Arbitrary Threshold Research Articles

High preoperative HbA1c does not affect early or late complication rates after bariatric surgery.

Preoperative HbA1c has been associated with an increased incidence of postoperative morbidity and mortality after abdominal and cardiovascular surgery. The literature on bariatric surgery is inconclusive and guidelines recommend postponement of surgery when HbA1c is above an arbitrary threshold (≥ 8.5%). In this study, we sought to understand the impact of preoperative HbA1c on early and late postoperative complications. We performed a retrospective analysis of prospectively collected data on obese patients with diabetes who underwent laparoscopic bariatric surgery. Patients were categorized into three groups according to their preoperative HbA1c level: < 6.5% (group 1), 6.5-8.4% (group 2) and ≥ 8.5% (group 3). Primary outcomes were early and late postoperative complications (< and > 30days, respectively) that were differentiated based on severity (major/minor). Secondary outcomes were length of stay (LOS), duration of surgery, and rate of readmission. In total, 6798 patients underwent laparoscopic bariatric surgery from 2006 to 2016, of which 1021 (15%) patients had Type 2 Diabetes (T2D). Complete data with a median follow-up of 45months (3-120) were available for 914 patients with HbA1c < 6.5% (n = 227, 24.9%), 6.5-8.4% (n = 532, 58.5%) and ≥ 8.5% (n = 152, 16.6%). Early major surgical complication rate was similar across the groups ranging from 2.6 to 3.3%. No associations between high preoperative HbA1c and late complications-medical as well as surgical-was observed. Groups 2 and 3 had statistically significant more pronounced inflammatory status. LOS (1.8-1.9days), readmission rates (1.7-2.0%) and surgical time was similar across the three groups. Elevated HbA1c is not associated with more early or late postoperative complications, longer LOS, longer surgical time or higher rates of readmission.

Accounting for careless and insufficient effort responding in large-scale survey data—development, evaluation, and application of a screen-time-based weighting procedure

Careless and insufficient effort responding (C/IER) poses a major threat to the quality of large-scale survey data. Traditional indicator-based procedures for its detection are limited in that they are only sensitive to specific types of C/IER behavior, such as straight lining or rapid responding, rely on arbitrary threshold settings, and do not allow taking the uncertainty of C/IER classification into account. Overcoming these limitations, we develop a two-step screen-time-based weighting procedure for computer-administered surveys. The procedure allows considering the uncertainty in C/IER identification, is agnostic towards the specific types of C/IE response patterns, and can feasibly be integrated with common analysis workflows for large-scale survey data. In Step 1, we draw on mixture modeling to identify subcomponents of log screen time distributions presumably stemming from C/IER. In Step 2, the analysis model of choice is applied to item response data, with respondents’ posterior class probabilities being employed to downweigh response patterns according to their probability of stemming from C/IER. We illustrate the approach on a sample of more than 400,000 respondents being administered 48 scales of the PISA 2018 background questionnaire. We gather supporting validity evidence by investigating relationships between C/IER proportions and screen characteristics that entail higher cognitive burden, such as screen position and text length, relating identified C/IER proportions to other indicators of C/IER as well as by investigating rank-order consistency in C/IER behavior across screens. Finally, in a re-analysis of the PISA 2018 background questionnaire data, we investigate the impact of the C/IER adjustments on country-level comparisons.

Effects of Image Size on Deep Learning

In this work, the best size for late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) images in the training dataset was determined to optimize deep learning training outcomes. Non-extra pixel and extra pixel interpolation algorithms were used to determine the new size of the LGE-MRI images. A novel strategy was introduced to handle interpolation masks and remove extra class labels in interpolated ground truth (GT) segmentation masks. The expectation maximization, weighted intensity, a priori information (EWA) algorithm was used for the quantification of myocardial infarction (MI) in automatically segmented LGE-MRI images. Arbitrary threshold, comparison of the sums, and sums of differences are methods used to estimate the relationship between semi-automatic or manual and fully automated quantification of myocardial infarction (MI) results. The relationship between semi-automatic and fully automated quantification of MI results was found to be closer in the case of bigger LGE MRI images (55.5% closer to manual results) than in the case of smaller LGE MRI images (22.2% closer to manual results).

Bayes factor functions for reporting outcomes of hypothesis tests

Bayes factors represent a useful alternative to P-values for reporting outcomes of hypothesis tests by providing direct measures of the relative support that data provide to competing hypotheses. Unfortunately, the competing hypotheses have to be specified, and the calculation of Bayes factors in high-dimensional settings can be difficult. To address these problems, we define Bayes factor functions (BFFs) directly from common test statistics. BFFs depend on a single noncentrality parameter that can be expressed as a function of standardized effects, and plots of BFFs versus effect size provide informative summaries of hypothesis tests that can be easily aggregated across studies. Such summaries eliminate the need for arbitrary P-value thresholds to define "statistical significance." Because BFFs are defined using nonlocal alternative prior densities, they provide more rapid accumulation of evidence in favor of true null hypotheses without sacrificing efficiency in supporting true alternative hypotheses. BFFs can be expressed in closed form and can be computed easily from z, t, χ2, and F statistics.

Toward a robust detection of viscous and turbulent flow regions using unsupervised machine learning

We propose an invariant feature space for the detection of viscous-dominated and turbulent regions (i.e., boundary layers and wakes). The developed methodology uses the principal invariants of the strain and rotational rate tensors as input to an unsupervised Machine Learning Gaussian mixture model. The selected feature space is independent of the coordinate frame used to generate the processed data, as it relies on the principal invariants of the strain and rotational rate, which are Galilean invariants. This methodology allows us to identify two distinct flow regions: a viscous-dominated, rotational region (a boundary layer and a wake region) and an inviscid, irrotational region (an outer flow region). We have tested the methodology on a laminar and a turbulent (using Large Eddy Simulation) case for flows past a circular cylinder at Re = 40 and Re = 3900 and a laminar flow around an airfoil at Re=1×105. The simulations have been conducted using a high-order nodal Discontinuous Galerkin Spectral Element Method. The results obtained are analyzed to show that Gaussian mixture clustering provides an effective identification method of viscous-dominated and rotational regions in the flow. We also include comparisons with traditional sensors to show that the proposed clustering does not depend on the selection of an arbitrary threshold, as required when using traditional sensors.

Mathematical modelling of the waning of anti-RBD IgG SARS-CoV-2 antibody titers after a two-dose BNT162b2 mRNA vaccination.

After exposure to SARS-CoV-2 and/or vaccination there is an increase in serum antibody titers followed by a non-linear waning. Our aim was to find out if this waning of antibody titers would fit to a mathematical model. We analyzed anti-RBD (receptor binding domain) IgG antibody titers and the breakthrough infections over a ten-month period following the second dose of the mRNA BNT162b2 (Pfizer-BioNtech.) vaccine, in a cohort of 54 health-care workers (HCWs) who were either never infected with SARS-CoV-2 (naïve, nHCW group, n=27) or previously infected with the virus (experienced, eHCW group, n=27). Two mathematical models, exponential and power law, were used to quantify antibody waning kinetics, and we compared the relative quality of the goodness of fit to the data between both models was compared using the Akaik Information Criterion. We found that the waning slopes were significantly more pronounced for the naïve when compared to the experienced HCWs in exponential (p-value: 1.801E-9) and power law (p-value: 9.399E-13) models. The waning of anti-RBD IgG antibody levels fitted significantly to both exponential (average-R2: 0.957 for nHCW and 0.954 for eHCW) and power law (average-R2: 0.991 for nHCW and 0.988 for eHCW) models, with a better fit to the power law model. In the nHCW group, titers would descend below an arbitrary 1000-units threshold at a median of 210.6 days (IQ range: 74.2). For the eHCW group, the same risk threshold would be reached at 440.0 days (IQ range: 135.2) post-vaccination. Two parsimonious models can explain the anti-RBD IgG antibody titer waning after vaccination. Regardless of the model used, eHCWs have lower waning slopes and longer persistence of antibody titers than nHCWs. Consequently, personalized vaccination booster schedules should be implemented according to the individual persistence of antibody levels.

The ‘Double Fuzzy Set’ Approach to Multidimensional Poverty Measurement: With a Focus on the Health Dimension

In more recent times, there is an increasing consensus in the field of development study to view poverty as a multidimensional deprivation beyond the more commonly used monetary perspective. Although the multidimensional poverty measurement is gaining more acceptance among policy makers, it is still based on the clear distinction between the poor and non-poor through an arbitrary threshold. One alternative to this shortcoming is offered by a multidimensional poverty measurement with a fuzzy-set approach in which it is possible to recognize deprivations as a matter of degree. The integrated fuzzy approach allows this possibility, although there is an unexplored opportunity of recognising that two or more dimensions can be attributed to a single item or attribute. This paper aims to contribute to this strand of research by introducing a ‘Double-Fuzzy’ approach. The methodology is applied to Tunisia using the Tunisian National Survey on Household Budget and Consumption (HBS) 2015.

Parent experiences of their children’s diagnosis with autism, attention deficit hyperactivity disorder, or both conditions

Purpose: A comparison of parents’ experiences of getting a diagnosis for their child with autism, attention deficit hyperactivity disorder (ADHD) and both diagnoses can inform our understanding of common and unique themes across these neurodevelopmental conditions. Method: A quantitative and qualitative online anonymous survey of 288 New Zealand parents of children diagnosed with autism (n = 111), ADHD (n = 93), or both conditions (n = 84) was conducted. Open-ended questions were analysed using reflexive thematic analysis. Results: Parents described an adversarial diagnosis and support system where seemingly arbitrary criteria and thresholds were applied. Key themes specific to the different diagnostic groups were also identified. Conclusions: Common themes of parents’ experience across the different neurodevelopmental conditions highlight the need for changes to the diagnostic process.

Cobind: quantitative analysis of the genomic overlaps.

Analyzing the overlap between two sets of genomic intervals is a frequent task in the field of bioinformatics. Typically, this is accomplished by counting the number (or proportion) of overlapped regions, which applies an arbitrary threshold to determine if two genomic intervals are overlapped. By making binary calls but disregarding the magnitude of the overlap, such an approach often leads to biased, non-reproducible, and incomparable results. We developed the cobind package, which incorporates six statistical measures: the Jaccard coefficient, Sørensen-Dice coefficient, Szymkiewicz-Simpson coefficient, collocation coefficient, pointwise mutual information (PMI), and normalized PMI. These measures allow for a quantitative assessment of the collocation strength between two sets of genomic intervals. To demonstrate the effectiveness of these methods, we applied them to analyze CTCF's binding sites identified from ChIP-seq, cancer-specific open-chromatin regions (OCRs) identified from ATAC-seq of 17 cancer types, and oligodendrocytes-specific OCRs identified from scATAC-seq. Our results indicated that these new approaches effectively re-discover CTCF's cofactors, as well as cancer-specific and oligodendrocytes-specific master regulators implicated in disease and cell type development. The cobind package is implemented in Python and freely available at https://cobind.readthedocs.io/en/latest/.

Reevaluation of dialect boundaries in the Yellowhammer (Emberiza citrinella) based on citizen science data: Should we split or lump?

Dialects are a specific form of geographic variation of birdsong with relatively sharp boundaries between distinct song characteristics, which provide opportunities for focused studies of processes underlying the emergence of spatial patterns in vocalization. Several songbird species that exhibit dialects became models for such research, and for some of them large-scale datasets were assembled that included recordings provided by the public. Among them, the Yellowhammer (Emberiza citrinella, Emberizidae) is particularly prominent, as it has been recently a subject of dedicated citizen science projects focusing on its dialect distribution. The most successful, in terms of public participation as well as the number and density of obtained recordings, was the Dialects of Czech Yellowhammers (DCY) project, which assembled detailed data at the whole-country level. A mosaic of almost all common song variants known across Europe was observed in Czechia, but the results indicated that some of the traditionally recognized Yellowhammer dialects may not represent geographically clustered song variants, at least not in Central Europe. We quantitatively analysed variation (frequency and temporal characteristics and modulation) of the terminal song element in three dialects defined by arbitrary frequency thresholds in DCY. Multivariate analyses indicated that pooling these to two distinct groups reflects the variation in the songs, as well as their spatial distribution, better than retaining the current classification to three dialects or their finer splitting to even more categories. We provide simple measures that may be used for classification of these Yellowhammer song variants in Central Europe. However, we warn from indiscriminate transposing of results from one region to another, as that may lead to substantial biases. Future studies of birdsong variation will benefit from big data assembled by citizen scientists, but to maximise their usefulness for further dialect research, careful delineation of dialect boundaries is essential.

A linear algorithm for the neighbor-component order connectivity of arbitrary trees

The graph parameter neighbor-connectivity was first investigated by Gunther and Hartnell in 1987 and provides important information on how reliable a network can be when failures of a node may impact its neighbors. In this model, the failure of a node causes the deletion of its closed neighborhood, i.e., the node and its adjacent neighbors as well. The minimum number of closed neighborhoods whose removal results in an empty, complete, or disconnected subgraph is called the neighbor-connectivity of the graph. Since then component order connectivity models have emerged to address inadequacies in the traditional models of connectivity, namely, that in many real-world networks, when disconnecting a graph one may be left with components that are large enough to still be operable. By adapting neighbor-connectivity to a component order model, we introduced neighbor-component order connectivity, defined as the minimum number of closed neighborhoods that must be removed from a network to ensure all remaining components have order less than some given threshold value. Given a threshold value of one, the neighbor-component order connectivity of a graph is equivalent to the well-known parameter domination number of the graph. The problem of computing the domination number of an arbitrary graph is NP-hard, and computing neighbor-component order connectivity of a graph for an arbitrary threshold value is also NP-hard. Here, we present a linear-time algorithm for computing the neighbor-component order connectivity of an arbitrary tree for an arbitrary threshold value, thus generalizing the classic linear algorithm of Cockayne, Goodman, and Hedetniemi for the domination number of the tree.

Exploring the Agreement Between Self-Reported Medication Adherence and Pharmacy Refill-Based Measures in Patients with Kidney Disease.

To assess the quantitative and categorical agreement between two methods of measuring medication adherence: pharmacy refill-based medication possession rates and self-reported medication adherence scale. Categorisation of adherence metrics using empirical cut-off scores can lead to misclassification, which can be overcome by expressing adherence as a continuous variable. Pharmacy refill-based adherence can be reported as actual rates, but the validity of expressing self-reported medication adherence scores as a continuous variable to reflect adherence is unknown and its quantitative agreement with refill-based adherence rates untested. Patients with kidney disease, including dialysis patients, from Illawarra Shoalhaven region of New South Wales, Australia were recruited between January 2015 and June 2016 to this cross-sectional study. Medication adherence was assessed using the self-reported Morisky Medication Adherence Scale (MMAS) and two pharmacy refill-based measures, Medication Possession Ratio (MPR) and Proportion of Days Covered (PDC) for antihypertensives and cardiometabolic drugs. Categorical and quantitative agreement between self-reported adherence and pharmacy refill-based adherence were assessed using tests of trend, analysis of covariance (ANCOVA), Cohen's kappa and Bland-Altman analysis. We recruited 113 patients. There was a significant declining trend of MPR (p < 0.001) and PDC (<0.001 for antihypertensives, p = 0.004 for cardiometabolic) scores among categories with worsening MMAS adherence. Adjusted ANCOVA showed significant association between self-report and pharmacy refill-based adherence (p < 0.001). Weighted Cohen's kappa statistics showed fair agreement between the self-report and pharmacy refill-based categories. Bland-Altman's analysis showed less than 5% of cases were outside the limits of agreement (-0.36 to 0.27) and the bias for MMAS was negative (-0.05 to -0.09), indicating MMAS did not overestimate adherence. There is modest agreement between pharmacy refill-based measures and self-report MMAS measures when assessed categorically or quantitatively. Assessing adherence as a continuous variable should be considered to overcome the challenges associated with categorization of adherence based on arbitrary thresholds.

TP53-altered acute myeloid leukemia and myelodysplastic syndrome with excess blasts should be approached as a single entity.

TP53-altered myelodysplastic syndrome with excess blasts and TP53-altered acute myeloid leukemia should be considered under one unifying classification term for their study in clinical trials. Ultimately, such a unification would simplify the screening processes for clinical trials and allow a focus on treating the patient for a genetically defined disorder rather than one based on an arbitrary blast threshold.

Risk scores’ performance and their impact on operative decision-making in left-sided endocarditis: a cohort study

The accuracy of contemporary risk scores in predicting perioperative mortality in infective endocarditis (IE) remains controversial. The aim is to evaluate the performance of existent mortality risk scores for cardiovascular surgery in IE and the impact on operability at high-risk thresholds. A single-center retrospective review of adult patients diagnosed with acute left-sided IE undergoing surgery from May 2014 to August 2019 (n = 142) was done. Individualized risk calculation was obtained according to the available mortality risk scores: EuroScore I and II, PALSUSE, Risk-E, Costa, De Feo-Cotrufo, AEPEI, STS-risk, STS-IE, APORTEI, and ICE-PCS scores. A cross-validation analysis was performed on the score with the best area under the curve (AUC). The 30-day survival was 96.5% (95%CI 91–98%). The score with worse area under the curve (AUC = 0.6) was the STS-IE score, while the higher was for the RISK-E score (AUC = 0.89). The AUC of the majority of risk scores suggested acceptable performance; however, statistically significant differences in expected versus observed mortalities were common. The cross-validation analysis showed that a large number of survivors (> 75%) would not have been operated if arbitrary high-risk threshold estimates had been used to deny surgery. The observed mortality in our cohort is significantly lower than is predicted by contemporary risk scores. Despite the reasonable numeric performance of the analyzed scores, their utility in judging the operability of a given patient remains questionable, as demonstrated in the cross-validation analysis. Future guidelines may advise that denial of surgery should only follow a highly experienced Endocarditis Team evaluation.

Unveiling Latent Structure of Venture Capital Syndication Networks

Venture capital (VC) is a form of private equity financing provided by VC institutions to startups with high growth potential due to innovative technology or novel business models but also high risks. To against uncertainties and benefit from mutual complementarity and sharing resources and information, making joint-investments with other VC institutions on the same startup are pervasive, which forms an ever-growing complex syndication network. Attaining objective classifications of VC institutions and revealing the latent structure of joint-investment behaviors between them can deepen our understanding of the VC industry and boost the healthy development of the market and economy. In this work, we devise an iterative Loubar method based on the Lorenz curve to make objective classification of VC institutions automatically, which does not require setting arbitrary thresholds and the number of categories. We further reveal distinct investment behaviors across categories, where the top-ranked group enters more industries and investment stages with a better performance. Through network embedding of joint investment relations, we unveil the existence of possible territories of top-ranked VC institutions, and the hidden structure of relations between VC institutions.

A Factor Mixture Model for Item Responses and Certainty of Response Indices to Identify Student Knowledge Profiles

AbstractThe certainty of response index (CRI) measures respondents' confidence level when answering an item. In conjunction with the answers to the items, previous studies have used descriptive statistics and arbitrary thresholds to identify student knowledge profiles with the CRIs. Whereas this approach overlooked the measurement error of the observed item responses and indices, we address this by proposing a factor mixture model that integrates a latent class model to detect student subgroups and a measurement model to control for student ability and confidence level. Applying the model to 773 seventh graders' responses to an algebra test, where some items were related to new material that had not been taught in class, we found two subgroups: (1) students who had high confidence in answering items involving the new material; and (2) students who had low confidence in answering items involving the new material but higher general self‐confidence than the first group. We regressed the posterior probability of the group membership on gender, prior achievement, and preview behavior and found preview behavior a significant factor associated with the membership. Finally, we discussed the implications of the current study for teaching practices and future research.

Structure-function clustering in weighted brain networks

Functional networks, which typically describe patterns of activity taking place across the cerebral cortex, are widely studied in neuroscience. The dynamical features of these networks, and in particular their deviation from the relatively static structural network, are thought to be key to higher brain function. The interactions between such structural networks and emergent function, and the multimodal neuroimaging approaches and common analysis according to frequency band motivate a multilayer network approach. However, many such investigations rely on arbitrary threshold choices that convert dense, weighted networks to sparse, binary structures. Here, we generalise a measure of multiplex clustering to describe weighted multiplexes with arbitrarily-many layers. Moreover, we extend a recently-developed measure of structure-function clustering (that describes the disparity between anatomical connectivity and functional networks) to the weighted case. To demonstrate its utility we combine human connectome data with simulated neural activity and bifurcation analysis. Our results indicate that this new measure can extract neurologically relevant features not readily apparent in analogous single-layer analyses. In particular, we are able to deduce dynamical regimes under which multistable patterns of neural activity emerge. Importantly, these findings suggest a role for brain operation just beyond criticality to promote cognitive flexibility.

Weighting by Gene Tree Uncertainty Improves Accuracy of Quartet-based Species Trees.

Phylogenomic analyses routinely estimate species trees using methods that account for gene tree discordance. However, the most scalable species tree inference methods, which summarize independently inferred gene trees to obtain a species tree, are sensitive to hard-to-avoid errors introduced in the gene tree estimation step. This dilemma has created much debate on the merits of concatenation versus summary methods and practical obstacles to using summary methods more widely and to the exclusion of concatenation. The most successful attempt at making summary methods resilient to noisy gene trees has been contracting low support branches from the gene trees. Unfortunately, this approach requires arbitrary thresholds and poses new challenges. Here, we introduce threshold-free weighting schemes for the quartet-based species tree inference, the metric used in the popular method ASTRAL. By reducing the impact of quartets with low support or long terminal branches (or both), weighting provides stronger theoretical guarantees and better empirical performance than the unweighted ASTRAL. Our simulations show that weighting improves accuracy across many conditions and reduces the gap with concatenation in conditions with low gene tree discordance and high noise. On empirical data, weighting improves congruence with concatenation and increases support. Together, our results show that weighting, enabled by a new optimization algorithm we introduce, improves the utility of summary methods and can reduce the incongruence often observed across analytical pipelines.

Hybrid features selection method using random forest and meerkat clan algorithm

In the majority of gene expression investigations, selecting relevant genes for sample classification is considered a frequent challenge, with researchers attempting to discover the minimum feasible number of genes while yet achieving excellent predictive performance. Various gene selection methods employ univariate (gene-by-gene) gene relevance rankings as well as arbitrary thresholds for selecting the number of genes, are only applicable to 2-class problems and use gene selection ranking criteria unrelated to the algorithm of classification. A modified random forest (MRF) algorithm depending on the meerkat clan algorithm (MCA) is provided in this work. It is one of the swarm intelligence algorithms and one of the most significant machine learning approaches in the decision tree. MCA is used to choose characteristics for the RF algorithm. In information systems, databases, and other applications, feature selection imputation is critical. The proposed algorithm was applied to three different databases, where the experimental results for accuracy and time proved the superiority of the proposed algorithm over the original algorithm.

Integration of Ecology, Larval Phenotypes, and Mate-Recognition Signals with Molecular and Morphological Data Indicate Taxonomic Inflation in Nyctibatrachus (Anura: Nyctibatrachidae)

Taxonomic studies over the past decade of the endemic Night Frog genus Nyctibatrachus (originally described in 1882) from Peninsular India have more than tripled, from 11 at the turn of this century to 36 by 2017. Despite these revisionary contributions, it is still challenging for field biologists to identify night frog species reliably, due to a near-complete absence of diagnostic, discrete character states or trait values. Worse, many questionably diagnosed night frog species' status has ostensibly been “supported” by phylogenies derived from sparsely sampled gene-trees that are based on a single locus or a handful of markers—with topology and arbitrary genetic distance thresholds of 3–6% used to support new species descriptions. We sought to re-evaluate and validate the species boundaries of six currently nominated species of Nyctibatrachus of the aliciae group (N. aliciae, N. periyar, N. deveni, N. pillaii), N. vasanthi, and N. poocha clade using a comprehensive integrative taxonomic approach that integrates classical taxonomy, molecular species delimitation analysis, statistical analysis of morphological characters of adults and larvae, analyses of bioacoustics, and natural history information. Our results indicate that recent descriptions of Nyctibatrachus deveni, N. periyar, and N. pillaii represent cases of taxonomic inflation (over-splitting), because the evidence cited in support of their recognition is irreproducible, subjective, and devoid of strong statistical support. We demonstrate the need for multidimensional species delimitation approaches in the celebrated Western Ghats biodiversity hotspot paleo-endemic genus Nyctibatrachus and suspect that this concerning trend of over-splitting amphibian species based on limited data and untenable support may be applicable to other amphibian groups.