Profile Likelihood for Hierarchical Models Using Data Doubling.

In scientific problems, an appropriate statistical model often involves a large number of canonical parameters. Often times, the quantities of scientific interest are real-valued functions of these canonical parameters. Statistical inference for a specified function of the canonical parameters can be carried out via the Bayesian approach by simply using the posterior distribution of the specified function of the parameter of interest. Frequentist inference is usually based on the profile likelihood for the parameter of interest. When the likelihood function is analytical, computing the profile likelihood is simply a constrained optimization problem with many numerical algorithms available. However, for hierarchical models, computing the likelihood function and hence the profile likelihood function is difficult because of the high-dimensional integration involved. We describe a simple computational method to compute profile likelihood for any specified function of the parameters of a general hierarchical model using data doubling. We provide a mathematical proof for the validity of the method under regularity conditions that assure that the distribution of the maximum likelihood estimator of the canonical parameters is non-singular, multivariate, and Gaussian.

Estimating the intensity of use by interacting predators and prey using camera traps.

Understanding how organisms distribute themselves in response to interacting species, ecosystems, climate, human development and time is fundamental to ecological study and practice. A measure to quantify the relationship among organisms and their environments is intensity of use: the rate of use of a specific resource in a defined unit of time. Estimating the intensity of use differs from estimating probabilities of occupancy or selection, which can remain constant even when the intensity of use varies. We describe a method to evaluate the intensity of use across conditions that vary in both space and time. We demonstrate its application on a large mammal community where linear developments and human activity are conjectured to influence the interactions between white-tailed deer (Odocoileus virginianus) and wolves (Canis lupus) with possible consequences on threatened woodland caribou (Rangifer tarandus caribou). We collect and quantify intensity of use data for multiple, interacting species with the goal of assessing management efficacy, including a habitat restoration strategy for linear developments. We test whether blocking linear developments by spreading logs across a 200-m interval can be applied as an immediate mitigation to reduce the intensities of use by humans, predator and prey species in a boreal caribou range. We deployed camera traps on linear developments with and without restoration treatments in a landscape exposed to both timber and oil development. We collected a three-year dataset and employed spatial recurrent event models to analyse intensity of use by an interacting human and large mammal community across a range of environmental and climatic conditions. Spatial recurrent event models revealed that intensity of use by humans influenced the intensity of use by all five large mammal species evaluated, and the intensities of use by wolves and deer were inextricably linked in space and time. Conditions that resist travel on linear developments had a strong negative effect on the intensity of human and large mammal use. Mitigation strategies that resist, or redirect, animal travel on linear developments can reduce the effects of resource development on interacting human and predator-prey interactions. Our approach is easily applied to other continuous time point-based survey methodologies and shows that measuring the intensity of use within animal communities can help scientists monitor, mitigate and understand ecological states and processes.

Defining the potential of aglycone modifications for affinity/selectivity enhancement against medically relevant lectins: synthesis, activity screening, and HSQC-based NMR analysis.

The emerging significance of lectins for pathophysiological processes provides incentive for the design of potent inhibitors. To this end, systematic assessment of contributions to affinity and selectivity by distinct types of synthetic tailoring of glycosides is a salient step, here taken for the aglyconic modifications of two disaccharide core structures. Firstly we report the synthesis of seven N-linked-lactosides and of eight O-linked N-acetyllactosamines, each substituted with a 1,2,3-triazole unit, prepared by copper-catalyzed azide-alkyne cycloaddition (CuAAC). The totally regioselective ß-D-(1 → 4) galactosylation of a 6-O-TBDPSi-protected N-acetylglucosamine acceptor provided efficient access to the N-acetyllactosamine precursor. The resulting compounds were then systematically tested for lectin reactivity in two binding assays of increasing biorelevance (inhibition of lectin binding to a surface-presented glycoprotein and to cell surfaces). As well as a plant toxin, we also screened the relative inhibitory potential with adhesion/growth-regulatory galectins (total of eight proteins). This type of modification yielded up to 2.5-fold enhancement for prototype proteins, with further increases for galectins-3 and -4. Moreover, the availability of (15)N-labeled proteins and full assignments enabled (1)H, (15)N HSQC-based measurements for hu- man galectins-1, -3, and -7 against p-nitrophenyl lactopyranoside, a frequently tested standard inhibitor containing an aromatic aglycone. The measurements confirmed the highest affinity against galectin-3 and detected chemical shift differences in its hydrophobic core upon ligand binding, besides common alterations around the canonical contact site for the lactoside residue. What can be accomplished in terms of affinity/selectivity by this type of core extension having been determined, the applied combined strategy should be instrumental for proceeding with defining structure-activity correlations at other bioinspired sites in glycans and beyond the tested lectin types.

Surgical Resection of the Liver Mass with Impending Rupture: Primary Osteosarcoma of Liver-a Histological Surprise and Literature Review.

Osteosarcomas occurring in the extraskeletal system are known as extraskeletal osteosarcomas (ESOS). They are rare, highly malignant tumors, associated with poor prognosis Di et al. World J Clin Cases 11(3):662-668 (2023). This is a case report of a gentleman nearing 60s with a liver mass with impending rupture. He underwent resection of the liver mass and was diagnosed to have with ESOS. He underwent a right hepatectomy on an urgent basis. He had an uneventful postoperative recovery. The histopathology report showed a poorly differentiated malignant neoplasm consistent with osteosarcoma. The patient is doing well 75 days after discharge and currently receiving adjuvant chemotherapy with ifosfamide, adriamycin, and cisplatin. We have also done a comprehensive literature review of this rare tumor. It is an elusive disease that is difficult to diagnose radiologically. The treatment includes a combination of surgery and adjuvant treatment.

Trans Enteric Rendezvous ERCP in a Patient with Loop Duodenal Switch.

Introduction: Performing endoscopic retrograde cholangiopancreatography (ERCP) in duodenal switch (DS) patients is challenging given their surgically altered anatomy. There have been very few reported cases of trans enteric rendezvous ERCP to relieve biliary obstruction in DS patients. More specifically, there has not been any reported cases of this procedure being performed in loop DS, also known as SADI (single anastomosis duodeno-ileostomy) or SIPS (stomach intestinal pylorus sparing procedure). Case Description: This case reports describes a 50-year-old male with prior loop DS who presented with gallstone pancreatitis. He underwent a laparoscopic cholecystectomy with positive intraoperative cholangiogram requiring the need for trans enteric rendezvous ERCP. Discussion: Although never reported, trans enteric rendezvous ERCP is a feasible approach in relieving biliary obstruction in patients with loop DS anatomy.

Simultaneous estimation of the temporal and spatial extent of animal migration using step lengths and turning angles.

BACKGROUND: Animals of many different species, trophic levels, and life history strategies migrate, and the improvement of animal tracking technology allows ecologists to collect increasing amounts of detailed data on these movements. Understanding when animals migrate is important for managing their populations, but is still difficult despite modelling advancements. METHODS: We designed a model that parametrically estimates the timing of migration from animal tracking data. Our model identifies the beginning and end of migratory movements as signaled by change-points in step length and turning angle distributions. To this end, we can also use the model to estimate how an animal's movement changes when it begins migrating. In addition to a thorough simulation analysis, we tested our model on three datasets: migratory ferruginous hawks (Buteo regalis) in the Great Plains, barren-ground caribou (Rangifer tarandus groenlandicus) in northern Canada, and non-migratory brown bears (Ursus arctos) from the Canadian Arctic. RESULTS: Our simulation analysis suggests that our model is most useful for datasets where an increase in movement speed or directional autocorrelation is clearly detectable. We estimated the beginning and end of migration in caribou and hawks to the nearest day, while confirming a lack of migratory behaviour in the brown bears. In addition to estimating when caribou and ferruginous hawks migrated, our model also identified differences in how they migrated; ferruginous hawks achieved efficient migrations by drastically increasing their movement rates while caribou migration was achieved through significant increases in directional persistence. CONCLUSIONS: Our approach is applicable to many animal movement studies and includes parameters that can facilitate comparison between different species or datasets. We hope that rigorous assessment of migration metrics will aid understanding of both how and why animals move.

Selection, use, choice and occupancy: clarifying concepts in resource selection studies.

1. During the last decade, there has been a proliferation of statistical methods for studying resource selection by animals. While statistical techniques are advancing at a fast pace, there is confusion in the conceptual understanding of the meaning of various quantities that these statistical techniques provide. 2. Terms such as selection, choice, use, occupancy and preference often are employed as if they are synonymous. Many practitioners are unclear about the distinctions between different concepts such as 'probability of selection,' 'probability of use,' 'choice probabilities' and 'probability of occupancy'. 3. Similarly, practitioners are not always clear about the differences between and relevance of 'relative probability of selection' vs. 'probability of selection' to effective management. 4. Practitioners also are unaware that they are using only a single statistical model for modelling resource selection, namely the exponential probability of selection, when other models might be more appropriate. Currently, such multimodel inference is lacking in the resource selection literature. 5. In this paper, we attempt to clarify the concepts and terminology used in animal resource studies by illustrating the relationships among these various concepts and providing their statistical underpinnings.

Predators choose prey over prey habitats: evidence from a lynx-hare system.

Resource selection is grounded in the understanding that animals select resources based on fitness requirements. Despite uncertainty in how mechanisms relate to the landscape, resource selection studies often assume, but rarely demonstrate, a relationship between modeled variables and fitness mechanisms. Using Canada lynx (Lynx canadensis) and snowshoe hare (Lepus americanus) as a model system, we assess whether prey habitat is a viable surrogate for encounters between predators and prey. We simultaneously collected winter track data for lynx and hare in two study areas. We used information criteria to determine whether selection by lynx is best characterized by a hare resource selection probability function (RSPF) or by the amount of hare resource use. Results show that lynx selection is better explained by the amount of hare use (SIC = -21.9; Schwarz's Information Criterion) than by hare RSPF (SIC = -16.71), and that hare RSPF cannot be assumed to reveal the amount of resource use, a primary mechanism of predator selection. Our study reveals an obvious but important distinction between selection and use that is applicable to all resource selection studies. We recommend that resource selection studies be coupled with mechanistic data (e.g., metrics of diet, forage, fitness, or abundance) when investigating mechanisms of resource selection.

Knowledge, attitude, and behaviours on diet, physical activity, and tobacco use among school students: A cross-sectional study in two Indian states.

Background: Non-communicable diseases (NCDs) are escalating in India and can be attributed to behavioural risk factors such as unhealthy diet, physical inactivity and tobacco use that began in early years. Understanding adolescents' knowledge, attitudes and behaviours (KAB) related to NCD risk factors would inform the development of school-based health programmes to prevent NCDs. Methods: Sixth-grade students (n=1026) in 20 schools (10 private, 10 public) from two Indian cities (n=667 from Pune; n=359 from Bengaluru) participated in a KAB survey in 2019. Differences in KAB by gender, school type within cities were investigated. Results: Knowledge about the harms of tobacco use was higher than knowledge about a healthy diet and the importance of physical activity. Only a small proportion of students did not eat breakfast (8.7%) or fruits (11.3%) daily. Only 33.4% of students read nutrition labels before choosing their food. Moderate-to-vigorous physical activity of less than an hour per day was reported by 42.5% of students. Approximately one-third of students had ever tried smoking tobacco (30.1%), smokeless tobacco (30.5%), and e-cigarettes (32.4%). Differences in these behaviours by gender and school type showed that both boys, girls and students of private and public schools are vulnerable. Conclusions: The findings highlight that knowledge is low for thematic areas like diet and physical activity. Low knowledge can be attributed to unfavourable behaviours like lack of reading nutrition labels and indulgence in sedentary activities. To protect India's young population (adolescents), there is a need to amplify health education activities and context-specific health intervention materials for them by engaging parents and communities. Thus, these programmes should be incorporated into the curriculum as part of the regular teaching, as they may induce positive changes in their knowledge and behaviours. In India, school health programmes should dedicate significant time to health promotion and NCD risk prevention.

Improved estimation of site occupancy using penalized likelihood.

When detection or occupancy probability is small or when the number of sites and number of visits per site is small, maximum likelihood estimators (MLE) of site occupancy parameters have large biases, are numerically unstable, and the corresponding confidence intervals have smaller than nominal coverage. We propose an alternative method of estimation, based on penalized likelihood. This method is numerically stable, the estimators have smaller mean square error than the MLE, and associated confidence intervals have close to nominal coverage.

A unifying theory for two-dimensional spatial redistribution kernels with applications in population spread modelling.

When building models to explain the dispersal patterns of organisms, ecologists often use an isotropic redistribution kernel to represent the distribution of movement distances based on phenomenological observations or biological considerations of the underlying physical movement mechanism. The Gaussian, two-dimensional (2D) Laplace and Bessel kernels are common choices for 2D space. All three are special (or limiting) cases of a kernel family, the Whittle-Matérn-Yasuda (WMY), first derived by Yasuda from an assumption of 2D Fickian diffusion with gamma-distributed settling times. We provide a novel derivation of this kernel family, using the simpler assumption of constant settling hazard, by means of a non-Fickian 2D diffusion equation representing movements through heterogeneous 2D media having a fractal structure. Our derivation reveals connections among a number of established redistribution kernels, unifying them under a single, flexible modelling framework. We demonstrate improvements in predictive performance in an established model for the spread of the mountain pine beetle upon replacing the Gaussian kernel by the Whittle-Matérn-Yasuda, and report similar results for a novel approximation, the product-Whittle-Matérn-Yasuda, that substantially speeds computations in applications to large datasets.

Hierarchical models in ecology: confidence intervals, hypothesis testing, and model selection using data cloning.

Hierarchical statistical models are increasingly being used to describe complex ecological processes. The data cloning (DC) method is a new general technique that uses Markov chain Monte Carlo (MCMC) algorithms to compute maximum likelihood (ML) estimates along with their asymptotic variance estimates for hierarchical models. Despite its generality, the method has two inferential limitations. First, it only provides Wald-type confidence intervals, known to be inaccurate in small samples. Second, it only yields ML parameter estimates, but not the maximized likelihood values used for profile likelihood intervals, likelihood ratio hypothesis tests, and information-theoretic model selection. Here we describe how to overcome these inferential limitations with a computationally efficient method for calculating likelihood ratios via data cloning. The ability to calculate likelihood ratios allows one to do hypothesis tests, construct accurate confidence intervals and undertake information-based model selection with hierarchical models in a frequentist context. To demonstrate the use of these tools with complex ecological models, we reanalyze part of Gause's classic Paramecium data with state-space population models containing both environmental noise and sampling error. The analysis results include improved confidence intervals for parameters, a hypothesis test of laboratory replication, and a comparison of the Beverton-Holt and the Ricker growth forms based on a model selection index.

A comparison of immunogenicity and safety of indigenously developed liquid (DTwPHB-Hib) pentavalent combination vaccine (Shan 5) with Easyfive (liq) and TritanrixHB + Hiberix (lyo) in Indian infants administered according to the EPI schedule.

The study was planned to assess and compare the immune response and safety of an indigenous DTPwHB-Hib pentavalent liquid combination vaccine (Shan 5) with Easyfive and TritanrixHB+ Hiberix, the two available pentavalent combination vaccines. Four hundred infants were randomized to receive three doses of either Shan 5 or one of the two comparators. Antibody analysis was performed prior to and four to six weeks post third vaccine dose. Solicited local and systemic events upto three days and unsolicited adverse events in the 30 days follow up period after each dose were recorded. A total of 365 subjects completed the study. Four to six weeks after third dose, 98.32% of the subjects in Shan 5 group had seroprotective Anti PRP-T IgG antibody concentrations (> or =0.15 microg/mL) as compared to 100% and 98.94% subjects in TritanrixHB + Hiberix and Easyfive groups respectively. Seroprotective levels for Anti-HBs (> or =10 mIU/mL) were observed in 97.77%, 97.83% and 98.94% subjects in Shan 5, TritanrixHB + Hiberix and Easyfive groups respectively. Comparable immune responses were observed for the three other components (D, T and P) in all the groups. Four Serious Adverse Events (SAEs) were reported (three with Shan 5 and one with Easyfive), all unrelated to the respective vaccines. Most commonly reported adverse events in all the groups were pain at injection site, mild fever (<103 degrees F) and minor swelling at injection site. The study proved that Shan 5 was safe and immunogenic compared to the two other licensed vaccines.

Errors in Statistical Inference Under Model Misspecification: Evidence, Hypothesis Testing, and AIC.

The methods for making statistical inferences in scientific analysis have diversified even within the frequentist branch of statistics, but comparison has been elusive. We approximate analytically and numerically the performance of Neyman-Pearson hypothesis testing, Fisher significance testing, information criteria, and evidential statistics (Royall, 1997). This last approach is implemented in the form of evidence functions: statistics for comparing two models by estimating, based on data, their relative distance to the generating process (i.e., truth) (Lele, 2004). A consequence of this definition is the salient property that the probabilities of misleading or weak evidence, error probabilities analogous to Type 1 and Type 2 errors in hypothesis testing, all approach 0 as sample size increases. Our comparison of these approaches focuses primarily on the frequency with which errors are made, both when models are correctly specified, and when they are misspecified, but also considers ease of interpretation. The error rates in evidential analysis all decrease to 0 as sample size increases even under model misspecification. Neyman-Pearson testing on the other hand, exhibits great difficulties under misspecification. The real Type 1 and Type 2 error rates can be less, equal to, or greater than the nominal rates depending on the nature of model misspecification. Under some reasonable circumstances, the probability of Type 1 error is an increasing function of sample size that can even approach 1! In contrast, under model misspecification an evidential analysis retains the desirable properties of always having a greater probability of selecting the best model over an inferior one and of having the probability of selecting the best model increase monotonically with sample size. We show that the evidence function concept fulfills the seeming objectives of model selection in ecology, both in a statistical as well as scientific sense, and that evidence functions are intuitive and easily grasped. We find that consistent information criteria are evidence functions but the MSE minimizing (or efficient) information criteria (e.g., AIC, AICc, TIC) are not. The error properties of the MSE minimizing criteria switch between those of evidence functions and those of Neyman-Pearson tests depending on models being compared.

Metal/Metal Oxide Nanoparticles: Toxicity, Applications, and Future Prospects.

The ever-growing resistance of pathogens to antibiotics and crop disease due to pest has triggered severe health concerns in recent years. Consequently, there is a need of powerful and protective materials for the eradication of diseases. Metal/metal oxide nanoparticles (M/MO NPs) are powerful agents due to their therapeutic effects in microbial infections. In this context, the present review article discusses the toxicity, fate, effects and applications of M/MO NPs. This review starts with an introduction, followed by toxicity aspects, antibacterial and testing methods and mechanism. In addition, discussion on the impact of different M/MO NPs and their characteristics such as size, shape, particle dissolution on their induced toxicity on food and plants, as well as applications in pesticides. Finally, prospective on current and future issues are presented.

Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods.

We introduce a new statistical computing method, called data cloning, to calculate maximum likelihood estimates and their standard errors for complex ecological models. Although the method uses the Bayesian framework and exploits the computational simplicity of the Markov chain Monte Carlo (MCMC) algorithms, it provides valid frequentist inferences such as the maximum likelihood estimates and their standard errors. The inferences are completely invariant to the choice of the prior distributions and therefore avoid the inherent subjectivity of the Bayesian approach. The data cloning method is easily implemented using standard MCMC software. Data cloning is particularly useful for analysing ecological situations in which hierarchical statistical models, such as state-space models and mixed effects models, are appropriate. We illustrate the method by fitting two nonlinear population dynamics models to data in the presence of process and observation noise.

Relative Selection Strength: Quantifying effect size in habitat- and step-selection inference.

Habitat-selection analysis lacks an appropriate measure of the ecological significance of the statistical estimates-a practical interpretation of the magnitude of the selection coefficients. There is a need for a standard approach that allows relating the strength of selection to a change in habitat conditions across space, a quantification of the estimated effect size that can be compared both within and across studies. We offer a solution, based on the epidemiological risk ratio, which we term the relative selection strength (RSS). For a "used-available" design with an exponential selection function, the RSS provides an appropriate interpretation of the magnitude of the estimated selection coefficients, conditional on all other covariates being fixed. This is similar to the interpretation of the regression coefficients in any multivariable regression analysis. Although technically correct, the conditional interpretation may be inappropriate when attempting to predict habitat use across a given landscape. Hence, we also provide a simple graphical tool that communicates both the conditional and average effect of the change in one covariate. The average-effect plot answers the question: What is the average change in the space use probability as we change the covariate of interest, while averaging over possible values of other covariates? We illustrate an application of the average-effect plot for the average effect of distance to road on space use for elk (Cervus elaphus) during the hunting season. We provide a list of potentially useful RSS expressions and discuss the utility of the RSS in the context of common ecological applications.