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Background Osteoporosis (OP) is a condition featured by bone mass loss and bone tissue microarchitectural alterations due to impaired tissue homeostasis favoring excessive bone resorption versus deposition. The trigger of such an impairment and the downstream molecular pathways involved are yet to be clarified. Long non-coding RNA (lncRNA) plays a role in gene transcription, protein expression and epigenetic regulation; and altered expression results in immune or metabolism related desease development. To determine whether lncRNAs are involved in osteoporosis, we analyzed the expression profile of lncRNAs and mRNAs in osteoporosis. Method Three pairs of osteoporosis patients (OP group) and healthy people controls (NC group) were screened by microarray. Quantitative polymerase chain reaction (qRT-PCR) was performed to confirm dysregulated lncRNA expressions in 5 pairs of OP and NC group tissues samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to construct the lncRNA-mRNA co-expression network. Result Through co-expression analysis, differently expressed transcripts were divided into modules, and lncRNAs were functionally annotated. We further analyzed the clinical significance of crucial lncRNAs from modules in public data. Finally, the expression of five lncRNAs, CUST_44695_PI430048170-GeneSymbol:CTA-384D8.35;CUST_39447_PI430048170,CUST_73298_PI430048170,CUST_108340_PI430048170,CUST_118927_PI430048170,this four lncRNAs have not been annotation genes and have not found GeneSymbols, and by quantitative RT-PCR, which may be associated with osteoporosis patients’ overall survival. Conclusion Analysis of this study revealed that dysregulated lncRNAs and mRNAs in osteoporosis patients and health people controls could affect the immune or metabolism system and musculoskeletal cell differentiation. The biological functions of those lncRNAs need to be further validated.

BACKGROUND: The use of exo-16,17-dihydro-gibberellin A5-13-acetate (DHGA 5 ) in agriculture has been limited by its low synthetic yield. This study was aimed at optimizing the synthetic route of DHGA 5 , designing and synthesizing new derivatives with strong plant growth inhibitory activities.RESULTS: Previous synthetic methods were replaced with a shorter, milder and faster reaction route with higher yield (76.3%) of DHGA 5 . Based on this novel route, a series of new derivatives were designed and synthesized using DHGA 5 as a lead compound and characterized and evaluated for biological activities in Arabidopsis thaliana. Among the 15 tested derivatives, compound 14j showed a lower medium inhibition concentration (IC 50 , 73 M) in Arabidopsis than that of DHGA 5 (91 M). Gibberellin deficient mutant assay further revealed that 14j had very different activities compared to DHGA 5 as it specifically inhibits gibberellin biosynthetic pathways. In addition, 14j does not influence the interaction between gibberellin receptors (GID1) and the master growth repressor (RGA) based on yeast two-hybrid assay. CONCLUSION: The optimized synthetic route provides a promising method for large-scale preparation of DHGA 5 . Our biological assays indicate that 14j likely acts on gibberellin signaling elements other than GID1. These results indicate that novel plant growth regulators can be developed. Synthesis of DHGA 5 (Compound 8)The optimized synthetic route of DHGA 5 (8) is shown in Fig. 3.Synthesis of (The intermediate 2 was prepared as described previously. 26 A mixture of GA 3 (10 g, 28.88 mmol), acetic anhydride (Ac 2 O) (20 mL) and 4-dimethylaminopyridine (DMAP) (36 mg, 0.28 mmol) in anhydrous pyridine (50 mL) was stirred at room temperature (rt) for Pest Manag Sci 2020; 76: 807-817

To obtain a speaker’s pronunciation characteristics, a method is proposed based on an idea from bionics, which uses spectrogram statistics to achieve a characteristic spectrogram to give a stable representation of the speaker’s pronunciation from a linear superposition of short-time spectrograms. To deal with the issue of slow network training and recognition speed for speaker recognition systems on resource-constrained devices, based on a traditional SOM neural network, an adaptive clustering self-organizing feature map SOM (AC-SOM) algorithm is proposed. This algorithm automatically adjusts the number of neurons in the competition layer based on the number of speakers to be recognized until the number of clusters matches the number of speakers. A 100-speaker database of characteristic spectrogram samples was built and applied to the proposed AC-SOM model, yielding a maximum training time of only 304 s, with a maximum sample recognition time of less than 28 ms. Comparing to other approaches, the proposed method offers greatly improved training and recognition speed without sacrificing too much recognition accuracy. The promising results suggest that the proposed method satisfies real-time data processing and execution requirements for edge intelligence systems better than other speaker recognition methods.