Comparing the efficiency of monofilament and regular nets for capturing bats

Regular nylon or polyester mist nets used for capturing bats have several drawbacks, particularly that they are inefficient at sampling insectivorous species. One possible alternative is to use monofilament nets, whose netting is made of single strands of yarn instead of several as regular nets, making them less detectable. To date, only one study has quantified the differences in capture rates between monofilament and regular mist nets for the study of bats, yet surprisingly, its findings suggest that the latter are more efficient than the former. Here, we provide further evidence of the differences in sampling efficiency between these two nets. We captured 90 individuals and 14 species in regular nets and 125 individuals and 20 species in monofilament nets. The use of monofilament nets increased overall capture rates, particularly for insectivorous species. Species accumulation curves indicate that samples based on regular nets are significantly underestimating species diversity, most notably as these nets fail at sampling rare species. We show that incorporating monofilament nets into bat studies offers an opportunity to expand records of different guilds and rare bat species and to improve our understanding of poorly known bat assemblages while using a popular, relatively cheap and portable sampling method.

Comparing the MCMC Efficiency of JAGS and Stan for the Multi-Level Intercept-Only Model in the Covariance- and Mean-Based and Classic Parametrization

Bayesian MCMC is a widely used model estimation technique, and software from the BUGS family, such as JAGS, have been popular for over two decades. Recently, Stan entered the market with promises of higher efficiency fueled by advanced and more sophisticated algorithms. With this study, we want to contribute empirical results to the discussion about the sampling efficiency of JAGS and Stan. We conducted three simulation studies in which we varied the number of warmup iterations, the prior informativeness, and sample sizes and employed the multi-level intercept-only model in the covariance- and mean-based and in the classic parametrization. The target outcome was MCMC efficiency measured as effective sample size per second (ESS/s). Based on our specific (and limited) study setup, we found that (1) MCMC efficiency is much higher for the covariance- and mean-based parametrization than for the classic parametrization, (2) Stan clearly outperforms JAGS when the covariance- and mean-based parametrization is used, and that (3) JAGS clearly outperforms Stan when the classic parametrization is used.

A Compressive Sensing Approach to Inferring Cognitive Representations with Reverse Correlation

Uncovering cognitive representations is an elusive goal that is increasingly pursued using the reverse correlation method. Employing reverse correlation often entails collecting thousands of stimulus-response pairs from human subjects, a burdensome task that limits the feasibility of many such studies. This methodological barrier can potentially be overcome using recent advances in signal processing designed to improve sampling efficiency, specifically compressive sensing. Here, compressive sensing is shown to be directly compatible with reverse correlation, and a trio of simulations are performed to demonstrate that compressive sensing can improve the accuracy of reconstructed representations while dramatically reducing the required number of samples. This work concludes by outlining the potential of compressive sensing to improve representation reconstruction throughout the field of neuroscience and beyond.

Consequences of changing water clarity on the fish and fisheries of the Laurentian Great Lakes

Human-driven environmental change underlies recent changes in water clarity in many of the world’s great lakes, yet our understanding of the consequences of these changes on the fish and fisheries they support remains incomplete. Herein, we offer a framework to organize current knowledge, guide future research, and help fisheries managers understand how water clarity can affect their valued populations. Emphasizing Laurentian Great Lakes findings where possible, we describe how changing water clarity can directly affect fish populations and communities by altering exposure to ultraviolet radiation, foraging success, predation risk, reproductive behavior, or territoriality. We also discuss how changing water clarity can affect fisheries harvest and assessment through effects on fisher behavior and sampling efficiency (i.e., catchability). Finally, we discuss whether changing water clarity can affect understudied aspects of fishery performance, including economic and community benefits. We conclude by identifying generalized predictions and discuss their implications for priority research questions for the Laurentian Great Lakes. Even though the motivation for this work was regional, the breadth of the review and generality of the framework are readily transferable to other freshwater and marine habitats.

Generalized parallel tempering on Bayesian inverse problems

In the current work we present two generalizations of the Parallel Tempering algorithm in the context of discrete-time Markov chain Monte Carlo methods for Bayesian inverse problems. These generalizations use state-dependent swapping rates, inspired by the so-called continuous time Infinite Swapping algorithm presented in Plattner et al. (J Chem Phys 135(13):134111, 2011). We analyze the reversibility and ergodicity properties of our generalized PT algorithms. Numerical results on sampling from different target distributions, show that the proposed methods significantly improve sampling efficiency over more traditional sampling algorithms such as Random Walk Metropolis, preconditioned Crank–Nicolson, and (standard) Parallel Tempering.

Sampling efficiency of Candida auris from healthcare surfaces: culture and nonculture detection methods

Sponges and swabs were evaluated for their ability to recover Candida auris dried 1 hour on steel and plastic surfaces. Culture recovery ranged from <0.1% (sponges) to 8.4% (swabs), and cells detected with an esterase activity assay revealed >50% recovery (swabs), indicating that cells may enter a viable but nonculturable state.

Analysis the level of Efficiency of Sharia Commercial Bank and Sharia Business Unit in Indonesia with Data Envelopment Analysis Method

This research has the purpose to measure efficiency and analyze the efficiency comparison between Sharia Commercial Bank (BUS) and Sharia Bussines Unit (UUS) in Indonesia and compare the efficiency during the period 2008-2013. There are 10 Islamic banks which are used as samples of this research consisting of 3 Sharia Commercial Bank (BUS) and 7 Sharia Business Unit (UUS) that were chosen by purposive sampling. Efficiency measurements in this research using Data Envelopment Analysis (DEA). To determine differences in efficiency between Sharia Commercial Bank (BUS) and Sharia Bussines Unit (UUS), this study uses a different test parametric independent sample t-test. The result of this research showed that there is no significant difference between the efficiency of Sharia Commercial Bank (BUS) and Sharia Bussines Unit (UUS), during the period of 2008-2013 with possible intervention t value (-0.429) < t table (2.002) and probability value =0.000.