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.

     

Yaks and sheep trigger different changes in the grasshopper assemblages of the Qilian Mountains via differentially altering plant assemblages

1. The Qilian Mountains represent one of the key livestock‐raising grasslands in China. The two main herbivore species raised in this area – yaks and sheep – are of critical economical value. Grasshoppers compete with these animals for available nutrients, creating multifaceted relationships between livestock, grasshoppers and plants. A clear understanding of such relationships is lacking and is urgently needed to guide conservation efforts. 2. This study aims to document the effects of yak and sheep grazing on grasshopper assemblages and to elucidate the underlying mechanisms of such effects. 3. It is shown here that yaks and sheep impact grasshopper assemblages differently. Grasshopper assemblages exhibited lower density, biodiversity, richness, and evenness of distribution in yak‐grazed pastures than in grazing‐free grasslands. Sheep‐grazed pastures exhibited a dramatically divergent picture, with elevated density, biodiversity and richness, and a slightly decreased evenness of distribution. Grasshoppers were generally larger in grazed pastures than in grazing‐free grasslands, especially in yak‐grazed plots. 4. The present study suggests that differences between yak and sheep pastures in plant assemblage structure and plant traits are probably the underlying forces driving the differences in grasshopper assemblage structure and grasshopper traits, respectively. 5. The study shows that the grasshopper habitat indicator species differ between yak and sheep pastures, raising the possibility that such indicators can be used to monitor grassland usage and degradation in the Qilian Mountains. 6. These results provide novel insights into the dynamic interactions of common domesticated herbivore species, grasshoppers and plants in Qilian Mountains, which augment current knowledge and may ultimately lead to better conservation practices.     

U-Shape Suppressive Effect of Phenol Red on the Epileptiform Burst Activity via Activation of Estrogen Receptors in Primary Hippocampal Culture

Phenol red is widely used in cell culture as a pH indicator. Recently, it also has been reported to have estrogen-like bioactivity and be capable of promoting cell proliferation in different cell lines. However, the effect of phenol red on primary neuronal culture has never been investigated. By using patch clamp technique, we demonstrated that hippocampal pyramidal neurons cultured in neurobasal medium containing no phenol red had large depolarization-associated epileptiform bursting activities, which were rarely seen in neurons cultured in phenol red-containing medium. Further experiment data indicate that the suppressive effect of the phenol red on the abnormal epileptiform burst neuronal activities was U-shape dose related, with the most effective concentration at 28 µM. In addition, this concentration related inhibitory effect of phenol red on the epileptiform neuronal discharges was mimicked by 17-β-estradiol, an estrogen receptor agonist, and inhibited by ICI-182,780, an estrogen receptor antagonist. Our results suggest that estrogen receptor activation by phenol red in the culture medium prevents formation of abnormal, epileptiform burst activity. These studies highlight the importance of phenol red as estrogen receptor stimulator and cautions of careful use of phenol red in cell culture media.     

A neural network study on the dynamic identification of a fermentation system

The objective of this paper is to propose neural networks for the study of dynamic identification and prediction of a fermentation system which produces mainly 2,3-butanediol (2,3-BDL). The metabolic products of the fermentation, acetic acid, acetoin, ethanol, and 2,3-BDL were measured on-line via a mass spectrometer modified by the insertion of a dimethylvinylsilicone membrane probe. The measured data at different sampling times were included as the input and output nodes, at different learning batches, of the network. A fermentation system is usually nonlinear and dynamic in nature. Measured fermentation data obtained from the complex metabolic pathways are often difficult to be entirely included in a static process model, therefore, a dynamic model was suggested instead. In this work, neural networks were provided by a dynamic learning and prediction process that moved along the time sequence batchwise. In other words, a scheme of two-dimensional moving window (number of input nodes by the number of training data) was proposed for reading in new data while forgetting part of the old data. Proper size of the network including proper number of input/output nodes were determined by trained with the real-time fermentation data. Different number of hidden nodes under the consideration of both learning performance and computation efficiency were tested. The data size for each learning batch was determined. The performance of the learning factors such as the learning coefficient η and the momentum term coefficient α were also discussed. The effect of different dynamic learning intervals, with different starting points and the same ending point, both on the learning and prediction performance were studied. On the other hand, the effect of different dynamic learning intervals, with the same starting point and different ending points, was also investigated. The size of data sampling interval was also discussed. The performance from four different types of transfer functions, x/(1+|x|), sgn(x)·x ²/(1+x ²), 2/(1+e ^{? x} )?1, and 1/(1+e ^{? x} ) was compared. A scaling factor b was added to the transfer function and the effect of this factor on the learning was also evaluated. The prediction results from the time-delayed neural networks were also studied.