Tibetan barley (Hordeum vulgare L., qingke) is the principal cereal cultivated on the Tibetan Plateau for at least 3,500 years, but its origin and domestication remain unclear. Here, based on deep-coverage whole-genome and published exome-capture resequencing data for a total of 437 accessions, we show that contemporary qingke is derived from eastern domesticated barley and it is introduced to southern Tibet most likely via north Pakistan, India, and Nepal between 4,500 and 3,500 years ago. The low genetic diversity of qingke suggests Tibet can be excluded as a center of origin or domestication for barley. The rapid decrease in genetic diversity from eastern domesticated barley to qingke can be explained by a founder effect from 4,500 to 2,000 years ago. The haplotypes of the five key domestication genes of barley support a feral or hybridization origin for Tibetan weedy barley and reject the hypothesis of native Tibetan wild barley.

The Tibetan hulless barley (Hordeum vulgare L. var. nudum), also called "Qingke" in Chinese and "Ne" in Tibetan, is the staple food for Tibetans and an important livestock feed in the Tibetan Plateau. The diploid nature and adaptation to diverse environments of the highland give it unique resources for genetic research and crop improvement. Here we produced a 3.89-Gb draft assembly of Tibetan hulless barley with 36,151 predicted protein-coding genes. Comparative analyses revealed the divergence times and synteny between barley and other representative Poaceae genomes. The expansion of the gene family related to stress responses was found in Tibetan hulless barley. Resequencing of 10 barley accessions uncovered high levels of genetic variation in Tibetan wild barley and genetic divergence between Tibetan and non-Tibetan barley genomes. Selective sweep analyses demonstrate adaptive correlations of genes under selection with extensive environmental variables. Our results not only construct a genomic framework for crop improvement but also provide evolutionary insights of highland adaptation of Tibetan hulless barley.Tibetan hulless barley | Triticeae evolution | genetic diversity | adaptation | selective sweep

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Salinity stress represents one of the most harmful abiotic stresses for agricultural productivity. Tibetan hulless barley is an important economic crop widely grown in highly stressful conditions in the Qinghai-Tibet Plateau and is often challenged by salinity stress. To investigate the temporal metabolic responses to salinity stress in hulless barley, we performed a widely targeted metabolomic analysis of 72 leaf samples from two contrasting cultivars. We identified 642 compounds 57 % of which were affected by salt stress in the two cultivars, principally amino acids and derivatives, organic acids, nucleotides, and derivatives and flavonoids. A total of 13 stress-related metabolites including piperidine, L-tryptophan, L-glutamic acid, L-saccharopine, L-phenylalanine, 6-methylcoumarin, cinnamic acid, inosine 5′-monophosphate, aminomalonic acid, 6-aminocaproic acid, putrescine, tyramine and abscisic acid (ABA) represent the core metabolome responsive to salinity stress in hulless barley regardless of the tolerance level. In particular, we found that the ABA signalling pathway is essential to salt stress response in hulless barley. The high tolerance of the cultivar 0119 is due to a metabolic reprogramming at key stress times. During the early salt stress stages (0–24 h), 0119 tended to save energy through reduced glycolysis, nucleotide metabolism and amino acid synthesis, while increased antioxidant compounds such as flavonoids. Under prolonged stress (48–72 h), 0119 significantly enhanced energy production and amino acid synthesis. In addition, some important compatible solutes were strongly accumulated. By comparing the two cultivars, nine salt-tolerance biomarkers, mostly unreported salt-tolerance compounds in plants, were uncovered. Our study indicated that the salt tolerant hulless barley cultivar invokes a tolerance strategy which is conserved in other plant species. Overall, we provide for the first time some extensive metabolic data and some important salt-tolerance biomarkers which may assist in efforts to improve hulless barley tolerance to salinity stress.

Activation functions facilitate deep neural networks by introducing non-linearity to the learning process. The non-linearity feature gives the neural network the ability to learn complex patterns. Recently, the most widely used activation function is the Rectified Linear Unit (ReLU). Though, other various existing activation including hand-designed alternatives to ReLU have been proposed. However, none has succeeded in replacing ReLU due to their existing inconsistencies. In this work, activation function called ReLU-Memristor-like Activation Function (RMAF) is proposed to leverage benefits of negative values in neural networks. RMAF introduces a constant parameter (α) and a threshold parameter (p) making the function smooth, non-monotonous, and introduces non-linearity in the network. Our experiments show that, the RMAF works better than ReLU and other activation functions on deeper models and across number of challenging datasets. Firstly, experiments are performed by training and classifying on multi-layer perceptron (MLP) over benchmark data such as the Wisconsin breast cancer, MNIST, Iris and Car evaluation. RMAF achieves high performance of 98.74%, 99.67%, 98.81% and 99.42% respectively, compared to Sigmoid, Tanh and ReLU. Secondly, experiments were performed on convolution neural network (ResNet) over MNIST, CIFAR-10 and CIFAR-100 data and observed the proposed activation function achieves higher performance accuracy of 99.73%, 98.77% and 79.82% respectively than Tanh, ReLU and Swish. Additionally, we experimented our work on deep networks i.e. squeeze network (SqueezeNet), Dense connected neural network (DenseNet121) and ImageNet dataset, which RMAF produced the best performance. We note that, the RMAF converges faster than the other functions and can replace ReLU in any neural network due to the efficiency, scalability and its similarity to both ReLU and Swish.