Session-Based Recommendation by Exploiting Substitutable and Complementary Relationships from Multi-behavior Data

Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions. Most existing studies on SR adopt advanced deep learning methods. However, the majority only consider a special behavior type (e.g., click), while those few considering multi-typed behaviors ignore to take full advantage of the relationships between products (items). In this case, the paper proposes a novel approach, called Substitutable and Complementary Relationships from Multi-behavior Data (denoted as SCRM) to better explore the relationships between products for effective recommendation. Specifically, we firstly construct substitutable and complementary graphs based on a user's sequential behaviors in every session by jointly considering `click' and `purchase' behaviors. We then design a denoising network to remove false relationships, and further consider constraints on the two relationships via a particularly designed loss function. Extensive experiments on two e-commerce datasets demonstrate the superiority of our model over state-of-the-art methods, and the effectiveness of every component in SCRM.Comment: 31 pages,11 figures, accepted by Data Mining and Knowledge Discovery(2023

Microstructural and functional impairment of the basal ganglia in Wilson’s disease: a multimodal neuroimaging study

ObjectivesMagnetic susceptibility changes in brain MRI of Wilson’s disease (WD) patients have been described in subcortical nuclei especially the basal ganglia. The objectives of this study were to investigate its relationship with other microstructural and functional alterations of the subcortical nuclei and the diagnostic utility of these MRI-related metrics.MethodsA total of 22 WD patients and 20 healthy controls (HCs) underwent 3.0T multimodal MRI scanning. Susceptibility, volume, diffusion microstructural indices and whole-brain functional connectivity of the putamen (PU), globus pallidus (GP), caudate nucleus (CN), and thalamus (TH) were analyzed. Receiver operating curve (ROC) was applied to evaluate the diagnostic value of the imaging data. Correlation analysis was performed to explore the connection between susceptibility change and microstructure and functional impairment of WD and screen for neuroimaging biomarkers of disease severity.ResultsWilson’s disease patients demonstrated increased susceptibility in the PU, GP, and TH, and widespread atrophy and microstructural impairments in the PU, GP, CN, and TH. Functional connectivity decreased within the basal ganglia and increased between the PU and cortex. The ROC model showed higher diagnostic value of isotropic volume fraction (ISOVF, in the neurite orientation dispersion and density imaging model) compared with susceptibility. Severity of neurological symptoms was correlated with volume and ISOVF. Susceptibility was positively correlated with ISOVF in GP.ConclusionMicrostructural impairment of the basal ganglia is related to excessive metal accumulation in WD. Brain atrophy and microstructural impairments are useful neuroimaging biomarkers for the neurological impairment of WD

The governance of urban energy transitions: A comparative study of solar water heating systems in two Chinese cities

This paper examines how urban energy transitions are unfolding in China, in relation to the deployment of solar water heating (SWH) systems in two Chinese cities, Rizhao and Shenzhen. Cities play a significant role in the energy transition in China. Scholarly efforts have looked into the translation of top-down visions into locally actionable policy. This article contributes to this body of research with an analysis of the urban governance of urban energy transitions in China, and how low carbon technologies are deployed in particular urban contexts. The comparative analysis of Rizhao and Shenzhen suggests that specific socio-spatial arrangements shape the evolutionary trajectories of urban energy transitions of SWH systems in both cities. In the case of Rizhao, policy approaches have been erratic. Nevertheless, governmental and civil society actors have worked to forge alignment among political visions, built environment constraints, and social practices. The proximity of an industrial cluster supporting SWH technology and the early uptake of this technology by households are two key factors that explain the rapid spread of SWH systems in Rizhao. In Shenzhen, the local government has promoted SWH systems through regulation and incentives in a top-down and coordinated manner. These programmes have been, however, abandoned, after they did not deliver the expected results. The two contrasting cases suggest that the urban energy transition in China is the result of the coordinated actions of multiple actors, and success depends on the fit between technologies and the urban development contexts, rather than on aggressive government-sponsored actions

Causality and Correlation Graph Modeling for Effective and Explainable Session-based Recommendation

Session-based recommendation which has been witnessed a booming interest recently, focuses on predicting a user's next interested item(s) based on an anonymous session. Most existing studies adopt complex deep learning techniques (e.g., graph neural networks) for effective session-based recommendation. However, they merely address co-occurrence between items, but fail to well distinguish causality and correlation relationship. Considering the varied interpretations and characteristics of causality and correlation relationship between items, in this study, we propose a novel method denoted as CGSR by jointly modeling causality and correlation relationship between items. In particular, we construct cause, effect and correlation graphs from sessions by simultaneously considering the false causality problem. We further design a graph neural network-based method for session-based recommendation. Extensive experiments on three datasets show that our model outperforms other state-of-the-art methods in terms of recommendation accuracy. Moreover, we further propose an explainable framework on CGSR, and demonstrate the explainability of our model via case studies on Amazon dataset.Comment: submitted to TOI

NIFK, an independent prognostic biomarker of hepatocellular carcinoma, is correlated with immune infiltration

Background: The molecular mechanisms that lead to hepatocellular carcinoma (HCC), a highly common malignant tumor, are currently unclear. In fact, while the nucleolar protein that interacts with the FHA domain of pKi-67 (NIFK) is known to promote lung cancer progression, its specific role in HCC remains unknown. Results: In HCC tissues, NIFK was significantly overexpressed in comparison with normal tissues. In The Cancer Genome Atlas (TCGA) database, NIFK expression showed a good prediction value according to an ROC curve and it was linked to poor progression-free interval, disease-specific survival and overall survival. Furthermore, the level of NIFK expression in HCC was significantly correlated with tumor stage, AFP (ng/mL), OS event, DSS event and PFI event. Based on GSEA evaluations, differentially expressed NIFK genes exhibited enrichment for fatty acid metabolism, oxidative phosphorylation as well as cancer, cell cycle, MAPK, TGF, WNT and NOTCH signaling pathways. The TIMER database analysis further revealed positive associations between NIFK expression and the immune cells, such as dendritic cells, neutrophils, macrophages, CD4+ T cells, CD8+ T cells and B cells. Also, the NIFK expression was positively correlated with immune checkpoints (PD1/PD-L1 and CTLA4). The experimental verification determined that NIFK knockdown could thwart HCC cellular properties of proliferation, metastasis and invasiveness. Univariate and multivariate Cox hazard regression validated NIFK expression as an independent prognostic marker in HCC. Conclusions: NIFK has theragnostic potential as a separate prognostic factor or novel biomarker involved in the immune infiltration of HCC.How to cite: Cheng F, Yuan L, Wu Z, et al. NIFK, an independent prognostic biomarker of hepatocellular carcinoma, is correlated with immune infiltration. Electron J Biotechnol 2023;63. https://doi.org/10.1016/j.ejbt.2023.01.003

Expression of Neural Crest Markers GLDC and ERRFI1 is Correlated with Melanoma Prognosis

Regulation of particular genes during the formation of neural crest (NC) cells is also described during progression of malignant melanoma. In this context, it is of paramount importance to develop neural crest models allowing the identification of candidate genes, which could be used as biomarkers for melanoma prognosis. Here, we used a human induced Pluripotent Stem Cells (iPSC)-based approach to present novel NC-associated genes, expression of which was upregulated in melanoma. A list of 8 candidate genes, based on highest upregulation, was tested for prognostic value in a tissue microarray analysis containing samples from advanced melanoma (good versus bad prognosis) as well as from high-risk primary melanomas (early metastasizing versus non or late-metastasizing). CD271, GLDC, and ERRFI1 showed significantly higher expression in metastatic patients who died early than the ones who survived at least 30 months. In addition, GLDC and TWIST showed a significantly higher immunohistochemistry (IHC) score in primary melanomas from patients who developed metastases within 12 months versus those who did not develop metastases in 30 months. In conclusion, our iPSC-based study reveals a significant association of NC marker GLDC protein expression with melanoma prognosis

Multifunctional glucose biosensors from Fe₃O₄ nanoparticles modified chitosan/graphene nanocomposites

Novel water-dispersible and biocompatible chitosan-functionalized graphene (CG) has been prepared by a one-step ball milling of carboxylic chitosan and graphite. Presence of nitrogen (from chitosan) at the surface of graphene enables the CG to be an outstanding catalyst for the electrochemical biosensors. The resulting CG shows lower ID/IG ratio in the Raman spectrum than other nitrogen-containing graphene prepared using different techniques. Magnetic Fe₃O₄ nanoparticles (MNP) are further introduced into the as-synthesized CG for multifunctional applications beyond biosensors such as magnetic resonance imaging (MRI). Carboxyl groups from CG is used to directly immobilize glucose oxidase (GOx) via covalent linkage while incorporation of MNP further facilitated enzyme loading and other unique properties. The resulting biosensor exhibits a good glucose detection response with a detection limit of 16 μM, a sensitivity of 5.658 mA/cm²/M, and a linear detection range up to 26 mM glucose. Formation of the multifunctional MNP/CG nanocomposites provides additional advantages for applications in more clinical areas such as in vivo biosensors and MRI agents.9 page(s