OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce

Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry for Identification of In Vitro and In Vivo Metabolites of Bornyl Gallate in Rats

Bornyl gallate (BG) is a potential drug candidate synthesized by the reaction of two natural products, gallic acid and borneol. Previous studies have strongly suggested that BG is worthy of further investigation due to antioxidant, antiatherosclerosis activities, and obvious activity of stimulating intersegmental vessel growth in zebrafish. This work was designed to elucidate the metabolic profile of BG through analyzing its metabolites in vitro and in vivo by a chromatographic separation coupled with a mass spectrometry. The metabolites of BG were characterized from the rat liver microsome incubation solution, as well as rat urine and plasma after oral administration. Chromatographic separation was performed on an Agilent TC-C18 column (250 mm × 4.6 mm, 5 μm) with gradient elution using methanol and water containing 0.2% (V : V) formic acid as the mobile phase. Metabolites identification involved analyzing the retention behaviors, changes of molecular weights and MS/MS fragment patterns of BG and the metabolites. Five compounds were identified as isomers of hydroxylated BG metabolites in vitro. The major metabolites of BG in rat urine and plasma proved to be BG-O-glucuronide and O-methyl BG-O-glucuronide. The proposed method confirmed to be a reliable and sensitive alternative for characterizing metabolic pathways of BG

Structural characteristics of corncob and eucalyptus contributed to sugar release during hydrothermal pretreatment and enzymatic hydrolysis

Explicitly understanding biomass recalcitrance through the characterization of biomass physicochemical properties may help to develop efficient pretreatment and enzymatic hydrolysis strategies. The lignin of corncob and eucalyptus contain the same main linkage bonds: beta-O-4 aryl ether bonds, beta-beta and beta-5 structures, but the lignin of eucalyptus was of the syringyl (S)-guaiacy (G) type, while that of corncob was SG-p-hydroxyphenyl (H) type, corresponding to lignin S/G ratios of 1.6 and 1.1 respectively. Under the optimum microwave-hydrothermal pretreatment condition of 180 degrees C for 30 min at a 12.5% substrate concentration, the maximum total xylose yield of corncob (64.7%) was lower than that of eucalyptus (79.2%). In contrast, corncob resulted in a greater increase in enzymatic digestibility, from 59.6 to 82.4%, after pretreatments, compared with 16.7 to 74.9% for eucalyptus. There was a positive correlation between the xylose yield and lignin S/G ratio, but the lignin content was negatively correlated with enzymatic digestibility. Furthermore, based on the non-destructive characterization of three-dimensional X-ray microscopy, not only was the increase in the number and size of surface pores beneficial to the accessibility of cellulose to cellulosic enzymes, but the swelling of cell wall could reduce the recalcitrance of sugar release

Phase-Exchange Solvent Pretreatment Improves the Enzymatic Digestibility of Cellulose and Total Sugar Recovery from Energy Sorghum

Traditional liquid hot water pretreatment (LHWP) has a high water consumption, a high reaction temperature, and low lignin removal, making it unsuitable for industrial applications of biomass conversion. In this study, we developed a new phase-exchange solvent pretreatment (PESP) based on the varied phase composition of furfural (FF)-water at different temperatures. Substitution of water with FF had no significant influence on xylan hydrolysis, but it improved the mass transfer performance. At the optimum conditions of 180 degrees C, an FF:water ratio of 30:70 (v/v), a solid:liquid ratio of 1:8 (w/v), and 0.2 wt % sulfuric acid for 30 min, the PESP of energy sorghum achieved a 74.98% total xylose yield and removed 85.08% of the lignin, while there was a selective distribution of sugar and lignin in the aqueous and organic phases, respectively. Moreover, 99.58% of the cellulase enzyme digestibility and 94.02% of the total sugar recovery were achieved after 72 h. This unusually high enzymatic digestibility could be attributed to the physicochemical changes in the substrate after the pretreatment. When compared with a traditional LHWP (5% solid loading), the water consumption decreased by approximately 72% and the lignin removal increased by 60.74%. These results demonstrate that PESP is a promising technology for biorefining lignocellulosic biomass with high efficiency and low energy consumption