Search

Abstract Background To investigate the association between serum branched chain amino acids (BCAAs), mammalian target of rapamycin (mTOR) levels and the risk of gestational diabetes mellitus (GDM) in pregnant women. Methods 1:1 matched case–control study was conducted including 66 GDM patients and 66 matched healthy pregnant women (± 3 years) in 2019, in China. Fasting bloods of pregnant women were collected in pregnancy at 24 ~ 28 weeks gestation. And the serum levels of valine (Val), leucine (Leu), isoleucine (Ile) and mTOR were determined. Conditional logistic regressions models were used to estimate the associations of BCAAs and mTOR concentrations with the risk of GDM. Results Concentrations of serum Val and mTOR in cases were significantly higher than that in controls (P

Abstract Optical microscopy techniques are a popular choice for visualizing micro-agents. They generate images with relatively high spatiotemporal resolution but do not reveal encoded information for distinguishing micro-agents and surroundings. This study presents multicolor fluorescence microscopy for rendering color-coded identification of mobile micro-agents and dynamic surroundings by spectral unmixing. We report multicolor microscopy performance by visualizing the attachment of single and cluster micro-agents to cancer spheroids formed with HeLa cells as a proof-of-concept for targeted drug delivery demonstration. A microfluidic chip is developed to immobilize a single spheroid for the attachment, provide a stable environment for multicolor microscopy, and create a 3D tumor model. In order to confirm that multicolor microscopy is able to visualize micro-agents in vascularized environments, in vitro vasculature network formed with endothelial cells and ex ovo chicken chorioallantoic membrane are employed as experimental models. Full visualization of our models is achieved by sequential excitation of the fluorophores in a round-robin manner and synchronous individual image acquisition from three-different spectrum bands. We experimentally demonstrate that multicolor microscopy spectrally decomposes micro-agents, organic bodies (cancer spheroids and vasculatures), and surrounding media utilizing fluorophores with well-separated spectrum characteristics and allows image acquisition with 1280 $$\times$$ × 1024 pixels up to 15 frames per second. Our results display that real-time multicolor microscopy provides increased understanding by color-coded visualization regarding the tracking of micro-agents, morphology of organic bodies, and clear distinction of surrounding media.

Analog compute-in-memory with resistive random access memory (RRAM) devices promises to overcome the data movement bottleneck in data-intensive artificial intelligence (AI) and machine learning. RRAM crossbar arrays improve the efficiency of vector-matrix multiplications (VMMs), which is a vital operation in these applications. The prototype IC is the first complete, fully integrated analog-RRAM CMOS coprocessor. This article focuses on the digital and analog circuitry that supports efficient and flexible RRAM-based computation. A passive $54\times108$ RRAM crossbar array performs VMM in the analog domain. Specialized mixed-signal circuits stimulate and read the outputs of the RRAM crossbar. The single-chip CMOS prototype includes a reduced instruction set computer (RISC) processor interfaced to a memory-mapped mixed-signal core. In the mixed-signal core, ADCs and DACs interface with the passive RRAM crossbar. The RISC processor controls the mixed-signal circuits and the algorithm data path. The system is fully programmable and supports forward and backward propagation. As proof of concept, a fully integrated 0.18- $\mu \text{m}$ CMOS prototype with a postprocessed RRAM array demonstrates several key functions of machine learning, including online learning. The mixed-signal core consumes 64 mW at an operating frequency of 148 MHz. The total system power consumption considering the mixed-signal circuitry, the digital processor, and the passive RRAM array is 307 mW. The maximum theoretical throughput is 2.6 GOPS at an efficiency of 8.5 GOPS/W.

Surface roughness and the material removal rate (MRR) are two important indicators during the grinding process. The former determines the surface quality while the latter reflects the grinding efficiency directly. In this paper, the two indicators are taken into consideration simultaneously and differently by converting them into a comprehensive goal with using weighting objective method. A prediction model was established for each comprehensive goal with each different combination of surface roughness and MRR weighting coefficient. The optimal value of abrasive size, contact force, belt linear speed, and feed speed were obtained under different grinding situations by using a central composite design (CCD) combined with response surface analysis. The experimental results showed that the comprehensive goal can be used effectively as an indicator to control the grinding performance and improve the optimization process.