Search

Rapidly evolving taxa are excellent models for understanding the mechanisms that give rise to biodiversity. However, developing an accurate historical framework for comparative analysis of such lineages remains a challenge due to ubiquitous incomplete lineage sorting (ILS) and introgression. Here, we use a whole-genome alignment, multiple locus-sampling strategies, and summary-tree and single nucleotide polymorphism-based species-tree methods to infer a species tree for eastern North American Neodiprion species, a clade of pine-feeding sawflies (Order: Hymenopteran; Family: Diprionidae). We recovered a well-supported species tree that-except for three uncertain relationships-was robust to different strategies for analyzing whole-genome data. Nevertheless, underlying gene-tree discordance was high. To understand this genealogical variation, we used multiple linear regression to model site concordance factors estimated in 50-kb windows as a function of several genomic predictor variables. We found that site concordance factors tended to be higher in regions of the genome with more parsimony-informative sites, fewer singletons, less missing data, lower GC content, more genes, lower recombination rates, and lower D-statistics (less introgression). Together, these results suggest that ILS, introgression, and genotyping error all shape the genomic landscape of gene-tree discordance in Neodiprion. More generally, our findings demonstrate how combining phylogenomic analysis with knowledge of local genomic features can reveal mechanisms that produce topological heterogeneity across genomes., (© The Author(s) 2024. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

The role of interspecific hybridization has recently seen increasing attention, especially in the context of diversification dynamics. Genomic research has now made it abundantly clear that both hybridization and introgression-the exchange of genetic material through hybridization and backcrossing-are far more common than previously thought. Besides cases of ongoing or recent genetic exchange between taxa, an increasing number of studies report "ancient introgression"- referring to results of hybridization that took place in the distant past. However, it is not clear whether commonly used methods for the detection of introgression are applicable to such old systems, given that most of these methods were originally developed for analyses at the level of populations and recently diverged species, affected by recent or ongoing genetic exchange. In particular, the assumption of constant evolutionary rates, which is implicit in many commonly used approaches, is more likely to be violated as evolutionary divergence increases. To test the limitations of introgression detection methods when being applied to old systems, we simulated thousands of genomic datasets under a wide range of settings, with varying degrees of among-species rate variation and introgression. Using these simulated datasets, we showed that some commonly applied statistical methods, including the D-statistic and certain tests based on sets of local phylogenetic trees, can produce false-positive signals of introgression between divergent taxa that have different rates of evolution. These misleading signals are caused by the presence of homoplasies occurring at different rates in different lineages. To distinguish between the patterns caused by rate variation and genuine introgression, we developed a new test that is based on the expected clustering of introgressed sites along the genome and implemented this test in the program Dsuite., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Systematic Biologists.)

Phylogenetic trees establish a historical context for the study of organismal form and function. Most phylogenetic trees are estimated using a model of evolution. For molecular data, modeling evolution is often based on biochemical observations about changes between character states. For example, there are 4 nucleotides, and we can make assumptions about the probability of transitions between them. By contrast, for morphological characters, we may not know a priori how many characters states there are per character, as both extant sampling and the fossil record may be highly incomplete, which leads to an observer bias. For a given character, the state space may be larger than what has been observed in the sample of taxa collected by the researcher. In this case, how many evolutionary rates are needed to even describe transitions between morphological character states may not be clear, potentially leading to model misspecification. To explore the impact of this model misspecification, we simulated character data with varying numbers of character states per character. We then used the data to estimate phylogenetic trees using models of evolution with the correct number of character states and an incorrect number of character states. The results of this study indicate that this observer bias may lead to phylogenetic error, particularly in the branch lengths of trees. If the state space is wrongly assumed to be too large, then we underestimate the branch lengths, and the opposite occurs when the state space is wrongly assumed to be too small., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Systematic Biologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site–for further information please contact journals.permissions@oup.com.)

Dating phylogenetic trees to obtain branch lengths in time units is essential for many downstream applications but has remained challenging. Dating requires inferring substitution rates that can change across the tree. While we can assume to have information about a small subset of nodes from the fossil record or sampling times (for fast-evolving organisms), inferring the ages of the other nodes essentially requires extrapolation and interpolation. Assuming a distribution of branch rates, we can formulate dating as a constrained maximum likelihood (ML) estimation problem. While ML dating methods exist, their accuracy degrades in the face of model misspecification, where the assumed parametric statistical distribution of branch rates vastly differs from the true distribution. Notably, most existing methods assume rigid, often unimodal, branch rate distributions. A second challenge is that the likelihood function involves an integral over the continuous domain of the rates, often leading to difficult non-convex optimization problems. To tackle both challenges, we propose a new method called Molecular Dating using Categorical-models (MD-Cat). MD-Cat uses a categorical model of rates inspired by non-parametric statistics and can approximate a large family of models by discretizing the rate distribution into k categories. Under this model, we can use the Expectation-Maximization algorithm to co-estimate rate categories and branch lengths in time units. Our model has fewer assumptions about the true distribution of branch rates than parametric models such as Gamma or LogNormal distribution. Our results on two simulated and real datasets of Angiosperms and HIV and a wide selection of rate distributions show that MD-Cat is often more accurate than the alternatives, especially on datasets with exponential or multimodal rate distributions., (© The Author(s) 2024. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site–for further information please contact journals.permissions@oup.com.)

Organizing and partitioning sets of chemical structures is of considerable practical significance, e.g., in compound library analysis and the postprocessing of screening hit lists. Approaches such as unsupervised clustering are computationally demanding and dataset-dependent; on the other hand, rule-based methods, such as those based on Murcko scaffolds, have linear time complexity but are often too fine-grained, leading to a large number of singletons or sparsely populated classes. An alternative rule-based method that seeks to achieve an optimal balance when grouping compounds into sets is the 'Scaffold Identification and Naming System' (SCINS). To facilitate public use of this previously published method, here, we provide an open-source Python implementation of SCINS, dependent only on RDKit. We show that SCINS can be useful in identifying sparsely and densely populated regions in chemical space in large databases, here exemplified with Enamine REAL Diverse and ChEMBL. We find that Enamine REAL Diverse covers a much smaller SCINS space relative to ChEMBL, whereas the opposite is true when Murcko and generic Murcko scaffolds are considered. Additionally, we show that SCINS can result in chemically intuitive grouping of medium-sized sets of bioactive compounds, which can be useful in compound selection from virtual screening campaigns as well as postprocessing of experimental hit lists. Hence, in this work, we provide both an open-source implementation of SCINS and its characterization with relevant use cases.

Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the 5 known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of 2 major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Systematic Biologists.)

The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost. Instead, phylogenetic pipelines generally consist of sequential analyses, whereby a single point estimate from a given analysis is used as input for the next analysis (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step, which can lead to inaccurate or spuriously confident results. Here, we formally develop and test a sequential inference approach for Bayesian phylogenetic inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior distribution produced in the previous step. Our sequential inference approach presented here not only accounts for uncertainty between analysis steps but also allows for greater flexibility in software choice (and hence model availability) and can be computationally more efficient than the traditional joint inference approach when multiple models are being tested. We show that our sequential inference approach is identical in practice to the joint inference approach only if sufficient information in the data is present (a narrow posterior distribution) and/or sufficiently many important samples are used. Conversely, we show that the common practice of using a single point estimate can be biased, for example, a single phylogeny estimate can transform an unrooted phylogeny into a time-calibrated phylogeny. We demonstrate the theory of sequential Bayesian inference using both a toy example and an empirical case study of divergence-time estimation in insects using a relaxed clock model from transcriptome data. In the empirical example, we estimate 3 posterior distributions of branch lengths from the same data (DNA character matrix with a GTR+Γ+I substitution model, an amino acid data matrix with empirical substitution models, and an amino acid data matrix with the PhyloBayes CAT-GTR model). Finally, we apply 3 different node-calibration strategies and show that divergence time estimates are affected by both the data source and underlying substitution process to estimate branch lengths as well as the node-calibration strategies. Thus, our new sequential Bayesian phylogenetic inference provides the opportunity to efficiently test different approaches for divergence time estimation, including branch-length estimation from other software., (© The Author(s) 2024. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Accurately reconstructing the reticulate histories of polyploids remains a central challenge for understanding plant evolution. Although phylogenetic networks can provide insights into relationships among polyploid lineages, inferring networks may be hindered by the complexities of homology determination in polyploid taxa. We use simulations to show that phasing alleles from allopolyploid individuals can improve phylogenetic network inference under the multispecies coalescent by obtaining the true network with fewer loci compared with haplotype consensus sequences or sequences with heterozygous bases represented as ambiguity codes. Phased allelic data can also improve divergence time estimates for networks, which is helpful for evaluating allopolyploid speciation hypotheses and proposing mechanisms of speciation. To achieve these outcomes in empirical data, we present a novel pipeline that leverages a recently developed phasing algorithm to reliably phase alleles from polyploids. This pipeline is especially appropriate for target enrichment data, where the depth of coverage is typically high enough to phase entire loci. We provide an empirical example in the North American Dryopteris fern complex that demonstrates insights from phased data as well as the challenges of network inference. We establish that our pipeline (PATÉ: Phased Alleles from Target Enrichment data) is capable of recovering a high proportion of phased loci from both diploids and polyploids. These data may improve network estimates compared with using haplotype consensus assemblies by accurately inferring the direction of gene flow, but statistical nonidentifiability of phylogenetic networks poses a barrier to inferring the evolutionary history of reticulate complexes., (© The Author(s) 2024. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

One primary advantage of receiver operating characteristic (ROC) analysis is considered to be its ability to quantify classification performance independently of factors such as prior probabilities and utilities of classification outcomes. This article argues the opposite. When evaluating classification performance, ROC analysis should consider prior probabilities and utilities. By developing expected utility lines (EU lines), this article shows the connection between a classifier's ROC curve and expected utility of classification. In particular, EU lines can be used to estimate expected utilities when classifiers operate at any ROC point for any given prior probabilities and utilities. EU lines are useful across all situations-no matter if one examines a single classifier or compares multiple classifiers, if one compares classifiers' potential to maximize expected utilities or classifiers' actual expected utilities, and if the ROC curves are full or partial, continuous or discrete. The connection between ROC and expected utility analyses reveals the common objective underlying these two methods: to maximize expected utility of classification. Particularly, ROC analysis is useful in choosing an optimal classifier and its optimal operating point to maximize expected utility. Yet, choosing a classifier and its operating point (i.e., changing conditional probabilities) is not the only way to increase expected utility. Inspired by parameters involved in estimating expected utility, this article also discusses other approaches to increase expected utility beyond ROC analysis. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Vegetation monitoring is considered an important application in remote sensing task due to variation of vegetation types and their distribution. The vegetation concentration around the Earth is increase in 5% in 2000 according to NASA monitoring. This increase is due to the Indian vegetable programs. In this research, the vegetation monitoring in Baghdad city was done using Normalized Difference Vegetation Index (NDVI) for temporal Landsat satellite images (Landsat 5 TM& Landsat 8 OIL). These images had been used and utilize in different times during the period from 2000, 2010, 2015 & 2017. The outcomes of the study demonstrate that a change in the vegetation Cover (VC) in Baghdad city. (NDVI) generally shows a low value of plant cover. The highest NDVI values were occur in 2000 and the lowest values for both years 2015-2017. This change is due to a correlation of climate indices such as precipitation, temperature, and dust storms. This study present that (NDVI) method is a powerful and useful way of monitoring vegetation. The calculation of vegetable areas show (43.3, 37.4, 9.1, and 22.7 Km2). The result were evaluated using (Environment for Visualizing Images ENVI) Ver. 4.8 package. [ABSTRACT FROM AUTHOR]

Disease detection is basically a principal aspect in ameliorating agricultural production. The presented research concentrates on devising Plant Leaf Disease (PLD) detection together with an identification process intended for larger fields of crop production. Here, an inclusive study on disease recognition together with the classification of plant leaves utilizing Image Processing (IP) methods is performed. Since this technique is unpredictable and inconsistent, the customary manual visual quality examination can't be systematically stated. Furthermore, an extraordinary quantity of expertise is involved in plant disease diagnostics as well as the inconsistent processing times. Therefore, IP is implemented for plant disease recognition. Next, an imperative role is played by the Deep Learning (DL) together with Machine Learning (ML) classifiers in leaf disease classification. Centered upon an assessment of the formerly recommended topnotch techniques, a comprehensive discussion on disease detection together with classification performance is given. Lastly, the challenges and also some prospects for future ameliorations are discussed as well as classified. [ABSTRACT FROM AUTHOR]

Detecting the types of anomalies that can occur throughout the milk processing process is an important task since it can assist providers in maintaining control over the process. The Raman spectrometer was used in conjunction with several classification approaches—linear discriminant analysis, decision tree, support vector machine, and k nearest neighbor—to establish a viable method for detecting different types of anomalies that may occur during the process—temperature and fat variation and added water or cleaning solution. Milk with 5% fat measured at 10°C was used as the reference milk for this study. Added water, cleaning solution, milk with various fat contents and different temperatures were used to detect abnormal conditions. While decision trees and linear discriminant analysis were unable to accurately categorize the various type of anomalies, the k nearest neighbor and support vector machine provided promising results. The accuracy of the support vector machine test set and the k nearest neighbor test set were 81.4% and 84.8%, respectively. As a result, it is reasonable to conclude that both algorithms are capable of appropriately classifying the various groups of samples. It can assist milk industries in determining what is wrong during milk processing. [ABSTRACT FROM AUTHOR]

Economic development of many countries depends mainly on agriculture. The role of agriculture has created a vast impact on various fields' namely industrial activities, employability and capital formation. Further, to improve the economic growth of a country, technology-based agriculture processes can be deployed in pre-production and post-production stage. This study mainly focuses on the various post-production activities such as grading, sorting, quality evaluation, monitoring, defects detection, recognition and maturity detection. Traditionally, various agricultural post-production stages such as grading, sorting and quality assessment is mostly done as manual process. Such inspections are prone to errors due to their ineffectiveness and unreliability. Thus, advancements in technology can be deployed for enhancing manual agricultural tasks. This study deals with image processing techniques in agricultural post-production stages. With the support of computer vision-based systems (CVS), efficiency in post-production various activities can be improved. Various feature properties like size, colour, shape of the agricultural products like vegetables and fruits can be used in CVS for grading and sorting. The steps involved in Computer Vision-based Grading System (CVGS) are image acquisition, preprocessing, image segmentation, features extraction and classification. Though many intelligent models exist for grading agricultural products, there are still broad challenges that need to be resolved. These challenges can be weighed against the advantages of CVGS and the grading process can be improved a lot. This work is based on the exhaustive survey of CVS and the steps involved in CVGS. The outcome of this survey is presented as a comparative analysis of various technologies for grading systems available in the market. [ABSTRACT FROM AUTHOR]

Increasing the financial institutions' deployment of capital in energy efficiency investments remains still a challenge. The present article is intended to investigate the benefits of the application of traditional classification methods, such as the ordinal logit, the ordinal probit and the linear discriminant analysis (LDA), as well as machine learning techniques, such as the k-Nearest Neighbors and the Support Vector Machines, in the development of models for predicting the performance of energy efficiency investments. We are dealing with the process of investments identification that can be considered attractive, in terms of fostering green growth, while also having an extremely strong capacity to meet their financial commitments and therefore bridging the gap between investors and project developers. In addition, the deduction of critical comparative insights regarding the application of these five widely used techniques, is anticipated. The validity of the attempt is verified through an illustrative testing procedure on the Energy Efficiency De-risking Project database. The qualitative and technical conclusions obtained demonstrate that machine learning methods moderately outperform traditional methods regarding their predictive accuracy. Finally, findings that confirm and expand the existing underlying research are also reported. [ABSTRACT FROM AUTHOR]

Classification is the last, and usually the most time-consuming step in recognition. Most recently proposed classification algorithms have adopted machine learning (ML) as the main classification approach, regardless of time consumption. This study proposes a statistical feature classification cubic spline interpolation (FC-CSI) algorithm to classify emotions in speech using a curve fitting technique. FC-CSI is utilized in a speech emotion recognition system (SERS). The idea is to sketch the cubic spline interpolation (CSI) for each audio file in a dataset and the mean cubic spline interpolations (MCSIs) representing each emotion in the dataset. CSI interpolation is generated by connecting the features extracted from each file in the feature extraction phase. The MCSI is generated by connecting the mean features of 70% of the files of each emotion in the dataset. Points on the CSI are considered the new generated features. To classify each audio file according to emotion, the Euclidian distance (ED) is found between each CSI and all MCSIs of all emotions in the dataset. Each audio file is classified according to the nearest MCSI to the CSI representing it. The three datasets used in this work are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Berlin (Emo-DB), and Surrey Audio-Visual Expressed Emotion (SAVEE). The proposed work shows fast classification and high accuracy of results. The classification accuracy, i.e., the proportion of samples assigned to the correct class, using FC-CSI without feature selection (FS), was 69.08%, 92.52%, and 89.1% with RAVDESS, Emo-DB, and SAVEE, respectively. The results of the proposed method were compared to those of a designed neural network called SER-NN. Comparisons were made with and without FS. FC-CSI outperformed SER-NN on Emo-DB and SAVEE, and underperformed on RAVDESS, without using an FS algorithm. It was noticed from experiments that FC-CSI operated faster than the same system utilizing SER-NN. [ABSTRACT FROM AUTHOR]

Internet traffic classification is a beneficial technique in the direction of intrusion detection and network monitoring. After several years of searching, there are still many open problems in Internet traffic classification. The hybrid classifier combines more than one classification method to identify Internet traffic. Using only one method to classify Internet traffic poses many risks. In addition, an online classifier is very important in order to manage threats on traffic such as denial of service, flooding attack and other similar threats. Therefore, this paper provides some information to differentiate between real and live internet traffic. In addition, this paper proposes a hybrid online classifier (HOC) system. HOC is based on two common classification methods, port-base and ML-base. HOC is able to perform an online classification since it can identify live Internet traffic at the same time as it is generated. HOC was used to classify three common Internet application classes, namely web, WhatsApp and Twitter. HOC produces more than 90% accuracy, which is higher than any individual classifiers. [ABSTRACT FROM AUTHOR]

Eastern Tunisia is dotted with varied wetlands such as lagoons, sabkhas, garaas... These wetlands, at the interface between land and water, are rich, diverse and dynamic environments. Remote sensing shows potential and efficiency in the detection and characterization of these environments, although these humid depressions are difficult to define. The implementation of new optical satellite sensors characterized by High and Very High Spatial Resolution (HSR and VHSR) and by a high temporal repetitiveness, completes the field data and allows to envisage a delineation of the wet depression and a detailed detection of its land use. The purpose of this article is to determine whether the VHSR image, with its very rich spatial resolution, offers an added value for the detection of these diversified depressions, in comparison with the HSR image with richer spectral resolution. The results of the analysis show that the Ikonos image at VHSR compensates for the spectral richness of Sentinel-2 images at HSR. The "object-oriented" classification method, widely used today in image processing for various applications, appears to be more suitable than the "pixel-based" method in VHSR image classification. The Normalized Difference Water Index (NDWI) and the Transformed Soil Adjusted Vegetation Index (TSAVI) have multiple interests in wetland detection, although moisture masks show some inefficiency in improving the quality of classifications. [ABSTRACT FROM AUTHOR]

This paper studies the strongly asymmetrical distribution of the population, quality of life and economic development in Portugal. It identifies homogeneous areas supported by an analysis of competitiveness, cohesion and environmental quality dimensions. Drawing on a multivariate approach, its findings reveal differences in terms of development across the country. The study identified groups of regions with common characteristics that go beyond the standard north-south or coastal-inland distinctions. Metropolitan areas and export-oriented regions are clearly distinguishable from the remainder of the country. Economic agglomeration indicators, as gross domestic product per capita, population density, resident foreign population and youth index, among others, are those that reveal greater diversity across the country. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the applicability and efficiency of Near-Infrared Spectroscopy (NIR) by using dispersive NIR and Fourier Transform NIR to analyse 267 samples of Brazilian wheat flour contaminated with deoxynivalenol (DON). For this, Partial Least-squares Discriminant Analysis (PLS-DA) and Principal Component Analysis-Linear Discriminant Analysis (PC-LDA) were used as discriminatory methods. Next, the samples were classified according to the maximum tolerated limits (MTL) for DON in Brazil, 750 μg kg−1, and two groups were established for the calibration set: category A (≤450 μg kg−1), non-contaminated or below the MTL; and category B (>450 μg kg−1), contaminated or above the MTL. Validation samples through PLS-DA showed correct classification rates in the range of 85–87.5% and presented a 10–15% error; for PC-LDA, the hit rate was over 85% with an error of 10–15%. The present findings demonstrate that NIR is an excellent alternative method to classify wheat flour samples according to DON content. [ABSTRACT FROM AUTHOR]

[Display omitted] The rise of technology has heightened work demands, adversely impacting mental health and fitness. The COVID-19 pandemic exacerbates psychological stress, emphasizing the need for non-pharmacological interventions like yoga. Yoga positively influences the autonomic nervous system, benefiting cardio-respiratory health, metabolic efficiency, and conditions like Type-2 diabetes, Chronic Venous disease, and obesity. This study employs a dataset with 100 samples and 43 features related to Chronic Venous Insufficiency (CVI). Logistic and Random Forest classifiers are validated using K-fold cross-validation, with feature selection optimizing prediction accuracy. Hybrid models, enhanced with optimization algorithms, predict Venous Clinical Severity Score (VCSS) before, one, and three months after yoga practices. The Random Forest classifier, particularly RFGT, proves highly accurate in categorizing baseline severity and identifying Mild and Moderate CVI cases. RFGT demonstrated AUC score of 0.9072, 0.8714, 0.7709, and 0.7200 in Absent, Mild, Moderate, and Severe patient groups classification before yoga practices (VCSS-Pre). These values were 0.9158, 0.8644, 0.8142, and 0.6333 for VCSS-1 and reported as 0.9269, 0.8399, 0.7838, and 0.7500 for patients' classification in VCSS-3. Predicting VCSS scores before yoga intervention assists in categorizing participants for personalized care and efficient resource allocation. The RFC-based models, notably RFGT, show high accuracy in identifying baseline severity, enabling early intervention for high-risk individuals. These models, especially RFGT, perform well in classifying Mild and Moderate CVI cases, informing lifestyle modifications. Predicting VCSS-1 scores evaluates the short-term impact of yoga practices, identifying individuals requiring additional support. RFGT aids in personalized recommendations based on specific factors, crucial for severe conditions. Predicting VCSS-3 scores assesses the sustained impact over three months, identifying intervention responders, particularly in Severe and Moderate groups. RFGT demonstrates optimal predictions, contributing to future interventions tailored to individual responses and improved outcomes. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed method may be used in the production process for fast identification of eggs originating from poultry infected with MS.Mycoplasma synoviae (MS) is a pathogen that causes economic losses in the poultry industry. It can be transmitted, amongst others, via the respiratory tract and spread relatively quickly. As such, MS infections are mainly controlled by maintaining MS-free breeder flocks. Routine diagnosis for the detection of MS may be based on serological, culture, and molecular tests. Here, we propose an optical solution where AI-based analysis of spectral data obtained from the light reflected from the eggshells is used to determine whether they originate from healthy or Mycoplasma synoviae-infected hens. The wavelengths proposed for spectral MS detection are limited to those of VIS and NIR DPSS lasers, which are freely accessible on market. The results are satisfactory: for white eggshells, the F-score is over 95% for five different combinations of wavelengths (using eight or nine wavelengths); for brown eggshells, the F-score is above 85%, also for five different combinations of 6–9 wavelengths. [ABSTRACT FROM AUTHOR]

Copyright of Medien & Kommunikationswissenschaft is the property of Nomos Verlagsgesellschaft mbH & Co. KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This research aims to analyze the diagnostic contribution of different discriminative regions of the breast ultrasound image and develop a more effective diagnosis method taking advantage of the discriminative regions' complementarity. Methods: First, the discriminative regions of the original breast ultrasound image as the inner region of the lesion, the marginal zone of the lesion, and the posterior echo region of the lesion were defined. The pretrained Inception‐V3 network was used to analyze the diagnostic contribution of these discriminative regions. Then, the network was applied to extract the deep features of the original image and the other three discriminative region images. Since there are many features, principal components analysis (PCA) was used to reduce the dimensionality of the extracted deep features. The selected deep features from different discriminative regions were fused to original image features and sent to the stacking ensemble learning classifier for classification experiments. In this study, 479 cases of breast ultrasound images, including 356 benign lesions and 123 malignant ones, were collected retrospectively and randomly divided into the training and validation set. Results: Experimental results show that by using Inception‐V3, the diagnostic performance of each discriminative region is different, and the diagnostic accuracy and the area under the ROC curve (AUC) of the lesion marginal zone image (78.3%, 0.798) are higher than those of the lesion inner region image (73.3%, 0.763) and the posterior echo region image (71.7%, 0.688), but lower than those of the original image (80.0%, 0.817). Furthermore, the best classification performance was obtained when all the four types of deep features (from the original image and three discriminative region images) were fused, and the ensemble learning for classification evaluation was employed. Compared with the original image, the classification accuracy and AUC increased from 80.83%, 0.818 to 85.00%, 0.872, and the classification sensitivity and specificity varied from 0.710, 0.798 to 0.871, 0.787. Conclusions: The inner region of the lesion, the marginal zone of the lesion, and the posterior echo region of the lesion play significant roles in the diagnosis of the breast ultrasound image. Deep feature fusion of these three kinds of images and the original image can effectively improve the accuracy of diagnosis. [ABSTRACT FROM AUTHOR]

Chronic Kidney Disease (CKD) is being typically observed as a health threatening issue, especially in developing countries, where receiving proper treatments are very expensive. Therefore, early prediction of CKD that protects the kidney and breaks the gradual progress of CKD has become an important issue for physicians and scientists. Internet of Things (IoT) as a useful paradigm in which, low cost body sensor and smart multimedia medical devices are applied to provide remote monitoring of kidney function, plays an important role, especially where the medical care centers are hardly available for most of people. To gain this objective, in this paper, a diagnostic prediction model for CKD and its severity is proposed that applies IoT multimedia data. Since the influencing features on CKD are enormous and also the volume of the IoT multimedia data is usually very huge, selecting different features based on physicians' clinical observations and experiences and also previous studies for CKD in different groups of multimedia datasets is carried out to assess the performance measures of CKD prediction and its level determination via different classification techniques. The experimental results reveal that the applied dataset with the proposed selected features produces 97% accuracy, 99% sensitivity and 95% specificity via applying decision tree (J48) classifier in comparison to Support Vector Machine (SVM), Multi-Layer Perception (MLP) and Naïve Bayes classifiers. Also, the proposed feature set can improve the execution time in comparison to other datasets with different features. [ABSTRACT FROM AUTHOR]

For taxonomy based classification of metagenomics assembled contigs, current methods use sequence similarity to identify their most likely taxonomy. However, in the related field of metagenomic binning, contigs are routinely clustered using information from both the contig sequences and their abundance. We introduce Taxometer, a neural network based method that improves the annotations and estimates the quality of any taxonomic classifier using contig abundance profiles and tetra-nucleotide frequencies. We apply Taxometer to five short-read CAMI2 datasets and find that it increases the average share of correct species-level contig annotations of the MMSeqs2 tool from 66.6% to 86.2%. Additionally, it reduce the share of wrong species-level annotations in the CAMI2 Rhizosphere dataset by an average of two-fold for Metabuli, Centrifuge, and Kraken2. Futhermore, we use Taxometer for benchmarking taxonomic classifiers on two complex long-read metagenomics data sets where ground truth is not known. Taxometer is available as open-source software and can enhance any taxonomic annotation of metagenomic contigs., (© 2024. The Author(s).)

Molecular sequence data from rapidly evolving organisms are often sampled at different points in time. Sampling times can then be used for molecular clock calibration. The root-to-tip (RTT) regression is an essential tool to assess the degree to which the data behave in a clock-like fashion. Here, we introduce Clockor2, a client-side web application for conducting RTT regression. Clockor2 allows users to quickly fit local and global molecular clocks, thus handling the increasing complexity of genomic datasets that sample beyond the assumption of homogeneous host populations. Clockor2 is efficient, handling trees of up to the order of 104 tips, with significant speed increases compared with other RTT regression applications. Although clockor2 is written as a web application, all data processing happens on the client-side, meaning that data never leave the user's computer. Clockor2 is freely available at https://clockor2.github.io/., (© The Author(s) 2024. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.)

Interspecific interactions, including host-symbiont associations, can profoundly affect the evolution of the interacting species. Given the phylogenies of host and symbiont clades and knowledge of which host species interact with which symbiont, two questions are often asked: "Do closely related hosts interact with closely related symbionts?" and "Do host and symbiont phylogenies mirror one another?." These questions are intertwined and can even collapse under specific situations, such that they are often confused one with the other. However, in most situations, a positive answer to the first question, hereafter referred to as "cophylogenetic signal," does not imply a close match between the host and symbiont phylogenies. It suggests only that past evolutionary history has contributed to shaping present-day interactions, which can arise, for example, through present-day trait matching, or from a single ancient vicariance event that increases the probability that closely related species overlap geographically. A positive answer to the second, referred to as "phylogenetic congruence," is more restrictive as it suggests a close match between the two phylogenies, which may happen, for example, if symbiont diversification tracks host diversification or if the diversifications of the two clades were subject to the same succession of vicariance events. Here we apply a set of methods (ParaFit, PACo, and eMPRess), whose significance is often interpreted as evidence for phylogenetic congruence, to simulations under 3 biologically realistic scenarios of trait matching, a single ancient vicariance event, and phylogenetic tracking with frequent cospeciation events. The latter is the only scenario that generates phylogenetic congruence, whereas the first 2 generate a cophylogenetic signal in the absence of phylogenetic congruence. We find that tests of global-fit methods (ParaFit and PACo) are significant under the 3 scenarios, whereas tests of event-based methods (eMPRess) are only significant under the scenario of phylogenetic tracking. Therefore, significant results from global-fit methods should be interpreted in terms of cophylogenetic signal and not phylogenetic congruence; such significant results can arise under scenarios when hosts and symbionts had independent evolutionary histories. Conversely, significant results from event-based methods suggest a strong form of dependency between hosts and symbionts evolutionary histories. Clarifying the patterns detected by different cophylogenetic methods is key to understanding how interspecific interactions shape and are shaped by evolution., (© The Author(s) 2024. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

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., (© The Author(s) 2024. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.)

When communities are assembled through processes such as filtering or limiting similarity acting on phylogenetically conserved traits, the evolutionary signature of those traits may be reflected in patterns of community membership. We show how the model of trait evolution underlying community-structuring traits can be inferred from community membership data using both a variation of a traditional eco-phylogenetic metric-the mean pairwise phylogenetic distance (MPD) between taxa-and a recent machine learning tool, Convolutional Kitchen Sinks (CKS). Both methods perform well across a range of phylogenetically informative evolutionary models, but CKS outperforms MPD as tree size increases. We demonstrate CKS by inferring the evolutionary history of freeze tolerance in angiosperms. Our analysis is consistent with a late burst model, suggesting freeze tolerance evolved recently. We suggest that multiple data types that are ordered on phylogenies, such as trait values, species interactions, or community presence/absence, are good candidates for CKS modeling because the generative models produce structured differences between neighboring points that CKS is well-suited for. We introduce the R package kitchen to perform CKS for generic application of the technique., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Systematic Biologists.)

Minimizing the number of selected features and maximizing the classification performance are two main objectives in feature selection, which can be formulated as a bi-objective optimization problem. Due to the complex interactions between features, a solution (i.e., feature subset) with poor objective values does not mean that all the features it selects are useless, as some of them combined with other complementary features can greatly improve the classification performance. Thus, it is necessary to consider not only the performance of feature subsets in the objective space, but also their differences in the search space, to explore more promising feature combinations. To this end, this paper proposes a tri-objective method for bi-objective feature selection in classification, which solves a bi-objective feature selection problem as a tri-objective problem by considering the diversity (differences) between feature subsets in the search space as the third objective. The selection based on the converted tri-objective method can maintain a balance between minimizing the number of selected features, maximizing the classification performance, and exploring more promising feature subsets. Furthermore, a novel initialization strategy and an offspring reproduction operator are proposed to promote the diversity of feature subsets in the objective space and improve the search ability, respectively. The proposed algorithm is compared with five multiobjective-based feature selection methods, six typical feature selection methods, and two peer methods with diversity as a helper objective. Experimental results on 20 real-world classification datasets suggest that the proposed method outperforms the compared methods in most scenarios., (© 2023 Massachusetts Institute of Technology.)

Objectives: To automatically construct a drug indication taxonomy from drug labels using generative Artificial Intelligence (AI) represented by the Large Language Model (LLM) GPT-4 and real-world evidence (RWE)., Materials and Methods: We extracted indication terms from 46 421 free-text drug labels using GPT-4, iteratively and recursively generated indication concepts and inferred indication concept-to-concept and concept-to-term subsumption relations by integrating GPT-4 with RWE, and created a drug indication taxonomy. Quantitative and qualitative evaluations involving domain experts were performed for cardiovascular (CVD), Endocrine, and Genitourinary system diseases., Results: 2909 drug indication terms were extracted and assigned into 24 high-level indication categories (ie, initially generated concepts), each of which was expanded into a sub-taxonomy. For example, the CVD sub-taxonomy contains 242 concepts, spanning a depth of 11, with 170 being leaf nodes. It collectively covers a total of 234 indication terms associated with 189 distinct drugs. The accuracies of GPT-4 on determining the drug indication hierarchy exceeded 0.7 with "good to very good" inter-rater reliability. However, the accuracies of the concept-to-term subsumption relation checking varied greatly, with "fair to moderate" reliability., Discussion and Conclusion: We successfully used generative AI and RWE to create a taxonomy, with drug indications adequately consistent with domain expert expectations. We show that LLMs are good at deriving their own concept hierarchies but still fall short in determining the subsumption relations between concepts and terms in unregulated language from free-text drug labels, which is the same hard task for human experts., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Tree diagrams are the prevailing form of visualization in biological classification and phylogenetics. Already during the time of the so-called Systematist Wars from the mid-1960s until the 1980s most journal articles and textbooks published by systematists contained tree diagrams. Although this episode of systematics is well studied by historians and philosophers of biology, most analyses prioritize scientific theories over practices and tend to emphasize conflicting theoretical assumptions. In this article, I offer an alternative perspective by viewing the conflict through the lens of representational practices with a case study on tree diagrams that were used by numerical taxonomists (phenograms) and cladists (cladograms). I argue that the current state of molecular phylogenetics should not be interpreted as the result of a competition of views within systematics. Instead, molecular phylogenetics arose independently of systematics and elements of cladistics and phenetics were integrated into the framework of molecular phylogenetics, facilitated by the compatibility of phenetic and cladistic practices with the quantitative approach of molecular phylogenetics. My study suggests that this episode of scientific change is more complex than common narratives of battles and winners or conflicts and compromises. Today, cladograms are still used and interpreted as specific types of molecular phylogenetic trees. While phenograms and cladograms represented different forms of knowledge during the time of the Systematist Wars, today they are both used to represent evolutionary relationships. This indicates that diagrams are versatile elements of scientific practice that can change their meaning, depending on the context of use within theoretical frameworks., (© 2024. The Author(s).)

Here, I review the dynamic history of prokaryotic phyla. Following leads set by Darwin, Haeckel and Woese, the concept of phylum has evolved from a group sharing common phenotypes to a set of organisms sharing a common ancestry, with modern taxonomy based on phylogenetic classifications drawn from macromolecular sequences. Phyla came as surprising latecomers to the formalities of prokaryotic nomenclature in 2021. Since then names have been validly published for 46 prokaryotic phyla, replacing some established names with neologisms, prompting criticism and debate within the scientific community. Molecular barcoding enabled phylogenetic analysis of microbial ecosystems without cultivation, leading to the identification of candidate divisions (or phyla) from diverse environments. The introduction of metagenome-assembled genomes marked a significant advance in identifying and classifying uncultured microbial phyla. The lumper-splitter dichotomy has led to disagreements, with experts cautioning against the pressure to create a profusion of new phyla and prominent databases adopting a conservative stance. The Candidatus designation has been widely used to provide provisional status to uncultured prokaryotic taxa, with phyla named under this convention now clearly surpassing those with validly published names. The Genome Taxonomy Database (GTDB) has offered a stable, standardized prokaryotic taxonomy with normalized taxonomic ranks, which has led to both lumping and splitting of pre-existing phyla. The GTDB framework introduced unwieldy alphanumeric placeholder labels, prompting recent publication of over 100 user-friendly Latinate names for unnamed prokaryotic phyla. Most candidate phyla remain 'known unknowns', with limited knowledge of their genomic diversity, ecological roles, or environments. Whether phyla still reflect significant evolutionary and ecological partitions across prokaryotic life remains an area of active debate. However, phyla remain of practical importance for microbiome analyses, particularly in clinical research. Despite potential diminishing returns in discovery of biodiversity, prokaryotic phyla offer extensive research opportunities for microbiologists for the foreseeable future.

The use of Artificial substrates (AS) as sampling devices addresses challenges in macrofaunal quantitative sampling. While effectively capturing biodiversity patterns, the time-intensitive identification process at the species level remains a substantial challenge. The Taxonomic Sufficiency approach (TS), where only taxa above species level are identified, arises as a potential solution to be tested across different environmental monitoring scenarios. In this paper, we analyzed three AS macrobenthic datasets to evaluate the odds of TS in improving the cost-effective ratio in AS monitoring studies and establish the highest resolution level to detect assemblage changes under different environmental factors. Results indicated that the family level emerged as a pragmatic compromise, balancing precision and taxonomic effort. Cost/benefit analysis supported TS efficiency, maintaining correlation stability until the family level. Results also showed that reducing resolution to family does not entail a significant Loss of Information. This study contributes to the discourse on TS applicability, highlighting its practicality in monitoring scenarios, including spatial-temporal studies, and rapid biodiversity assessments. Additionally, it highlights the "second best approach" of family-level practicality depending on the specific monitoring scenario and recognizes the importance of the species-level "best approach" before applying TS in monitoring studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Although species are central units for biological research, recent findings in genomics are raising awareness that what we call species can be ill-founded entities due to solely morphology-based, regional species descriptions. This particularly applies to groups characterized by intricate evolutionary processes such as hybridization, polyploidy, or asexuality. Here, challenges of current integrative taxonomy (genetics/genomics + morphology + ecology, etc.) become apparent: different favored species concepts, lack of universal characters/markers, missing appropriate analytical tools for intricate evolutionary processes, and highly subjective ranking and fusion of datasets. Now, integrative taxonomy combined with artificial intelligence under a unified species concept can enable automated feature learning and data integration, and thus reduce subjectivity in species delimitation. This approach will likely accelerate revising and unraveling eukaryotic biodiversity., Competing Interests: Declaration of interests No interests are declared., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Over the years, many studies have been carried out to reduce and eliminate the effects of diseases on human health. Gene expression data sets play a critical role in diagnosing and treating diseases. These data sets consist of thousands of genes and a small number of sample sizes. This situation creates the curse of dimensionality and it becomes problematic to analyze such data sets. One of the most effective strategies to solve this problem is feature selection methods. Feature selection is a preprocessing step to improve classification performance by selecting the most relevant and informative features while increasing the accuracy of classification. In this article, we propose a new statistically based filter method for the feature selection approach named Effective Range-based Feature Selection Algorithm (FSAER). As an extension of the previous Effective Range based Gene Selection (ERGS) and Improved Feature Selection based on Effective Range (IFSER) algorithms, our novel method includes the advantages of both methods while taking into account the disjoint area. To illustrate the efficacy of the proposed algorithm, the experiments have been conducted on six benchmark gene expression data sets. The results of the FSAER and the other filter methods have been compared in terms of classification accuracies to demonstrate the effectiveness of the proposed method. For classification methods, support vector machines, naive Bayes classifier, and k-nearest neighbor algorithms have been used.

The objective of this paper is twofold. First, I present a framework called historical coherentism (Chang, 2004; Tal, 2016; Van fraassen 2008) and argue that it is the best epistemological framework available to tackle the problem of coordination, an epistemic conundrum that arises with every attempt to provide empirical content to scientific theories, models or statements. Second, I argue that the problem of coordination, which has so far been theorized only in the context of measurement practices (Reichenbach, 1927; Chang, 2001; Tal, 2012; Van fraassen 2008), can be generalized beyond the philosophy of measurement. Specifically, it will be shown that the problem is embodied in classificatory practices and that, consequently, historical coherentism is well suited to analyze these practices as well as metrological ones. As a case study, I look at a contemporary debate in phylogenetics, regarding the evolutionary origin of a newly identified archaeal phylum called Methanonatronarchaeia. Exploring this debate through the lens of historical coherentism provides a detailed understanding of the dynamics of the field and a foothold for critical analyses of the standard rationale used by practitioners., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author. Published by Elsevier Ltd.. All rights reserved.)

Phylogenies are central to many research areas in biology and commonly estimated using likelihood-based methods. Unfortunately, any likelihood-based method, including Bayesian inference, can be restrictively slow for large datasets-with many taxa and/or many sites in the sequence alignment-or complex substitutions models. The primary limiting factor when using large datasets and/or complex models in probabilistic phylogenetic analyses is the likelihood calculation, which dominates the total computation time. To address this bottleneck, we incorporated the high-performance phylogenetic library BEAGLE into RevBayes, which enables multi-threading on multi-core CPUs and GPUs, as well as hardware specific vectorized instructions for faster likelihood calculations. Our new implementation of RevBayes+BEAGLE retains the flexibility and dynamic nature that users expect from vanilla RevBayes. In addition, we implemented native parallelization within RevBayes without an external library using the message passing interface (MPI); RevBayes+MPI. We evaluated our new implementation of RevBayes+BEAGLE using multi-threading on CPUs and 2 different powerful GPUs (NVidia Titan V and NVIDIA A100) against our native implementation of RevBayes+MPI. We found good improvements in speedup when multiple cores were used, with up to 20-fold speedup when using multiple CPU cores and over 90-fold speedup when using multiple GPU cores. The improvement depended on the data type used, DNA or amino acids, and the size of the alignment, but less on the size of the tree. We additionally investigated the cost of rescaling partial likelihoods to avoid numerical underflow and showed that unnecessarily frequent and inefficient rescaling can increase runtimes up to 4-fold. Finally, we presented and compared a new approach to store partial likelihoods on branches instead of nodes that can speed up computations up to 1.7 times but comes at twice the memory requirements., (© The Author(s) 2024. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For commercial re.use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site.for further information please contact journals.permissions@oup.com.)

The evolution of gene families is complex, involving gene-level evolutionary events such as gene duplication, horizontal gene transfer, and gene loss, and other processes such as incomplete lineage sorting (ILS). Because of this, topological differences often exist between gene trees and species trees. A number of models have been recently developed to explain these discrepancies, the most realistic of which attempts to consider both gene-level events and ILS. When unified in a single model, the interaction between ILS and gene-level events can cause polymorphism in gene copy number, which we refer to as copy number hemiplasy (CNH). In this paper, we extend the Wright-Fisher process to include duplications and losses over several species, and show that the probability of CNH for this process can be significant. We study how well two unified models-multilocus multispecies coalescent (MLMSC), which models CNH, and duplication, loss, and coalescence (DLCoal), which does not-approximate the Wright-Fisher process with duplication and loss. We then study the effect of CNH on gene family evolution by comparing MLMSC and DLCoal. We generate comparable gene trees under both models, showing significant differences in various summary statistics; most importantly, CNH reduces the number of gene copies greatly. If this is not taken into account, the traditional method of estimating duplication rates (by counting the number of gene copies) becomes inaccurate. The simulated gene trees are also used for species tree inference with the summary methods ASTRAL and ASTRAL-Pro, demonstrating that their accuracy, based on CNH-unaware simulations calibrated on real data, may have been overestimated., (© The Author(s) 2024. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For commercial re.use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site.for further information please contact journals.permissions@oup.com.)

Comparative analysis of variables across phylogenetically linked observations can reveal mechanisms and insights in evolutionary biology. As the taxonomic breadth of the sample of interest increases, challenges of data sparsity, poor phylogenetic resolution, and complicated evolutionary dynamics emerge. Here, we investigate a cross-eukaryotic question where all these problems exist: which organismal ecology features are correlated with gene retention in mitochondrial and chloroplast DNA (organelle DNA or oDNA). Through a wide palette of synthetic control studies, we first characterize the specificity and sensitivity of a collection of parametric and non-parametric phylogenetic comparative approaches to identify relationships in the face of such sparse and awkward datasets. This analysis is not directly focused on oDNA, and so provides generalizable insights into comparative approaches with challenging data. We then combine and curate ecological data coupled to oDNA genome information across eukaryotes, including a new semi-automated approach for gathering data on organismal traits from less systematized open-access resources including encyclopedia articles on species and taxa. The curation process also involved resolving several issues with existing datasets, including enforcing the clade-specificity of several ecological features and fixing incorrect annotations. Combining this unique dataset with our benchmarked comparative approaches, we confirm support for several known links between organismal ecology and organelle gene retention, identify several previously unidentified relationships constituting possible ecological contributors to oDNA genome evolution, and provide support for a recently hypothesized link between environmental demand and oDNA retention. We, with caution, discuss the implications of these findings for organelle evolution and of this pipeline for broad comparative analyses in other fields., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Systematic Biologists.)

The Andes mountains of western South America are a globally important biodiversity hotspot, yet there is a paucity of resolved phylogenies for plant clades from this region. Filling an important gap in our understanding of the World's richest flora, we present the first phylogeny of Freziera (Pentaphylacaceae), an Andean-centered, cloud forest radiation. Our dataset was obtained via hybrid-enriched target sequence capture of Angiosperms353 universal loci for 50 of the ca. 75 spp., obtained almost entirely from herbarium specimens. We identify high phylogenomic complexity in Freziera, including the presence of data artifacts. Via by-eye observation of gene trees, detailed examination of warnings from recently improved assembly pipelines, and gene tree filtering, we identified that artifactual orthologs (i.e., the presence of only one copy of a multicopy gene due to differential assembly) were an important source of gene tree heterogeneity that had a negative impact on phylogenetic inference and support. These artifactual orthologs may be common in plant phylogenomic datasets, where multiple instances of genome duplication are common. After accounting for artifactual orthologs as source of gene tree error, we identified a significant, but nonspecific signal of introgression using Patterson's D and f4 statistics. Despite phylogenomic complexity, we were able to resolve Freziera into 9 well-supported subclades whose evolution has been shaped by multiple evolutionary processes, including incomplete lineage sorting, historical gene flow, and gene duplication. Our results highlight the complexities of plant phylogenomics, which are heightened in Andean radiations, and show the impact of filtering data processing artifacts and standard filtering approaches on phylogenetic inference., (© The Author(s) 2024. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Scientific names permit humans and search engines to access knowledge about the biodiversity that surrounds us, and names linked to DNA sequences are playing an ever-greater role in search-and-match identification procedures. Here, we analyze how users and curators of the National Center for Biotechnology Information (NCBI) are flagging and curating sequences derived from nomenclatural type material, which is the only way to improve the quality of DNA-based identification in the long run. For prokaryotes, 18,281 genome assemblies from type strains have been curated by NCBI staff and improve the quality of prokaryote naming. For Fungi, type-derived sequences representing over 21,000 species are now essential for fungus naming and identification. For the remaining eukaryotes, however, the numbers of sequences identifiable as type-derived are minuscule, representing only 739 species of arthropods, 1542 vertebrates, and 125 embryophytes. An increase in the production and curation of such sequences will come from (i) sequencing of types or topotypic specimens in museum collections, (ii) the March 2023 rule changes at the International Nucleotide Sequence Database Collaboration requiring more metadata for specimens, and (iii) efforts by data submitters to facilitate curation, including informing NCBI curators about a specimen's type status. We illustrate different type-data submission journeys and provide best-practice examples from a range of organisms. Expanding the number of type-derived sequences in DNA databases, especially of eukaryotes, is crucial for capturing, documenting, and protecting biodiversity., (Published by Oxford University Press on behalf of the Society of Systematic Biologists 2023.)

Chronograms-phylogenies with branch lengths proportional to time-represent key data on timing of evolutionary events, allowing us to study natural processes in many areas of biological research. Chronograms also provide valuable information that can be used for education, science communication, and conservation policy decisions. Yet, achieving a high-quality reconstruction of a chronogram is a difficult and resource-consuming task. Here we present DateLife, a phylogenetic software implemented as an R package and an R Shiny web application available at www.datelife.org, that provides services for efficient and easy discovery, summary, reuse, and reanalysis of node age data mined from a curated database of expert, peer-reviewed, and openly available chronograms. The main DateLife workflow starts with one or more scientific taxon names provided by a user. Names are processed and standardized to a unified taxonomy, allowing DateLife to run a name match across its local chronogram database that is curated from Open Tree of Life's phylogenetic repository, and extract all chronograms that contain at least two queried taxon names, along with their metadata. Finally, node ages from matching chronograms are mapped using the congruification algorithm to corresponding nodes on a tree topology, either extracted from Open Tree of Life's synthetic phylogeny or one provided by the user. Congruified node ages are used as secondary calibrations to date the chosen topology, with or without initial branch lengths, using different phylogenetic dating methods such as BLADJ, treePL, PATHd8, and MrBayes. We performed a cross-validation test to compare node ages resulting from a DateLife analysis (i.e, phylogenetic dating using secondary calibrations) to those from the original chronograms (i.e, obtained with primary calibrations), and found that DateLife's node age estimates are consistent with the age estimates from the original chronograms, with the largest variation in ages occurring around topologically deeper nodes. Because the results from any software for scientific analysis can only be as good as the data used as input, we highlight the importance of considering the results of a DateLife analysis in the context of the input chronograms. DateLife can help to increase awareness of the existing disparities among alternative hypotheses of dates for the same diversification events, and to support exploration of the effect of alternative chronogram hypotheses on downstream analyses, providing a framework for a more informed interpretation of evolutionary results., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Systematic Biologists.)

Hundreds or thousands of loci are now routinely used in modern phylogenomic studies. Concatenation approaches to tree inference assume that there is a single topology for the entire dataset, but different loci may have different evolutionary histories due to incomplete lineage sorting (ILS), introgression, and/or horizontal gene transfer; even single loci may not be treelike due to recombination. To overcome this shortcoming, we introduce an implementation of a multi-tree mixture model that we call mixtures across sites and trees (MAST). This model extends a prior implementation by Boussau et al. (2009) by allowing users to estimate the weight of each of a set of pre-specified bifurcating trees in a single alignment. The MAST model allows each tree to have its own weight, topology, branch lengths, substitution model, nucleotide or amino acid frequencies, and model of rate heterogeneity across sites. We implemented the MAST model in a maximum-likelihood framework in the popular phylogenetic software, IQ-TREE. Simulations show that we can accurately recover the true model parameters, including branch lengths and tree weights for a given set of tree topologies, under a wide range of biologically realistic scenarios. We also show that we can use standard statistical inference approaches to reject a single-tree model when data are simulated under multiple trees (and vice versa). We applied the MAST model to multiple primate datasets and found that it can recover the signal of ILS in the Great Apes, as well as the asymmetry in minor trees caused by introgression among several macaque species. When applied to a dataset of 4 Platyrrhine species for which standard concatenated maximum likelihood (ML) and gene tree approaches disagree, we observe that MAST gives the highest weight (i.e., the largest proportion of sites) to the tree also supported by gene tree approaches. These results suggest that the MAST model is able to analyze a concatenated alignment using ML while avoiding some of the biases that come with assuming there is only a single tree. We discuss how the MAST model can be extended in the future., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Systematic Biologists.)

Background: Taxonomies are needed for automated analysis of clinical data in healthcare. Few reviews of the taxonomy development methods used in health sciences are found. This systematic review aimed to describe the scope of the available taxonomies relative to patient safety, the methods used for taxonomy development, and the strengths and limitations of the methods. The purpose of this systematic review is to guide future taxonomy development projects., Methods: The CINAHL, PubMed, Scopus, and Web of Science databases were searched for studies from January 2012 to April 25, 2023. Two authors selected the studies using inclusion and exclusion criteria and critical appraisal checklists. The data were analysed inductively, and the results were reported narratively., Results: The studies (n = 13) across healthcare concerned mainly taxonomies of adverse events and medication safety but little for specialised fields and information technology. Critical appraisal indicated inadequate reporting of the used taxonomy development methods. Ten phases of taxonomy development were identified: (1) defining purpose and (2) the theory base for development, (3) relevant data sources' identification, (4) main terms' identification and definitions, (5) items' coding and pooling, (6) reliability and validity evaluation of coding and/or codes, (7) development of a hierarchical structure, (8) testing the structure, (9) piloting the taxonomy and (10) reporting application and validation of the final taxonomy. Seventeen statistical tests and seven software systems were utilised, but automated data extraction methods were used rarely. Multimethod and multi-stakeholder approach, code- and hierarchy testing and piloting were strengths and time consumption and small samples in testing limitations., Conclusion: New taxonomies are needed on diverse specialities and information technology related to patient safety. Structured method is needed for taxonomy development, reporting and appraisal to strengthen taxonomies' quality. A new guide was proposed for taxonomy development, for which testing is required. Prospero registration number CRD42023411022., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)