恶性疟原虫抗原表位的亚单位疫苗与DNA疫苗的联合免疫

构建了含有恶性疟原虫抗原基因 ( AWTE)的真核表达质粒 p CMV- AWTE,以及能在大肠杆菌中得到分泌性表达的原核表达质粒 p MC0 5 ,表达的蛋白 AWTE保持了疟原虫抗原的抗原性。将 p CMV- AWTE以及 AWTE两者混合各 1 0μg鼻腔免疫小鼠 ,一次后诱导机体产生了较高水平的体液免疫及细胞免疫     

Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma

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Diversity of endophytic bacteria and fungi in seeds of Elymus nutans growing in four locations of Qinghai Tibet Plateau,China

Plant and Soil - Seeds are involved in the transmission of microorganisms from one plant generation to the next, acting as initial inoculum for the plant microbiome, therefore provide a key source...     

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.

     

Host-Parasite Interactions and Purifying Selection in a Microsporidian Parasite of Honey Bees

To clarify the mechanisms of Nosema ceranae parasitism, we deep-sequenced both honey bee host and parasite mRNAs throughout a complete 6-day infection cycle. By time-series analysis, 1122 parasite genes were significantly differently expressed during the reproduction cycle, clustering into 4 expression patterns. We found reactive mitochondrial oxygen species modulator 1 of the host to be significantly down regulated during the entire infection period. Our data support the hypothesis that apoptosis of honey bee cells was suppressed during infection. We further analyzed genome-wide genetic diversity of this parasite by comparing samples collected from the same site in 2007 and 2013. The number of SNP positions per gene and the proportion of non-synonymous substitutions per gene were significantly reduced over this time period, suggesting purifying selection on the parasite genome and supporting the hypothesis that a subset of N. ceranae strains might be dominating infection.     

A yeast's eye view of mammalian reproduction: cross-species gene co-expression in meiotic prophase

Background  

Meiotic prophase is a critical stage in sexual reproduction. Aberrant chromosome recombination during this stage is a leading cause of human miscarriages and birth defects. However, due to the experimental intractability of mammalian gonads, only a very limited number of meiotic genes have been characterized. Here we aim to identify novel meiotic genes important in human reproduction through computational mining of cross-species and cross-sex time-series expression data from budding yeast, mouse postnatal testis, mouse embryonic ovary, and human fetal ovary.