Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning

Session-based recommendation techniques aim to capture dynamic user behavior by analyzing past interactions. However, existing methods heavily rely on historical item ID sequences to extract user preferences, leading to challenges such as popular bias and cold-start problems. In this paper, we propose a hybrid multimodal approach for session-based recommendation to address these challenges. Our approach combines different modalities, including textual content and item IDs, leveraging the complementary nature of these modalities using CatBoost. To learn universal item representations, we design a language representation-based item retrieval architecture that extracts features from the textual content utilizing pre-trained language models. Furthermore, we introduce a novel Decoupled Contrastive Learning method to enhance the effectiveness of the language representation. This technique decouples the sequence representation and item representation space, facilitating bidirectional alignment through dual-queue contrastive learning. Simultaneously, the momentum queue provides a large number of negative samples, effectively enhancing the effectiveness of contrastive learning. Our approach yielded competitive results, securing a 5th place ranking in KDD CUP 2023 Task 1. We have released the source code and pre-trained models associated with this work

Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain

The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of ‘‘hidden responders’’ which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL https://github.com/zhaoyw456/DIPRAS

Compressed glassy carbon: An ultrastrong and elastic interpenetrating graphene network

Carbon’s unique ability to have both sp2 and sp3 bonding states gives rise to a range of physical attributes, including excellent mechanical and electrical properties. We show that a series of lightweight, ultrastrong, hard, elastic, and conductive carbons are recovered after compressing sp2-hybridized glassy carbon at various temperatures. Compression induces the local buckling of graphene sheets through sp3 nodes to form interpenetrating graphene networks with long-range disorder and short-range order on the nanometer scale. The compressed glassy carbons have extraordinary specific compressive strengths—more than two times that of commonly used ceramics—and simultaneously exhibit robust elastic recovery in response to local deformations. This type of carbon is an optimal ultralight, ultrastrong material for a wide range of multifunctional applications, and the synthesis methodology demonstrates potential to access entirely new metastable materials with exceptional properties

A critical role of RBM8a in proliferation and differentiation of embryonic neural progenitors

BACKGROUND: Nonsense mediated mRNA decay (NMD) is an RNA surveillance mechanism that controls RNA stability and ensures the speedy degradation of erroneous and unnecessary transcripts. This mechanism depends on several core factors in the exon junction complex (EJC), eIF4A3, RBM8a, Magoh, and BTZ, as well as peripheral factors to distinguish premature stop codons (PTCs) from normal stop codons in transcripts. Recently, emerging evidence has indicated that NMD factors are associated with neurodevelopmental disorders such as autism spectrum disorder (ASD) and intellectual disability (ID). However, the mechanism in which these factors control embryonic brain development is not clear. RESULT: We found that RBM8a is critical for proliferation and differentiation in cortical neural progenitor cells (NPCs). RBM8a is highly expressed in the subventricular zone (SVZ) of the early embryonic cortex, suggesting that RBM8a may play a role in regulating NPCs. RBM8a overexpression stimulates embryonic NPC proliferation and suppresses neuronal differentiation. Conversely, knockdown of RBM8a in the neocortex reduces NPC proliferation and promotes premature neuronal differentiation. Moreover, overexpression of RBM8a suppresses cell cycle exit and keeps cortical NPCs in a proliferative state. To uncover the underlying mechanisms of this phenotype, genome-wide RNAseq was used to identify potential downstream genes of RBM8a in the brain, which have been implicated in autism and neurodevelopmental disorders. Interestingly, autism and schizophrenia risk genes are highly represented in downstream transcripts of RBM8a. In addition, RBM8a regulates multiple alternative splicing genes and NMD targets that are implicated in ASD. Taken together, this data suggests a novel role of RBM8a in the regulation of neurodevelopment. CONCLUSIONS: Our studies provide some insight into causes of mental illnesses and will facilitate the development of new therapeutic strategies for neurodevelopmental illnesses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13064-015-0045-7) contains supplementary material, which is available to authorized users

Modeling Axial Relocation of Fragmented Fuel During Loss of Coolant Conditions by Using ABAQUS

Copyright © 2020 ASME. In a light-water reactor, during normal operating condition, the UO2 nuclear fuel pellets undergo fragmentation primarily due to presence of thermal stresses, fission gas development and pellet-clad mechanical interaction. Under Loss of Coolant Accident (LOCA) conditions, a portion of fuel fragments can freely move downwards to the ballooning region due to the significant cladding deformation. The fuel relocation can localize the heat load and in turn accelerate the cladding balloon and burst process. Cladding burst is of great concern because of the potential for fuel dispersal into coolant and clad structural stability. In our work, we built up a finite element model considering cladding balloon, fuel relocation and its resultant thermal feedback during LOCA condition with ABAQUS. The clad balloon model includes phase transformation, swelling, thermal and irradiation creep, irradiation hardening and annealing and other important thermal-mechanical properties. The mass of relocation model was verified against the analytical cases of single balloon and twin balloons. The cladding balloon model combined with fuel thermal conductivity degradation was verified against fuel performance code, FRAPTRAN. Finally, with the evolution of pellet-cladding gap, the fuel mass relocation was calculated and compared against the IFA-650.4 transient test from the Halden reactor

Timing Determination of Invasive Fungal Infection Prophylaxis According to Immune Function in HSCT Patients

Patients who receive a hematopoietic stem cell transplantation (HSCT) exhibit an immune defect after recovering from neutropenia. The current guidelines do not recommend fungal prophylaxis in these patients, except for grades III to IV GVHD in HSCT. Thus, the timing for the initiation and cessation of IFI prophylaxis in immune-compromised patients remains a challenging endeavor. We retrospectively analyzed patients who received auto or allo-HSCT and monitored their immune function after recovering from neutropenia by measuring the levels of IgG, IgA, IgM, as well as the number of T, B, NK cells. We found that the level of IgG and NK cell count exhibited a significant difference with the incidence of IFI by logistic regression (p = 0.000 vs. 0.000, respectively) and conditional logistic regression (p = 0.009 vs. p = 0.002). The initiation of IFI prophylaxis was determined to be IgG < 7 mg/mL and NK cell count < 6.5 × 104/mL by an receiver operating characteristic curve separately. Tests in parallel increased the test sensitivity and specificity. Thus, the optimal timing for initiating prophylaxis in patients after HSCT could be IgG < 7 mg/mL or NK cell count < 6.5 × 104/mL. Future large-scale prospective clinical trials are required to verify these findings. Patients who are immuno-compromised after auto or allo-HSCT may benefit from a lower fungi infection incidence with immune surveillance and proper fungal prophylaxis