Activity budget and spatial distribution of Bennett's wallabies (Macropus rufogriseus) in open versus closed exhibit designs

Although many studies investigating the impacts of zoo exhibit designs on captive animals exist, none have been performed on how they influence the behavior and welfare of captive Bennett's wallabies (Macropus rufogriseus). Here, we assess the impact of exhibit design on the activity budget and spatial distribution of Bennett's wallabies. We compared animal behavior in two open exhibits (i.e. physical interaction between animals and visitors permitted) to two closed exhibits (i.e. physical interaction between animals and visitors prohibited). Behavioral data were collected using focal sampling, and spatial distribution was recorded on exhibit maps at regular time intervals. We found a significant increase in feeding and interactive behaviors in closed exhibits in comparison to open exhibits. However, other behaviors such as resting, locomotion, and vigilance did not vary with design. Functional use of space was similar between both designs; however, the effect of habituation may be relevant to consider in future studies. Although some support for visitor effects were present, our study provided no evidence for strong impacts of exhibit design on Bennett's wallaby welfare. Our study emphasizes the need for additional research into the impacts of how zoo environments affect Bennett's wallaby behavior and welfare.     

On the Role of Baseline Measurements for Crossover Designs under the Self and Mixed Carryover Effects Model

Summary .  It is well known that optimal designs are strongly model dependent. In this article, we apply the Lagrange multiplier approach to the optimal design problem, using a recently proposed model for carryover effects. Generally, crossover designs are not recommended when carryover effects are present and when the primary goal is to obtain an unbiased estimate of the treatment effect. In some cases, baseline measurements are believed to improve design efficiency. This article examines the impact of baselines on optimal designs using two different assumptions about carryover effects during baseline periods and employing a nontraditional crossover design model. As anticipated, baseline observations improve design efficiency considerably for two-period designs, which use the data in the first period only to obtain unbiased estimates of treatment effects, while the improvement is rather modest for three- or four-period designs. Further, we find little additional benefits for measuring baselines at each treatment period as compared to measuring baselines only in the first period. Although our study of baselines did not change the results on optimal designs that are reported in the literature, the problem of strong model dependency problem is generally recognized. The advantage of using multiperiod designs is rather evident, as we found that extending two-period designs to three- or four-period designs significantly reduced variability in estimating the direct treatment effect contrast.     

A model-independent approach to the theory of experimental designs and a special discussion of bib. i general definition and designs with one or two block factors

Experimental designs are definded by introducing an assignment matrix Z. It is shown by block designs and double block designs that using Z or an operator on Z otherwise defined, well known designs can be got as special cases. Till now we didn' find an experimental design which could not be defined by our matrix Z. The definitions of properties of experimental designs can be given independently of the model of the statistical analysis. This is shown for the property of balance of block designs.     

Single subject (small N) research designs and zoo research

This paper presents single case (small N) research designs as a design option for research in zoos. Because of the small numbers of animals in zoos, traditional large group designs using inferential statistics are often not feasible. Single subject research designs are discussed with respect to logic, specific design options, and validity. Specific examples are used throughout to demonstrate how these designs would be used in zoo research. © 1996 Wiley-Liss, Inc.     

PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design

Protein design algorithms must search an enormous conformational space to identify favorable conformations. As a result, those that perform this search with guarantees of accuracy generally start with a conformational pruning step, such as dead-end elimination (DEE). However, the mathematical assumptions of DEE-based pruning algorithms have up to now severely restricted the biophysical model that can feasibly be used in protein design. To lift these restrictions, I propose to prune local unrealistic geometries (PLUG) using a linear programming-based method. PLUG's biophysical model consists only of well-known lower bounds on interatomic distances. PLUG is intended as preprocessing for energy-based protein design calculations, whose biophysical model need not support DEE pruning. Based on 96 test cases, PLUG is at least as effective at pruning as DEE for larger protein designs—the type that most require pruning. When combined with the LUTE protein design algorithm, PLUG greatly facilitates designs that account for continuous entropy, large multistate designs with continuous flexibility, and designs with extensive continuous backbone flexibility and advanced nonpairwise energy functions. Many of these designs are tractable only with PLUG, either for empirical reasons (LUTE's machine learning step achieves an accurate fit only after PLUG pruning), or for theoretical reasons (many energy functions are fundamentally incompatible with DEE).     

Optimal nested row-column designs with specified components

We consider nested row-column designs where each of the rowand column component designs is specified. For the case thateach of the component designs has second-order balance, we definesuch a nested row-column design to be special if it is generallybalanced, with the smallest possible number of canonical treatmentcontrasts having the lower canonical efficiency factor in bothcomponents. We show that if any special row-column design existsthen it is A-optimal over all nested row-column designs withthe given components.     

Using partition designs to enhance purification process understanding

Characterization of purification processes by identifying significant input parameters and establishing predictive models is vital to developing robust processes. Current experimental design approaches restrict analysis to one process step at a time, which can severely limit the ability to identify interactions between process steps. This can be overcome by the use of partition designs which can model multiple, sequential process steps simultaneously. This paper presents the application of partition designs to a monoclonal antibody purification process. Three sequential purification steps were modeled using both traditional experimental designs and partition designs and the results compared as a proof of concept study. The partition and traditional design approaches identified the same input parameters within each process step that significantly affected the product quality output examined. The partition design also identified significant interactions between input parameters across process steps that could not be uncovered by the traditional approach. Biotechnol. Bioeng. 2010;107: 814–824. © 2010 Wiley Periodicals, Inc.     

On the Convergence of an Algorithm of Jones

Pearce (1976) proposed the use of the variance matrix for design block experiments. Jones (1976) gave an iterative formula for, but stated that the iteration failed to converge for some block designs. We derive necessary and sufficient conditions for the convergence of JONES' iteration. We also note that a simple modification to JONES' iteration will ensure its convergence.     

Optimal adaptive two‐stage designs for phase II cancer clinical trials

In oncology, single‐arm two‐stage designs with binary endpoint are widely applied in phase II for the development of cytotoxic cancer therapies. Simon's optimal design with prefixed sample sizes in both stages minimizes the expected sample size under the null hypothesis and is one of the most popular designs. The search algorithms that are currently used to identify phase II designs showing prespecified characteristics are computationally intensive. For this reason, most authors impose restrictions on their search procedure. However, it remains unclear to what extent this approach influences the optimality of the resulting designs. This article describes an extension to fixed sample size phase II designs by allowing the sample size of stage two to depend on the number of responses observed in the first stage. Furthermore, we present a more efficient numerical algorithm that allows for an exhaustive search of designs. Comparisons between designs presented in the literature and the proposed optimal adaptive designs show that while the improvements are generally moderate, notable reductions in the average sample size can be achieved for specific parameter constellations when applying the new method and search strategy.     

Comparison of cohort and nested case-control designs for estimating the effect of time-varying drug exposure on the risk of adverse event in the presence of ties

Cohort and nested case-control (NCC) designs are frequently used in pharmacoepidemiology to assess the associations of drug exposure that can vary over time with the risk of an adverse event. Although it is typically expected that estimates from NCC analyses are similar to those from the full cohort analysis, with moderate loss of precision, only few studies have actually compared their respective performance for estimating the effects of time-varying exposures (TVE). We used simulations to compare the properties of the resulting estimators of these designs for both time-invariant exposure and TVE. We varied exposure prevalence, proportion of subjects experiencing the event, hazard ratio, and control-to-case ratio and considered matching on confounders. Using both designs, we also estimated the real-world associations of time-invariant ever use of menopausal hormone therapy (MHT) at baseline and updated, time-varying MHT use with breast cancer incidence. In all simulated scenarios, the cohort-based estimates had small relative bias and greater precision than the NCC design. NCC estimates displayed bias to the null that decreased with a greater number of controls per case. This bias markedly increased with higher proportion of events. Bias was seen with Breslow's and Efron's approximations for handling tied event times but was greatly reduced with the exact method or when NCC analyses were matched on confounders. When analyzing the MHT-breast cancer association, differences between the two designs were consistent with simulated data. Once ties were taken correctly into account, NCC estimates were very similar to those of the full cohort analysis.     

Sample Size Requirements in Studies of the Etiologic Fraction

This paper outlines methods of determining sample size for epidemiologic research in studies of the etiologic fraction. The basic model with a dichotomous disease and a single dichotomous exposure factor is considered. To determine sample size, the researcher must specify: the magnitude of the etiologic fraction ε to be detected as statistically significant, the level of significance α, the power 1 - β of the test, p the proportion of the population exposed to the risk factor and R the proportion of the population with the disease. Sample size formulas and tables are presented for the case-control, cohort and cross-sectional designs. Optimal allocation considerations are examined to minimize cost for a specified power. Extensive use is made of Walter's results concerning the asymptotic variance of the maximum likelihood estimator of the etiologic fraction for the three epidemiologic study designs.     

Application of the Biased Spring Balance Weighing Design Theory to Estimation of Differences of Line Effects for Legume Content

The incidence matrix of a BIB design for v treatments has been used to construct a biased spring balance weighing design. Conditions under which an optimum biased spring balance weighing design exists are given. It is also shown how this theory may be utilized to obtain treatment and experiment designs to estimate differences in legume content between pair of lines in an experiment overseeded with grass species.     

Fast gap‐free enumeration of conformations and sequences for protein design

Despite significant successes in structure‐based computational protein design in recent years, protein design algorithms must be improved to increase the biological accuracy of new designs. Protein design algorithms search through an exponential number of protein conformations, protein ensembles, and amino acid sequences in an attempt to find globally optimal structures with a desired biological function. To improve the biological accuracy of protein designs, it is necessary to increase both the amount of protein flexibility allowed during the search and the overall size of the design, while guaranteeing that the lowest‐energy structures and sequences are found. DEE/A*‐based algorithms are the most prevalent provable algorithms in the field of protein design and can provably enumerate a gap‐free list of low‐energy protein conformations, which is necessary for ensemble‐based algorithms that predict protein binding. We present two classes of algorithmic improvements to the A* algorithm that greatly increase the efficiency of A*. First, we analyze the effect of ordering the expansion of mutable residue positions within the A* tree and present a dynamic residue ordering that reduces the number of A* nodes that must be visited during the search. Second, we propose new methods to improve the conformational bounds used to estimate the energies of partial conformations during the A* search. The residue ordering techniques and improved bounds can be combined for additional increases in A* efficiency. Our enhancements enable all A*‐based methods to more fully search protein conformation space, which will ultimately improve the accuracy of complex biomedically relevant designs. Proteins 2015; 83:1859–1877. © 2015 Wiley Periodicals, Inc.     

Highly efficient stepped wedge designs for clusters of unequal size

The stepped wedge design (SWD) is a form of cluster randomized trial, usually comparing two treatments, which is divided into time periods and sequences, with clusters allocated to sequences. Typically all sequences start with the standard treatment and end with the new treatment, with the change happening at different times in the different sequences. The clusters will usually differ in size but this is overlooked in much of the existing literature. This paper considers the case when clusters have different sizes and determines how efficient designs can be found. The approach uses an approximation to the variance of the treatment effect, which is expressed in terms of the proportions of clusters and of individuals allocated to each sequence of the design. The roles of these sets of proportions in determining an efficient design are discussed and illustrated using two SWDs, one in the treatment of sexually transmitted diseases and one in renal replacement therapy. Cluster-balanced designs, which allocate equal numbers of clusters to each sequence, are shown to have excellent statistical and practical properties; suggestions are made about the practical application of the results for these designs. The paper concentrates on the cross-sectional case, where subjects are measured once, but it is briefly indicated how the methods can be extended to the closed-cohort design.     

Analysis of the effect of design parameters and their interactions on the strength of dental restorations with endodontic posts,using finite element models and statistical analysis

Many previous studies, both in vitro and with model simulations, have been conducted in an attempt to reach a full understanding of how the different design parameters of an endodontically restored tooth affect its mechanical strength. However, differences in the experimental set-up or modelling conditions and the limited number of parameters studied in each case prevent us from obtaining clear conclusions about the real significance of each parameter. In this work, a new approach is proposed for this purpose based on the combination of a validated three-dimensional parametric biomechanical model of the restored tooth and statistical analysis using full factorial analysis of variance. A two-step approach with two virtual tests (with, respectively, 128 and 81 finite element models) was used in the present work to study the effect of several design parameters on the strength of a restored incisor, using full factorial designs. Within the limitations of this study, and for cases where the parameters are within the ranges that were tested, the conclusions indicate that the material of the post is the most significant factor as far as its strength is concerned, the use of a low Young's modulus being preferable for this component. Once the post material has been chosen, the geometry of the post is of less importance than the Young's modulus selected for the core or, especially, for the crown.     

Developing Regenerate: A circular economy engagement tool for the assessment of new and existing buildings

The transition toward a circular economy (CE) is key in decarbonizing the built environment. Despite this, knowledge of—and engagement with—CE philosophies remains limited within the construction industry. Discussion with practitioners reveals this to be contributed to by a lack of clarity regarding CE principles, with numerous organizations recommending implementation of differing and sometimes conflicting principles. In addition, a systematic assessment of how building designs consider CE is made difficult by the multiple design areas required to be considered and the large amount of design data required to do so. The absence of a systematic CE assessment causes a lack of comparability across designs, preventing benchmarking of CE practices in building design at present. This paper details the development of Regenerate, a CE engagement tool for the assessment of new and existing buildings, established in an effort to overcome the aforementioned barriers to the adoption of CE within the construction sector. A CE design workflow for the built environment is proposed, comprising four overarching circularity principles (Design for Adaptability; Design for Deconstructability; Circular Material Selection; Resource Efficiency) and contributing design actions. In addition to engaging stakeholders by enabling the assessment of building designs, the tool retrieves key data for further research. Information on completed design actions as well as recycling and waste metrics is collected to facilitate future CE benchmarking. “Bill of materials” data (i.e., material quantities) is also compiled, with this being key in material stock modeling research and embodied carbon benchmarking.     

Task Design Influences Prosociality in Captive Chimpanzees (Pan troglodytes)

Chimpanzees confer benefits on group members, both in the wild and in captive populations. Experimental studies of how animals allocate resources can provide useful insights about the motivations underlying prosocial behavior, and understanding the relationship between task design and prosocial behavior provides an important foundation for future research exploring these animals'' social preferences. A number of studies have been designed to assess chimpanzees'' preferences for outcomes that benefit others (prosocial preferences), but these studies vary greatly in both the results obtained and the methods used, and in most cases employ procedures that reduce critical features of naturalistic social interactions, such as partner choice. The focus of the current study is on understanding the link between experimental methodology and prosocial behavior in captive chimpanzees, rather than on describing these animals'' social motivations themselves. We introduce a task design that avoids isolating subjects and allows them to freely decide whether to participate in the experiment. We explore key elements of the methods utilized in previous experiments in an effort to evaluate two possibilities that have been offered to explain why different experimental designs produce different results: (a) chimpanzees are less likely to deliver food to others when they obtain food for themselves, and (b) evidence of prosociality may be obscured by more “complex” experimental apparatuses (e.g., those including more components or alternative choices). Our results suggest that the complexity of laboratory tasks may generate observed variation in prosocial behavior in laboratory experiments, and highlights the need for more naturalistic research designs while also providing one example of such a paradigm.     

A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds

The accurate design of new protein–protein interactions is a longstanding goal of computational protein design. However, most computationally designed interfaces fail to form experimentally. This investigation compares five previously described successful de novo interface designs with 158 failures. Both sets of proteins were designed with the molecular modeling program Rosetta. Designs were considered a success if a high‐resolution crystal structure of the complex closely matched the design model and the equilibrium dissociation constant for binding was less than 10 μM. The successes and failures represent a wide variety of interface types and design goals including heterodimers, homodimers, peptide‐protein interactions, one‐sided designs (i.e., where only one of the proteins was mutated) and two‐sided designs. The most striking feature of the successful designs is that they have fewer polar atoms at their interfaces than many of the failed designs. Designs that attempted to create extensive sets of interface‐spanning hydrogen bonds resulted in no detectable binding. In contrast, polar atoms make up more than 40% of the interface area of many natural dimers, and native interfaces often contain extensive hydrogen bonding networks. These results suggest that Rosetta may not be accurately balancing hydrogen bonding and electrostatic energies against desolvation penalties and that design processes may not include sufficient sampling to identify side chains in preordered conformations that can fully satisfy the hydrogen bonding potential of the interface.     

Centronuclear (myotubular) myopathy

In recent years, the use of adaptive design methods in clinical research and development based on accrued data has become very popular due to its flexibility and efficiency. Based on adaptations applied, adaptive designs can be classified into three categories: prospective, concurrent (ad hoc), and retrospective adaptive designs. An adaptive design allows modifications made to trial and/or statistical procedures of ongoing clinical trials. However, it is a concern that the actual patient population after the adaptations could deviate from the originally target patient population and consequently the overall type I error (to erroneously claim efficacy for an infective drug) rate may not be controlled. In addition, major adaptations of trial and/or statistical procedures of on-going trials may result in a totally different trial that is unable to address the scientific/medical questions the trial intends to answer. In this article, several commonly considered adaptive designs in clinical trials are reviewed. Impacts of ad hoc adaptations (protocol amendments), challenges in by design (prospective) adaptations, and obstacles of retrospective adaptations are described. Strategies for the use of adaptive design in clinical development of rare diseases are discussed. Some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given. Practical issues that are commonly encountered when implementing adaptive design methods in clinical trials are also discussed.     

De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD)

The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of “human Abs” based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed “forward folding” with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.