Abscisic acid-triggered guard cell l-cysteine desulfhydrase function and in situ hydrogen sulfide production contributes to heme oxygenase-modulated stomatal closure

Recent studies have demonstrated that hydrogen sulfide (H₂S) produced through the activity of l -cysteine desulfhydrase (DES1) is an important gaseous signaling molecule in plants that could participate in abscisic acid (ABA)-induced stomatal closure. However, the coupling of the DES1/H₂S signaling pathways to guard cell movement has not been thoroughly elucidated. The results presented here provide genetic evidence for a physiologically relevant signaling pathway that governs guard cell in situ DES1/H₂S function in stomatal closure. We discovered that ABA-activated DES1 produces H₂S in guard cells. The impaired guard cell ABA phenotype of the des1 mutant can be fully complemented when DES1/H₂S function has been specifically rescued in guard cells and epidermal cells, but not mesophyll cells. This research further characterized DES1/H₂S function in the regulation of LONG HYPOCOTYL1 (HY1, a member of the heme oxygenase family) signaling. ABA-induced DES1 expression and H₂S production are hyper-activated in the hy1 mutant, both of which can be fully abolished by the addition of H₂S scavenger. Impaired guard cell ABA phenotype of des1/hy1 can be restored by H₂S donors. Taken together, this research indicated that guard cell in situ DES1 function is involved in ABA-induced stomatal closure, which also acts as a pivotal hub in regulating HY1 signaling.     

A Stochastic Model for Detecting Overlapping and Hierarchical Community Structure

Community detection is a fundamental problem in the analysis of complex networks. Recently, many researchers have concentrated on the detection of overlapping communities, where a vertex may belong to more than one community. However, most current methods require the number (or the size) of the communities as a priori information, which is usually unavailable in real-world networks. Thus, a practical algorithm should not only find the overlapping community structure, but also automatically determine the number of communities. Furthermore, it is preferable if this method is able to reveal the hierarchical structure of networks as well. In this work, we firstly propose a generative model that employs a nonnegative matrix factorization (NMF) formulization with a l_2,1 norm regularization term, balanced by a resolution parameter. The NMF has the nature that provides overlapping community structure by assigning soft membership variables to each vertex; the l_2,1 regularization term is a technique of group sparsity which can automatically determine the number of communities by penalizing too many nonempty communities; and hence the resolution parameter enables us to explore the hierarchical structure of networks. Thereafter, we derive the multiplicative update rule to learn the model parameters, and offer the proof of its correctness. Finally, we test our approach on a variety of synthetic and real-world networks, and compare it with some state-of-the-art algorithms. The results validate the superior performance of our new method.     

First Report of Cucumber mosaic virus Infecting Chinese Mallow in China

The virus in naturally infected, stunted Chinese mallow plants and mosaic leaves was identified as Cucumber mosaic virus (CMV). Six symptomatic plants and one symptomless plant were collected in Chongqing, China. DAS‐ELISA suggested CMV was likely associated with the diseased Chinese mallow. Double‐stranded RNA was extracted from the samples, analysed by RT‐PCR, and the coding sequences of their coat proteins (CPs) were sequenced. The results further confirmed CMV was the pathogen causing Chinese mallow stunted, mosaic disease. The isolate was named CMV‐DXC. The full sequence of CMV‐DXC CP was determined, and it had the highest nucleotide identity (99.4%) of those of CMV‐lily, CMV‐WSJ and CMV‐Hnt, respectively. Phylogenetic analysis shows that CMV‐DXC belongs to CMV subgroup II. To our knowledge, this is the first report of CMV infecting Chinese mallow in China.     

金沙江干热河谷区田菁根瘤菌多样性与系统发育

[目的]揭示金沙江干热河谷区这一特殊地理环境下田菁根瘤菌的多样性和系统发育地位. [方法]采用了数值分类、16S rDNA PCR-RFLP、16S rDNA和GSⅡ序列分析方法.[结果]数值分类结果表明:在93%的水平上,待测菌株分布于6个群,其中4个群分别与R.tropici、 R.etli、 S.saheli、A.rubi的参比菌株聚在一起,两个群没有参比菌株与之聚群.16S rDNA PCR-RFLP结果与数值分类基本一致,只有两个独立群有所差异.16S rDNA序列分析表明:两独立群中心菌株SCAU176 和SCAU144与R.huautlense聚在一起,与该种同源性分别为100%和98.9%.GSⅡ序列分析中SCAU176 和SCAU144单独聚在一起,与最近的参比菌株R.tropici的同源性系数在90%以下.[结论]金沙江干热河谷区田菁根瘤菌具有较为丰富的多样性,在系统发育地位上分布于Sinorhizobium、Agrobacterium和Rhizobium三个属.     

Temperature Sensor Based on Hollow Fiber Filled with Graphene-Ag Composite Nanowire and Liquid

A temperature sensor based on hollow fiber (HF) filled with graphene-Ag composite nanowire and liquid is presented. The coupling properties and sensing performance are numerically analyzed by finite element method using wavelength and amplitude interrogations. Results show that the sensor exhibiting strong birefringence with x-polarized peak provides much higher sensitivities and better signal-to-noise ratio (SNR) than y-polarized, which is more suitable for temperature detection. The graphene-Ag composite nanowire can not only solve the oxidation problem but also avoid the metal coating. Moreover, it provides better performance than other similar works like Au-Ag nanowire-filled, Au nanowire-filled, and Ag nanowire-filled sensors. Contrary to the blue shift of traditional SPR temperature sensors, the resonance peak shifts to the longer wavelength in our device when temperature increases and the high sensitivity 9.44 nm/ °C is obtained. The influences of nanowire diameter, grapheme-layer thickness on the designed sensor, are also investigated. This work can provide a reference for developing a high sensitivity, real-time, remote sensing, and distributed temperature SPR sensor.     

Analysis of Graphene-Based Photonic Crystal Fiber Sensor Using Birefringence and Surface Plasmon Resonance

Plasmonics - We present and numerically characterize a photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) sensor. By adjusting the air hole sizes of the PCF, the effective...     

Uncovering Community Structures with Initialized Bayesian Nonnegative Matrix Factorization

Uncovering community structures is important for understanding networks. Currently, several nonnegative matrix factorization algorithms have been proposed for discovering community structure in complex networks. However, these algorithms exhibit some drawbacks, such as unstable results and inefficient running times. In view of the problems, a novel approach that utilizes an initialized Bayesian nonnegative matrix factorization model for determining community membership is proposed. First, based on singular value decomposition, we obtain simple initialized matrix factorizations from approximate decompositions of the complex network’s adjacency matrix. Then, within a few iterations, the final matrix factorizations are achieved by the Bayesian nonnegative matrix factorization method with the initialized matrix factorizations. Thus, the network’s community structure can be determined by judging the classification of nodes with a final matrix factor. Experimental results show that the proposed method is highly accurate and offers competitive performance to that of the state-of-the-art methods even though it is not designed for the purpose of modularity maximization.     

Polarization Characteristics of High-Birefringence Photonic Crystal Fiber Selectively Coated with Silver Layers

The polarization characteristics of high-birefringence photonic crystal fiber (HB-PCF) selectively coated with silver layers are numerically investigated using the full-vector finite element method (FEM). The fundamental mode coupling properties and polarization splitting effect are discussed in detail. Results show that the resonance wavelength, resonance strength, and splitting distance between two polarized modes can be adjusted significantly by changing the fiber structure, the diameter of silver rings, and the thickness of silver layers. A single-polarization filter at 1310 nm bands is proposed with the corresponding loss 500 dB/cm and full width half maximum (FWHM) only 23 nm. This work is very helpful for further studies in polarization-dependent wavelength-selective applications or other fiber-based plasmonic devices.

     

CircRNA hsa_circRNA_101996 increases cervical cancer proliferation and invasion through activating TPX2 expression by restraining miR-8075

In recent years, circular RNAs have been shown to serve as essential regulators in several human cancers. Nevertheless, the function and mechanism of CircRNA in cervical cancer remain elusive. In the present study, we showed that hsa_circRNA_101996 was highly expressed in cervical cancer tissues compared with matched normal tissues by bioinformatics analysis. We showed that the expression level of hsa_circRNA_101996 in cervical cancer tissues was positively correlated with TNM stage, tumor size, and lymph node metastasis. Moreover, higher levels of hsa_circRNA_101996 were related to poor outcomes of cervical cancer patients. We found that knockdown of hsa_circRNA_101996 significantly inhibited the proliferation, cell cycle, migration, and invasion of cervical cancer cells. Mechanistically, we demonstrated that hsa_circRNA_101996 served as a sponge of miR-8075, which targeted TPX2 in cervical cancer cells. We showed that miR-8075 that was downregulated in cervical cancer tissues repressed cervical cancer cell proliferation, migration, and invasion. Furthermore, we validated that upregulation of TPX2 by hsa_circRNA_101996-mediated inhibition of miR-8075 contributed to cervical cancer proliferation, migration, and invasion. Taken together, our findings revealed a novel mechanism that hsa_circRNA_101996-miR-8075-TPX2 network promoted cervical cancer progression.     

Detection and evolutionary analysis of soybean miRNAs responsive to soybean mosaic virus

MicroRNAs (miRNA) are a class of non-coding RNAs that have important gene regulatory roles in various organisms. However, the miRNAs involved in soybean’s response to soybean mosaic virus (SMV) are unknown. To identify novel miRNAs and biotic-stress regulated small RNAs that are involved in soybean’s response to SMV, two small RNA libraries were constructed from mock-inoculated and SMV-infected soybean leaves and sequenced. This led to the discovery of 179 miRNAs, representing 52 families, among which five miRNAs belonging to three families were novel miRNAs in soybean. A large proportion (71.5 %) of miRNAs arose from segmental duplication, similar to the process that drives the evolution of protein-coding genes. In addition, we predicted 346 potential targets of these identified miRNAs, and verified 12 targets by modified 5′-RACE analysis. Finally, three miRNAs (miR160, miR393 and miR1510) that are involved in plant resistance were observed to respond to SMV infection. The interaction between miRNAs and resistance-related genes provides a novel mechanism for pathogens to evade host recognition.