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.     

Hybrid nanoreservoirs based on dextran-capped dendritic mesoporous silica nanoparticles for CD133-targeted drug delivery

In this study, the chemical features of dendritic mesoporous silica nanoparticles (DMSNs) provided the opportunity to design a nanostructure with the capability to intelligently transport the payload to the tumor cells. In this regard, doxorubicin (DOX)-encapsulated DMSNs was electrostatically surface-coated with polycarboxylic acid dextran (PCAD) to provide biocompatible dextran-capped DMSNs (PCAD-DMSN@DOX) with controlled pH-dependent drug release. Moreover, a RNA aptamer against a cancer stem cell (CSC) marker, CD133 was covalently attached to the carboxyl groups of DEX to produce a CD133-PCAD-DMSN@DOX. Then, the fabricated nanosystem was utilized to efficiently deliver DOX to CD133+ colorectal cancer cells (HT29). The in vitro evaluation in terms of cellular uptake and cytotoxicity demonstrated that the CD133-PCAD-DMSN@DOX specifically targets HT29 as a CD133 overexpressed cancer cells confirmed by flow cytometry and 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide assay. The potentially promising intelligent-targeted platform suggests that targeted dextran-capped DMSNs may find impressive application in cancer therapy.     

On the Stability of Clathrate Hydrates Encaging Polar Guest Molecules: Contrast in the Hydrogen Bonds of Methylamine and Methanol Hydrates

Abstract

The stability of clathrate hydrates encaging highly polar guests has been investigated in order to explain the experimental observation that some amines form clathrate hydrates but alcohols act as inhibitor to hydrate formation. We choose methylamine and methanol as guest species and examine the stable structure, at which the total potential energy has a minimum value. At the local minima of those two hydrates, the potential energies of water-water and guest-water, and their hydrogen bonded networks are compared. It is found that methanol does not retain the host lattice structure, while the host-network structure is kept in the presence of methylamine. It is shown that the difference in the magnitude of the partial charge on the hydrogen atom between the hydroxyl and amino groups plays a much more significant role on the stability of both clathrate hydrates than the difference in molecular geometry. This is supported from the result of a methylamine-like model that has the same partial charges on the atoms in the hydrophilic site as methanol.     

Quercetin-induced yeast apoptosis through mitochondrial dysfunction under the accumulation of magnesium in Candida albicans

Latterly, the upsurge in use of antifungal drugs has brought about the emergence of several drug-resistance strains, making it skeptical to continue relying on current therapeutic regime. In the necessity of resistance-free antifungal agent, flavonoids presented possibilities of replacing existing drugs, displaying antifungal activity against pathogenic fungi. Among them, quercetin, one of the most representative flavonoids, exhibited antifungal activity against Candida albicans. To inspect the further understanding regarding quercetin, the antifungal mode of action of quercetin was investigated. In the initial step, the apoptosis was monitored after quercetin treatment. Moreover, intracellular levels of Mg²⁺ was assessed and was determined that Mg²⁺ increase occurred under the influence of quercetin. In addition, several features of mitochondrial dysfunction were monitored. Mitochondrial dysfunction triggers decrease in mitochondrial redox levels and leads to disruption in mitochondrial antioxidant system. Increased intracellular ROS and decreased intracellular redox levels were also displayed, indicating the occurrence of overall disruption in antioxidant systems. Sequentially, DNA fragmentation was observed and this DNA damage in turn induces apoptosis. In analyses, hexaamminecobalt(III) chloride (Cohex) was applied to inhibit Mg²⁺ transport between cytosol and mitochondria. Cohex attenuated the effects induced by quercetin, which demonstrates that the presence of Mg²⁺ is essential in quercetin-induced apoptosis.     

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.     

Accurate MS-based Rab10 Phosphorylation Stoichiometry Determination as Readout for LRRK2 Activity in Parkinson's Disease

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Highlights

• •MS-based clinical assay that accurately determines phospho Rab10 occupancy.
• •Stable isotope labeled phosphopeptide injected as a standard with endogenous tryptic phospho Rab peptide for accurate ratio determination.
• •Determination of pRab levels in neutrophils of Parkinson disease patients.
• •Relevance of pRab levels as marker of PD.

     

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.