Hybrid Augmented Automated Graph Contrastive Learning

Graph augmentations are essential for graph contrastive learning. Most existing works use pre-defined random augmentations, which are usually unable to adapt to different input graphs and fail to consider the impact of different nodes and edges on graph semantics. To address this issue, we propose a framework called Hybrid Augmented Automated Graph Contrastive Learning (HAGCL). HAGCL consists of a feature-level learnable view generator and an edge-level learnable view generator. The view generators are end-to-end differentiable to learn the probability distribution of views conditioned on the input graph. It insures to learn the most semantically meaningful structure in terms of features and topology, respectively. Furthermore, we propose an improved joint training strategy, which can achieve better results than previous works without resorting to any weak label information in the downstream tasks and extensive evaluation of additional work

Detect Depression from Social Networks with Sentiment Knowledge Sharing

Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing attention, with a significant portion of individuals resorting to social networks as an outlet for expressing emotions. Using deep learning techniques to discern potential signs of depression from social network messages facilitates the early identification of mental health conditions. Current efforts in detecting depression through social networks typically rely solely on analyzing the textual content, overlooking other potential information. In this work, we conduct a thorough investigation that unveils a strong correlation between depression and negative emotional states. The integration of such associations as external knowledge can provide valuable insights for detecting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK

Characterization of the Differential Aroma Compounds among 10 Different Kinds of Premium Soy Sauce

Investigation of the aroma differences among different kinds of soy sauces is beneficial for controlling their flavor quality and processing improvement from the perspectives of raw materials and brewing techniques. The aroma compounds in ten premium soy sauces (CB, HT1, HT2, LH, LJJ1, LJJ2, QH, XH1, XH2, WZ) were qualitative and quantitative analyzed by solid phase extraction and solid-phase microextraction combined with gas chromatography-mass spectrometry (GC-MS). The contributions of aroma compounds to the aroma characteristics of premium soy sauce was determined by sensory evaluation, calculation of aroma activity value (OAV) and partial least squares regression analysis (PLSR). A total of 86 volatile compounds were identified in 10 premium soy sauces, 44 of them were both detected in 10 soy sauce. The 30 aroma compounds with OAV≥1 were detected, the 5-ethyl-4-hydroxy-2-methyl-3(2H)-furanone showed the highest OAV (373~4698), followed by 4-methoxy-2,5-dimethyl-3(2H)-furanone (0~1473). WZ soy sauce had a strong smoky aroma due to the highest variety of phenolic and ketone compounds. The overall aroma profile of CB soy sauce was the weakest with the lowest concentration of ethanol (25.775 μg/L), but the highest content of pyrazine compounds (182.796 μg/L), of which 2,6-dimethylpyrazine was 66.256 μg/L. XH1 soy sauce had a strong sauce aroma and alcoholic notes, due to the highest ethanol content (147.257 μg/L) and higher phenolic content, for example the concentration of 4-ethyl-2-methoxyphenol (18240.479 μg/L) was the highest. XH2 soy sauce had a strong malty aroma. The content of 2-methyl-1-propanol (51.223 μg/L) and 2,3-butanediol (57921.798 μg/L) in LH soy sauce was the highest among others. The content of 1-octen-3-ol (61.219 μg/L) in HT1 soy sauce was the highest. Combination of OAV and PLSR analysis confirmed the ethyl acetate, 3-hydroxy-2-butanone, 2,3-butanediol, 3-ethyl-2,5-dimethylpyrazine, 4-methoxy-2,5-dimethyl-3(2H)-furanone, 4-ethylguaiacol and 4-ethylphenol were the key aroma-active components that contribute to the aroma differences among 10 kinds of premium soy sauce

One-step Method to Fabricate Poly(ethylene terephthalate)/Gd(OH)3 Magnetic Nanofibers tTowards MRI-active Materials with High T1 Relaxivity and Long-term Visibility

Magnetic resonance imaging (MRI)-active polymers exhibit unique advantages for in vivo diagnosis. Here, in order to endow electrospun fibers with long-term T1 positive MRI visibility, MRI contrast agent (CA), Gd(OH)3, is introduced in a new, extremely convenient method. Crucially, GdCl3 is reacted with NaOH in situ during electrospinning, with flexibility to deliver both well-dispersed and aggregated Gd(OH)3 clusters within a poly(ethylene terephthalate) (PET) matrix. T1 and T2 relaxivities of Gd(OH)3 in PET nanofibers are studied. Well-dispersed Gd(OH)3 (sub-nanometer in size) exhibits 34 times higher T1 relaxivity than aggregated nanoparticles when embedded within the fibers. The morphology, structure, magnetic properties, tensile properties, imaging performance and biosafety of the PET/Gd(OH)3 composite fibers are evaluated to identify the optimum conditions to produce new materials with balanced properties, excellent in vivo positive contrast and approximately 139 days imaging lifetime. Comparing this sample with a commercial CA, only 0.32 wt.% Gd loading is needed to attain similar MRI signal intensity. In summary, PET/Gd(OH)3 long-term MRI-active fibers show great potential for future biomedical applications and the study also provides a promising new general strategy to enhance the MRI T1 positive contrast of electrospun fibers of a whole host of other systems