Study of the Structure and Bioactivity of Polysaccharides from Different Parts of Stemona tuberosa Lour.

The polysaccharides from Stemona tuberosa Lour, a kind of plant used in Chinese herbal medicine, have various pharmacological activities, such as anti-inflammatory and antioxidant properties. However, the effects of the extraction methods and the activity of polysaccharides from different parts are still unknown. Therefore, this study aimed to evaluate the effects of different extraction methods on the yields, chemical compositions, and bioactivity of polysaccharides extracted from different parts of Stemona tuberosa Lour. Six polysaccharides were extracted from the leaves, roots, and stems of Stemona tuberosa Lour through the use of hot water (i.e., SPS-L1, SPS-R1, and SPS-S1) and an ultrasound-assisted method (i.e., SPS-L2, SPS-R2, and SPS-S2). The results showed that the physicochemical properties, structural properties, and biological activity of the polysaccharides varied with the extraction methods and parts. SPS-R1 and SPS-R2 had higher extraction yields and total sugar contents than those of the other SPSs (SPS-L1, SPS-L2, SPS-S1, and SPS-S2). SPS-L1 had favorable antioxidant activity and the ability to downregulate MUC5AC expression. An investigation of the anti-inflammatory properties showed that SPS-R1 and SPS-R2 had greater anti-inflammatory activities, while SPS-R2 demonstrated the strongest anti-inflammatory potential. The results of this study indicated that SPS-L1 and SPS-L2, which were extracted from non-medicinal parts, may serve as potent natural antioxidants, but further study is necessary to explore their potential applications in the treatment of diseases. The positive anti-inflammatory effects of SPS-R1 and SPS-R2 in the roots may be further exploited in drugs for the treatment of inflammation.

Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks.

The field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In this work, we apply a neuroscience-inspired machine learning model called echo state network (ESN) to the critical task of symbol detection in massive MIMO-OFDM systems, a key technology for 6G networks. Our work encompasses the design of a hardware-accelerated reservoir neuron architecture to speed up the ESN-based symbol detector. The design is then validated through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. The experiment results show the great performance and scalability of our symbol detector design across a range of MIMO configurations, compared with traditional MIMO symbol detection methods like linear minimum mean square error. Our findings also confirm the performance and feasibility of our entire system, reflected in low bit error rates, low resource utilization, and high throughput.

Template electrodeposition of ordered bismuth telluride nanowire arrays.

Thermoelectric bismuth telluride nanowire arrays have been synthesized by direct-current electrode-position into porous anodic alumina membranes both galvanostatically and potentiostatically. The as-synthesized Bi2Te3 nanowire arrays are highly ordered in large area, stoichiometric, uniform, with high aspect ratio (above 100) and high filling ratio (>90%) of the membrane. The effects of different electrochemical deposition parameters on crystal structures, morphology and composition have been investigated. Field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) have been used to characterize the physical and chemical properties of the nanowires.