Pattern analysis of stem cell differentiation during <Emphasis Type="Italic">in vitro Arabidopsis</Emphasis> organogenesis

Plant somatic cells have the capability to switch their cell fates from differentiated to undifferentiated status under proper culture conditions, which is designated as totipotency. As a result, plant cells can easily regenerate new tissues or organs from a wide variety of explants. However, the mechanism by which plant cells have such remarkable regeneration ability is still largely unknown. In this study, we used a set of meristem-specific marker genes to analyze the patterns of stem cell differentiation in the processes of somatic embryogenesis as well as shoot or root organogenesis in vitro. Our studies furnish preliminary and important information on the patterns of the de novo stem cell differentiation during various types of in vitro organogenesis.     

Enhancement of cell growth by intracellularly expressed herpes simplex virus thymidine kinase

A recombinant cell line (NIH3T3:pLtkSN) was made by infecting parental cells (NIH3T3) with a recombinant retrovirus (pLtkSN) encoding herpes simplex virus thymidine kinase (HSVtk) gene. The cells expressing HSVtk (NIH3T3:pLtkSN) grew 2.3 times more than the parental cells (NIH3T3) in Dulbecco's Modified Eagles Media containing 10% (v/v) horse serum. The NIH3T3:pLtkSN cells also showed a significant enhancement in the maximal cell concentration and the specific growth rate even at 2.5% serum concentration. The specific O2 uptake rate of NIH3T3 was 2.1 times greater than that of NIH3T3:pLtkSN. Under both O2-limited and O2-unlimited conditions, it appears that HSVtk plays an important role in enhancing the growth characteristics of animal cells.     

Solution structure of the N‐terminal domain of DC‐UbP/UBTD2 and its interaction with ubiquitin

DC‐UbP/UBTD2 is a ubiquitin (Ub) domain‐containing protein first identified from dendritic cells, and is implicated in ubiquitination pathway. The solution structure and backbone dynamics of the C‐terminal Ub‐like (UbL) domain were elucidated in our previous work. To further understand the biological function of DC‐UbP, we then solved the solution structure of the N‐terminal domain of DC‐UbP (DC‐UbP_N) and studied its Ub binding properties by NMR techniques. The results show that DC‐UbP_N holds a novel structural fold and acts as a Ub‐binding domain (UBD) but with low affinity. This implies that the DC‐UbP protein, composing of a combination of both UbL and UBD domains, might play an important role in regulating protein ubiquitination and delivery of ubiquitinated substrates in eukaryotic cells.     

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.