchical interactions among the ECC risk factors. Oral hygiene plays a pivotal role in early childhood caries and can modulate positively or negatively their development. Supervised toothbrushing is a crucial intervention to be included in the daily educational and clinical practice and in the school-based oral health promotion programs. Trial registration: Clinical trial registration number: NCT02798809. [ABSTRACT FROM AUTHOR]

When the in/consistency in Pairwise Comparisons (PCs) is taken into consideration as the subarea of the Multi Attribute Decision Making (MADM) scientific field, it has many repercussions in various types of research areas including different modelling scenarios e.g. reduction of inconsistency during PCs, deriving appropriate consistency thresholds for inconsistent Pairwise Comparison Matrices (PCMs), completing of incomplete PCMs, aggregating of individual PCMs in relation to Group Decision Making (GDM) aspects, and PCMs in/consistency relation to credibility of Priority Vectors (PV) derived from PCMs with the application of various Priorities Deriving Methods (PDMs). The examination objective in the latter area of research is the uncertainty related to the inexactitude of prioritization based on derived PVs. However, only few research studies examine this problem from the perspective of PCM applicability for credible designation of decision maker's (DM) priorities in the way that leads to minimization of the prioritization uncertainty related to possible, and sometimes very probable, ranking fluctuations. This problem constitutes the primary area of interest for this research paper as no research study was thus far identified that examines this problem from the perspective of consistent PCMs. Hence, a research gap was identified. Thus, the objective of this research paper is to fill in this scientific gap. The research findings have serious repercussions in relation to prioritization quality with the application of PCs methodology, mostly in relation to the interpretation and reliability evaluation of prioritization results. Firstly, the research study outcome changes the perspective of the rank reversal phenomenon, which shed new light on many research studies that have been presented in the subject's literature for many decades. Secondly, the research study results throw new light on the discussion concerning the fuzziness of AHP's results. Last but not least, the effect of the research opens the unique opportunity to evaluate the prioritization outcome obtained within the process of consistent PCs from the well-known perspective of statistical hypothesis testing i.e. the probability designation of the chance that accepted ranking results which were considered as correct due to low probability of change may be incorrect, hence they should be rejected, and the probability designation of the chance that rejected ranking results which were considered as incorrect due to high probability of change may be correct and should be accepted. The paramount finding of the research is the fact that consistent PCMs provide PVs, which elements cannot be considered as established, but only approximated within certain confidence intervals estimated with a certain level of probability. As problems related to heuristics can be analyzed only via a computer simulation process, because they cannot be mathematically determined, the problem examined in this research paper is examined via Monte Carlo simulations, appropriately coded and executed with the application of Wolfram's Mathematica Software. It is believed that this research findings should be very important and useful for all decision makers and researchers during their problems' examinations that relate to prioritization processes with the application of PCs methodology. [ABSTRACT FROM AUTHOR]

Background: Composite multidimensional indices are broadly used to measure child poverty and social exclusion. Many of such indices are based on EU-SILC data or similar large scale complex sampling surveys, with the household as unit of analysis. Indicators related to households with or without children may quantify the intended attribute differently depending on the household structure and characteristics of individuals, potentially compromising the assessment. Methods: We conducted statistical modelling and hypotheses tests using a two-parameter logistic item response model (IRM) and the likelihood-ratio test for DIF verification. Methods were applied to 2020 EU-SILC Portuguese data comprising 11,367 households representing a population of 4,099,052. Statistical analysis have allowed for the survey sampling design. Conclusion: Our findings demonstrate differential item functioning in the assessment material deprivation in households with or without children. [ABSTRACT FROM AUTHOR]

Mapper, a topological algorithm, is frequently used as an exploratory tool to build a graphical representation of data. This representation can help to gain a better understanding of the intrinsic shape of high-dimensional genomic data and to retain information that may be lost using standard dimension-reduction algorithms. We propose a novel workflow to process and analyze RNA-seq data from tumor and healthy subjects integrating Mapper, differential gene expression, and spectral shape analysis. Precisely, we show that a Gaussian mixture approximation method can be used to produce graphical structures that successfully separate tumor and healthy subjects, and produce two subgroups of tumor subjects. A further analysis using DESeq2, a popular tool for the detection of differentially expressed genes, shows that these two subgroups of tumor cells bear two distinct gene regulations, suggesting two discrete paths for forming lung cancer, which could not be highlighted by other popular clustering methods, including t-distributed stochastic neighbor embedding (t-SNE). Although Mapper shows promise in analyzing high-dimensional data, tools to statistically analyze Mapper graphical structures are limited in the existing literature. In this paper, we develop a scoring method using heat kernel signatures that provides an empirical setting for statistical inferences such as hypothesis testing, sensitivity analysis, and correlation analysis. [ABSTRACT FROM AUTHOR]

To determine the relevance of morphometric properties attributed to the meat yield and fatness index of the saltwater hard clam Meretrix meretrix. A new strain of M. meretrix with red shell color was produced after five generations of selection within a family of full-sibs. 7 morphometric traits, including shell length (SL), shell height (SH), shell width (SW), ligament length (LL), projection length (PL), projection width (PW), and live body weight (LW), and 2 meat characteristics, including meat yield (MY) and fatness index (FI) were measured from 50 individuals of three-year-old M. meretrix. The correlation coefficients, path coefficients, determination coefficients among attributes were analyzed. The results indicated that correlation achieved very significant levels (P<0.01). In addition, the multiple regression equations were formulated by considering the meat yield and fatness index as the dependent variables, respectively, and 7 other morphometric traits as independent variables. The correlation indices (R2) of morphometric traits against the meat yield and fatness index of clams were 0.901 and 0,929, respectively, indicating that the live body weight and shell length were the common main factors influencing the meat characteristics. By testing the significance of partial regression coefficient and gradually removing the non-significant morphometric traits, a multiple regression equation was established to estimate the relationship between shell length (SL, mm), live body weight (LW, g), ligament length (LL, mm) and meat yield (MY, %), fat index (FI, %): MY (%) = 0.432SL+0.251LW and FI (%) = 0.156SL+0.067LL+0.42LW-3.533. The study draws a conclusion that live body weight and shell length have a predominant direct effect on the meat yield and fatness index, which provides theoretical information for the breeding of M. meretrix. [ABSTRACT FROM AUTHOR]

Introduction: Few studies including contradictory results have addressed the acute effects of the 11+ on motor performance, indicating a potentially reduced applicability of the program for warming up before competitions. This study aims to compare the acute effects of a soccer-specific warm-up (Football+) and the 11+ on motor performance. Materials and methods: Thirty-eight volunteer collegiate players (22 males; age = 21.1±1.9 years, height = 1.81± 0.06 m, weight = 73.4± 9.5 kg; 16 females; age = 21.3±1.5 years; height = 1.71± 0.07 m, weight = 67.8± 8.5 kg) underwent the 11+ and the Football+ in a randomized crossover design with a one-week washout. The Football+ starts with a self-estimated 40–50 percent running, followed by dynamic stretching of the hip muscles, shoulder contact, controlled lunge, Copenhagen exercise, and modified Nordic hamstring exercise. The second part involves roughly intensive small-sided games, followed by plyometric and anaerobic exercises in the third part. The warm ups' effects on performance were determined by a linear sprinting test (20 m), countermovement jump performance (CMJ), Illinois agility (IA), and dribbling speed (DS) tests. Within-subject differences were reported as the means and SD. Pairwise t tests at the significance level of p<0.05 were used to calculate the significant differences. Results: Overall, except for the CMJ (mean = -0.43±3.20 cm, p = 0.21, d = -0.13), significant differences for the 20 m sprint (mean = 0.04±0.10 s, p = 0.005, d = 0.42), IA (mean = 0.65±0.45 s, p = 0.01, d = 1.43), and DS (mean = 0.60±1.58 s, p = 0.012, d = 0.38) were observed. In females, significant differences observed only for IA (mean difference = 0.52±0.42 s, p<0.001, d = 1.24) and DS (mean difference = 1.29±1,77 s, p = 0.005, d = 0.73), with the Football+ showing superiority. In males, significant differences were found only for 20 m sprinting (mean difference = 0.06±0.09, p = 0.005, d = 0.60) and IA (mean difference = 0.74±0.46, p<0.001, d = 1.62), with the Football+ having superiority. Discussion: Although practicable for injury prevention, the 11+ may not optimize acute performance and prepare players for high-intensity physical tasks as well as a well-structured, roughly intensive warm-up. Further gender-specific studies should evaluate the long-term effects of the Football+ on performance and injury prevention. [ABSTRACT FROM AUTHOR]

Diagnostic screening models for the interpretation of null hypothesis significance test (NHST) results have been influential in highlighting the effect of selective publication on the reproducibility of the published literature, leading to John Ioannidis' much-cited claim that most published research findings are false. These models, however, are typically based on the assumption that hypotheses are dichotomously true or false, without considering that effect sizes for different hypotheses are not the same. To address this limitation, we develop a simulation model that overcomes this by modeling effect sizes explicitly using different continuous distributions, while retaining other aspects of previous models such as publication bias and the pursuit of statistical significance. Our results show that the combination of selective publication, bias, low statistical power and unlikely hypotheses consistently leads to high proportions of false positives, irrespective of the effect size distribution assumed. Using continuous effect sizes also allows us to evaluate the degree of effect size overestimation and prevalence of estimates with the wrong sign in the literature, showing that the same factors that drive false-positive results also lead to errors in estimating effect size direction and magnitude. Nevertheless, the relative influence of these factors on different metrics varies depending on the distribution assumed for effect sizes. The model is made available as an R ShinyApp interface, allowing one to explore features of the literature in various scenarios. [ABSTRACT FROM AUTHOR]

In the case of comparing means of various groups, data exploration and comparison for affecting factors or relative indices would be involved. This process is not only complex requiring extensive statistical knowledge and methods, but also challenging for the complex installation of existing tools for users who lack of statistical knowledge and coding experience. Like, the normal distribution and equal variance are crucial premises of parametric statistical analysis. But some studies reported that associated data from various industries violated the normal distribution and equal variance, parametric analysis still involved leading to invalid results. This is owing to that the normal distribution tests and homogeneity of variance test for different variables are time-cost and error-prone, posing an urgent need for an automatic and user-friendly analysis application, not only integrating normal distribution tests and homogeneity of variance test, but also associated the following statistical analysis. To address this, we developed a Shiny/R application, moreThanANOVA, which is an interactive, user-friendly, open-source and cloud-based visualization application to achieve automatic distribution tests, and correlative significance tests, then customize post-hoc analysis based on the considerations to the trade-off of Type I and Type II errors (deployed at https://hanchen.shinyapps.io/moreThanANOVA/). moreThanANOVA enables novice users to perform their complex statistical analyses quickly and credibly with interactive visualization and download publication-ready graphs for further analysis. [ABSTRACT FROM AUTHOR]

The event related P300 potentials, positive waveforms in electroencephalography (EEG) signals, are often utilized in brain computer interfaces (BCI). Many studies have been carried out to improve the performance of P300 speller systems either by developing signal processing algorithms and classifiers with different architectures or by designing new paradigms. In this study, a new paradigm is proposed for this purpose. The proposed paradigm combines two remarkable properties of being a 3D animation and utilizing column-only flashings as opposed to classical paradigms which are based on row-column flashings in 2D manner. The new paradigm is utilized in a traditional two-layer artificial neural networks model with a single output neuron, and numerous experiments are conducted to evaluate and compare the performance of the proposed paradigm with that of the classical approach. The experimental results, including statistical significance tests, are presented for single and multiple EEG electrode usage combinations in 1, 3 and 15 flashing repetitions to detect P300 waves as well as to recognize target characters. Using the proposed paradigm, the best average classification accuracy rates on the test data are improved from 89.97% to 93.90% (an improvement of 4.36%) for 1 flashing, from 97.11% to 98.10% (an improvement of 1.01%) for 3 flashings and from 99.70% to 99.81% (an improvement of 0.11%) for 15 flashings when all electrodes, included in the study, are utilized. On the other hand, the accuracy rates are improved by 9.69% for 1 flashing, 4.72% for 3 flashings and 1.73% for 15 flashings when the proposed paradigm is utilized with a single EEG electrode (P8). It is observed that the proposed speller paradigm is especially useful in BCI systems designed for few EEG electrodes usage, and hence, it is more suitable for practical implementations. Moreover, all participants, given a subjective test, declared that the proposed paradigm is more user-friendly than classical ones. [ABSTRACT FROM AUTHOR]

Cervical cancer remains one of the top causes of cancer-related morbidity and mortality all over the world. Currently, however, there are no published studies to assess the knowledge of HPV and cervical cancer in Kazakhstan. This study aimed to assess the awareness of HPV, the knowledge of HPV as a cause of cervical cancer, and the awareness of HPV vaccination among Kazakhstani women visiting gynecological clinics across the country. In addition, the study aimed to identify the factors associated with the awareness of HPV and the HPV vaccine and knowledge of HPV as a major cause of cervical cancer. This was a cross-sectional survey-based study with 2,272 women aged between 18–70 years attending gynecological clinics, who were administered paper-based questionnaires. Data analysis included descriptive statistics consisting of mean values, standard deviations, and frequencies, where applicable. Differences in categorical variables between groups were analyzed using the Chi-square test with a significance value of <0.005. Crude odds ratio (OR) and adjusted odds ratio (AOR) with 95% corresponding confidence intervals were calculated in regression analysis using univariate and multivariable logistic regression models. The mean age of participants was 36.33±10.09 years. More than half (53%) of the participants had been screened for cervical cancer. Among those who were aware of HPV, 46% knew that HPV causes cervical cancer and 52% were aware of the HPV vaccine. The key factors related to outcome variables were age, ethnicity, education, family, number of deliveries, and menarche. From a subgroup analysis, results from the HPV test and Pap smear test were factors related to dependent variables such as awareness of HPV and awareness of HPV vaccination. [ABSTRACT FROM AUTHOR]

Background: Null Hypothesis Significance Testing (NHST) is the most familiar statistical procedure for making inferences about population effects. Important problems associated with this method have been addressed and various alternatives that overcome these problems have been developed. Despite its many well-documented drawbacks, NHST remains the prevailing method for drawing conclusions from data. Reasons for this have been insufficiently investigated. Therefore, the aim of our study was to explore the perceived barriers and facilitators related to the use of NHST and alternative statistical procedures among relevant stakeholders in the scientific system. Methods: Individual semi-structured interviews and focus groups were conducted with junior and senior researchers, lecturers in statistics, editors of scientific journals and program leaders of funding agencies. During the focus groups, important themes that emerged from the interviews were discussed. Data analysis was performed using the constant comparison method, allowing emerging (sub)themes to be fully explored. A theory substantiating the prevailing use of NHST was developed based on the main themes and subthemes we identified. Results: Twenty-nine interviews and six focus groups were conducted. Several interrelated facilitators and barriers associated with the use of NHST and alternative statistical procedures were identified. These factors were subsumed under three main themes: the scientific climate, scientific duty, and reactivity. As a result of the factors, most participants feel dependent in their actions upon others, have become reactive, and await action and initiatives from others. This may explain why NHST is still the standard and ubiquitously used by almost everyone involved. Conclusion: Our findings demonstrate how perceived barriers to shift away from NHST set a high threshold for actual behavioral change and create a circle of interdependency between stakeholders. By taking small steps it should be possible to decrease the scientific community's strong dependence on NHST and p-values. [ABSTRACT FROM AUTHOR]

In a balanced design, researchers allocate the same number of units across all treatment groups. It has been believed as a rule of thumb among some researchers in agriculture. Sometimes, an unbalanced design outperforms a balanced design. Given a specific parameter of interest, researchers can design an experiment by unevenly distributing experimental units to increase statistical information about the parameter of interest. An additional way of improving an experiment is an adaptive design (e.g., spending the total sample size in multiple steps). It is helpful to have some knowledge about the parameter of interest to design an experiment. In the initial phase of an experiment, a researcher may spend a portion of the total sample size to learn about the parameter of interest. In the later phase, the remaining portion of the sample size can be distributed in order to gain more information about the parameter of interest. Though such ideas have existed in statistical literature, they have not been applied broadly in agricultural studies. In this article, we used simulations to demonstrate the superiority of the experimental designs over the balanced designs under three practical situations: comparing two groups, studying a dose-response relationship with right-censored data, and studying a synergetic effect of two treatments. The simulations showed that an objective-specific design provides smaller error in parameter estimation and higher statistical power in hypothesis testing when compared to a balanced design. We also conducted an adaptive experimental design applied to a dose-response study with right-censored data to quantify the effect of ethanol on weed control. Retrospective simulations supported the benefit of this adaptive design as well. All researchers face different practical situations, and appropriate experimental designs will help utilize available resources efficiently. [ABSTRACT FROM AUTHOR]