Non‐pollinating cheater wasps benefit from seasonally poor performance of the mutualistic pollinating wasps at the northern limit of the range of Ficus microcarpa

1. Species interactions in tightly bound ecological mutualisms often feature highly specialised species' roles in which competitive exclusion may preclude multi‐species coexistence. Among the 800 fig (Ficus) species, it was originally considered that each was pollinated by their own wasp (Agaonidae). However, recent investigations show that this ‘one‐to‐one’ rule often breaks down, as fig species regularly host multiple agaonids but in ways suggesting that competitive processes still mediate biodiversity outcomes. 2. A phenological survey was conducted of the fig–fig wasp pair, Ficus microcarpa and its associated pollinating wasp, alongside its sister species, the cheating wasp, in Xishuangbanna, China. 3. Reproductive output underwent extreme seasonal variation. Seed and pollinator production fell markedly during cooler, drier months, although high levels of fig production continued. However, this resource was predominantly utilised by the cheater species, which offers no pollination services. Pollinators and cheaters rarely co‐occur, suggesting that temporal coexistence is constrained by competition for access to figs. 4. The overall findings indicate periodic rearrangements of mutualism dynamics, probably resulting from a strongly seasonal environment. Sympatric co‐occurrence may result from a window of opportunity for a functionally divergent agaonid, potentially due to constraints on the main pollinator in adapting to variable year‐round conditions that prevent competitive exclusion.     

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.

     

An eight-nucleotide sequence in the potato virus X 3' untranslated region is required for both host protein binding and viral multiplication.

Gel retardation and UV-cross-linking techniques were used to demonstrate that two tobacco proteins, with approximate molecular masses of 28 and 32 kDa, bind to a site within the 3' region of potato virus X (PVX) genomic RNA. The protein binding is specific, in that a 50-fold excess of unlabeled probe prevents formation of the complexes but no reduction is observed with a 2,000-fold molar excess of yeast tRNA. Complex formation is inhibited by poly(U) but is relatively unaffected by poly(A), poly(G), or poly(C-I). PVX RNA-host protein complex formation occurs in vitro at salt concentrations up to 400 mM. Deletion mapping indicates that the proteins bind within the 3' untranslated region (UTR) of PVX genomic RNA and that an 8-nucleotide U-rich sequence (5'-UAUUUUCU) is required for the binding. Deletion of the 8-nucleotide U-rich region from the 3' UTR of a sensitive PVX reporter virus that carries the luciferase gene in place of the PVX coat protein gene results in a more than 70,000-fold reduction in luciferase expression in tobacco protoplasts. RNA probes carrying the sequence GCGC in place of the central four contiguous uridines of the 8-nucleotide U-rich motif fail to bind host protein at detectable levels, and the same mutation, when introduced into the PVX reporter virus, eliminates viral multiplication. Mutations of 1 or 2 nucleotides within the same four uridines reduced both binding of host proteins and replication of reporter virus. These results indicate that the 8-nucleotide U-rich motif within the PVX 3' UTR is important for some aspect of viral multiplication and suggest that host protein binding plays a role in the process.     

The protective effects of MSC‐EXO against pulmonary hypertension through regulating Wnt5a/BMP signalling pathway

The aim of the study was to explore the mechanism of mesenchymal stem cell‐derived exosomes (MSC‐EXO) to protect against experimentally induced pulmonary hypertension (PH). Monocrotaline (MCT)‐induced rat model of PH was successfully established by a single intraperitoneal injection of 50 mg/kg MCT, 3 weeks later the animals were treated with MSC‐EXO via tail vein injection. Post‐operation, our results showed that MSC‐EXO could significantly reduce right ventricular systolic pressure (RVSP) and the right ventricular hypertrophy index, attenuate pulmonary vascular remodelling and lung fibrosis in vivo. In vitro experiment, the hypoxia models of pulmonary artery endothelial cell (PAEC) and pulmonary vascular smooth muscle cell (PASMC) were used. We found that the expression levels of Wnt5a, Wnt11, BMPR2, BMP4 and BMP9 were increased, but β‐catenin, cyclin D1 and TGF‐β1 were decreased in MSC‐EXO group as compared with MCT or hypoxia group in vivo or vitro. However, these increased could be blocked when cells were transfected with Wnt5a siRNA in vitro. Taken together, these results suggested that the mechanism of MSC‐EXO to prevent PH vascular remodelling may be via regulation of Wnt5a/BMP signalling pathway.     

Origin and phylogeny of Oryza species with the CD genome based on multiple-gene sequence data

The CD genome species in the genus Oryza are endemic to Latin America, including O. alta, O. grandiglumis and O. latifolia. Origins and phylogenetic relationship of these species have long been in dispute and are still ambiguous due to their homogeneous genome type, similar morphological characteristics and overlapping distribution. In the present study, we sequenced two chloroplast fragments (matK and trnL-trnF) and portions of three nuclear genes (Adh1, Adh2 and GPA1) from sixteen accessions representing seven species with the C, CD, and E genomes, as well as one G genome species as the outgroup. Phylogenetic analyses using parsimony and distance methods strongly supported that the CD genome originated from a single hybridization event, and that the C genome species (O. officinalis or O. rhizomatis instead of O. eichingeri) served as the maternal parent while the E genome species (O. australiensis) was the paternal donor during the formation of CD genome. In addition, the consistent phylogenetic relationships among the CCDD species indicated that significant divergence existed between O. latifolia and the other two (O. alta and O. grandiglumis), which corroborated the suggestion of treating the latter two as a single species or as taxa within species.We thank Tao Sang of Michigan State University (East Lansing, USA) and Bao-rong Lu of Fudan University (Shanghai, China) for their encouragement and assistance. We are also grateful to the International Rice Research Institute (Manila, Philippines) for providing plant material for this study. This research was supported by the Chinese Academy of Sciences (kscxz-sw-101A), the National Natural Science Foundation of China (30025005) and the Program for Key International S & T Cooperation Project of P. R. China (2001CB711103).     

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.