Enzymic synthesis ofL-Lysine fromDL-α-Amino-ε-caprolactam by new microbial strains

The production of L-lysine fromDL-α-amino-ε-caprolactam (DL-ACL) by new strains producingL-α-amino-ε-caprolactamase and aminocaprolactam racemase is described. Optimal conditions for hydrolysis ofL-ACL byCryptococcus sp. and for racemization of ACL by cells of a strain isolated in nature and identified asPseudomonas sp. were determined. Synthesis ofL-α-amino-ε-caprolactamase is induced byDL-ACL orL-lysine with the same effectivity. A positive effect of phosphates (potassium salts) on reduction of the induction lag was detected, the synthesis of this enzyme was found to be repressed by glucose and some possibilities of the reversion of this repressive effect were demonstrated. Under conditions optimal for the production of both enzymes a quantitative theoretical conversion of 10 % aqueousDL-ACL toL-lysine by a mixture of native cells in a mass ratio of 1: 2 (producer of ACL-hydrolase to producer of ACL-racemase) occurred in 8 h at 40 °C and pH 8.0     

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.

     

Putative candidate genes for blood pressure control in the SHR.BN-RNO8 congenic substrains

The SHR-Lx congenic strain carrying a differential segment of chromosome 8 of BN and PD origin was recently shown to exhibit a significant decrease in blood pressure as compared to the SHR strain. There were two positional candidate genes for blood pressure control mapped to the differential segment: the rat kidney epithelial potassium channel gene (Kcnj1) and brain dopamine receptor 2 gene (Drd2). Bot these genes were separated into SHR.BN-RNO8 congenic substrains. In this communication, we are presenting the assignment of two further putative candidate genes, which might be involved in blood pressure control to the BN/PD differential segment of the SHR-Lx congenic strain. These are: the gene coding for smooth muscle cell specific protein 22 (Sm22) defined by the D8Mcw1 marker and neuronal nicotinic acetylcholine receptor gene cluster, defined by the D8Bord1 marker. Moreover, the glutamate receptor gene Grik4 which also maps to the differential segment of the SHR-Lx should be taken into account. The genetic separation of all these putative candidate genes of blood pressure control is being performed by recombinations and subsequent selection using (SHR×SHR-Lx) intercross population.