A study on giant panda recognition based on images of a large proportion of captive pandas

1. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.
2. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.
3. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.
4. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.

     

Parameter estimation for the distribution of single cell lag times

In Quantitative Microbial Risk Assessment, it is vital to understand how lag times of individual cells are distributed over a bacterial population. Such identified distributions can be used to predict the time by which, in a growth-supporting environment, a few pathogenic cells can multiply to a poisoning concentration level.We model the lag time of a single cell, inoculated into a new environment, by the delay of the growth function characterizing the generated subpopulation. We introduce an easy-to-implement procedure, based on the method of moments, to estimate the parameters of the distribution of single cell lag times. The advantage of the method is especially apparent for cases where the initial number of cells is small and random, and the culture is detectable only in the exponential growth phase.     

Can transposable element copy number distribution parameters be estimated from natural populations of Drosophila melanogaster?

The copy frequency distribution of a transposable element family in a Drosophila melanogaster natural population is generally characterised by the values of the Charlesworths' model parameters α and β (Charlesworth & Charlesworth, 1983). The estimation of these parameters is made using the observed distribution of the occupied sites in a population sample. Several results have been interpreted as due either to the influence of stochastic factors or to deterministic factors (transposition, excision, selection…). The accuracy of this method was tested by estimations performed on samples from simulated populations. The results show that with the sample size usually used for natural population studies, the confidence intervals are too large to reasonably deduce either the element copy number distribution or the values of transposition and excision rate and selective coefficients.     

Confidence Intervals for the Risk Ratio in Cohort Studies under Inverse Sampling

Three simple interval estimates for the risk ratio in inverse sampling are considered. The first two interval estimates are derived on the basis of Fieller's Theorem and the delta method with the logarithmic transformation, respectively. The third interval estimate is derived on the basis of an F-test statistic proposed by BENNETT (1981) for testing equal probabilities of a disease between two comparison groups when the disease is rare. To evaluate the performance of these three methods, a Monte Carlo simulation is used to compare the actual coverage probability with the nominal confidence level for each method and to estimate the expected length of the corresponding confidence interval in a variety of situations. On the basis of the results found in the simulation, we have concluded that the method with the logarithmic transformation is either equivalent to or better than the other two methods for all situations considered here.     

Comparing Stochastically Restricted Linear Estimators in a Regression Model

Conditions for superiority of the minimum dispersion estimator over another with respect to the covariance matrix are derived when the vector parameter of a regression model is subject to competing stochastic restrictions. The restrictions may also consist both of a deterministic part and a stochastic part.     

Linearized Growth Curve,Proper Age and Age at Menarche

A mathematical model of the linearized growth curve and its physiological interpretation by the introduction of proper age, which is proportional to the chronological age, are presented here. In the second phase, but not in the first phase, this constant of proportionality is highly correlated with the age at menarche.     

Seemingly Unrelated Generalized Linear Models

By using deviance standardized residuals, the seemingly unrelated regression estimation procedure is extended to generalized linear models, and fitted by an iterative procedure. The matrix of cross products of standardized residuals is asymptotically multivariate normal, and can be used for further multivariate analyses and for hypothesis testing.     

Ants adjust their pheromone deposition to a changing environment and their probability of making errors

Animals must contend with an ever-changing environment. Social animals, especially eusocial insects such as ants and bees, rely heavily on communication for their success. However, in a changing environment, communicated information can become rapidly outdated. This is a particular problem for pheromone trail using ants, as once deposited pheromones cannot be removed. Here, we study the response of ant foragers to an environmental change. Ants were trained to one feeder location, and the feeder was then moved to a different location. We found that ants responded to an environmental change by strongly upregulating pheromone deposition immediately after experiencing the change. This may help maintain the colony''s foraging flexibility, and allow multiple food locations to be exploited simultaneously. Our treatment also caused uncertainty in the foragers, by making their memories less reliable. Ants which had made an error but eventually found the food source upregulated pheromone deposition when returning to the nest. Intriguingly, ants on their way towards the food source downregulated pheromone deposition if they were going to make an error. This may suggest that individual ants can measure the reliability of their own memories and respond appropriately.     

The Use of Proton Chemical Shifts to Define the Solution Structure of a Dimeric Peptide

For flexible peptides, nuclear Overhauser Effects (NOE) experiments do not provide enough information to ensure a correct definition of their solution structure. The use of distance constraints, derived from the knowledge of proton chemical shifts, is developed to restrict the number of possible conformations. In the case of flexible molecules, randomization appears as an important factor of the correct estimation of the chemical shifts from the 3D structure. The refinement of the solution structure of the highly flexible AVP-like parallel dimer is described to illustrate this process.