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

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Autumnal moth – why autumnal?

1. As for some other spring‐feeding moths, adult flight of Epirrita autumnata (Lepidoptera: Geometridae) occurs in late autumn. Late‐season flight is a result of a prolonged pupal period. Potential evolutionary explanations for this phenological pattern are evaluated. 2. In a laboratory rearing, there was a weak correlation between pupation date and the time of adult emergence. A substantial genetic difference in pupal period was found between two geographic populations. Adaptive evolution of eclosion time can thus be expected. 3. Metabolic costs of a prolonged pupal period were found to be moderate but still of some ecological significance. Pupal mortality is likely to form the main cost of the prolonged pupal period. 4. Mortality rates of adults, exposed in the field, showed a declining temporal trend from late summer to normal eclosion time in autumn. Lower predation pressure on adults may constitute the decisive selective advantage of late‐season flight. It is suggested that ants, not birds, were the main predators responsible for the temporal trend. 5. Egg mortality was estimated to be low; it is thus unlikely that the late adult period is selected for to reduce the time during which eggs are exposed to predators. 6. In a laboratory experiment, oviposition success was maximal at the time of actual flight peak of E. autumnata, however penalties resulting from sub‐optimal timing of oviposition remained limited.     

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.

     

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.     

Functional analysis of a miRNA‐like small RNA derived from Bombyx mori cytoplasmic polyhedrosis virus

Bombyx mori cytoplasmic polyhedrosis virus (BmCPV) is a major pathogen of the economic insect silkworm, Bombyx mori. Virus‐encoded microRNAs (miRNAs) have been proven to play important roles in host–pathogen interactions. In this study we identified a BmCPV‐derived miRNA‐like 21 nt small RNA, BmCPV‐miR‐1, from the small RNA deep sequencing of BmCPV‐infected silkworm larvae by stem‐loop quantitative real‐time PCR (qPCR) and investigated its functions with qPCR and lentiviral expression systems. Bombyx mori inhibitor of apoptosis protein (BmIAP) gene was predicted by both target prediction software miRanda and Targetscan to be one of its target genes with a binding site for BmCPV‐miR‐1 at the 5′ untranslated region. It was found that the expression of BmCPV‐miR‐1 and its target gene BmIAP were both up‐regulated in BmCPV‐infected larvae. At the same time, it was confirmed that BmCPV‐miR‐1 could up‐regulate the expression of BmIAP gene in HEK293T cells with lentiviral expression systems and in BmN cells by transfecting mimics. Furthermore, BmCPV‐miR‐1 mimics could up‐regulate the expression level of BmIAP gene in midgut and fat body in the silkworm. In the midgut of BmCPV‐infected larvae, BmCPV‐miR‐1 mimics could be further up‐regulated and inhibitors could lower the virus‐mediated expression of BmIAP gene. With the viral genomic RNA segments S1 and S10 as indicators, BmCPV‐miR‐1 mimics could up‐regulate and inhibitors down‐regulate their replication in the infected silkworm. These results implied that BmCPV‐miR‐1 could inhibit cell apoptosis in the infected silkworm through up‐regulating BmIAP expression, providing the virus with a better cell circumstance for its replication.