The protective value of the colour and shape of the mountain katydid's antipredator defence

Deimatic behaviour is performed by prey when attacked by predators as part of an antipredator strategy. The behaviour is part of a sequence that consists of several defences, for example they can be preceded by camouflage and followed by a hidden putatively aposematic signal that is only revealed when the deimatic behaviour is performed. When displaying their hidden signal, mountain katydids (Acripeza reticulata) hold their wings vertically, exposing striking red and black stripes with blue spots and oozing an alkaloid-rich chemical defence derived from its Senecio diet. Understanding differences and interactions between deimatism and aposematism has proven problematic, so in this study we isolated the putative aposematic signal of the mountain katydid's antipredator strategy to measure its survival value in the absence of their deimatic behaviour. We manipulated two aspects of the mountain katydid's signal, colour pattern and whole body shape during display. We deployed five kinds of clay models, one negative control and four katydid-like treatments, in 15 grids across part of the mountain katydid's distribution to test the hypothesis that their hidden signal is aposematic. If this hypothesis holds true, we expected that the models, which most closely resembled real katydids would be attacked the least. Instead, we found that models that most closely resembled real katydids were the most likely to be attacked. We suggest several ideas to explain these results, including that the deimatic phase of the katydid's display, the change from a camouflaged state to exposing its hidden signal, may have important protective value.     

Testing different 3D techniques using geometric morphometrics: Implications for cranial fluctuating asymmetry in humans

This study aimed to test the performance of 3D digitizer, CT scanner, and surface scanner in detecting cranial fluctuating asymmetry. Sets of 32 landmarks (6 in the midline and 13 bilateral) were acquired from 14 archeological crania using a 3D digitizer, and from 3D models generated from a CT scanner and surface scanner using Viewbox 4. Levels of shape variation were analyzed in MorphoJ using Procrustes analysis of variance and Principal component analysis. Intra-observer error accounted for 1.7%, 1.8%, and 4.5% of total shape variation for 3D digitizer, CT scanner, and surface scanner respectively. Fluctuating asymmetry accounted for 15%–16% of total shape variation. Variation between techniques accounted for 18% of total shape variation. We found a higher level of missing landmarks in our surface scan data than for both 3D digitizer and CT scanner data, and both 3D model-based techniques sometimes obscured taphonomic damage. All three 3D techniques are appropriate for measuring cranial fluctuating asymmetry. We advise against combining data collected with different techniques.     

Principles of nonlinear regression modeling

Summary Five principles govern the selection of nonlinear regression models for bacterial growth. Examples are given of the various ways in which researchers have approached the problems of nonlinear regression modeling together with some discussion of linear modeling.Reference to a brand or firm name does not constitute endorsement by the US Department of Agriculture over others of a similar nature not mentioned.     

Evaluating treatment effects in group sequential multivariate longitudinal studies with covariate adjustment

Jeffries et al. (2018) investigated testing for a treatment difference in the setting of a randomized clinical trial with a single outcome measured longitudinally over a series of common follow-up times while adjusting for covariates. That paper examined the null hypothesis of no difference at any follow-up time versus the alternative of a difference for at least one follow-up time. We extend those results here by considering multivariate outcome measurements, where each individual outcome is examined at common follow-up times. We consider the case where there is interest in first testing for a treatment difference in a global function of the outcomes (e.g., weighted average or sum) with subsequent interest in examining the individual outcomes, should the global function show a treatment difference. Testing is conducted for each follow-up time and may be performed in the setting of a group sequential trial. Testing procedures are developed to determine follow-up times for which a global treatment difference exists and which individual combinations of outcome and follow-up time show evidence of a difference while controlling for multiplicity in outcomes, follow-up, and interim analyses. These approaches are examined in a study evaluating the effects of tissue plasminogen activator on longitudinally obtained stroke severity measurements.     

Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time-dependent covariate.     

Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach

This paper is concerned with using multivariate binary observations to estimate the probabilities of unobserved classes with scientific meanings. We focus on the setting where additional information about sample similarities is available and represented by a rooted weighted tree. Every leaf in the given tree contains multiple samples. Shorter distances over the tree between the leaves indicate a priori higher similarity in class probability vectors. We propose a novel data integrative extension to classical latent class models with tree-structured shrinkage. The proposed approach enables (1) borrowing of information across leaves, (2) estimating data-driven leaf groups with distinct vectors of class probabilities, and (3) individual-level probabilistic class assignment given the observed multivariate binary measurements. We derive and implement a scalable posterior inference algorithm in a variational Bayes framework. Extensive simulations show more accurate estimation of class probabilities than alternatives that suboptimally use the additional sample similarity information. A zoonotic infectious disease application is used to illustrate the proposed approach. The paper concludes by a brief discussion on model limitations and extensions.     

Forest resistance to drought in a humid tropical dipterocarp forest in the Western Ghats of India

Droughts are linked to tree die-offs in the biodiverse humid tropics. We assessed drought response of a Dipterocarp Forest and found a marginal decrease in tree survival, indicating drought resistance. Understory and emergent species were sensitive to drought. Urgent focus is needed to understand drought impacts and plant physiological responses in South Asian forests.     

Chiral symmetry breaking: Experimental results and computer analysis of a liquid-phase autoxidation

The autoxidation of tetralin is treated as a model reaction system to define the applicability of stereospecific autocatalysis. This concept, predicting a spontaneous amplification of enantiomeric excess generated by an autocatalytic chemical reaction, is used in several theoretical models as an explanation for the origin of natural optical activity. The reaction system investigated obeys the basic criteria of these models: a chiral intermediate (tetralin hydroperoxide) is produced from an achiral substrate (tetralin) via an autocatalytic pathway where the feedback mechanism is expected to generate a state of broken chiral symmetry. In order to test the amplification capacity of this reaction a computer analysis of the kinetic scheme is performed. This simulation is derived from the known kinetic scheme of autoxidation and is validated by fitting the experimentally observed data of hydroperoxide evolution. Calculations show that this model allows powerful amplification of enantiomeric excess and a transient amplification of the optical rotation. It is also demonstrated that the model system exhibits pronounced sensitivity toward any loss of absolute configuration of the involved chiral species. Since an amplification effect results exclusively at a high degree of stereoselectivity, it is concluded that stereospecific autocatalysis is possible in systems which show template reactions, crystallization, or colloidal effects. © 1993 Wiley-Liss, Inc.     

A mechanistic spatio-temporal modeling of COVID-19 data

Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used. Point processes are the natural tool to perform such analyses. We analyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19) cases detected in Valencia (Spain) during the first 11 months (February 2020 to January 2021) of the pandemic. In particular, we propose a mechanistic spatio-temporal model for the first-order intensity function of the point process. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while similar studies have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations. The results suggest that there has only been a mild level of spatio-temporal interaction between cases in the study area, which to a large extent corresponds to people living in the same residential location. Extending our proposed model to larger areas could help us gain knowledge on the propagation of COVID-19 across cities with high mobility levels.