Variance In Size Research Articles

First Results of a Geometric Morphometric Analysis of the Leaf Size and Shape Variation in Quercus petraea Across a Wide European Area

The high leaf morphological variability of European white oaks is largely documented in the botanical literature, and several papers have been published in the last two decades focusing on inter- and intraspecific leaf phenotypic plasticity. Studies involving landmark-based geometric morphometrics proved to be useful in highlighting relationships between leaf size and shape variation and environmental factors, phylogenetic patterns, or hybridization events. In this paper, the leaf size and shape variations of 18 populations of Quercus petraea distributed throughout a wide geographical area were analyzed by means of geometric morphometric methods (GMMs). This study involved 10 European countries and investigated the intraspecific leaf variability of Q. petraea within a wide latitudinal and longitudinal gradient. Analyses of variance for shape and centroid size were performed through Procrustes ANOVA. Multivariate analysis procedures, partial least squares method, and regression analyses were used to highlight possible patterns of covariation between leaf shape and size and geographical/environmental variables. The results revealed that the Q. petraea populations analyzed mainly differed in their leaf size, where a decrease was observed according to a north to south geographical gradient. Both leaf size and shape were found to be significantly related to latitude, and, to a lesser extent, to mean annual temperature and the leaf isotopic signature of 15N. All the other variables considered did not provide significant results. Unexpected differences observed comparing the leaf traits of geographically strictly adjacent populations suggest the involvement of local hybridization/introgression events. However, with a few exceptions, Q. petraea turned out to be quite conservative in its leaf shape and size at both the local and continental scale.

Influence of pores on the fatigue life of welded joints and investigation of acceptance criteria defined in standard documents

AbstractInner defects in welded joints constitute concurrent sites for fatigue failure in welds, especially when high fatigue resistance is enforced at weld toe and root. Existing standards limit the size of pores and other inner defects in proportion to the sheet or weld thickness, but for larger dimensions cutoff values are in place. This leads to overly high acceptance criteria when it comes to thicker sheets, as for example those used for wind turbine towers. To analyze the influence of pores on the fatigue life, specimens with targeted pore sizes larger than the usually accepted were tested under fatigue loading. In the present study, a numerical model for fatigue assessment was developed based on the experimental results, which allowed further variance of pore sizes and locations. It is shown, that for larger weldments the acceptable pore size can be increased by up to 87.5 % without the necessity to reduce the applicable design curve. But, the location of the pore with regard to the specimens surface has a large influence, with pores in a distance to the surface smaller than 13 % of the sheet thickness leading to a reduction in the applicable design curve by up to 4 FAT classes — an effect that is currently not considered in common weld quality documents.

Clade Size Statistics Under Ford’s α-Model

Given a labeled tree topology t of n taxa, consider a population P of k leaves chosen among those of t. The clade of P is the minimal subtree P^ of t containing P, and its size |P^| is provided by the number of leaves in the clade. We study distributive properties of the clade size variable |P^| considered over labeled topologies of size n generated at random in the framework of Ford’s α-model. Under this model, starting from the one-taxon labeled topology, a random labeled topology is produced iteratively by a sequence of α-insertions, each of which adds a pendant edge to either a pendant or internal edge of a labeled topology, with a probability that depends on the parameter α∈[0,1]. Different values of α determine different probability distributions over the set of labeled topologies of given size n, with the special cases α=0 and α=1/2 respectively corresponding to the Yule and uniform distributions. In the first part of the manuscript, we consider a labeled topology t of size n generated by a sequence of random α-insertions starting from a fixed labeled topology t∗ of given size k, and determine the probability mass function, mean, and variance of the clade size |P^| in t when P is chosen as the set of leaves of t inherited from t∗. In the second part of the paper, we calculate the probability that a set P of k leaves chosen at random in a Ford-distributed labeled topology of size n is monophyletic, that is, the probability that |P^|=k. Our investigations extend previous results on clade size statistics obtained for Yule and uniformly distributed labeled topologies.

The relationship between dividend policy and bank size: Evidence from Jordan

The growing need to comprehend how dividend policy affects bank size, particularly in emerging markets like Jordan, makes this study relevant. Bank size, often measured by total assets, is a key indicator of financial strength and stability. This study aims to examine the relationship between various measures of dividend policy – dividend per share, dividend yield, and dividend per share to earnings per share ratio – and bank size in Jordanian banks, using earnings per share as a control variable.The study employs ordinary least squares regression analysis to investigate the relationship between these variables over a sample of Jordanian banks. Three regression models were constructed to evaluate the impact of each dividend measure on bank size. The results indicate a significant positive relationship between dividend per share and bank size, and between the dividends per share to earnings per share ratio and bank size. The results show that approximately 43.9% of the variance in bank size is explained by the Dividends per share and Earnings per share, and a significant positive correlation is observed between total assets (bank size) and dividend per share, with a coefficient of 53%. Dividend yield, however, showed no significant impact on bank size.The results support that Jordanian banks with a sound dividend policy on dividend per share and its continuity with earnings exhibit higher asset growth. In this respect, bank growth appears to be highly dependent on a prudent dividend policy even from an emerging markets perspective.

Systematic Review and Meta-Analysis in Randomized Controlled Trials of Anti-Hypertensive Drugs and the Risk of Cancer

This paper evaluates the potential carcinogenicity of anti-hypertensive medications by performing a meta-analysis on the risk of cancer associated with their use. The analysis incorporated data from 12 studies, encompassing a total of 16,711,997 observations, and focused on randomized controlled trials of anti-hypertensive drugs. The primary objective was to compare the cancer risk across different classes of these medications while considering their blood pressure-lowering effects. The meta-analysis employed a random-effects model to synthesize the results. The mean effect size was found to be 1.178, with a 95% confidence interval ranging from 1.027 to 1.351. A Z-test conducted to test the null hypothesis that the mean effect size equals 1 yielded a Z-value of 2.343 and a p-value of less than 0.019, leading to the rejection of the null hypothesis. This indicates a statistically significant association between the use of anti-hypertensive drugs and an increased risk of cancer. The heterogeneity among the included studies was significant, as evidenced by a Q-statistic of 85.118 with 11 degrees of freedom and a p-value of less than 0.001. The I-squared statistic was 87%, suggesting that 87% of the variance in observed effects was due to differences in true effects rather than sampling error. The tau-squared value, representing the variance of true effect sizes, was 0.040 in log units, while tau, the standard deviation of true effect sizes, was 0.201 in log units. Assuming a normal distribution of true effects, the prediction interval for the true effect size was estimated to be between 0.733 and 1.894, meaning that the true effect size in 95% of all studies would fall within this range. Overall, the findings suggest a potential risk of cancer associated with anti-hypertensive drugs, which warrants careful consideration when weighing their benefits and risks. The computations for the analysis were conducted using Comprehensive Meta-Analysis.

Coefficients in Taylor's law increase with the time scale of water clarity measurements in a global suite of lakes.

Identifying the scaling rules describing ecological patterns across time and space is a central challenge in ecology. Taylor's law of fluctuation scaling, which states that the variance of a population's size or density is proportional to a positive power of the mean size or density, has been widely observed in population dynamics and characterizes variability in multiple scientific domains. However, it is unclear if this phenomenon accurately describes ecological patterns across many orders of magnitude in time, and therefore links otherwise disparate observations. Here, we use water clarity observations from 10,531 days of high-frequency measurements in 35 globally distributed lakes, and lower-frequency measurements over multiple decades from 6342 lakes to test this unknown. We focus on water clarity as an integrative ecological characteristic that responds to both biotic and abiotic drivers. We provide the first documentation that variations in ecological measurements across diverse sites and temporal scales exhibit variance patterns consistent with Taylor's law, and that model coefficients increase in a predictable yet non-linear manner with decreasing observation frequency. This discovery effectively links high-frequency sensor network observations with long-term historical monitoring records, thereby affording new opportunities to understand and predict ecological dynamics on time scales from days to decades.

Increasing environmental fluctuations can dampen variability of endogenously cycling populations.

Understanding how populations respond to increasingly variable conditions is a major objective for natural resource managers forecasting extinction risk. The lesson from current modelling is clear: increasing environmental variability increases population abundance variability. We show that this paradigm fails to describe a broad class of empirically observed dynamics, namely endogenously driven population cycles. In contrast to the dominant paradigm, these populations can exhibit reduced long-run population variance under increasing environmental variability. We provide evidence for a mechanistic explanation of this phenomenon that relies on how stochasticity interacts with long transient dynamics present in the deterministic cycling model. This interaction stands in contrast to the often assumed additivity of stochastic and deterministic drivers of population fluctuations. We show evidence for the phenomenon in two cyclical populations: flour beetles and Canadian lynx. We quantify the impact of the phenomenon with new theory that partitions the effects of nonlinear dynamics and stochastic variation on dynamical systems. In both empirical examples, the partitioning shows that the interaction between deterministic and stochastic dynamics reduces the variance in population size. Our results highlight that previous predictions about extinction under environmental variability may prove inadequate to understand the effects of climate change in some populations.

Ship detection using ensemble deep learning techniques from synthetic aperture radar imagery

Synthetic Aperture Radar (SAR) integrated with deep learning has been widely used in several military and civilian applications, such as border patrolling, to monitor and regulate the movement of people and goods across land, air, and maritime borders. Amongst these, maritime borders confront different threats and challenges. Therefore, SAR-based ship detection becomes essential for naval surveillance in marine traffic management, oil spill detection, illegal fishing, and maritime piracy. However, the model becomes insensitive to small ships due to the wide-scale variance and uneven distribution of ship sizes in SAR images. This increases the difficulties associated with ship recognition, which triggers several false alarms. To effectively address these difficulties, the present work proposes an ensemble model (eYOLO) based on YOLOv4 and YOLOv5. The model utilizes a weighted box fusion technique to fuse the outputs of YOLOv4 and YOLOv5. Also, a generalized intersection over union loss has been adopted in eYOLO which ensures the increased generalization capability of the model with reduced scale sensitivity. The model has been developed end-to-end, and its performance has been validated against other reported results using an open-source SAR-ship dataset. The obtained results authorize the effectiveness of eYOLO in multi-scale ship detection with an F1score and mAP of 91.49% and 92.00%, respectively. This highlights the efficacy of eYOLO in multi-scale ship detection using SAR imagery.

Structural diversity and distribution of NMCP-class nuclear lamina proteins in streptophytic algae.

Nuclear Matrix Constituent Proteins (NMCPs) in plants function like animal lamins, providing the structural foundation of the nuclear lamina and regulating nuclear organization and morphology. Although they are well-characterized in angiosperms, the presence and structure of NMCPs in more distantly related species, such as streptophytic algae, are relatively unknown. The rapid evolution of NMCPs throughout the plant lineage has caused a divergence in protein sequence that makes similarity-based searches less effective. Structural features are more likely to be conserved compared to primary amino acid sequence; therefore, we developed a filtration protocol to search for diverged NMCPs based on four physical characteristics: intrinsically disordered content, isoelectric point, number of amino acids, and the presence of a central coiled-coil domain. By setting parameters to recognize the properties of bona fide NMCP proteins in angiosperms, we filtered eight complete proteomes from streptophytic algae species and identified strong NMCP candidates in six taxa in the Classes Zygnematophyceae, Charophyceae, and Klebsormidophyceae. Through analysis of these proteins, we observed structural variance in domain size between NMCPs in algae and land plants, as well as a single block of amino acid conservation. Our analysis indicates that NMCPs are absent in the Mesostigmatophyceae. The presence versus absence of NMCP proteins does not correlate with the distribution of different forms of mitosis (e.g., closed/semi-closed/open) but does correspond to the transition from unicellularity to multicellularity in the streptophytic algae, suggesting that an NMCP-based nucleoskeleton plays important roles in supporting cell-to-cell interactions.

Size and Shape Modulation of Cu2S Nanoplates via Chemical Reduction with NaOH and NH3·H2O.

Copper(I) sulfide (Cu2S) has electrical, optical, and thermoelectric properties that make it a promising material for a variety of applications, including energy conversion and antibacterial coatings. Nevertheless, the current synthesis and morphological modulation of Cu2S typically focuses on thermolysis of the copper and sulfur precursors, is procedurally complex, and demands expensive equipment. In this article, a facile, high-yield, three-step, low-temperature aqueous synthesis alternative for Cu2S nanoplates is introduced. By variations of the reaction temperature, reducing agent concentration, and pH modifier (NaOH or NH3·H2O), the morphological characteristics can be controlled. As confirmed with scanning and transmission electron microscopy, the lateral extent of the synthesized Cu2S nanoplates can be tuned from around 30 nm to around 300 nm simply by varying the heating conditions from 10-100 °C. A similar effect is more subtly observed by varying the concentration of the reducing agent. In addition to size variance, the morphological properties of the Cu2S nanoplates can be changed by using different bases for the reaction. Characterization of the composition and crystalline structure of the materials has also been performed using energy-dispersive spectroscopy and X-ray diffraction, and optical properties are investigated by UV-visible and near-infrared spectroscopy (UV-vis-NIR). The synthesis pathway described in this paper can be easily performed and feasibly scaled, which is advantageous as the retrieved material is suitable for diverse applications, such as its use in battery electrodes, photonic and charged-particle sensors, and radiation shielding.

116 Contribution of X-chromosome to the genetic variance of litter size and stillbirth in pigs

Abstract Genes with important roles in reproduction traits have been described in pig X-chromosome (SSCX), which may contribute considerably to genetic variance and could impact the accuracy of genomic prediction. The phenotypic data set includes information on 82,580 litter size records from 34,346 sows. Additionally, there were 108,006 in the pedigree, and genotypes for 41,492 SNPs, of which 1,435 in SSCX, were available for 30,000 animals. Three different approaches were used to estimate variance components for the total number born and number of stillborn. In the first approach, only SNPs in the autosomes were used and the model included the additive genetic (u), permanent environment (p), and residual (e) random effects. In the second approach, the same model was adopted; however, the data included SNPs in the autosomes and in the X chromosome. For both models, it was assumed that u~N(0,H⊗Vu), e~N(0,I⊗Ve) and p~N(0,I⊗Vp), wherein H is a relationship matrix that combines pedigree and genomic information; and, Vu, Vp, and Ve are the (co)variance matrices for the random additive genetic, permanent environment, and residual effects, respectively. In the third approach, the random additive genetic effects associated with the X-chromosome (s) were included in the model described before. Then, it was assumed that s~N(0,Hx⊗Vs), where Hx is the genomic relationship matrix for the X-chromosome and Vs is the additive genetic variance associated with the SSCX. All analyses were carried out using GIBBSF90+. Table 1 shows variance components for the three approaches. For both traits, additive genetic variance and heritabilities were similar across the approaches. The additive genetic variance for the SSCX was small, accounting for approximately two percent of the total additive genetic variance. Including the X chromosome as a separate effect and using Hx as a covariance matrix captured only a small part of the additive genetic variance. The additive variance across approaches did not differ. For total number born and number of stillborn, using the SSCX SNPs to build Hx did not provide extra benefit compared with fitting those SNP in the regular H matrix.

Radiomics analysis of cerebral blood flow suggests a possible link between perfusion homogeneity and poor glioblastoma multiforme prognosis

Objectives. This study investigates the association between cerebral blood flow (CBF) and overall survival (OS) in glioblastoma multiforme (GBM) patients receiving chemoradiation. Identifying CBF biomarkers could help predict patient response to this treatment, facilitating the development of personalized therapeutic strategies. Materials and Methods. This retrospective study analyzed CBF data from dynamic susceptibility contrast (DSC) MRI in 30 newly diagnosed GBM patients (WHO grade IV). Radiomics features were extracted from CBF maps, tested for robustness, and correlated with OS. Kaplan-Meier analysis was used to assess the predictive value of radiomic features significantly associated with OS, aiming to stratify patients into groups with distinct post-treatment survival outcomes. Results. While mean relative CBF and CBV failed to serve as independent prognostic markers for OS, the prognostic potential of radiomic features extracted from CBF maps was explored. Ten out of forty-three radiomic features with highest intraclass correlation coefficients (ICC > 0.9), were selected for characterization. While Correlation and Zone Size Variance (ZSV) features showed significant OS correlations, indicating prognostic potential, Kaplan-Meier analysis did not significantly stratify patients based on these features. Visual analysis of the graphs revealed a predominant association between the identified radiomic features and OS under two years. Focusing on this subgroup, Correlation, ZSV, and Gray-Level Nonuniformity (GLN) emerged as significant, suggesting that a lack of heterogeneity in perfusion patterns may be indicative of a poorer outcome. Kaplan-Meier analysis effectively stratified this cohort based on the features mentioned above. Receiver operating characteristic (ROC) analysis further validated their prognostic value, with ZSV demonstrating the highest sensitivity and specificity (0.75 and 0.85, respectively). Conclusion. Our findings underscored radiomics features sensitive to CBF heterogeneity as pivotal predictors for patient stratification. Our results suggest that these markers may have the potential to identify patients who are unlikely to benefit from standard chemoradiation therapy.

Beyond head and wings: Unveiling influence of diet, body size, and phylogeny on the evolution of the femur in phyllostomid bats.

Phyllostomidae, the most diverse family of Neotropical bats, encompass 230 species with varied dietary habits and food acquisition methods. Their feeding niche diversification has shaped skull and wing morphologies through natural selection, reflecting food processing and flight strategies. Yet, evolution of bat hindlimbs, especially in phyllostomids, remains little understood. Previous studies highlighted the femur's morphology as a key to understanding the evolution of quadrupedalism in yangochiropteran bats, including the adept walking observed in vampire bats (Desmodontinae). Here, we aimed to describe the femoral morphological variation in Phyllostomidae, correlating this with body size and assessing the effects of phylogenetic history, dietary habits, and hindlimb usage. Analyzing 15 femoral traits from 45 species across 9 subfamilies through phylogenetically informed methods, we discovered a significant phylogenetic structure in femoral morphology. Allometric analysis indicated that body mass accounts for about 85% of the variance in phyllostomid femoral size and about 11% in femoral shape. Relatively smaller femurs showed to be typical in Stenodermatinae, Lonchophyllinae, and Glossophaginae, in contrast to the larger femurs of Phyllostominae, Desmodontinae, Micronycterinae, and Lonchorrhininae. Furthermore, extensive femur shape variation was detected, with the most distinct morphologies in vampire bats, followed by frugivorous species. Adaptive evolutionary models related to diet more effectively explained variations in femoral relative size and shape than stochastic models. Contrary to the conventional belief of limited functional demand on bat femurs, our findings suggest that femoral morphology is significantly influenced by functional demands associated with diet and food capture, in addition to being partially structured by body size and shared evolutionary history.

Basic Psychological Needs and Extremism: Understanding Theories Through Meta-Analysis

ABSTRACT Extremist attacks pose a dangerous threat to society and past work suggests that many factors can contribute to radicalization. The present project aims to combine literature from multiple theories of radicalization to meta-analyze the association between basic psychological needs and extremism. From 46 articles containing 75 samples, 248 effect sizes were retrieved (N = 30,082). There is not a significant overall association between basic psychological needs and extremism. However, the operationalization of the need explains a significant amount of variance in the effect sizes, such that need satisfaction is negatively associated with extremism (r = −0.09, 95 percent CI [−0.15, −0.03]) whereas desire for a need is positively associated with extremism (r = 0.21, 95 percent CI [0.14, 0.28]). This finding suggests that how researchers define basic psychological needs is important when examining the role of needs in radicalization and theorists should continue to incorporate them in their models. Furthermore, the nature of extremism explains a significant amount of variance in the effect sizes, such that basic psychological needs are positively associated with Islamist extremism and negatively associated with far-left extremism. Directions for future research and implications for countering violent extremism interventions are discussed.

Prevalence and Predictors of Long COVID-19 and the Average Time to Diagnosis in the General Population: A Systematic Review, Meta-Analysis and Meta-Regression

Objectives: We aimed to assess the prevalence of long COVID-19 and estimate the average time to its diagnosis and meta-regression for covariates. Methods: We conducted a systematic review, meta-analysis, and meta-regression from 43 studies (367,236 patients) (June 2020–August 2022). With the random-effects model, the pooled prevalence of long COVID-19 was measured. Publication bias was ascertained, and meta-regression analysis was performed on predetermined covariates. The trial was registered with PROSPERO (CRD42022328509). Results: The pooled prevalence of long COVID-19 was 42.5% (95% CI 36% to 49.3%), with 25% and 66% at four and two months, respectively. Mostly, long COVID-19 signs and symptoms occurred at three (54.3%) to six (57%) months (p < 0.0001), further increasing at 12 months (57.9%, p = 0.0148). Hypertension was significantly associated with long COVID-19 at 32% (0.322 (95% CI 0.166, 0.532) (p < 0.001) and hospital re-admission contributed to 17% (Q = 8.70, df = 1, p = 0.0032) (R2 = 0.17). All the covariates explained at least some of the variance in effect size on long COVID-19 at 53% (Q = 38.81, df = 19, p = 0.0047) (R2 analog = 0.53). Conclusion: The prevalence of long COVID-19 was 42.5% when linked with a cardiovascular disorder. Hospital re-admission majorly predicted the incidence of long COVID-19. Clinical and methodological characteristics in a specific study contributed to over 50% of long COVID-19 events, with most signs and symptoms occurring between 3 and 6 months and increasing at 12 months.

Electron Microscopic Mapping of Mitochondrial Morphology in the Cochlear Nerve Fibers.

To enable nervous system function, neurons are powered in a use-dependent manner by mitochondria undergoing morphological-functional adaptation. In a well-studied model system-the mammalian cochlea, auditory nerve fibers (ANFs) display distinct electrophysiological properties, which is essential for collectively sampling acoustic information of a large dynamic range. How exactly the associated mitochondrial networks are deployed in functionally differentiated ANFs remains scarcely interrogated. Here, we leverage volume electron microscopy and machine-learning-assisted image analysis to phenotype mitochondrial morphology and distribution along ANFs of full-length in the mouse cochlea inner spiral bundle. This reveals greater variance in mitochondrial size with increased ANF habenula to terminal path length. Particularly, we analyzed the ANF terminal-residing mitochondria, which are critical for local calcium uptake during sustained afferent activities. Our results suggest that terminal-specific enrichment of mitochondria, in addition to terminal size and overall mitochondrial abundance of the ANF, correlates with heterogenous mitochondrial contents of the terminal.

How well do ridge parameter estimators proposed so far perform in terms of normality, outlier detection, and MSE criteria?

Ridge regression is a commonly used prediction method in cases of multicollinearity among regressors in multiple linear regression model. In this study, the performances of 366 different estimators proposed in the literature for ridge parameter estimation were evaluated. The criteria of estimators’ ratio of generating outliers and the proportion of normality besides the Mean Square Error (MSE) criterion were used for diagnosing ridge parameter estimators. Besides, the effect of outliers and deviations from normality generated by the ridge parameter estimators on the MSE was revealed by Spearman’s Rho correlation analysis. To do so, a Monte Carlo simulation was designed for 2420 different cases, considering the simulation parameters comprehensively in terms of the number of regressors, sample size, collinearity level and error variances. The results revealed that some ridge parameter estimators recommended according to MSE criterion, do not perform well in terms of outlier generation and normality criteria. Significant inter-linear correlations were also found among the ridge parameter estimators’ MSE performance criterion, ratios of outlier generation criterion and normality criterion. Thus, it is suggested that an effective ridge parameter estimator should perform well in terms of outlier generation and normality criteria, as well as having small MSE value.

Impact of digital tools in oncology: A global analysis of the adoption of ONCOassist and NCCN Chemotherapy Order Templates (NCCN Templates) in collaboration with OncoAlert.

e13662 Background: The integration of digital technology has significantly transformed clinical practice, especially in oncology. ONCOassist, an application that integrates content from the NCCN Templates offers professionals quick and up-to-date access to information crucial for cancer treatment. Methods: To analyze and determine whether factors such as the Human Development Index (HDI) or Gross National Income (GNI) influence the adoption of the NCCN Templates, an in-application survey was distributed to health professionals who accessed NCCN content. The survey asked two questions: (1) would respondents use a specific NCCN protocol for a current patient (2) if not, the reasons why they did not utilize it. Finally, how the acceptance of the NCCN Templates was related to HDI or GNI and specialty was analyzed. Results: The survey was responded to by 2522 health professionals from 122 countries. The NCCN Templates were widely accepted (80%) across different geographic and economic contexts, indicating their global applicability and trust. Generally, GNI is categorized by high income, upper middle income, lower middle income, and low income, while HDI includes very high, high, medium, and low income. For the lowest levels of GNI and HDI, there were few responses (1% by GNI and 8% by HDI), and for purposes of this abstract, they were combined with the next highest level, lower middle income, and medium HDI. Using GNI, the rates of using the NCCN Templates in countries with high income (72%) were lower than those with upper-middle income (83%) and lower middle/low income (86%). In a similar inverse tendency, based on HDI, the utilization rates of the NCCN Templates were 70%, 83%, and 86% in countries with very-high, high, and medium/low HDI, respectively. When stratified by specialty, the absolute numbers showed that most respondents belonged to the field of the breast (36%), followed by gastrointestinal (28%) and lung cancers (10%). However, when we analyzed percentages, we observed that the decision-making process regarding protocol adoption was consistent among specialties despite the variance in specialist population size. When we analyzed the reasons for not utilizing NCCN Templates, “Other reasons” was the primary response (51%). Among the remaining answers, using specific local protocols was the primary reason (47% of the remaining), followed by the lack of drug availability (18% of the remaining) and reimbursement issues (18% of the remaining). Conclusions: There was a general acceptance of the NCCN Templates among healthcare professionals globally. The willingness to utilize NCCN Templates content was not strongly determined by the HDI or GNI, suggesting a perception of their utility beyond economic barriers. The protocol has the potential to be used in daily clinical practice, improving decision-making.

A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons

In this second part of the study, using a ‘clean’ dataset without very low precision landmarks and outliers, I describe how to compare mandibular size and shape using Procrustes methods in adult North American marmots. After demonstrating that sex differences are negligible, females and males are pooled together with specimens of unknown sex and species are compared using a battery of tests, that estimate both statistical significance and effect size. The importance of allometric variation and its potential effect on shape differences is also explored. Finally, to provide potential clues on founder effects, I compare the magnitude of variance in mandibular size and shape between the Vancouver Island marmot (VAN) and the hoary marmot, its sister species on the mainland. In almost all main analyses, I explore the sensitivity of results to heterogeneous sample size and small samples using subsamples and randomized selection experiments. For both size and shape, I find a degree of overlap among species variation but, with very few exceptions, mean interspecific differences are well supported in all analyses. Shape, in particular, is an accurate predictor of taxonomic affiliation. Allometry in adults, however, explains a modest amount of within-species shape change. Yet, there is a degree of divergence in allometric trajectories that seems consistent with subgeneric separation. VAN is the most distinctive species for mandibular shape and mandibular morphology suggests a long history of reduced variation in this insular population. Geometric morphometrics (GMM) is a powerful tool to aid taxonomic research. Regardless of the effectiveness of this family of methods and the apparent robustness of results obtained with GMM, however, large samples and careful measurements remain essential for accuracy. Even with excellent data, morphometrics is important, but its findings must be corroborated with an integrative approach that combines multiple lines of evidence to taxonomic assessment. The analytical protocol I suggest is described in detail, with a summary checklist, in the Appendix, not to miss important steps. All the analyses can be replicated using the entire dataset, which is freely available online. Beginners may follow all the steps, whereas more experienced researchers can focus on one specific aspect and read only the relevant chapter. There are limitations, but the protocol is flexible and easy to improve or implement using a programming language such as R.

Interlocking characteristic and its correlation with interlayer shear strength in typical double-layered asphalt systems

ABSTRACT Interlayer bonding is crucial to maintaining the structural integrity of asphalt pavement structures. Addressing a crucial research gap, this study uniquely investigated the impact of interface morphology on interlocking properties and interlayer shear strength in three typical double-layered asphalt systems: AC-13/AC-20, SMA-13/AC-20, and OGFC-13/AC-20. Through the innovative application of nondestructive separation technology and three-dimensional (3D) laser scanning, interface morphology characteristics were thoroughly analyzed. This approach enabled the detailed extraction of texture parameters, unveiling the complex relationship between interface morphology and interlayer bonding strength. Interface shear tests further assessed the difference in shear strength, revealing significant differences in texture characteristics influenced by the mineral size variance in the upper layer. This analysis led to the development of a predictive model for interlayer shear strength based on morphological characteristics, offering a new tool for approximating the shear bonding condition, despite challenges in capturing the complex interaction between interface morphology and interlocking effects. The comprehensive examination of 3D interface morphology, combined with the shear strength analysis, clarified the mechanisms of interlayer bonding in relation to interface morphology. This breakthrough provides essential insights into the role of interface morphology in bonding efficacy and represents a significant advancement in understanding of interlayer bonding mechanisms.