Identification of long-lived proteins in the mitochondria reveals increased stability of the electron transport chain

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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.     

Optional tool use: The case of wild bearded capuchins (Sapajus libidinosus) cracking cashew nuts by biting or by using percussors

Tool use in humans can be optional, that is, the same person can use different tools or no tool to achieve a given goal. Strategies to reach the same goal may differ across individuals and cultures and at the intra‐individual level. This is the first experimental study at the intra‐individual level on the optional use of a tool in wild nonhuman primates. We investigated optional tool use by wild bearded capuchins (Sapajus libidinosus) of Fazenda Boa Vista (FBV; Piauí, Brazil). These monkeys habitually succeed in cracking open the mesocarp of dry cashew nuts (Anacardium spp.) by pounding them with stones and/or by biting. We assessed whether availability of a stone and resistance of the nut affected capuchins' choice to pound or to bite the nuts and their rates of success. Sixteen capuchins (1–16 years) received small and large dry cashew nuts by an anvil together with a stone (Stone condition) or without a stone (No‐Stone condition). In the Stone conditions, subjects used it to crack the nut in 89.1% (large nuts) and 90.1% (small nut) of the trials. Nut size significantly affected the number of strikes used to open it. Availability of the stone significantly increased the average percent of success. In the No‐Stone conditions, monkeys searched for and used other percussors to crack the nuts in 54% of trials. In all conditions, age affects percentage of success and number of strikes to reach success. We argue that exclusive use of stones in other sites may be due to the higher abundance of stones at these sites compared with FBV. Since capuchins opened cashews with a tool 1–2 years earlier than they succeed at cracking more resistant palm nuts, we suggest that success at opening cashew nuts with percussors may support the monkeys' persistent efforts to crack palm nuts.     

Long-term changes in growth and age at maturity of mackerel, Scomber scombrus L., from the North Sea

Mean length and mean weight at age of Atlantic mackerel in the North Sea increased significantly between 1960 and 1979, then, decreased markedly during the 1980s. In contrast, median age at maturity decreased (c. 1-1.5 years) from 1960. Median length at maturity remained stable until the 1980s, when it declined sharply. The significance of these changes is discussed in relation to variation in sea temperature, food availability and stock size. It is speculated that the changes during the 1980s resulted from immigration of western stock mackerel.     

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.     

Long‐term population dynamics of a coral reef gastropod and responses to disturbance

The age‐specific density of the red‐lipped stromb Strombus luhuanus (Mollusca: Gastropoda) was monitored over 13 years (1981–1993) at four locations on the intertidal reef flat at Heron Island, Great Barrier Reef. Densities were highly variable, but there were persistent, location‐specific differences in population density, age structure and adult body size, the latter indicating that the populations were not extensively linked by adult movement. There was relatively high recruitment at most locations in 1984, 1989 and 1993, each occurring approximately 2 years after El Niño/Southern Oscillation events, although recruit density during these years varied in both space and time. The studied strombs experienced three disturbance events: (i) experimental harvesting at two locations (1984–1985); (ii) siltation from a harbour dredging operation (1987–1988); and (iii) a severe cyclone (1992). Resilience to harvesting at a local scale (0.5–2 ha) was high: density had recovered within a year, due to immigration of adults and older juveniles. Strombus luhuanus responded much more strongly to broad‐scale changes to its environment than to localized harvesting. After dredging, there was a progressive density decline coupled with low recruitment at two locations, and a later decline at a third location, followed by a recruitment‐driven rebound after the cyclone. Generalized environmental effects of siltation and the cyclone were also reflected in substantial changes in algal cover. Long‐term variations in environmental conditions probably cause high temporal variation over large spatial scales through effects on the survival of larvae or recruits. Localized short‐term field monitoring of such species would give a misleading picture of key factors affecting population dynamics.     

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.