No abstract

Artificial intelligence is facilitating human life in many aspects. Previous artificial intelligence has been mainly focused on computer algorithms (e.g. deep-learning and extremelearning) and integrated circuits. Recently, all-optical diffractive deep neural networks (D 2 NN) were realized by using passive structures, which can perform complicated functions designed by computer-based neural networks at the light speed. However, once a passive D 2 NN architecture is fabricated, its function will be fixed. Here, we propose a programmable artificial intelligence machine (PAIM) that can execute various intellectual tasks by realizing hierarchical connections of brain neurons via a multi-layer digital-coding metasurface array. Integrated with two amplifier chips in each meta-atom, its transmission coefficient covers a dynamic range of 35 dB (from -40 dB to -5 dB), which is the basis to construct the reprogrammable physical layers of D 2 NN, in which the digital meta-atoms make the artificial neurons alive. We experimentally show that PAIM can handle various deep-learning tasks for wave sensing, including image classifications, mobile communication coder-decoder, and real-time multi-beam focusing. In particular, we propose a reinforcement learning algorithm for on-site learning and discrete optimization algorithm for digital coding, making PAIM have autonomous intelligence ability and perform self-learning tasks without the support of extra computer.

SummarySoybean cyst nematode (SCN, Heterodera glycines) is the most devastating pest affecting soybean production worldwide. SCN resistance requires both the GmSHMT08 and the GmSNAP18 in ‘Peking’‐type resistance. Here, we describe the molecular interaction between GmSHMT08 and GmSNAP18, which is potentiated by a pathogenesis‐related protein GmPR08‐Bet VI. Like GmSNAP18 and GmSHMT08, GmPR08‐Bet VI expression was induced in response to SCN and its overexpression decreased SCN cysts by 65% in infected transgenic soybean roots. Overexpression of GmPR08‐Bet VI did not have an effect on SCN resistance when the two cytokinin‐binding sites in GmPR08‐Bet VI were mutated, indicating a new role of GmPR08‐Bet VI in SCN resistance. GmPR08‐Bet VI was mapped to a QTL for resistance to SCN using different mapping populations. GmSHMT08, GmSNAP18 and GmPR08‐Bet VI localize to the cytosol and plasma membrane. GmSNAP18 expression and localization hyper‐accumulated at the plasma membrane and was specific to the root cells surrounding the nematode in SCN‐resistant soybeans. Genes encoding key components of the salicylic acid signalling pathway were induced under SCN infection. GmSNAP18 and GmPR08‐Bet VI were also induced under salicylic acid and cytokinin exogenous treatments, while GmSHMT08 was induced only when the resistant GmSNAP18 was present, pointing to the presence of a molecular crosstalk between SCN‐resistant genes and defence genes. Expression analysis of GmSHMT08 and GmSNAP18 identified the need of a minimum expression requirement to trigger the SCN resistance reaction. These results provide insight into a new response mechanism towards plant nematode resistance involving haplotype compatibility, gene dosage and hormone signalling.

BACKGROUND Early gastric cancer (EGC), compared with advanced gastric cancer (AGC), has a higher 5-year survival rate. However, due to the lack of typical symptoms and the difficulty in diagnosing EGC, no effective biomarkers exist for the detection of EGC, and gastroscopy is the only detection method. AIM To provide new biomarkers with high specificity and sensitivity through analyzed the differentially expressed microRNAs (miRNAs) in EGC and AGC and compared them with those in benign gastritis (BG). METHODS We examined the differentially expressed miRNAs in the plasma of 30 patients with EGC, AGC, and BG by miRNA chip analysis. Then, we analyzed and selected the significantly different miRNAs using bioinformatics. Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) confirmed the relative transcription level of these miRNAs in another 122 patients, including patients with EGC, AGC, Helicobacter pylori ( H. pylori )-negative gastritis (Control-1), and H. pylori -positive atrophic gastritis (Control-2). To establish a diagnostic model for the detection of plasma miRNA in EGC, we chose miRNAs that can be used to determine EGC and AGC from Control-1 and Control-2 and miRNAs in EGC from all other groups. RESULTS Among the expression profiles of the miRNA chips in the three groups in the discovery set, of 117 aberrantly expressed miRNAs, 30 confirmed target prediction, whereas 14 were included as potential miRNAs. The RT-qPCR results showed that 14 potential miRNAs expression profiles in the two groups exhibited no differences in terms of H. pylori -negative gastritis (Control-1) and H. pylori -positive atrophic gastritis (Control-2). Hence, these two groups were incorporated into the Control group. A combination of four types of miRNAs, miR-7641, miR-425-5p, miR-1180-3p and miR-122-5p, were used to effectively distinguish the Cancer group (EGC + AGC) from the Control group [area under the curve (AUC) = 0.799, 95% confidence interval (CI): 0.691-0.908, P < 0.001]. Additionally, miR-425-5p, miR-24-3p, miR-1180-3p and miR-122-5p were utilized to distinguish EGC from the Control group (AUC = 0.829, 95%CI: 0.657-1.000, P = 0.001). Moreover, the miR-24-3p expression level in EGC was lower than that in the AGC (AUC = 0.782, 95%CI: 0.571-0.993, P = 0.029), and the miR-4632-5p expression level in EGC was significantly higher than that in AGC (AUC = 0.791, 95%CI: 0.574-1.000, P = 0.024). CONCLUSION The differentially expressed circulatory plasma miR-425-5p, miR-1180-3p, miR-122-5p, miR-24-3p and miR-4632-5p can be regarded as a new potential biomarker panel for the diagnosis of EGC. The prediction and early diagnosis of EGC can b...

No abstract