Modeling the effects of particles,interstitials, vacancies and tip geometry on indentation-induced plasticity

In this work, which is a follow-up of an earlier work by the authors in this journal, we discuss the effects of particles, interstitials, vacancies and indenter-tip geometry on indentation-induced plasticity. A nano-indentation model based on a close-packed array of straws was used. Upon indentation with a cylindrical indenter, the results observed in situ, match qualitatively computer atomistic and finite-element simulation models. This model helps explain the phenomenon and physical reasons for annihilation of vacancies. It also shows the strong effect of interstitials on the inception of plasticity at their sites. It shows that a rigid particle acts as a stress concentrator even away from the indenter. On indentation with a flat indenter, curling of atoms at the indenter edges was observed, along with slip lines reported in the literature.     

Characterization and partial purification of <Emphasis Type="Italic">Candida albicans</Emphasis> Secretory IL-12 Inhibitory Factor

Background  

We have previously shown that supernatant from Candida albicans (CA) culture contains a Secretory Interleukin (IL)-12 Inhibitory Factor (CA-SIIF), which inhibits IL-12 production by human monocytes. However, the effect of CA-SIIF on secretion of other cytokines by monocytes is unknown, and detailed characterization of this factor has not been performed.     

Molecular cloning and DNA sequence analysis of the human guanine nucleotide-binding protein Go alpha

The nucleotide sequence of human Go alpha was determined from a partial human brain cDNA clone and the sequence of the first two 5' coding exons of a human genomic Go alpha clone. Comparison of this sequence with bovine and rat Go alpha shows greater than 90% homology at the nucleotide and deduced amino acid level. There is 100% identity at the amino acid level for the cholera and pertussis toxin-catalyzed ADP ribosylation sites, the putative guanine nucleotide binding, and the GTP hydrolysis sites.     

Nonselective inhibition of messenger RNA translation by highly purified low molecular weight RNA species from ribosomal salt wash of chick embryonic muscle

A low molecular weight RNA species, in the 70–90 nucleotide size range (iRNA), has been purified from the ribosomal salt wash of chick embryonic muscle by a combination of DEAE-cellulose and hydroxyapatite chromatography. This method yields iRNA free from contaminating tRNA and gives better and more reproducible yields than those obtained with our previous method involving lengthy dialysis of the salt wash. The iRNA at a concentration of 20–80 ng range strongly inhibits the translation of homologous and heterologous mRNAs i.e. chick muscle poly(A)⁺mRNA and rabbit globin mRNA; uncapped mRNA; and poly(A)^-mRNA in micrococcal nuclease-treated reticulocyte lysate indicating that inhibition by iRNA is nonselective in nature. The translation of endogenous globin mRNA and polysomes in the lysate is strikingly less sensitive to iRNA suggesting that the initiation step is primarily affected by iRNA. The iRNA does not appear to be double-stranded RNA. It is concluded that iRNA is distinct from other low molecular weight RNA species described in the literature which modulate protein synthesis in cell-free systems.     

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.