Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21.51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1

Graph based Label Enhancement for Multi-instance Multi-label learning

Multi-instance multi-label (MIML) learning is widely applicated in numerous domains, such as the image classification where one image contains multiple instances correlated with multiple logic labels simultaneously. The related labels in existing MIML are all assumed as logical labels with equal significance. However, in practical applications in MIML, significance of each label for multiple instances per bag (such as an image) is significant different. Ignoring labeling significance will greatly lose the semantic information of the object, so that MIML is not applicable in complex scenes with a poor learning performance. To this end, this paper proposed a novel MIML framework based on graph label enhancement, namely GLEMIML, to improve the classification performance of MIML by leveraging label significance. GLEMIML first recognizes the correlations among instances by establishing the graph and then migrates the implicit information mined from the feature space to the label space via nonlinear mapping, thus recovering the label significance. Finally, GLEMIML is trained on the enhanced data through matching and interaction mechanisms. GLEMIML (AvgRank: 1.44) can effectively improve the performance of MIML by mining the label distribution mechanism and show better results than the SOTA method (AvgRank: 2.92) on multiple benchmark datasets.Comment: 7 pages,2 figure

The expression and role of protein kinase C (PKC) epsilon in clear cell renal cell carcinoma

Protein kinase C epsilon (PKCε), an oncogene overexpressed in several human cancers, is involved in cell proliferation, migration, invasion, and survival. However, its roles in clear cell renal cell carcinoma (RCC) are unclear. This study aimed to investigate the functions of PKCε in RCC, especially in clear cell RCC, to determine the possibility of using it as a therapeutic target. By immunohistochemistry, we found that the expression of PKCε was up-regulated in RCCs and was associated with tumor Fuhrman grade and T stage in clear cell RCCs. Clone formation, wound healing, and Borden assays showed that down-regulating PKCε by RNA interference resulted in inhibition of the growth, migration, and invasion of clear cell RCC cell line 769P and, more importantly, sensitized cells to chemotherapeutic drugs as indicated by enhanced activity of caspase-3 in PKCε siRNA-transfected cells. These results indicate that the overexpression of PKCε is associated with an aggressive phenotype of clear cell RCC and may be a potential therapeutic target for this disease

The role of C-peptide in diabetes and its complications: an updated review

Worldwide, diabetes and its complications have seriously affected people’s quality of life and become a serious public health problem. C-peptide is not only an indicator of pancreatic β-cell function, but also a biologically active peptide that can bind to cell membrane surface signaling molecules and activate downstream signaling pathways to play antioxidant, anti-apoptotic and inflammatory roles, or regulate cellular transcription through internalization. It is complex how C-peptide is related to diabetic complications. Both deficiencies and overproduction can lead to complications, but their mechanisms of action may be different. C-peptide replacement therapy has shown beneficial effects on diabetic complications in animal models when C-peptide is deficient, but results from clinical trials have been unsatisfactory. The complex pattern of the relationship between C-peptide and diabetic chronic complications has not yet been fully understood. Future basic and clinical studies of C-peptide replacement therapies will need to focus on baseline levels of C-peptide in addition to more attention also needs to be paid to post-treatment C-peptide levels to explore the optimal range of fasting C-peptide and postprandial C-peptide maintenance

Microstructures, Mechanical Properties and Transformation Behavior in Ni49.6Ti35.4Hf15 Alloy Produced with High-Pressure Torsion

High-pressure torsion (HPT) was applied for the Ni49.6Ti35.4Hf15 (at.%) alloy up to 1/4, 2 and 16 turns under a pressure of 4.0 GPa. The samples were examined using X-ray diffraction (XRD), transmission electron microscope (TEM) and microhardness measurements. The results indicate that the mixture of an amorphous and nanocrystalline microstructure developed in the investigated NiTiHf alloy as the number of HPT turns was increased to two. The average hardness of the samples increased from 330 Hv to 500 Hv after 16 turns of HPT. Very fine martensite developed when the HPT-processed samples were annealed at 550 ⁰C and the finer microstructures were attained with higher HPT turns. Differential scanning calorimetry (DSC) tests were performed in the post-HPT annealing samples to clarify the transformation behavior after severe plastic deformation in HPT and subsequent annealing, so as to provide an experimental basis for the application of the shape memory alloy. The transformation temperature of the alloy decreased remarkably when the number of turns of HPT reached 16. It is suggested that the deformation extent and annealing temperatures should be considered in order to maintain a high transformation temperature while utilizing the strengthening effect of HPT in the NiTiHf alloy

Recent development and applications of advanced materials via direct ink writing

Direct ink writing (DIW), a type of extrusion-based 3D printing method, enables the rapid design and building of size- and shape-scalable 3D structures in a low-cost and green manner without the need for specific size reactors and secondary substrates compared to traditional synthesis methods. Coupling the use of sol-gel inks with optimized rheological properties (elastoviscosity and shear stress) and a wide range of nanomaterials enhances the mechanical and electrical conductivity of printed products. In this review, the recent development in DIW methods, critical requirements for printable DIW inks, and applications of DIW-printed products in medical, energy storage, and environmental treatment are reviewed. A perspective outlook associated with limitations from current DIW research is proposed for the breakthrough development of such technology in the future

Transarterial chemoembolization combined with molecularly targeted agents plus immune checkpoint inhibitors for unresectable hepatocellular carcinoma: a retrospective cohort study

PurposeTo retrospectively evaluate and compare treatment effectiveness and safety between transarterial chemoembolization (TACE) combined with molecularly targeted agents plus immune checkpoint inhibitors (TACE+T+I) and TACE combined with molecularly targeted agents (TACE+T) for unresectable hepatocellular carcinoma (uHCC).MethodsWe retrospectively analyzed the data of patients with unresectable HCC from January 2018 to June 2022. The patients were screened based on the inclusion criteria and were divided into the triple combination group (TACE+T+I) and the double combination group (TACE+T). The primary outcomes were overall survival (OS), progression-free survival (PFS), and adverse events (AEs). The secondary outcomes were objective response rate (ORR) and disease control rate (DCR). Risk factors associated with PFS and OS were determined by Cox regression analysis.ResultsA total of 87 patients were enrolled in this study, including 42 patients in the TACE+T+I group and 45 patients in the TACE+T group. Over a median follow-up of 29.00 and 26.70 months, patients who received TACE+T+I therapy achieved a significantly longer median OS (24.00 vs. 21.40 months, p = 0.007) and median PFS (9.70 vs. 7.00 months, p = 0.017); no grade 4 AEs or treatment-related death occurred in the two groups. Grade 3 AEs attributed to systemic agents in the two groups showed no significant difference (19.0% vs. 15.6%, p = 0.667). Patients in the TACE+T+I group demonstrated better tumor response when compared with patients in the TACE+T group, with an ORR of 52.4% vs. 17.8% (p = 0.001). No significant difference was observed in DCR between the two groups (83.3% vs. 77.8%, p = 0.514). Cox regression analysis showed that only the treatment method was an independent factor of OS, and both age and treatment method were independent factors related to PFS.ConclusionCompared with TACE plus molecularly targeted agents (TACE+T), the triple therapy (TACE+T+I) could improve survival and tumor response in unresectable HCC with manageable toxicities

Integrated metabolome and transcriptome analyses provide insight into the effect of red and blue LEDs on the quality of sweet potato leaves

Red and blue light-emitting diodes (LEDs) affect the quality of sweet potato leaves and their nutritional profile. Vines cultivated under blue LEDs had higher soluble protein contents, total phenolic compounds, flavonoids, and total antioxidant activity. Conversely, chlorophyll, soluble sugar, protein, and vitamin C contents were higher in leaves grown under red LEDs. Red and blue light increased the accumulation of 77 and 18 metabolites, respectively. Alpha-linoleic and linolenic acid metabolism were the most significantly enriched pathways based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A total of 615 genes were differentially expressed between sweet potato leaves exposed to red and blue LEDs. Among these, 510 differentially expressed genes were upregulated in leaves grown under blue light compared with those grown under red light, while the remaining 105 genes were expressed at higher levels in the latter than in the former. Among the KEGG enrichment pathways, blue light significantly induced anthocyanin and carotenoid biosynthesis structural genes. This study provides a scientific reference basis for using light to alter metabolites to improve the quality of edible sweet potato leaves