Divergence Estimates Research Articles

Evolution of fertilization ability in obligatorily outcrossing populations of Caenorhabditis elegans.

In species reproducing by selfing, the traits connected with outcrossing typically undergo degeneration, a phenomenon called selfing syndrome. In Caenorhabditis elegans nematodes, selfing syndrome affects many traits involved in mating, rendering cross-fertilization highly inefficient. In this study, we investigated the evolution of cross-fertilization efficiency in populations genetically modified to reproduce by obligatory outcrossing. Following the genetic modification, replicate obligatorily outcrossing were maintained for over 100 generations, at either optimal (20°C) or elevated (24°C) temperatures, as a part of a broader experimental evolution program. Subsequently, fertilization rates were assayed in the evolving populations, as well as their ancestors who had the obligatory outcrossing introduced but did not go through experimental evolution. Fertilization effectivity was measured by tracking the fractions of fertilized females in age-synchronized populations, through 8 h since reaching adulthood. In order to check the robustness of our measurements, each evolving population was assayed in two or three independent replicate blocks. Indeed, we found high levels of among-block variability in the fertilization trajectories, and in the estimates of divergence between evolving populations and their ancestors. We also identified five populations which appear to have evolved increased fertilization efficiency, relative to their ancestors. However, due to the abovementioned high variability, this set of populations should be treated as candidate, with further replications needed to either confirm or disprove their divergence from ancestors. Furthermore, we also discuss additional observations we have made concerning fertilization trajectories.

A machine learning approach to deal with ambiguity in the humanitarian decision‐making

One of the major challenges for humanitarian organizations in response planning is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes from different sources in postdisaster settings may involve missing elements and inconsistencies, which can hamper effective humanitarian decision‐making. In this paper, we propose a new methodological framework based on graph clustering and stochastic optimization to support humanitarian decision‐makers in analyzing the implications of divergent estimates from multiple data sources on final decisions and efficiently integrating these estimates into decision‐making. To the best of our knowledge, the integration of ambiguous information into decision‐making by combining a cluster machine learning method with stochastic optimization has not been done before. We illustrate the proposed approach on a realistic case study that focuses on locating shelters to serve internally displaced people (IDP) in a conflict setting, specifically, the Syrian civil war. We use the needs assessment data from two different reliable sources to estimate the shelter needs in Idleb, a district of Syria. The analysis of data provided by two assessment sources has indicated a high degree of ambiguity due to inconsistent estimates. We apply the proposed methodology to integrate divergent estimates in making shelter location decisions. The results highlight that our methodology leads to higher satisfaction of demand for shelters than other approaches such as a classical stochastic programming model. Moreover, we show that our solution integrates information coming from both sources more efficiently thereby hedging against the ambiguity more effectively. With the newly proposed methodology, the decision‐maker is able to analyze the degree of ambiguity in the data and the degree of consensus between different data sources to ultimately make better decisions for delivering humanitarian aid.

Contrasting Population Projections to Induce Divergent Estimates of Landslides Exposure Under Climate Change

AbstractAt first glance, assessing future landslide‐exposed population appears to be a straightforward task if landslide hazard estimates, climate change, and population projections are available. However, the intersection of landslide hazard with socioeconomic elements may result in significant variation of estimated landslide exposure due to considerable variations in population projections. This study aims to investigate the effects of different sources of population data on the evaluation of landslide‐exposed population in China under four Shared Socioeconomic Pathways (SSPs) scenarios. We utilize multiple global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 and six high‐resolution spatially explicit static and dynamic population data sets to drive available landslide models. The results indicate an overall rise in landslide hazard projections, with an increase in the potential impact area of 0.4%–2.7% and an increase in the landslide frequency of 4.7%–20.1%, depending on the SSPs scenarios and future periods. However, the likely changes in future landslide exposed population, as modeled by incorporating population data from different sources with landslide hazard, yield divergent outcomes depending on the population data source. Thus, some of the projections depict an increase in future landslide exposure, while others show a clear decrease. The nationwide divergence ranged from −64% to +48%. These divergent findings were mainly attributed to differences in population data and a lesser extent to variations in GCMs. The present findings highlight the need to pay closer attention to the dynamic evolution of the elements at risk and the associated data uncertainties.

The Ga1 locus of the genus Zea is associated with novel genome structures derived from multiple, independent non-homologous recombination events.

The Ga1 locus controls cross incompatibility between field corn and popcorn. The Ga1-S haplotype contains two types of pectin methylesterase (PME) genes, ZmPme3 and several copies of ZmGa1P, that are expressed in silk and pollen, respectively. The ga1 haplotype contains non-functional tandem repeat sequences related to ZmPme3 and ZmGa1P. This haplotype can cross pollinate freely and is widely present in field corn. The primary objective of this study is to characterize the repeat sequences from a diverse collection of maize and teosinte lines and use this information to understand the evolution of the Ga1 locus. First, we characterized the complexity of the Ga1 genome region in high quality maize genome assemblies which led to their categorization into five groups based on the number and type of PME-like sequences found at this region. Second, we studied duplication events that led to the ga1 and Ga1-S repeats using maximum-likelihood phylogenetic reconstruction. Divergence estimates of the ga1 haplotype suggest that the duplication events occurred more than 600 kya whereas those in Ga1-S occurred at three time points i.e., > 600 kya, ∼ 260 kya and ∼ 100 kya. These estimates suggest that the ga1 and Ga1-S tandem duplication events occurred independently. Finally, analysis of ZmPme3 and ZmGa1P homologs in Zea and Tripsacum genomes suggests that ga1 and Ga1-S repeats originated from an ancestral pair of PME genes that duplicated and diverged through two evolutionary branches prior to the domestication of maize.

Guidelines and quantitative standards for improved cetacean taxonomy using full mitochondrial genomes.

In many organisms, especially those of conservation concern, traditional lines of evidence for taxonomic delineation, such as morphological data, are often difficult to obtain. In these cases, genetic data are often the only source of information available for taxonomic studies. In particular, population surveys of mitochondrial genomes offer increased resolution and precision in support of taxonomic decisions relative to conventional use of the control region or other gene fragments of the mitochondrial genome. To improve quantitative guidelines for taxonomic decisions in cetaceans, we build on a previous effort targeting the control region and evaluate, for whole mitogenome sequences, a suite of divergence and diagnosability estimates for pairs of recognized cetacean populations, subspecies, and species. From this overview, we recommend new guidelines based on complete mitogenomes, combined with other types of evidence for isolation and divergence, which will improve resolution for taxonomic decisions, especially in the face of small sample sizes or low levels of genetic diversity. We further use simulated data to assist interpretations of divergence in the context of varying forms of historical demography, culture, and ecology.

Comparing Methods for Estimating Demographics in Racially Polarized Voting Analyses

We consider the cascading effects of researcher decisions throughout the process of quantifying racially polarized voting (RPV). We contrast three methods of estimating precinct racial composition, Bayesian Improved Surname Geocoding (BISG), fully Bayesian BISG, and Citizen Voting Age Population (CVAP), and two algorithms for performing ecological inference (EI), King’s EI and EI:RxC using eiCompare. Using data from two different elections we identify circumstances in which different combinations of methods produce divergent results, comparing against ground-truth data where available. We first find that BISG outperforms CVAP at estimating racial composition, though fully Bayesian BISG does not yield further improvements. Next, in a statewide election, we find that all combinations of methods yield similarly reliable estimates of RPV. However, county-level analyses and results from a non-partisan school board election reveal that BISG and CVAP produce divergent estimates of Black preferences in elections with low turnout and few precincts. Our results suggest that methodological choices can meaningfully alter conclusions about RPV, particularly in smaller, low-turnout elections.

Data-driven modeling on the global annual soil nitrous oxide emissions: Spatial pattern and attributes

Previous assessments generated divergent estimates of global terrestrial soil nitrous oxide (N2O) emission and its spatial distributions, which did not match the observed data well. The objectives of this study were to generate a global map of terrestrial soil N2O emissions based on field observations (n = 5549) and quantify the contribution of different variables for predicting the global variation of N2O emissions. We provided spatially explicit maps of annual soil N2O emission rates across forest, grassland and cropland using the random forest approach. The global mean soil N2O emission rate in our data-driven model was 0.059 ± 0.006 g N m−2 year−1, which was lower than the estimates from previous model ensembles. Soil N2O emissions were higher in the northern than southern hemisphere. The average annual soil N2O emission rate of cropland (0.094 ± 0.009 g N m−2 year−1) was higher than that of forest (0.039 ± 0.004 g N m−2 year−1) and grassland (0.045 ± 0.007 g N m−2 year−1). In addition, we found that soil nitrogen substrates dominated the changes in soil N2O emissions and the relative importance of nitrate, ammonium, and fertilizer in predicting soil N2O emissions was greater than that of mean annual temperature and precipitation. Our data-driven model results implied that previous process-based model may overestimate the global soil N2O emission rates due to limited validation data and incomplete assumptions on related-mechanisms. This study highlights the importance of global field observations in N2O emission estimation, which can provide an independent dataset to constrain previous process-based models for better prediction.

Utility of pre-chemoradiotherapy Pan-Immune-Inflammation-Value for predicting the osteoradionecrosis rates in locally advanced nasopharyngeal cancers.

The aim of this retrospective study was to explore whether pretreatment Pan-Immune-Inflammation-Value (PIV) measurements might predict the risk of mandibular osteoradionecrosis (ORN) in patients receiving concurrent chemoradiotherapy (CCRT) for locally advanced nasopharyngeal cancer (LA-NPC). The platelet, monocyte, neutrophil, and lymphocyte counts acquired on the first day of CCRT were used to compute pretreatment PIV levels: PIV = (Platelets × Monocytes × Neutrophils) ÷ Lymphocytes. Receiver operating characteristic curve analysis was used to determine the association between ORN rates and PIV levels. Spearman correlation analysis was used to examine the probable intergroup correlations. The potential link between the pretreatment PIV levels and the post-treatment ORN rates was determined as the primary objective. 21 (10.0%) of 210 eligible patients were diagnosed with ORN. The optimal pre-CCRT PIV cutoff was 833, which separated patients into two PIV groups with divergent ORN prevalence estimates: Group1: PIV < 833 (N = 153), and Group2: PIV ≥ 833 (N = 57). The comparison analysis found that the PIV ≥ 833 cohort had significantly higher ORN rates than the PIV < 833 cohort (29.8% vs. 2.6%; P < 0.001). Other characteristics linked to significantly higher ORN rates were the patient's continuing smoking, the use of the Three-dimensional conformal radiation therapy technique, the mean mandibular dose of ≥ 58.1 Gy, the number of tooth extractions before CCRT ≥ 4, and the presence of tooth extractions after CCRT. The independent importance of all factors on higher ORN occurrence rates were retained in multivariate analysis (P < 0.05). Our findings revealed astrong link between aggravated inflammatory response and ORN genesis, with high pretreatment PIV levels related to significantly higher ORN rates.

An adaptive Kalman filtering algorithm based on maximum likelihood estimation

Traditional adaptive Kalman filtering algorithms based on innovation are often used to solve the problem of reduced or even divergent filtering estimation accuracy under abnormal measurement noise. However, these algorithms are usually characterized by difficulties in selecting window width and window weight, which cannot simultaneously take into account the filtering tracking sensitivity and filtering accuracy. In this paper, an adaptive Kalman filtering algorithm based on maximum likelihood estimation is proposed, which determines the window size and window weight size under the kth moment by designing a window adaptive selection function and a weight function to change the innovation covariance at the kth moment, which in turn changes the measurement noise covariance at the kth moment, so that the measurement noise covariance is no longer a fixed single value, but can better adapt to the changes in the environment, reflecting good adaptive characteristics. The simulation results based on GPS/SINS integrated navigation system demonstrate that the new filtering algorithm of this paper reflects higher filtering accuracy and stronger robustness under the carrier in multiple motion states and accompanied by time-varying measurement noise interference. Compared with the traditional adaptive Kalman filtering algorithm based on innovation, the accuracy of attitude angle estimation error under this method is improved by 119.97%; the accuracy of velocity estimation error is improved by 264.42%; the accuracy of position estimation error is improved by 156.69%.

Parameter estimation procedures for exponential-family random graph models on count-valued networks: A comparative simulation study

The exponential-family random graph models (ERGMs) have emerged as an important framework for modeling social networks for a wide variety of relational types. ERGMs for valued networks are less well-developed than their unvalued counterparts, and pose particular computational challenges. Network data with edge values on the non-negative integers (count-valued networks) is an important such case, with examples ranging from the magnitude of migration and trade flows between places to the frequency of interactions and encounters between individuals. Here, we propose an efficient parallelizable subsampled maximum pseudo-likelihood estimation (MPLE) scheme for count-valued ERGMs, and compare its performance with existing Contrastive Divergence (CD) and Monte Carlo Maximum Likelihood Estimation (MCMLE) approaches via a simulation study based on migration flow networks in two U.S. states. Our results suggest that edge value variance is a key factor in method performance, while network size mainly influences their relative merits in computational time. For small-variance networks, all methods perform well in point estimations while CD greatly overestimates uncertainties, and MPLE underestimates them for dependence terms; all methods have fast estimation for small networks, but CD and subsampled multi-core MPLE provides speed advantages as network size increases. For large-variance networks, both MPLE and MCMLE offer high-quality estimates of coefficients and their uncertainty, but MPLE is significantly faster than MCMLE; MPLE is also a better seeding method for MCMLE than CD, as the latter makes MCMLE more prone to convergence failure. The study suggests that MCMLE and MPLE should be the default approach to estimate ERGMs for small-variance and large-variance valued networks, respectively. We also offer further suggestions regarding choice of computational method for valued ERGMs based on data structure, available computational resources and analytical goals.

Updated phylogeny of Vestimentifera (Siboglinidae, Polychaeta, Annelida) based on mitochondrial genomes, with a new species

Siboglinid tubeworms are found at chemosynthetic environments worldwide and the Vestimentifera clade is particularly well known for their reliance on chemoautotrophic bacterial symbionts for nutrition. The mitochondrial genomes have been published for nine vestimentiferan species to date. This study provides new complete mitochondrial genomes for ten further Vestimentifera, including the first mitochondrial genomes sequenced for Alaysia spiralis, Arcovestia ivanovi, Lamellibrachia barhami, Lamellibrachia columna, Lamellibrachia donwalshi, and unnamed species of Alaysia and Oasisia. Phylogenetic analyses combining fifteen mitochondrial genes and the nuclear 18S rRNA gene recovered Lamellibrachia as sister to the remaining Vestimentifera and Riftia pachyptila as separate from the other vent-endemic taxa. Implications and auxiliary analyses regarding differing phylogenetic tree topologies, substitution saturation, ancestral state reconstruction, and divergence estimates are also discussed. Additionally, a new species of Alaysia is described from the Manus Basin.

Conjugate priors and bias reduction for logistic regression models

We address the issue of divergent maximum likelihood estimates for logistic regression models by considering a conjugate prior penalty which always produces finite estimates. We show that the proposed method is closely related to the reduced-bias approach of Firth (1993), and that the induced penalized likelihood can be expressed as a genuine binomial likelihood, replacing the original data with pseudo-counts.

Evolution of the spinach sex-linked region within a rarely recombining pericentromeric region.

Sex chromosomes have evolved independently in many different plant lineages. Here, we describe reference genomes for spinach (Spinacia oleracea) X and Y haplotypes by sequencing homozygous XX females and YY males. The long arm of the 185 Mb chromosome 4 carries a 13 Mb X-linked region (XLR) and 24.1 Mb Y-linked region (YLR), of which 10 Mb is Y-specific. We describe evidence that this reflects insertions of autosomal sequences creating a "Y duplication region" or "YDR" whose presence probably directly reduces genetic recombination in the immediately flanking regions, although both the X and Y Sex-Linked Regions are within a large pericentromeric region of chromosome 4 that recombines rarely in meiosis of both sexes. Sequence divergence estimates using synonymous sites indicate that YDR genes started diverging from their likely autosomal progenitors about 3 MYA, around the time when the flanking YLR stopped recombining with the XLR. These flanking regions have a higher density of repetitive sequences in the YY than the XX assembly and include slightly more pseudogenes compared with the XLR, and the YLR has lost about 11% of the ancestral genes, suggesting some degeneration. Insertion of a male-determining factor would have caused Y-linkage across the entire pericentromeric region, creating physically small, highly recombining, terminal pseudo-autosomal regions. These findings provide a broader understanding of the origin of sex chromosomes in spinach.

Martingale Methods for Sequential Estimation of Convex Functionals and Divergences

We present a unified technique for sequential estimation of convex divergences between distributions, including integral probability metrics like the kernel maximum mean discrepancy, $\varphi$-divergences like the Kullback-Leibler divergence, and optimal transport costs, such as powers of Wasserstein distances. This is achieved by observing that empirical convex divergences are (partially ordered) reverse submartingales with respect to the exchangeable filtration, coupled with maximal inequalities for such processes. These techniques appear to be complementary and powerful additions to the existing literature on both confidence sequences and convex divergences. We construct an offline-to-sequential device that converts a wide array of existing offline concentration inequalities into time-uniform confidence sequences that can be continuously monitored, providing valid tests or confidence intervals at arbitrary stopping times. The resulting sequential bounds pay only an iterated logarithmic price over the corresponding fixed-time bounds, retaining the same dependence on problem parameters (like dimension or alphabet size if applicable). These results are also applicable to more general convex functionals -- like the negative differential entropy, suprema of empirical processes, and V-Statistics -- and to more general processes satisfying a key leave-one-out property.

Robust Z-Estimators for Semiparametric Moment Condition Models.

In the present paper, we introduce a class of robust Z-estimators for moment condition models. These new estimators can be seen as robust alternatives for the minimum empirical divergence estimators. By using the multidimensional Huber function, we first define robust estimators of the element that realizes the supremum in the dual form of the divergence. A linear relationship between the influence function of a minimum empirical divergence estimator and the influence function of the estimator of the element that realizes the supremum in the dual form of the divergence led to the idea of defining new Z-estimators for the parameter of the model, by using robust estimators in the dual form of the divergence. The asymptotic properties of the proposed estimators were proven, including here the consistency and their asymptotic normality. Then, the influence functions of the estimators were derived, and their robustness is demonstrated.

An Improved Framework for Estimating Organic Carbon Content of Mangrove Soils Using loss-on-ignition and Coastal Environmental Setting

The use of loss on ignition (LOI) measurements of soil organic matter (SOM) to estimate soil organic carbon (OC) content is a decades-old practice. While there are limitations and uncertainties to this approach, it continues to be necessary for many coastal wetlands researchers and conservation practitioners without access to an elemental analyzer. Multiple measurement, reporting, and verification (MRV) standards recognize the need (and uncertainty) for using this method. However, no framework exists to explain the substantial differences among equations that relate SOM to OC; consequently, equation selection can be a haphazard process leading to widely divergent and inaccurate estimates. To address this lack of clarity, we used a dataset of 1,246 soil samples from 17 mangrove regions in North, Central, and South America, and calculated SOM to OC conversion equations for six unique types of coastal environmental setting. A framework is provided for understanding differences and selecting an equation based on a study region’s SOM content and whether mineral sediments are primarily terrigenous or carbonate in origin. This approach identifies the positive dependence of conversion equation slopes on regional mean SOM content and indicates a distinction between carbonate settings with mean (± 1 S.E.) OC:SOM of 0.47 (0.002) and terrigenous settings with mean OC:SOM of 0.32 (0.018). This framework, focusing on unique coastal environmental settings, is a reminder of the global variability in mangrove soil OC content and encourages continued investigation of broadscale factors that contribute to soil formation and change in blue carbon settings.

Using the household hunger scale to improve analysis and classification of severe food insecurity in famine-risk conditions: Evidence from three countries

The currently accepted means of categorizing household food insecurity and identifying famine or famine risk is through Integrated Food Security Phase Classification (IPC) analysis. IPC guidelines set criteria for the measurement and determination of famine, but recent analyses in famine-risk countries have faced several challenges. Commonly used indicators capture different aspects of food security and often produce very divergent estimates of the prevalence of food insecurity. This led to the usage of the Household Hunger Scale (HHS) as the presumed “anchor” indicator for IPC acute food insecurity analysis, with all other indicators of food consumption calibrated to the HHS. But because there was no gold standard, only an “anchor,” this presumption had never been tested. Further, given that HHS was the only indicator that could specify the difference between the two most severe categories of IPC Phase Classification (Phase 4 “Emergency” and Phase 5 “Famine”), analysis of food insecurity at the extreme end of the IPC scale relies heavily on HHS, rather than the standard panoply of indicators used for acute food insecurity analysis at lesser levels of severity. This study sought to test how well HHS differentiates between IPC Phase 4 and Phase 5 and to investigate the validity of the usage of HHS as the “anchor” indicator across the spectrum of IPC analysis. Data was collected in seven different severely food-insecure areas of South Sudan, Kenya, and Somalia. The findings show that, overall, the HHS performs reasonably well as an “anchor indicator” across the IPC scale and is able to differentiate the majority of cases in Phases 4 and 5 but tends to over-classify (over-estimate the severity of food insecurity at the household level)—especially at the lower end of the IPC scale. At the high end of the scale, these results indicate that HHS is good at flagging highly food-insecure households but may require additional information to rigorously differentiate households in Phase 4 from those in Phase 5. The study identified ways to improve classification by HHS by some simple additional questions or observations to differentiate between Phase 4 and Phase 5. In an era of extreme scarcity of humanitarian funding, this has substantial implications for resource allocation and humanitarian prioritization.

Molecular identification of Anaplasma platys in cattle by nested PCR.

Anaplasmosis is a zoonotic disease caused by Gram-negative bacterium from Anaplasmataceae family. Anaplasma causes high economic losses worldwide. 16S rRNA analysis was used to diagnose Anaplasma platys in Cattle. Phylogenetic tree and estimation of evolutionary divergence between A. platys isolates were performed. A total of 60 blood samples were collected from a cattle farm in AL-Diwaniyah province. 16S rRNA gene was identified using nested PCR. Overall, 40% of cattle that were chosen to collect the blood were identified to be infected with A. platys. The results have shown presence of targeting partial region of 16S rRNA gene in 24 samples out of 60. Sequencing results of 10 samples have revealed that the phylogenetic tree was divided in to two separate clades. Five isolates of A. platys-Iraq (accession no. OP646782, OP646783, OP646784, OP646790, and OP646791) were located in one clade with the A. platys-China (accession no. MN193068.1). While, five isolates (accession no. OP646785, OP646786, OP646787, OP646788, OP646789) were in different clade with two isolates of A. platys-Africa and A. platys-Zambia in distinct branches, close to the Rickettsiales. The phylogenetic study of A. platys sequences indicated that the isolates were collected from a cattle farm in Al-Dewaniyah were similar and close related to A. platys-China, A. platys-Zambia and A. platys-Africa). This study suggests that cattle can be considered a reservoir of A. platys.

Genetic divergence and correlation estimates for quantitative traits in Salix

Genetic divergence and correlation estimates for quantitative traits in Salix

Estimation of genetic divergence and determining selection criteria in sesame (Sesamum indicum)

Twenty eight mutant lines of sesame were evaluated in randomized block design at Agricultural Research Station, Mandor, Jodhpur, Rajasthan, during kharif, 2018. ANOVA stated the existence of significant difference for all the characters studied. The percentage contribution towards genetic divergence was found highest for test weight followed by number of capsules/plant and seed yield/plant.Character association study indicated that simultaneous selection for the traits like harvest index, oil content, capsule length and number of capsules and direct selection for protein content, capsules/plant, length of capsule, days to maturity and harvest index will be advantageous for aiding yield improvement. Out of ten independent characters studied in path coefficient three traits, viz. number of capsules/plant, capsule length and harvest index expressed maximum directas well as indirect contributions through other characters on seed yield. Hence, these characters may be considered as important quantitative traits in sesame improvementprogramme and direct selection for these traits will be useful.