Dynamic evolution characteristics and hazard assessment of compound drought/waterlogging and low temperature events for maize.

Hazard assessment is fundamental in the field of disaster risk management. With the increase in global warming, compound water and temperature events have become more frequent. Current research lacks risk assessments of low temperatures and their compound events, necessitating relevant hazard assessment work to improve the accuracy and diversity of maize disaster prevention and mitigation strategies. This study comparatively analyzed the dynamic evolution characteristics and hazards of compound drought/waterlogging and low temperature events (CDLEs and CWLEs) for maize in the Songliao Plain during different growth periods from 1981 to 2020. First, composite drought/waterlogging and low temperature magnitude indices (CDLMI and CWLMI) were constructed to quantify the intensity of CDLEs and CWLEs by fitting non-exceedance probabilities. Next, static and dynamic hazard assessment models were developed by fitting probability density and cumulative probability density curves to CDLMI and CWLMI. The results showed that the correlations between SPRI and LTI across different decades were mainly negative during the three growth periods. The hazard ratings for both CDLEs and CWLEs were relatively high in the northern part of the study area, consistent with the higher occurrence, duration, and severity of both CDLEs and CWLEs at higher latitudes. Relative to 2001-2010, the center of gravity of hazard shifted southward for CDLEs and northward for CWLEs in 2011-2020. The mean duration, frequency, and hazard were generally higher for CWLEs, but CDLEs were associated with more severe maize yield reductions. This study provides new insights into compound disaster risk assessment, and the research methodology can be generalized to other agricultural growing areas to promote sustainable development of agricultural systems and food security.

Pairwise summation as a method for the additive combination of probabilities in qualitative risk assessments.

Qualitative frameworks are widely employed to tackle urgent animal or public health issues when data are scarce and/or urgent decisions need to be made. In qualitative models, the degree of belief regarding the probabilities of the events occurring along the risk pathway(s) and the outcomes is described in nonnumerical terms, typically using words such as Low, Medium, or High. The main methodological challenge, intrinsic in qualitative models, relates to performing mathematical operations and adherence to the rule of probabilities when probabilities are nonnumerical. Although methods to obtain the qualitative probability from the conditional realization of n events are well-established and consistent with the multiplication rule of probabilities, there is a lack of accepted methods for addressing situations where the probability of an event occurring can increase, and the rule of probability P(AUB) = P(A) + P(B) - P(A∩B) should apply. In this work, we propose a method based on the pairwise summation to fill this methodological gap. Our method was tested on two qualitative models and compared by means of scenario analysis to other approaches found in literature. The qualitative nature of the models prevented formal validation; however, when using the pairwise summation, results consistently appeared more coherent with probability rules. Even if the final qualitative estimate can only represent an approximation of the actual probability of the event occurring, qualitative models have proven to be effective in providing scientific-based evidence to support decision-making. The method proposed in this study contributes to reducing the subjectivity that characterizes qualitative models, improving transparency and reproducibility.

Optimized Derivation of Predicted No-Effect Concentrations (PNECs) for Eight Polycyclic Aromatic Hydrocarbons (PAHs) Using HC10 Based on Acute Toxicity Data.

For persistent organic pollutants, a concern of environmental supervision, predicted no-effect concentrations (PNECs) are often used in ecological risk assessment, which is commonly derived from the hazardous concentration of 5% (HC5) of the species sensitivity distribution (SSD). To address the problem of a lack of toxicity data, the objectives of this study are to propose and apply two improvement ideas for SSD application, taking polycyclic aromatic hydrocarbons (PAHs) as an example: whether the chronic PNEC can be derived from the acute SSD curve; whether the PNEC may be calculated by HC10 to avoid solely statistical extrapolation. In this study, the acute SSD curves for eight PAHs and the chronic SSD curves for three PAHs were constructed. The quantity relationship of HC5s between the acute and chronic SSD curves was explored, and the value of the assessment factor when using HC10 to calculate PNEC was derived. The results showed that, for PAHs, the chronic PNEC can be estimated by multiplying the acute PNEC by 0.1, and the value of the assessment factor corresponding to HC10 is 10. For acenaphthene, anthracene, benzo[a]pyrene, fluoranthene, fluorene, naphthalene, phenanthrene, and pyrene, the chronic PNECs based on the acute HC10s were 0.8120, 0.008925, 0.005202, 0.07602, 2.328, 12.75, 0.5731, and 0.05360 µg/L, respectively.

Analyzing clustered continuous response variables with ordinal regression models.

Continuous response data are regularly transformed to meet regression modeling assumptions. However, approaches taken to identify the appropriate transformation can be ad hoc and can increase model uncertainty. Further, the resulting transformations often vary across studies leading to difficulties with synthesizing and interpreting results. When a continuous response variable is measured repeatedly within individuals or when continuous responses arise from clusters, analyses have the additional challenge caused by within-individual or within-cluster correlations. We extend a widely used ordinal regression model, the cumulative probability model (CPM), to fit clustered, continuous response data using generalized estimating equations for ordinal responses. With the proposed approach, estimates of marginal model parameters, cumulative distribution functions , expectations, and quantiles conditional on covariates can be obtained without pretransformation of the response data. While computational challenges arise with large numbers of distinct values of the continuous response variable, we propose feasible and computationally efficient approaches to fit CPMs under commonly used working correlation structures. We study finite sample operating characteristics of the estimators via simulation and illustrate their implementation with two data examples. One studies predictors of CD4:CD8 ratios in a cohort living with HIV, and the other investigates the association of a single nucleotide polymorphism and lung function decline in a cohort with early chronic obstructive pulmonary disease.

A theoretical base for non-invasive prenatal paternity testing.

There is an increasing demand for prenatal paternity testing in the forensic applications, which identify biological fathers before the birth of children. Currently, one of the most effective and safe Non-Invasive Prenatal Paternity Testing (NIPPT) methods is high-throughput Next-Generation Sequencing (NGS)-based SNP genotyping of cell-free DNA in maternal peripheral blood. To the best of our knowledge, nearly all methods being used in such applications are based on traditional postnatal paternity tests and/or statistical models of conventional polymorphism sites. These methods have shown unsatisfactory performance due to the uncertainty of fetal genotype. In this study, we propose a cutting-edge methodology called the Prenatal paternity Test Analysis System (PTAS) for cell-free fetal DNA-based NIPPT using NGS-based SNP genotyping. With the implementation of our proposed PTAS methodology, 63 out of 64 early-pregnancy (i.e., less than seven weeks) samples can be precisely identified to determine paternity, except for one sample that does not meet quality control requirements. Although the fetal fraction of the non-identified sample is extremely low (0.51%), its paternity can still be detected by our proposed PTAS methodology through unique molecular identifier tagging. Paternity of the total 313 samples for mid-to-late pregnancy (i.e., more than seven weeks) can be accurately identified. Extensive experiments indicate that our methodology makes a significant breakthrough in the NIPPT theory and will bring substantial benefits to forensic applications.

Optimization of sowing date for spring maize in China's loess plateau based on presowing temperature and soil water content.

BACKGROUND: The sowing date of spring maize in China's Loess Plateau is often restricted by the presowing temperature and soil water content (SWC). The effective measurement of the effects of presowing temperature and SWC on the sowing date is a major concern for agricultural production in this region. In this paper, we considered the average daily air temperature of ˃10 °C in the 7 days before sowing. The Decision Support System for Agrotechnology Transfer (DSSAT) model was used to simulate a spring maize yield under distinct combinations of SWC and sowing date for 51 years (1970-2020). Subsequently, through the cumulative probability distribution function, the contribution of presowing SWC to the spring maize yield was quantified, and the optimal sowing date of spring maize in each production location was determined. RESULTS: The results revealed that the daily average temperature of ˃10 °C for 7 days consecutively can be used effectively as the basis for the simulation of spring maize sowing date. The presowing SWC affected the spring maize yield but did not change the optimal sowing date. When the SWC of each study site is about 70% of the field capacity, Wenshui and Yuanping can properly delay sowing, and Lin county can sow early to obtain a higher yield. CONCLUSION: This study provides an effective approach for optimizing presowing soil moisture management and the sowing date of spring maize in the semiarid regions of the Loess Plateau. © 2023 Society of Chemical Industry.

Preimplantation genetic testing for aneuploidy: helpful but not a first choice.

PURPOSE: This retrospective cohort study aimed to assess and compare the outcomes between cumulative live birth of patients with and without PGT-A and also between prior unsuccessful IVF cycles and PGT-A cycles among patients who experienced IVF but without live birth delivery, and to clarify the effective usage of PGT-A as an in vitro fertilization (IVF) add-on. METHODS: A total of 2113 females undergoing IVF with at least one blastocyst were reviewed. Patients in the PGT-A and non-PGT-A groups were further categorized into first-time IVF and prior unsuccessful IVF groups (previous IVF experience but without live birth delivery). RESULTS: In the PGT-A group, there were additional oocyte retrieval cycles, fewer transfer cycles per patient, higher clinical pregnancy rates per embryo transfer, and lower miscarriage rates per clinical pregnancy as compared to the non-PGT-A group, all showing significant differences. However, the first-time IVF group with PGT-A had a significantly longer duration from the first oocyte retrieval to the first live birth delivery (LBD) and a significantly lower LBD rate per patient than the non-PGT-A group. The cumulative probability for a first LBD with PGT-A was inferior in the first-time IVF group for women < 35 years, marginally superior in the prior unsuccessful IVF group of women aged 38-40 years, and similar for other groups. CONCLUSION: PGT-A should not be recommended to all patients; however, if the first IVF treatment failed, PGT-A may reduce the patient's burden regardless of age.

Asymptotic Properties for Cumulative Probability Models for Continuous Outcomes.

Regression models for continuous outcomes frequently require a transformation of the outcome, which is often specified a priori or estimated from a parametric family. Cumulative probability models (CPMs) nonparametrically estimate the transformation by treating the continuous outcome as if it is ordered categorically. They thus represent a flexible analysis approach for continuous outcomes. However, it is difficult to establish asymptotic properties for CPMs due to the potentially unbounded range of the transformation. Here we show asymptotic properties for CPMs when applied to slightly modified data where bounds, one lower and one upper, are chosen and the outcomes outside the bounds are set as two ordinal categories. We prove the uniform consistency of the estimated regression coefficients and of the estimated transformation function between the bounds. We also describe their joint asymptotic distribution, and show that the estimated regression coefficients attain the semiparametric efficiency bound. We show with simulations that results from this approach and those from using the CPM on the original data are very similar when a small fraction of the data are modified. We reanalyze a dataset of HIV-positive patients with CPMs to illustrate and compare the approaches.

Research on a Fractal Dimension Calculation Method for a Nano-Polymer Microspheres Dispersed System.

Polymer microspheres (PMs) are a kind of self-similar volume expansion particle, and their fractal dimension varies with hydration swelling. However, there is no unique fractal dimension calculation method for their characteristics. A new model is established in this paper, which is particular to calculate the fractal dimension of PMs. We carried out swelling hydration experiments and scanning electron microscope (SEM) experiments to verify the new model. The new model and the box-counting model were used to calculate the fractal dimensions of PMs based on the hydration experiment results. Then, a comparison of the calculation results of the two methods was used to verify the validity of the model. Finally, according to the new model calculation results, the fractal dimension characteristics of PMs were analyzed. The research results indicate that the new model successfully correlates the cumulative probability of the PMs dispersed system with the fractal dimension and makes fractal dimension calculation of PMs more accurate and convenient. Based on the experiment results, the new model was used to calculate the fractal dimension of PMs and the box-counting model, and its findings were all 2.638 at initial state hydration and 2.739 and 2.741 at hydration time as of day 1. This result verifies the correctness of the new model. According to the hydration swelling experiments and the new model calculation results, the fractal dimension is linear correlated to the average particle size of PMs and the standard deviation average particle size. This means the fractal dimension of PMs represents the space occupancy ability and space occupancy effectiveness.

Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens.

Most phase I trials in oncology aim to find the maximum tolerated dose (MTD) based on the occurrence of dose limiting toxicities (DLT). Evaluating the schedule of administration in addition to the dose may improve drug tolerance. Moreover, for some molecules, a bivariate toxicity endpoint may be more appropriate than a single endpoint. However, standard dose-finding designs do not account for multiple dose regimens and bivariate toxicity endpoint within the same design. In this context, following a phase I motivating trial, we proposed modeling the first type of DLT, cytokine release syndrome, with the entire dose regimen using pharmacokinetics and pharmacodynamics (PK/PD), whereas the other DLT (DLTo ) was modeled with the cumulative dose. We developed three approaches to model the joint distribution of DLT, defining it as a bivariate binary outcome from the two toxicity types, under various assumptions about the correlation between toxicities: an independent model, a copula model and a conditional model. Our Bayesian approaches were developed to be applied at the end of the dose-allocation stage of the trial, once all data, including PK/PD measurements, were available. The approaches were evaluated through an extensive simulation study that showed that they can improve the performance of selecting the true MTD-regimen compared to the recommendation of the dose-allocation method implemented. Our joint approaches can also predict the DLT probabilities of new dose regimens that were not tested in the study and could be investigated in further stages of the trial.

Age Conditional Probability of Ocular and Ocular Adnexal Malignancies.

AIM: The aim of this study was to discuss and illustrate the role age-conditional probability has in communicating risk of developing ocular and ocular adnexal malignancies. METHODS: Cross-sectional incidence for retinoblastoma, uveal melanoma, conjunctival melanoma, and lacrimal gland carcinomas from 2000 to 2017 were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Incidence rates were age-adjusted to the 2000 United States population. Age-adjusted incidence was converted to age-interval and cumulative risks. Outcomes were examined in 20-year intervals and cumulatively for adult cancers and yearly for retinoblastoma. RESULTS: The risk of each malignancy displayed age-dependent variation. For adult malignancies, men were at higher risk at most age intervals. Uveal melanoma had the greatest cumulative lifetime risk. The probability of developing retinoblastoma declines precipitously after age 3 years. CONCLUSIONS: Age-conditional probability of developing cancer is a conceptually friendly means of understanding and communicating risk. It is particularly useful in comparing the risks of uncommon or rare cancers, such as those found in and around the eye. The assessment of risk in terms of age-conditional probability is a versatile and an underutilized pedagogical tool.

High-Speed Video Cryomicroscopy for Measurement of Intracellular Ice Formation Kinetics.

Quantitative information about the kinetics and cumulative probability of intracellular ice formation is necessary to develop minimally damaging freezing procedures for the cryopreservation of cells and tissues. Conventional cryomicroscopic assays, which rely on indirect evidence of intracellular freezing (e.g., opacity changes in the cell cytoplasm), can yield significant errors in the estimated kinetics. In contrast, the formation and growth of intracellular ice crystals can be accurately detected using temporally resolved imaging methods (i.e., video recording at sub-millisecond resolution). Here, detailed methods for the setup and operation of a high-speed video cryomicroscope system are described, including protocols for imaging of intracellular ice crystallization events and stochastic analysis of the ice formation kinetics in a cell population. Recommendations are provided for temperature profile design, sample preparation, and configuration of the video acquisition parameters. Throughout this chapter, the protocols incorporate best practices that have been drawn from two decades of experience with high-speed video cryomicroscopy in our laboratory.

Variability in microbial inactivation: From deterministic Bigelow model to probability distribution of single cell inactivation times.

Phenotypic heterogeneity seems to be an important component leading to biological individuality and is of great importance in the case of microbial inactivation. Bacterial cells are characterized by their own resistance to stresses. This inherent stochasticity is reflected in microbial survival curve which, in this context, can be considered as cumulative probability distribution of lethal events. The objective of the present study was to present an overview on the assessment and quantification of variability in microbial inactivation originating from single cells and discuss this heterogeneity in the context of predicting microbial behavior and Risk assessment studies. The detailed knowledge of the distribution of the single cells' inactivation times can be the basis for stochastic inactivation models which, in turn, may be employed in a risk - based food safety approach.

Distribution and correlation of radon and uranium and associated hydrogeochemical processes in alluvial aquifers of northwest India.

The spatial and vertical distributions of radon and uranium are evaluated in relation to the hydrogeology, geomorphology, and hydrochemistry of southwest Punjab. Radon activity of the groundwater ranges from 580 to 3633 Bq/m3 (shallow groundwater 580 to 2438 Bq/m3 and deep groundwater 964 to 3633 Bq/m3), and uranium concentration varies from 24.4 to 253 µg/L (shallow groundwater 24.4 to 253 µg/L and deep groundwater 27.6 to 76.3 µg/L). Shallow groundwater shows higher U concentration compared with deeper ones, which can be attributed to the presence of dissolved oxygen (DO) and NO3- as oxidants and HCO3- as stabilizing agent in shallow zone. Unlike uranium, the radon activities were found to be similar in both shallow and deep groundwater. Rnexcess over secular equilibrium was used to confirm the possibility of additional sources of radon, such as secondary minerals present in the subsurface. Surface manifestations show significant influence on radon and uranium distributions in the shallow zone but not in deep zone due to limited hydraulic connectivity. Depth profiles and correlations of radon and uranium with trace elements and hydrochemical parameters indicate that groundwater exhibits different redox characteristics in shallow (younger and oxidizing) and deep zones (older and reducing). The present study provides critical information that can be helpful for planning sustainable groundwater development in this region and other similar regions without contaminating the relatively safer deep aquifers.

Estimation of the adjusted cause-specific cumulative probability using flexible regression models for the cause-specific hazards.

In competing risks setting, we account for death according to a specific cause and the quantities of interest are usually the cause-specific hazards (CSHs) and the cause-specific cumulative probabilities. A cause-specific cumulative probability can be obtained with a combination of the CSHs or via the subdistribution hazard. Here, we modeled the CSH with flexible hazard-based regression models using B-splines for the baseline hazard and time-dependent (TD) effects. We derived the variance of the cause-specific cumulative probabilities at the population level using the multivariate delta method and showed how we could easily quantify the impact of a covariate on the cumulative probability scale using covariate-adjusted cause-specific cumulative probabilities and their difference. We conducted a simulation study to evaluate the performance of this approach in its ability to estimate the cumulative probabilities using different functions for the cause-specific log baseline hazard and with or without a TD effect. In the scenario with TD effect, we tested both well-specified and misspecified models. We showed that the flexible regression models perform nearly as well as the nonparametric method, if we allow enough flexibility for the baseline hazards. Moreover, neglecting the TD effect hardly affects the cumulative probabilities estimates of the whole population but impacts them in the various subgroups. We illustrated our approach using data from people diagnosed with monoclonal gammopathy of undetermined significance and provided the R-code to derive those quantities, as an extension of the R-package mexhaz.

Covariate-adjusted Spearman's rank correlation with probability-scale residuals.

It is desirable to adjust Spearman's rank correlation for covariates, yet existing approaches have limitations. For example, the traditionally defined partial Spearman's correlation does not have a sensible population parameter, and the conditional Spearman's correlation defined with copulas cannot be easily generalized to discrete variables. We define population parameters for both partial and conditional Spearman's correlation through concordance-discordance probabilities. The definitions are natural extensions of Spearman's rank correlation in the presence of covariates and are general for any orderable random variables. We show that they can be neatly expressed using probability-scale residuals (PSRs). This connection allows us to derive simple estimators. Our partial estimator for Spearman's correlation between X and Y adjusted for Z is the correlation of PSRs from models of X on Z and of Y on Z, which is analogous to the partial Pearson's correlation derived as the correlation of observed-minus-expected residuals. Our conditional estimator is the conditional correlation of PSRs. We describe estimation and inference, and highlight the use of semiparametric cumulative probability models, which allow preservation of the rank-based nature of Spearman's correlation. We conduct simulations to evaluate the performance of our estimators and compare them with other popular measures of association, demonstrating their robustness and efficiency. We illustrate our method in two applications, a biomarker study and a large survey.

Grounding a natural background level for fluoride in a potentially contaminated crystalline aquifer in south India.

Fluoride contamination is one of the most alarming issues for those countries that depend on groundwater drinking water supply. A careful examination of the hydrogeochemical conditions and routine monitoring of fluoride level are therefore quintessential. Estimation of natural background level (NBL) of fluoride becomes significant information for assessing the current and future contamination episodes. Vellore District in Tamil Nadu is a hard rock terrain known for its F-rich groundwater. In this study, we attempted to form a benchmark for fluoride using hydrochemical pre-selection (based on TDS and NO3) and cumulative probability plots (CPP). Principle components analysis is (PCA) applied to evaluate the corresponding factor grouping of the total of 68 samples, which is later mapped using geostatistical tool in ArcGIS. From the CPP, we derived the NBL of F as 0.75 mg/L. This value is compared with the observed concentration in each sample and they were spatially plotted based on the NBL. Resultant plot suggests that W-NW part of the study area has exceeded and E-EW regions are below the NBL of F. Spatial variation of the factor scores also supported this observation. Grounding an NBL and extending it to other parts of the potential contaminated aquifers are highly recommended for better understanding and management of the water supply systems.

Systematic analysis of residual density suggests that a major limitation in well-refined X-ray structures of proteins is the omission of ordered solvent.

In the residual electron density map of a fully refined X-ray protein model, there should be no peaks arising from modeling errors or missing atoms. Any residual peaks that do occur should be contributed by random residual intensity differences between the model and the data. If the model is incomplete (i.e., some atoms are missing), there will be more positive peaks than negative ones. On the other hand, if the model includes inappropriately located atoms, there will be an excess of negative peaks. In this study, random residual peaks are quantified using the probability density function P(x), which is defined as the probability for a peak having peak height between x and x + dx. It is found that P(x) is single-exponential and symmetric for both positive and negative peaks. Thus, P(x) can be used to discriminate residual peaks contributed by random noise in complete models from residual peaks being attributable to modeling errors in incomplete models. For a number of representative structures in the PDB it is found that P(x) has far more large (greater than 5 sigma) positive peaks than large negative peaks. This excess of large positive peaks suggests that the main defect in these refined structures is the omission of ordered water molecules.

Does adaptive strategy for delayed seed dispersion affect extinction probability of a desert species? an assessment using the population viability analysis and glass house experiment

Canopy seed bank is an important adaptive evolutionary trait that provides various types of protection to the seeds. However, costing of such evolutionary trait on plant survival is largely unknown. Present investigation provided a new insight on the serotonious habit of Blepharis sindica associated with its endangerment status. Extinction probabilities of two available population of B. sindica were quantified using two types of census data, i.e., fruiting body number and actual population size. Population Viability Analysis (PVA) revealed that delayed seed release tendency (higher fruiting body number) was not synchronized with actual ground conditions (lower population size). PVA analysis based on actual population size indicated that both the available populations would vanish within 20 years. The mean time of extinction calculated from both type census data indicated its extinction within 48 years. For assessing the conservation criteria, a glass house experiment was carried out with different soil types and compositions. Pure sand and higher proportions of sand -silt were more suitable compared to clay; further, gravelly surface was the most unsuitable habitat for this species. Collection of the seeds from mature fruits/capsule and their sowing with moderate moisture availability with sandy soil could be recommended.