Functional Response Data Research Articles

LOGGIC/FIREFLY-2: a phase 3, randomized trial of tovorafenib vs. chemotherapy in pediatric and young adult patients with newly diagnosed low-grade glioma harboring an activating RAF alteration

BackgroundPediatric low-grade glioma (pLGG) is essentially a single pathway disease, with most tumors driven by genomic alterations affecting the mitogen-activated protein kinase/ERK (MAPK) pathway, predominantly KIAA1549::BRAF fusions and BRAF V600E mutations. This makes pLGG an ideal candidate for MAPK pathway-targeted treatments. The type I BRAF inhibitor, dabrafenib, in combination with the MEK inhibitor, trametinib, has been approved by the United States Food and Drug Administration for the systemic treatment of BRAF V600E-mutated pLGG. However, this combination is not approved for the treatment of patients with tumors harboring BRAF fusions as type I RAF inhibitors are ineffective in this setting and may paradoxically enhance tumor growth. The type II RAF inhibitor, tovorafenib (formerly DAY101, TAK-580, MLN2480), has shown promising activity and good tolerability in patients with BRAF-altered pLGG in the phase 2 FIREFLY-1 study, with an objective response rate (ORR) per Response Assessment in Neuro-Oncology high-grade glioma (RANO-HGG) criteria of 67%. Tumor response was independent of histologic subtype, BRAF alteration type (fusion vs. mutation), number of prior lines of therapy, and prior MAPK-pathway inhibitor use.MethodsLOGGIC/FIREFLY-2 is a two-arm, randomized, open-label, multicenter, global, phase 3 trial to evaluate the efficacy, safety, and tolerability of tovorafenib monotherapy vs. current standard of care (SoC) chemotherapy in patients < 25 years of age with pLGG harboring an activating RAF alteration who require first-line systemic therapy. Patients are randomized 1:1 to either tovorafenib, administered once weekly at 420 mg/m2 (not to exceed 600 mg), or investigator’s choice of prespecified SoC chemotherapy regimens. The primary objective is to compare ORR between the two treatment arms, as assessed by independent review per RANO-LGG criteria. Secondary objectives include comparisons of progression-free survival, duration of response, safety, neurologic function, and clinical benefit rate.DiscussionThe promising tovorafenib activity data, CNS-penetration properties, strong scientific rationale combined with the manageable tolerability and safety profile seen in patients with pLGG led to the SIOPe-BTG-LGG working group to nominate tovorafenib for comparison with SoC chemotherapy in this first-line phase 3 trial. The efficacy, safety, and functional response data generated from the trial may define a new SoC treatment for newly diagnosed pLGG.Trial registrationClinicalTrials.gov: NCT05566795. Registered on October 4, 2022.

Curve Fitting Algorithm of Functional Radiation-Response Data Using Bayesian Hierarchical Gaussian Process Regression Model

We present a nonparametric Bayesian hierarchical (NBH) model and develop a variational approximation (VA) algorithm for the curve fitting of the functional radiation response data. The NBH model is based on a Bayesian hierarchical (BH) model with a Gaussian-Inverse Wishart process (G-IWP) prior, which simultaneously smooths multiple functional observations and estimates mean-covariance functions. We use the automatic differentiation variational inference (ADVI) algorithm with a Gaussian distribution as the variational distribution for approximating the posterior distribution of parameters of interest, which is applicable to a wide class of probabilistic models and can also be implemented in Stan (a probabilistic programming system). Using the NBH model and the Gaussian ADVI algorithm, we fit a dataset for the semiconductor obtained from the radiation response map (RRM) of South Korea.

Robust Inference for Partially Observed Functional Response Data.

Irregular functional data in which densely sampled curves are observed over different ranges pose a challenge for modeling and inference, and sensitivity to outlier curves is a concern in applications. Motivated by applications in quantitative ultrasound signal analysis, this paper investigates a class of robust M-estimators for partially observed functional data including functional location and quantile estimators. Consistency of the estimators is established under general conditions on the partial observation process. Under smoothness conditions on the class of M-estimators, asymptotic Gaussian process approximations are established and used for large sample inference. The large sample approximations justify a bootstrap approximation for robust inferences about the functional response process. The performance is demonstrated in simulations and in the analysis of irregular functional data from quantitative ultrasound analysis.

Abstract 4150: Enriching for adenosine antagonist patient responses through deep learning

Abstract Background: Adenosine signaling is a key metabolic pathway regulating tumor immunity. The conversion of inflammatory extracellular ATP into immunosuppressive adenosine invokes signaling of the A2a receptor (A2AR) in the tumor microenvironment. This dampens immune responses and creates a pro-tumor niche. A number of novel IO drugs targeting the adenosine pathway through inhibition of the ectonucleotidases CD39 and CD73 or the A2AR/BR are in clinical trials, including our non-CNS penetrant A2AR-selective antagonist, EXS21546 (NCT04727138). While early clinical results from other adenosine receptor-inhibitors (A2ARi) have shown modest monotherapy activity in nonspecific patient populations, we believe greater success can be achieved by leveraging methods that enable the evaluation of single-cell effects in patient samples preclinically. Using our deep learning driven image analysis platform, we define an adenosine-induced, tumor protective immunosuppression biomarker to augment A2AR antagonist responder identification. Here we describe efforts to transcriptionally and functionally map the adenosine suppressed immune potential and activation by inhibition of A2AR with EXS21546 in primary material. The goal is to reveal gene signatures indicative of adenosine immunosuppression deployable in clinical studies, increasing the likelihood of trial success by identifying patients that have the highest efficiency potential for A2AR targeted therapy. Methods: Leveraging patient material as disease relevant model systems, and collecting baseline and treatment condition transcriptomics, we model the patient specific anti-cancer immune repertoire and validate patient selection methods functionally with a translatable high content imaging platform amenable to primary human material supported by end-to-end deep learning driven image analysis. Results: Combining single cell transcriptomic and functional response data, we demonstrate preclinical mechanistic studies of A2AR antagonism on infiltrating immune cells, with the ultimate aim of discovering predictive algorithms to enrich patients more likely respond to adenosine pathway inhibitors. Patient selection gene signatures are functionally validated using a high content imaging platform with proven translational capabilities (Kornauth et al, Cancer Disc., 2021), demonstrating the association of anti-cancer immune activity with inhibition of adenosine signaling by EXS21546. Signatures and patient selection algorithms are cross-validated with publicly available data. Discussion: Gathering multiple layers of data from primary tumor tissues, we reveal and map the association of immune response potential to A2AR inhibition in cancer. Patient stratification gene signatures identified have the aim to be implemented in future studies of our candidate A2ARi EXS21546, to deliver the right drug at the right time in the right patients. Citation Format: Isabella Alt, Robert Shelke, Anna Lobley, Claudia Baumgaertler, Maja Stulic, Pierre Fons, Mark Whittaker, Klaus Hackner, Lucia Dzurillova, Edga Petru, Laudia Hadjari, Judith Lafleur, Josef Singer, Nikolaus Krall, Lukas Hefler, Thorsten Füreder, Christina Taubert, Christophe Boudesco, Andrew Payne, Gregory Ian Vladimer. Enriching for adenosine antagonist patient responses through deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4150.

A Framework for Inverse Prediction Using Functional Response Data

Abstract Inverse prediction models have commonly been developed to handle scalar data from physical experiments. However, it is not uncommon for data to be collected in functional form. When data are collected in functional form, it must be aggregated to fit the form of traditional methods, which often results in a loss of information. For expensive experiments, this loss of information can be costly. This paper introduces the functional inverse prediction (FIP) framework, a general approach which uses the full information in functional response data to provide inverse predictions with probabilistic prediction uncertainties obtained with the bootstrap. The FIP framework is a general methodology that can be modified by practitioners to accommodate many different applications and types of data. We demonstrate the framework, highlighting points of flexibility, with a simulation example and applications to weather data and to nuclear forensics. Results show how functional models can improve the accuracy and precision of predictions.

Highlights from Recent Cancer Literature

Highlights from Recent Cancer Literature

Functional response regression model on correlated longitudinal microbiome sequencing data

Functional regression has been widely used on longitudinal data, but it is not clear how to apply functional regression to microbiome sequencing data. We propose a novel functional response regression model analyzing correlated longitudinal microbiome sequencing data, which extends the classic functional response regression model only working for independent functional responses. We derive the theory of generalized least squares estimators for predictors’ effects when functional responses are correlated, and develop a data transformation technique to solve the computational challenge for analyzing correlated functional response data using existing functional regression method. We show by extensive simulations that our proposed method provides unbiased estimations for predictors’ effect, and our model has accurate type I error and power performance for correlated functional response data, compared with classic functional response regression model. Finally we implement our method to a real infant gut microbiome study to evaluate the relationship of clinical factors to predominant taxa along time.

The Cancer Surfaceome Atlas integrates genomic, functional and drug response data to identify actionable targets.

Cell-surface proteins (SPs) are a rich source of immune and targeted therapies. By systematically integrating single-cell and bulk genomics, functional studies and target actionability, in the present study we comprehensively identify and annotate genes encoding SPs (GESPs) pan-cancer. We characterize GESP expression patterns, recurrent genomic alterations, essentiality, receptor-ligand interactions and therapeutic potential. We also find that mRNA expression of GESPs is cancer-type specific and positively correlates with protein expression, and that certain GESP subgroups function as common or specific essential genes for tumor cell growth. We also predict receptor-ligand interactions substantially deregulated in cancer and, using systems biology approaches, we identify cancer-specific GESPs with therapeutic potential. We have made this resource available through the Cancer Surfaceome Atlas ( http://fcgportal.org/TCSA ) within the Functional Cancer Genome data portal.

Efficient Estimation for Varying-Coefficient Mixed Effects Models with Functional Response Data

In this article, we focus on the estimation of varying-coefficient mixed effects models for longitudinal and sparse functional response data, by using the generalized least squares method coupling a modified local kernel smoothing technique. This approach provides a useful framework that simultaneously takes into account the within-subject covariance and all observation information in the estimation to improve efficiency. We establish both uniform consistency and pointwise asymptotic normality for the proposed estimators of varying-coefficient functions. Numerical studies are carried out to illustrate the finite sample performance of the proposed procedure. An application to the white matter tract dataset obtained from Alzheimer’s Disease Neuroimaging Initiative study is also provided.

Abstract B070: LOCUS database enables rapid query and targeted selection of preclinical tumor models based on cancer genomics and pharmacologic response

Abstract Preclincal drug testing in oncology research has benefited from the development of thousands of tumors models—both in vitro and in vivo. Within BioDuro laboratories alone, there is a collection of &amp;gt;500 models, including traditional 2D human cell lines grown in vitro, primary 3D lines, cell-derived xenograft (CDX), patient derived xenograft (PDX) and syngeneic murine models (SYN). Identifying the most relevant models to support cancer drug discovery is a challenge. In an era of precision medicine, the need to understand tumor model characteristics, such as functional drug response profiles or genomic status, has become a critical path. In this report, we present LOCUS, a SQL backed database with graphical user interface, populated with functional pharmacologic response and genomic data for hundreds of tumor models including in vitro 2D, 3D, CDX, PDX and SYN. Models are readily searched by any number of conditions, including cancer type, model type, specific mutation, or target expression level. LOCUS combines relational databases for pharmacology and genomics data with scientific statistical models through a spring integrator. A scientific data centered domain architecture allows for pharmacogenomic queries and analysis. Pharmacogenomic data is pushed to dynamic scientific statistical models running on a python environment, the results of which are visualized to a Java EE UI Framework. All tumor models are genomically characterized, with searchable fields for single nucleotide variations, insertion deletions, and transcript expression levels. Extracted RNA was profiled using next generation sequencing performed on Illumina HiSeq using a paired end protocol. For xenograft samples, reads were first aligned and filtered by the mouse reference genome using the Bowtie2 algorithm. The remaining reads were aligned to the GRCh38 human reference genome using the MapSplice2 algorithm. Transcript- and Gene-level expression were determined by counting the number of sequence alignments per exon according to the RefSeq gene model, and computing Fragments Per Killobase Million (FPKM) and Transcripts Per Million (TPM) values using best practices. Opossum software was used to further prepare alignments produced by MapSplice2 algorithm for small variant calling. The Platypus variant caller was used to identify single nucleotide variant, small insertion, and small deletion events. The variation events were annotated using SnpEff and custom perl scripts to overlay known somatic mutations with oncogenic significance. LOCUS provides users with rapid access to hundreds of pharmacology and genomic data sets that enable rational selection of tumor models for preclinical oncology drug testing. The database and structure are readily scaled, supporting future growth of thousands of more tumor models and characterization. Citation Format: Armand Amin, Sandeep Sanga, Shanshan Gan, Ruyi Li, Todd Blewett, Zhuyao Wang, Thomas B Broudy. LOCUS database enables rapid query and targeted selection of preclinical tumor models based on cancer genomics and pharmacologic response [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr B070. doi:10.1158/1535-7163.TARG-19-B070

Abstract 458: Precision systems medicine in acute myeloid leukemia: real-time translation of tailored therapeutic opportunities arising from ex-vivo drug sensitivity testing and molecular profiling

Abstract Acute myeloid leukemia (AML) is an aggressive disease of clonal hematopoietic progenitor cells. Here, we applied ex-vivo drug sensitivity and resistance testing on AML patient cells with 362 emerging and 153 approved cancer drugs together with genomic and transcriptomic profiling to identify and tailor therapies for patients with advanced disease. Ex-vivo testing with freshly isolated patient cells revealed cancer-specific efficacies of approved drugs in 97% of the 164 patient cases, including 47% of the cases with no actionable driver mutations. We identified 142 statistically significant associations between drug responses and somatic mutations, including increased sensitivity to JAK inhibitors in patients with NPM1 mutations. Transcriptomic profiles predicted drug responses better than genomics and helped to identify additional response markers, especially beyond mutations. For example, overexpression of HOX family genes was associated with sensitivity to JAK inhibitors in patients with NPM1 mutation. In a prospective study, we translated the functional drug response and molecular profile data to the clinic and suggested tailored therapy with targeted drugs for 26 relapsed or refractory AML patients. In an observational intervention study, acting on these recommendations resulted in a temporary complete clinical remission or leukemia-free state in 39% of the cases. In summary, we conclude that ex-vivo testing of drugs on patient AML cells i) revealed clinically actionable drug efficacies in almost all AML patients, including patients with no actionable mutations, ii) predicted cases with actionable driver mutations with no pharmacological dependency on the target, and iii) enabled real-time tailoring of therapy with 39% clinical response rate in chemorefractory advanced AML. Taken together, we believe this real-time systems medicine approach could become a powerful strategy for tailoring therapies for individual patients in the future. Citation Format: Disha Malani, Ashwni Kumar, Bhagwan Yadav, Mika Kontro, Swapnil Potdar, Oscar Bruck, Säri Kytölä, Jani Saarela, Samuli Eldfors, Riikka Karjalainen, Muntasir M. Majumder, Imre Västrik, Pekka Ellonen, Matti Kankainen, Minna Suvela, Siv Knappila, Alun Parson, Aino Palva, Pirkko Mattila, Evgeny Kulesskiy, Laura Turunen, Karoliina Laamanen, Elina Lehtinen, Maria Nurmi, Katja Suomi, Astrid Muruimägi, Bjorn T. Gjertsen, Satu Mustjoki, Simon Anders, Maija Wolf, Tero Aittokallio, Krister Wennerberg, Caroline Heckman, Kimmo Porkka, Olli Kallioniemi. Precision systems medicine in acute myeloid leukemia: real-time translation of tailored therapeutic opportunities arising from ex-vivo drug sensitivity testing and molecular profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 458.

Abstract 2472: LOCUS: Queryable database of cancer genomics and pharmacologic response enables rapid selection of in vitro and in vivo preclinical tumor models

Abstract Preclincal drug testing in oncology research has benefited from the development of thousands of tumors models—both in vitro and in vivo. Within BioDuro laboratories alone, there is a collection of &amp;gt;500 models, including traditional 2D human cell lines grown in vitro, primary 3D lines, cell-derived xenograft (CDX), patient derived xenograft (PDX) and syngeneic murine models (SYN). Identifying the most relevant models to support cancer drug discovery is a challenge. In an era of precision medicine, the need to understand tumor model characteristics, such as functional drug response profiles or genomic status, has become a critical path. In this report, we present LOCUS, a SQL backed database with graphical user interface, populated with functional pharmacologic response and genomic data for hundreds of tumor models including in vitro 2D, 3D, CDX, PDX and SYN. Models are readily searched by any number of conditions, including cancer type, model type, specific mutation, or target expression level. LOCUS combines relational databases for pharmacology and genomics data with scientific statistical models through a spring integrator. A scientific data centered domain architecture allows for pharmacogenomic queries and analysis. Pharmacogenomic data is pushed to dynamic scientific statistical models running on a python environment, the results of which are visualized to a Java EE UI Framework. All tumor models are genomically characterized, with searchable fields for single nucleotide variations, insertion deletions, and transcript expression levels. Extracted RNA was profiled using next generation sequencing performed on Illumina HiSeq using a paired end protocol. For xenograft samples, reads were first aligned and filtered by the mouse reference genome using the Bowtie2 algorithm. The remaining reads were aligned to the GRCh38 human reference genome using the MapSplice2 algorithm. Transcript- and Gene-level expression were determined by counting the number of sequence alignments per exon according to the RefSeq gene model, and computing Fragments Per Killobase Million (FPKM) and Transcripts Per Million (TPM) values using best practices. Opossum software was used to further prepare alignments produced by MapSplice2 algorithm for small variant calling. The Platypus variant caller was used to identify single nucleotide variant, small insertion, and small deletion events. The variation events were annotated using SnpEff and custom perl scripts to overlay known somatic mutations with oncogenic significance. LOCUS provides users with rapid access to hundreds of pharmacology and genomic data sets that enable rational selection of tumor models for preclinical oncology drug testing. The database and structure are readily scaled, supporting future growth of thousands of more tumor models and characterization. Citation Format: Armand Amin, Sandeep Sanga, Shanshan Gan, Yong Hu, Thomas Broudy. LOCUS: Queryable database of cancer genomics and pharmacologic response enables rapid selection of in vitro and in vivo preclinical tumor models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2472.

Applications and limitations of fitting of the operational model to determine relative efficacies of agonists

Proper determination of agonist efficacy is essential in the assessment of agonist selectivity and signalling bias. Agonist efficacy is a relative term that is dependent on the system in which it is measured, especially being dependent on receptor expression level. The operational model (OM) of functional receptor agonism is a useful means for the determination of agonist functional efficacy using the maximal response to agonist and ratio of agonist functional potency to its equilibrium dissociation constant (KA) at the active state of the receptor. However, the functional efficacy parameter τ is inter-dependent on two other parameters of OM; agonist’s KA and the highest response that could be evoked in the system by any stimulus (EMAX). Thus, fitting of OM to functional response data is a tricky process. In this work we analyse pitfalls of fitting OM to experimental data and propose a rigorous fitting procedure where KA and EMAX are derived from half-efficient concentration of agonist and apparent maximal responses obtained from a series of functional response curves. Subsequently, OM with fixed KA and EMAX is fitted to functional response data to obtain τ. The procedure was verified at M2 and M4 muscarinic receptors fused with the G15 G-protein α-subunit. The procedure, however, is applicable to any receptor-effector system.

Size-dependent functional response ofXenopus laevisfeeding on mosquito larvae

Predators can play an important role in regulating prey abundance and diversity, determining food web structure and function, and contributing to important ecosystem services, including the regulation of agricultural pests and disease vectors. Thus, the ability to predict predator impact on prey is an important goal in ecology. Often, predators of the same species are assumed to be functionally equivalent, despite considerable individual variation in predator traits known to be important for shaping predator–prey interactions, like body size. This assumption may greatly oversimplify our understanding of within-species functional diversity and undermine our ability to predict predator effects on prey. Here, we examine the degree to which predator–prey interactions are functionally homogenous across a natural range of predator body sizes. Specifically, we quantify the size-dependence of the functional response of African clawed frogs (Xenopus laevis) preying on mosquito larvae (Culex pipiens). Three size classes of predators, small (15–30 mm snout-vent length), medium (50–60 mm) and large (105–120 mm), were presented with five densities of prey to determine functional response type and to estimate search efficiency and handling time parameters generated from the models. The results of mesocosm experiments showed that type of functional response of X. laevis changed with size: small predators exhibited a Type II response, while medium and large predators exhibited Type III responses. Functional response data showed an inversely proportional relationship between predator attack rate and predator size. Small and medium predators had highest and lowest handling time, respectively. The change in functional response with the size of predator suggests that predators with overlapping cohorts may have a dynamic impact on prey populations. Therefore, predicting the functional response of a single size-matched predator in an experiment may misrepresent the predator’s potential impact on a prey population.

UNIFORMLY VALID POST-REGULARIZATION CONFIDENCE REGIONS FOR MANY FUNCTIONAL PARAMETERS IN Z-ESTIMATION FRAMEWORK.

In this paper, we develop procedures to construct simultaneous confidence bands for potentially infinite-dimensional parameters after model selection for general moment condition models where is potentially much larger than the sample size of available data, n. This allows us to cover settings with functional response data where each of the parameters is a function. The procedure is based on the construction of score functions that satisfy Neyman orthogonality condition approximately. The proposed simultaneous confidence bands rely on uniform central limit theorems for high-dimensional vectors (and not on Donsker arguments as we allow for ). To construct the bands, we employ a multiplier bootstrap procedure which is computationally efficient as it only involves resampling the estimated score functions (and does not require resolving the high-dimensional optimization problems). We formally apply the general theory to inference on regression coefficient process in the distribution regression model with a logistic link, where two implementations are analyzed in detail. Simulations and an application to real data are provided to help illustrate the applicability of the results.

Prognostic factors of antiangiogenic therapy response in neovascular macular degeneration

to study the influence of prognostic factors on the dynamics of functional and morphological response during anti-VEGF therapy in patients with nAMD. Retrospective analysis of anti-angiogenic therapy results was performed on the data of 1020 patients with nAMD. Some patients were put into two subgroups: 100 patients with retinal pigment epithelium detachment (PED) and 100 patients without PED. Comparative analysis of functional and morphological parameters was performed in the subgroups. Baseline visual acuity (VA) in patients with PED (0.14±0.1) was significantly (р<0.05) lower than in patients without PED (0.32±0.21) and had negative correlation with PED presence (r=-0.61, р<0.05). By the end of the 12 months follow-up Best Corrected Visual Acuity (BCVA) was 0.52±0.28 and 0.62±0.28 (р>0.05) in patients with PED and without PED respectively. Mean number of injections in patients with PED was 5.7±2.5 compared to 3.5±1.2 in patients without PED (р<0.05). After 12 months VA in patients with PED was associated with baseline BCVA (r=0.59, р<0.05), VA after 4th injection (r=0.84, р<0.05) and baseline retinal thickness (r=0.60, р<0.05). PED in patients with nAMD was shown to be an unfavorable factor associated with significantly lower baseline BCVA that requires careful monitoring. At least three 'loading' injections are required to determine drug responsiveness and acquire functional and morphological response data. Correlation of BCVA values at different timepoints (baseline/after 3 'loading' doses/after 12 month) was found.

A Functional Varying-Coefficient Single-Index Model for Functional Response Data

Motivated by the analysis of imaging data, we propose a novel functional varying-coefficient single-index model (FVCSIM) to carry out the regression analysis of functional response data on a set of covariates of interest. FVCSIM represents a new extension of varying-coefficient single-index models for scalar responses collected from cross-sectional and longitudinal studies. An efficient estimation procedure is developed to iteratively estimate varying coefficient functions, link functions, index parameter vectors, and the covariance function of individual functions. We systematically examine the asymptotic properties of all estimators including the weak convergence of the estimated varying coefficient functions, the asymptotic distribution of the estimated index parameter vectors, and the uniform convergence rate of the estimated covariance function and their spectrum. Simulation studies are carried out to assess the finite-sample performance of the proposed procedure. We apply FVCSIM to investigate the development of white matter diffusivities along the corpus callosum skeleton obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Supplementary material for this article is available online.

Wrecking the Curve: Altered Functional Response of Tetragoneuria (Odonata: Corduliidae) Naiads Infected with Metacercariae of Haematoloechus floedae.

The ubiquity of host-parasite interactions and their potential for substantial representation, in terms of overall biomass, within ecosystems suggests that parasites have the capacity to influence energy flow within an ecosystem. Although the influence of certain parasites on prey behavior has been well documented, parasites could also exert an influence on ecosystem dynamics by influencing predator feeding behavior. The functional response of Tetragoneuria naiads was characterized by presenting naiads with varying abundances of Daphnia magna , after which a subset of the naiads were exposed to cercariae of Haematoloechus floedae, and the feeding trials repeated for both the control and exposed odonates. A type II functional response was chosen as an appropriate model for comparison. An indicator variable approach to nonlinear regression of the functional response data indicated that infected odonate naiads spent significantly more time foraging than they did before infection, whereas there was no significant change in the functional response of the control naiads. Infected odonates also had a slower rate of growth. These results imply a metabolic cost to infection of Tetragoneuria naiads by H. floedae that might be associated with the encapsulating response to the metacercariae that was observed in infected naiads.

A Functional Varying-Coefficient Single-Index Model for Functional Response Data

A Functional Varying-Coefficient Single-Index Model for Functional Response Data

Bayesian inference and model choice for Holling’s disc equation: a case study on an insect predator-prey system

The dynamics of predator-prey systems relate strongly to the density (in)dependent attributes of the predator’s feeding rate, i.e., its functional response. The outcome of functional response models is often used in theoretical or applied ecology in order to extract information about the mechanisms associated with the feeding behavior of predators. The focus of this study centres upon Holling’s type II functional response model, commonly known as the disc equation, which describes an inverse-density dependent mortality caused by a single predator to its prey. A common method to provide inference on functional response data involves nonlinear least squares optimization, assuming independent Gaussian errors, an assumption often violated in practice due to the heteroscedasticity which is typically present in the data. Moreover, as prey depletion is common in functional response experiments, the differential form of disc equation ought to be used in principle. We introduce a related statistical model and adopt a Bayesian approach for estimating parameters in ordinary differential equation models. In addition, we explore model uncertainty via Bayes factors. Our approach is illustrated via the analysis of several data sets concerning the functional response of a widespread ladybird beetle (Propylea quatuordecimpunctata) to its prey (Aphis fabae), predicting the efficiency of this predator on a common and important aphid species. The results showed that the approach developed in this study is towards a direction for accurate estimation of the parameters that determine the shape of the functional response of a predator without having to make unnecessary assumptions. The R (www.r-project.org) code for fitting the proposed model to experimental data is made freely available.