The effects of three different grinding methods in DNA extraction of cowpea (Vigna unguiculata L. Walp)

Rapid DNA extraction is a prerequisite for molecular studies. Generally, plant tissue is ground in liquid nitrogen to isolate DNA; but, liquid nitrogen is dangerous and volatile. Besides, liquid nitrogen is not always available in many developing countries. To investigate if high quality DNA could be obtained for downstream PCR analysis without liquid nitrogen, the cowpea DNA was extracted by Hexadecyl trimethyl ammonium bromide cetyl trimethylammonium bromide (CTAB) method and sodium dodecyl sulphate (SDS) method, respectively, each with three different grinding methods, including ground in liquid nitrogen, in preheated mortar and in non-preheated mortar. The DNA was compared according to their yield, purity, integrity and functionality. The results showed that high quality DNA could be obtained by three grinding methods both in CTAB method and SDS method. Without liquid nitrogen, grinding plant tissue in preheated or non-preheated mortar with extraction buffer to extract DNA is feasible.Keywords: Cowpea (Vigna unguiculata), grinding method, liquid nitrogen, DNA extractionAfrican Journal of Biotechnology Vol. 12(16), pp. 1946-195

Genome and pan-genome assembly of asparagus bean (Vigna unguiculata ssp. sesquipedialis) reveal the genetic basis of cold adaptation

Asparagus bean (Vigna unguiculata ssp. sesquipedialis) is an important cowpea subspecies. We assembled the genomes of Ningjiang 3 (NJ, 550.31 Mb) and Dubai bean (DB, 564.12 Mb) for comparative genomics analysis. The whole-genome duplication events of DB and NJ occurred at 64.55 and 64.81 Mya, respectively, while the divergence between soybean and Vigna occurred in the Paleogene period. NJ genes underwent positive selection and amplification in response to temperature and abiotic stress. In species-specific gene families, NJ is mainly enriched in response to abiotic stress, while DB is primarily enriched in respiration and photosynthesis. We established the pan-genomes of four accessions (NJ, DB, IT97K-499-35 and Xiabao II) and identified 20,336 (70.5%) core genes present in all the accessions, 6,507 (55.56%) variable genes in two individuals, and 2,004 (6.95%) unique genes. The final pan genome is 616.35 Mb, and the core genome is 399.78 Mb. The variable genes are manifested mainly in stress response functions, ABC transporters, seed storage, and dormancy control. In the pan-genome sequence variation analysis, genes affected by presence/absence variants were enriched in biological processes associated with defense responses, immune system processes, signal transduction, and agronomic traits. The results of the present study provide genetic data that could facilitate efficient asparagus bean genetic improvement, especially in producing cold-adapted asparagus bean

Transcriptome profiling of Cd resistance by exogenous treatment of melatonin in Italian Lettuce (Lactuca sativa L. var. ramose)

This dataset contains the data related to the transcriptome project involved in the Cd and melatonin treatment on Italian Lettuce (Lactuca sativa L. var. ramose).Contents:1. Lettuce_Annotation.xls: annotation of consensus transcripts of Italian Lettuce based on seven databases (nr, KEGG, SwissProt, KOG, GO, nt and pfam).2. Gene expression profiles of Italian Lettuce under different Cd and melatonin treatments.</div

FPKM.xls

通过三代转录组结合二代转录组对生菜进行高通量重组，并转化为基因序列进行注释，得到其基因表达量与注释信息

Annotation.xls

通过三代转录组结合二代转录组对生菜进行高通量测序，并对其测序的基因序列进行注释，得到其基因表达量与注释信息

SCANet: A lightweight deep learning network for massive MIMO CSI feedback based on spatial and channel attention mechanism

Multi-user massive multiple-input multiple-output (MIMO) communication systems consume too much downlink bandwidth due to the huge channel state information (CSI) feedback, deep learning-based CSI feedback approaches fortunately can alleviate the feedback overhead while obtaining an accurate CSI. However, there is a trade-off between the high feedback performance and low computational complexity. In this paper, a low-complexity CSI feedback approach is proposed based on spatial and channel attention mechanism, namely the Spatial and Channel Attention Network (SCANet). Specifically, the spatial and channel attention mechanism makes the network's attention mainly focus on the specific spatial regions and key feature channels. We devise a serial architecture in the encoder that composes of Spatial and Channel Attention Block (SCAB) and Encoder Transformer Block. Moreover, we design a hybrid architecture in the decoder that composes of the CNNs Block and Decoder Transformer Block. These designs enable the network to effectively extract both global and local CSI features. Computer simulations in both the indoor and outdoor scenarios show that under the same system configurations, the proposed low-complexity SCANet achieves almost the same performance as the state-of-the-art network while reducing the computational complexity by 85.76% fewer floating-point operations per second (FLOPS) on average. © 202