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.     

DISC1 gene polymorphisms and the risk of schizophrenia in an Iranian population: A preliminary study

Background: Schizophrenia, schizoaffective disorder, and bipolar illness are common psychological disorders with high heritability and variable phenotypes. The disrupted in schizophrenia 1 ( DISC1) gene, on chromosome 1q42, has an essential role in neurite outgrowth and cell signaling. The purpose of this study was to investigate the association of three single-nucleotide polymorphisms (SNPs; rs6675281, rs2255340, and rs2738864) with schizophrenia disorder. These three SNPs were chosen as they had been used in most of the previous studies. Methods: In a case-control study of Iranian population for the first time 778 blood samples were collected including, 402 schizophrenic patients and 376 healthy controls. Genomic DNA was extracted from peripheral blood using DNA extraction kit (BioFlux Co). The genotypes of rs6675281, rs2255340, and rs2738864 were detected by nested allele-specific multiplex polymersae chain reaction (PCR). Results: Our data revealed that the three SNPs are significantly associated with schizophrenia (rs2255349 C>T: confidence interval (CI), 2.115 to 3.268; P = 0.0000 OR: 2.629; rs2738864 C>T: CI, 1.538 to 2.339; P = 0.0000 OR: 1.897; rs6675281 C>T: CI, 2.788 to 4.662; P = 0.0009241 OR: 3.605). Through applying the expectation-maximization (EM) algorithm, we calculated the haplotype frequency, and finally performed haplotype analysis with Bonferroni correction and data preprocessing methods and the results showed rs66875281 to have the highest association. Discussion: Our findings primarily showed that DISC1 gene polymorphisms contribute to schizophrenia risk and have a significant association with this disorder among Iranian population. The strategy was found to be easy, rapid, specific, and consistent for the co-occurring detection of the DISC1 polymorphisms. We could finally confirm that the polymorphisms are related to schizophrenia studied in Iranian population.     

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.     

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.     

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.     

Genome‐wide scans to detect positive selection in Large White and Tongcheng pigs

Due to the direction, intensity, duration and consistency of genetic selection, especially recent artificial selection, the production performance of domestic pigs has been greatly changed. Therefore, we reasoned that there must be footprints or selection signatures that had been left during domestication. In this study, with porcine 60K BeadChip genotyping data from both commercial Large White and local Chinese Tongcheng pigs, we calculated the extended haplotype homozygosity values of the two breeds using the long‐range haplotype method to detect selection signatures. We found 34 candidate regions, including 61 known genes, from Large White pigs and 25 regions comprising 57 known genes from Tongcheng pigs. Many selection signatures were found on SSC1, SSC4, SSC7 and SSC14 regions in both populations. According to quantitative trait loci and network pathway analyses, most of the regions and genes were linked to growth, reproduction and immune responses. In addition, the average genetic differentiation coefficient F_ST was 0.254, which means that there had already been a significant differentiation between the breeds. The findings from this study can contribute to further research on molecular mechanisms of pig evolution and domestication and also provide valuable references for improvement of their breeding and cultivation.     

Estimation of Individual Growth Curves by Empirically Weighted Least Squares

The simultaneous estimation of individual growth curves and a mean growth curve is accomplished by weighted least squares. A polynomial curve is fitted for each individual and the polynomial parameters are linear functions of parameters corresponding to covariates. A simple, computationally efficient variance-covariance estimator is derived. The resultant estimate is used in the weighted least squares estimation. The results are compared to empirical Bayes estimation.