Likelihood Of Data Research Articles

Is it all Phacelia pinnatifida? Molecular delimitations and taxonomic revision of Phacelia sect. Glandulosae in South America based on ddRADseq

AbstractPhacelia sect. Glandulosae, with ca. 62 taxa, is the most diverse of the 11 sections of the genus Phacelia (Hydrophyllaceae). The family is largely restricted to North and Central America, but the genus Phacelia has an amphitropical distribution with nine species reported from western South America, five of which have been assigned to P. sect. Glandulosae. These species are found in arid to semi‐arid Andean habitats between 1000 and 5000 m a.s.l. Species delimitation has long been confounded for this plant group in South America by some degree of taxonomic chaos – lack of typifications, dozens of synonyms, different names used for the same taxon between different countries – and poor representation in molecular phylogenies. Here, we try to improve our understanding by employing a ddRADseq strategy to investigate phylogenetic relationships of all South American representatives of P. sect. Glandulosae based on a total of 33 sampled specimens. Both maximum likelihood and species tree inference analyses of the ddRADseq data resulted in a well‐resolved topology. All five species currently recognized in P. sect. Glandulosae in South America form a highly supported monophylum including P. nana, previously considered as belonging to P. sect. Euglypta. However, the currently recognized morphospecies form a clade with very short branch lengths indicating coalescence processes at the level of population, except for P. artemisioides, which is clearly sister to the rest. Additionally, different morphospecies and ecotypes are largely retrieved as poly‐ and/or paraphyletic. Based on these results and on re‐evaluation of the morphological characters traditionally used to delineate species within P. sect. Glandulosae, we argue that these characters are of limited diagnostic value. We recommend recognizing only two South American species within this section: polymorphic P. pinnatifida and its closely related sister species P. artemisioides. A revised formal taxonomy is provided.

Additions to Lentitheciaceae (Pleosporales, Dothideomycetes): Pseudosetoseptoria jiangxiensis sp. nov. from decaying culms of bamboo in China

In terms of morphology, ecology, and phylogeny, Lentitheciaceae species exhibit an enormous diversity. They have a cosmopolitan distribution worldwide, and lead diverse lifestyles. A novel species in Lentitheciaceae, Pseudosetoseptoria jiangxiensis occurring on decaying culms of bamboo in Jiangxi province, China is described and illustrated. Morphologically, Pseudosetoseptoria jiangxiensis has dark brown to black, semi-immersed, carbonaceous, pycnidial conidiomata and hyaline, obovoid, elliptic, or nearly spherical, aseptate conidia with minute guttules. Maximum likelihood and Bayesian inference analyses of combined ITS, LSU, SSU and tef1-α sequence data were used to clarify the phylogenetic affinities of the species.

Distribution-agnostic landslide hazard modelling via Graph Transformers

In statistical applications, choosing a suitable data distribution or likelihood that matches the nature of the response variable is required. To spatially predict the planimetric area of a landslide population, the most tested likelihood corresponds to the Log-Gaussian case. This causes a limitation that hinders the ability to accurately model both very small and very large landslides, with the latter potentially leading to a dangerous underestimation of the hazard. Here, we test a distribution-agnostic solution via a Graph Transformer Neural Network (GTNN) and implement a loss function capable of forcing the model to capture both the bulk and the right tail of the landslide area distribution. An additional problem with this type of data-driven hazard assessment is that one often excludes slopes with landslide areas equal to zero from the regression procedure, as this may bias the prediction towards small values. Due to the nature of GTNNs, we present a solution where all the landslide area information is passed to the model, as one would expect for architectures built for image analysis. The results are promising, with the landslide area distribution generated by the Wenchuan earthquake being suitably estimated, including both zeros, the bulk and the extremely large cases. We consider this a step forward in the landslide hazard modelling literature, with implications for what the scientific community could achieve in light of a future space–time and/or risk assessment extension of the current protocol.

Hybrid Open-Set Segmentation With Synthetic Negative Data.

Open-set segmentation can be conceived by complementing closed-set classification with anomaly detection. Many of the existing dense anomaly detectors operate through generative modelling of regular data or by discriminating with respect to negative data. These two approaches optimize different objectives and therefore exhibit different failure modes. Consequently, we propose a novel anomaly score that fuses generative and discriminative cues. Our score can be implemented by upgrading any closed-set segmentation model with dense estimates of dataset posterior and unnormalized data likelihood. The resulting dense hybrid open-set models require negative training images that can be sampled from an auxiliary negative dataset, from a jointly trained generative model, or from a mixture of both sources. We evaluate our contributions on benchmarks for dense anomaly detection and open-set segmentation. The experiments reveal strong open-set performance in spite of negligible computational overhead.

Cross-fade sampling: extremely efficient Bayesian inversion for a variety of geophysical problems

SUMMARY This paper introduces cross-fade sampling, a computationally efficient Markov Chain Monte Carlo simulation method that uses a semi-analytical approach to quickly solve Bayesian inverse problems that do not themselves have an analytical solution. Cross-fading is efficient in two ways. First, it requires fewer samples to obtain the same quality simulation of the target probability density function (PDF). Secondly, it is much faster to evaluate the posterior probability of each sample than conventional sampling methods for simulating Bayesian posterior PDFs. Conventional methods require evaluating the prior probability (which describes your a priori constraints) and data likelihood (which describes the fit between the observations and the predictions of the model) for each sample model. However, cross-fading does not require evaluating the data likelihood, meaning that ‘big data’ can be fit with zero additional computational cost. Further, the cross-fading approach can be used to calculate the marginal likelihood associated with a model design, facilitating model comparison and Bayesian model averaging. Topics covered in this paper include derivation of the cross-fade approach and how it can be used to simulate Bayesian posterior PDFs and compute the marginal likelihood, discussion of the class of problems to which cross-fading can be applied (with examples from earthquake statistics, earthquake ground motion modelling, volcanic eruption forecasting, and finite fault slip modelling), demonstration of efficiency relative to existing sampling methods and discussion of how cross-fading can be used to account for prediction errors (i.e. epistemic errors) as part of the geophysical inverse problem.

A Hard Re-descending Hybrid Robust Regression Estimation Technique Using Direct Weights

A hybrid approach of M and R estimators using an iterative procedure is proposed to detect outliers and estimation of regression parameters for linear models. We consider the deviation of each residual from its median to measure the likelihood of the corresponding data point to be an outlier. Also, the proposed work develops a reliable algorithm to estimate parameters of regression model that is unaffected by outliers. The significance of the proposed work is a novel hybrid approach of weighing the observations based on the order of residuals. Our proposal is illustrated using Monte Carlo simulation and analysed for few benchmark data sets.

Mitochondrial DNA-based reassessment of Antennablennius Fowler (Blenniidae: Salariini) from the north-western Indian Ocean, with resurrection of A. persicus (Regan)

Seven Operational Taxonomic Units within the blenniid genus Antennablennius are recognised from the north-western Indian Ocean, based on the Cytochrome oxidase-c subunit I gene (CO1). Five of them can be referred to nominal species with certainty. Two additional distinct lineages originate from the Persian Gulf and the Gulf of Oman, and the Socotra Archipelago. They were initially, morphologically, identified as A. adenensis Fraser-Brunner, 1951, yet could hitherto not be referred to this species with certainty, requiring further genetic data. Detailed descriptions accompanied by photographs are provided for six species, including A. adenensis. Antennablennius persicus (Regan, 1905) is resurrected from the synonymy of A. bifilum (Günther, 1861) and re-described based on specimens from the Iranian coast of the Persian Gulf and Gulf of Oman, increasing the number of valid species in the genus to eight. The former differs from the latter species in details of morphology and coloration, supported by genetic data. A revised key to all species of the genus is included. A phylogenetic investigation of the genus based on Maximum Likelihood (ML) and Bayesian Inference (BI) analyses of the CO1 data is presented for the first time.

A Bayesian bias-adjusted random-effects model for synthesizing evidence from randomized controlled trials and nonrandomized studies of interventions.

An important consideration when combining RCTs and NRSIs is how to address their potential biases in the pooled estimates. This study aimed to propose a Bayesian bias-adjusted random effects model for the synthesis of evidence from RCTs and NRSIs. We present a Bayesian bias-adjusted random effects model based on power prior method, which combines the likelihood contribution of the NRSIs, raised to the power parameter of alpha, with the likelihood of the RCT data, modeled with an additive bias. The method was illustrated using a meta-analysis on the association between low-dose methotrexate exposure and melanoma. We also combined RCTs and NRSIs using the naïve data synthesis. The results including only RCTs has a posterior median and 95% credible interval (CrI) of 1.18 (0.31-4.04), the posterior probability of any harm (>1.0) and a meaningful association (>1.15) were 0.61 and 0.52, respectively. The posterior median and 95% CrI based on the naïve data synthesis resulted in 1.17 (0.96-1.47), and the posterior probability of any harm and a meaningful association were 0.96 and 0.60, respectively. For the Bayesian bias-adjusted analysis, the median OR was 1.16 (95% CrI: 0.83-1.71), and the posterior probabilities of any and a meaningful clinical association were 0.88 and 0.53, respectively. The results indicated that integrating NRSIs into meta-analysis could increase the certainty of the body of evidence. However, directly combining RCTs and NRSIs in the same meta-analysis without distinction may lead to misleading conclusions.

On the detection of population heterogeneity in causation between two variables: Finite mixture modeling of data collected from twin pairs.

Causal inference is inherently complex, often dependent on key assumptions that are sometimes overlooked. One such assumption is the potential for unidirectional or bidirectional causality, while another is population homogeneity, which suggests that the causal direction between two variables remains consistent across the study sample. Discerning these processes requires meticulous data collection through an appropriate research design and the use of suitable software to define and fit alternative models. In psychiatry, the co-occurrence of different disorders is common and can stem from various origins. A patient diagnosed with two disorders might have one recognized as primary and the other as secondary, suggesting the existence of two types of comorbidity within the population. For example, in some individuals, depression might lead to substance use, while in others, substance use could lead to depression. Identifying the primary disorder is crucial for developing effective treatment plans. This article explores the use of finite mixture models to depict within-sample heterogeneity. We begin with the Direction of Causation (DoC) model for twin data and extend it to a mixture distribution model. This extension allows for the calculation of the likelihood of each individual's data for the two alternate causal directions. Given twin data, there are four possible pairwise combinations of causal direction. Through simulations, we investigate the Direction of Causation Twin Mixture (mixCLPM) model's potential to detect and model heterogeneity due to varying causal directions.

Approximate Gibbs sampler for efficient inference of hierarchical Bayesian models for grouped count data

Hierarchical Bayesian Poisson regression models (HBPRMs) provide a flexible modelling approach of the relationship between predictors and count response variables. The applications of HBPRMs to large-scale datasets require efficient inference algorithms due to the high computational cost of inferring many model parameters based on random sampling. Although Markov Chain Monte Carlo (MCMC) algorithms have been widely used for Bayesian inference, sampling using this class of algorithms is time-consuming for applications with large-scale data and time-sensitive decision-making, partially due to the non-conjugacy of many models. To overcome this limitation, this research develops an approximate Gibbs sampler (AGS) to efficiently learn the HBPRMs while maintaining the inference accuracy. In the proposed sampler, the data likelihood is approximated with Gaussian distribution such that the conditional posterior of the coefficients has a closed-form solution. Numerical experiments using real and synthetic datasets with small and large counts demonstrate the superior performance of AGS in comparison to the state-of-the-art sampling algorithm, especially for large datasets.

Caustic area biases and how to deal with them

Quadruply imaged strongly lensed point sources (quads) are routinely used for measurements of the expansion rate of the Universe with time delays. It has recently been suggested that any quad lens is subject to a Malmquist-like bias that causes the inferred area enclosed within the tangential caustic to be systematically underestimated, and that such a bias might translate into a corresponding bias on the expansion parameter. In this work we extended that analysis by also considering the effect of Eddington bias. We find that the sign and amplitude of the combined bias depend on the functional form of the caustic area distribution of the lens population and on the noise associated with the caustic area estimation process. Based on simulations, we estimate that the corresponding impact on $H_0$ is of the order of a percent or smaller. If the likelihood of the lensing data is known, then the bias can be accounted for when modelling the lens population. However, ignoring the criteria used to select a quad might lead to a bias at the lens modelling stage that causes the inferred caustic area to be overestimated. Such a bias disappears for lens models that are well constrained by the data.

An overview of Melanommataceae (Pleosporales, Dothideomycetes): Current insight into the host associations and geographical distribution with some interesting novel additions from plant litter.

Melanommataceous species exhibit high diversity with a cosmopolitan distribution worldwide and show a prominent saprobic lifestyle. In this study, we explored five saprobic species collected from plant litter substrates from terrestrial habitats in China and Thailand. A combination of morphological characteristics and multi-locus phylogenetic analyses was used to determine their taxonomic classifications. Maximum Likelihood and Bayesian Inference analyses of combined LSU, SSU, ITS and tef1-α sequence data were used to clarify the phylogenetic affinities of the species. Byssosphaeriapoaceicola and Herpotrichiazingiberacearum are introduced as new species, while three new host records, Bertiellafici, By.siamensis and Melanommapopulicola are also reported from litter of Cinnamomumverum, Citrustrifoliata and Fagussylvatica, respectively. Yet, despite the rising interest in the melanommataceous species, there is a considerable gap in knowledge on their host associations and geographical distributions. Consequently, we compiled the host-species associations and geographical distributions of all the so far known melanommataceous species.

Quantile regression and smoothed empirical likelihood for non-ignorable missing data based on semi-parametric response models

Quantile regression and smoothed empirical likelihood for non-ignorable missing data based on semi-parametric response models

Joint modeling of gene-environment correlations and interactions using polygenic risk scores in case-control studies.

Polygenic risk scores (PRSs) are rapidly emerging as a way to measure disease risk by aggregating multiple genetic variants. Understanding the interplay of the PRS with environmental factors is critical for interpreting and applying PRSs in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using the PRS in case-control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the 2 models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared with standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis, for gene-environment correlations. Finally, using simulated case-control data sets within the UK Biobank study, we demonstrate the power of our method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and the PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.

Multiple evidence reveals a new species of Neocucurbitaria (Cucurbitariaceae, Pleosporales) from China

Xanthoceras sorbifolium (yellowhorn) is an endemic edible oil crop in China. A novel Neocucurbitaria species associated with dead stems of yellowhorn in Jilin Province (northeast China) is described and illustrated. It is characterized by stromata with 1–5 locules and a subhyaline pycnidial wall under the inner layer. Full morphological details and a comparison with closely related species are also provided. The new species differs from the closely related species N. ribicola, N. rhamni, N. rhamnicola and N. rhamnioides by its distinct conidiogenous cells and conidia. Phylogenetic analysis (maximum likelihood and Bayesian inference method) of combined ITS, LSU, rpb2, tef1-α and tub2 sequence data reveal that two strains of the fungus differ from all other species of Neocucurbitaria (Cucurbitariaceae). Therefore, we introduce N. subribicola as a new species, and this is the first report of the asexual morph of Neocucurbitaria in China

Random-Effects Substitution Models for Phylogenetics via Scalable Gradient Approximations.

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches.

Fatigue Risk Management Preferences for Consumer Sleep Technologies and Data Sharing in Aviation.

INTRODUCTION: Employees from any type of aviation services industry were asked to give their opinions about the usefulness of consumer sleep technologies (CSTs) during operations and their willingness to share data from CSTs with their organizations for fatigue risk management purposes under a variety of circumstances.METHODS: Respondents provided information about position in aviation and use of CST devices. Respondents ranked sleep issues and feedback metrics by perceived level of importance to operational performance. Respondents rated their likelihood to share data with their organization under a series of hypothetical situations.RESULTS: Between January-July 2023, 149 (N = 149) aviation professionals responded. Pilots comprised 72% (N = 108) of respondents; 84% (N = 125) of all respondents worked short- or medium-haul operations. "Nighttime operations" and "inconsistent sleep routines" ranked as the most important issues affecting sleep. "Sleep quality history" and "projected alertness levels" ranked as most important feedback metrics for personal management of fatigue. Respondents were split between CST users (N = 64) and nonusers (N = 68). CST users did not indicate a strong preference for a specific device brand. The most-reported reason for not using a CST was due to not owning one or no perceived need. Respondents indicated greater likelihood of data sharing under conditions where the device was provided to them by their organization.DISCUSSION: These results suggest that aviation professionals are more concerned about schedule-related disturbances to sleep than they are about endogenous sleep problems. Organizations may be able to increase compliance to data collection for fatigue risk management by providing employees with company-owned CSTs of any brand.Devine JK, Choynowski J, Hursh SR. Fatigue risk management preferences for consumer sleep technologies and data sharing in aviation. Aerosp Med Hum Perform. 2024; 95(5):265-272.

Generative probabilistic prediction of precipitation induced landslide deformation with variational autoencoder and gated recurrent unit

Landslides, prevalent in mountainous areas, are typically triggered by tectonic movements, climatic changes, and human activities. They pose catastrophic risks, especially when occurring near settlements and infrastructure. Therefore, detecting, monitoring, and predicting landslide deformations is essential for geo-risk mitigation. The mainstream of the previous studies have often focused on deterministic models for immediate landslide prediction. However, in most of them, the aspect of prediction uncertainties are not sufficiently addressed. This paper introduces an innovative probabilistic prediction method using a Variational Autoencoder (VAE) combined with Gated Recurrent Unit (GRU) to forecast landslide deformations from a generative standpoint. Our approach consists of two main elements: firstly, training the VAE-GRU model to maximize the variational lower bound on the likelihood of historical precipitation data; secondly, using the learned approximated posterior distribution to predict imminent deformations from a generative angle. To assess the prediction quality, we use four widely-used metrics: Prediction Interval Coverage Probability (PICP), Prediction Interval Normalized Average Width (PINAW), Coverage Width-Based Criterion (CWC), and Prediction Interval Normalized Root Mean Square Width (PINRW). The results demonstrate that our proposed VAE-GRU framework surpasses traditional state-of-the-art (SOTA) probabilistic deformation prediction algorithms in terms of accuracy and reliability.

ConDENSE: Conditional Density Estimation for Time Series Anomaly Detection

In recent years deep learning methods, based on reconstruction errors, have facilitated huge improvements in unsupervised anomaly detection. These methods make the limiting assumption that the greater the distance between an observation and a prediction the lower the likelihood of that observation. In this paper we propose conDENSE, a novel anomaly detection algorithm, which does not use reconstruction errors but rather uses conditional density estimation in masked autoregressive flows. By directly estimating the likelihood of data, our model moves beyond approximating expected behaviour with a single point estimate, as is the case in reconstruction error models. We show how conditioning on a dense representation of the current trajectory, extracted from a variational autoencoder with a gated recurrent unit (GRU VAE), produces a model that is suitable for periodic datasets, while also improving performance on non-periodic datasets. Experiments on 31 time-series, including real-world anomaly detection benchmark datasets and synthetically generated data, show that the model can outperform state-of-the-art deep learning methods.

Screening as a stage in the prevention of oral mucosal diseases in patients undergoing chemotherapy for malignant growths

Relevance. The incidence of malignant growths in the maxillofacial area has consistently increased over the years. Chemotherapy stands out as one of the most effective treatments for cancerous tumors. Chemotherapeutic drugs possess the capability to either inhibit the proliferation of malignant cells or cause damage to them. However, while acting on abnormal cells, cytostatic drugs also impact actively dividing cells in normal tissues, including the mucous membranes in the oral cavity. As a result, various complications are observed in every patient undergoing treatment for malignant neoplasms (MN), particularly chemotherapy (CT). The nature and severity of side effects undergo changes based on the specific drugs and cytostatic treatment regimen employed. Adverse reactions and side effects inherent to each drug category manifest at different points throughout a chemotherapy course and its aftermath. Oral mucosal (OM) complications, such as oral mucositis, may ensue as a consequence of CT side effects.Materials and methods. In order to assess the effectiveness of screening for OM disorders arising as side effects of chemotherapy, a study involving 37 patients undergoing chemotherapy for an underlying condition was conducted. The screening study comprised two stages: Stage I involved a quality-of-life assessment utilizing questionnaires developed by the authors, while Stage II entailed a clinical examination of the oral mucosa. The obtained data were subjected to statistical analysis methods. Variation statistics were employed to assess the likelihood of data errors.Results. Comprehensive OM diagnostics in patients undergoing chemotherapy for an underlying oncology condition enable the early identification of oral mucosal diseases and the prevention of complications. It is recommended to conduct a thorough clinical examination and employ toluidine blue staining during each follow-up visit. Any observed clinical or stained tissue changes, the emergence of new erythematous lesions, erosions, or ulcers may serve as indicators of malignant transformation.Conclusion. It is advisable to incorporate screening diagnostics into each patient's dental visit to proactively prevent the onset of various pathological conditions.