Interaction of hemojuvelin with neogenin results in iron accumulation in human embryonic kidney 293 cells

Type 2 hereditary hemochromatosis (HH) or juvenile hemochromatosis is an early onset, genetically heterogeneous, autosomal recessive disorder of iron overload. Type 2A HH is caused by mutations in the recently cloned hemojuvelin gene (HJV; also called HFE2) (Papanikolaou, G., Samuels, M. E., Ludwig, E. H., MacDonald, M. L., Franchini, P. L., Dube, M. P., Andres, L., MacFarlane, J., Sakellaropoulos, N., Politou, M., Nemeth, E., Thompson, J., Risler, J. K., Zaborowska, C., Babakaiff, R., Radomski, C. C., Pape, T. D., Davidas, O., Christakis, J., Brissot, P., Lockitch, G., Ganz, T., Hayden, M. R., and Goldberg, Y. P. (2004) Nat. Genet. 36, 77–82), whereas Type 2B HH is caused by mutations in hepcidin. HJV is highly expressed in both skeletal muscle and liver. Mutations in HJV are implicated in the majority of diagnosed juvenile hemochromatosis patients. In this study, we stably transfected HJV cDNA into human embryonic kidney 293 cells and characterized the processing of HJV and its effect on iron homeostasis. Our results indicate that HJV is a glycosylphosphatidylinositol-linked protein and undergoes a partial autocatalytic cleavage during its intracellular processing. HJV co-immunoprecipitated with neogenin, a receptor involved in a variety of cellular signaling processes. It did not interact with the closely related receptor DCC (deleted in Colon Cancer). In addition, the HJV G320V mutant implicated in Type 2A HH did not co-immunoprecipitate with neogenin. Immunoblot analysis of ferritin levels and transferrin-55Fe accumulation studies indicated that the HJV-induced increase in intracellular iron levels in human embryonic kidney 293 cells is dependent on the presence of neogenin in the cells, thus linking these two proteins to intracellular iron homeostasis

Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss

Contrastive Learning (CL) has achieved impressive performance in self-supervised learning tasks, showing superior generalization ability. Inspired by the success, adopting CL into collaborative filtering (CF) is prevailing in semi-supervised top-K recommendations. The basic idea is to routinely conduct heuristic-based data augmentation and apply contrastive losses (e.g., InfoNCE) on the augmented views. Yet, some CF-tailored challenges make this adoption suboptimal, such as the issue of out-of-distribution, the risk of false negatives, and the nature of top-K evaluation. They necessitate the CL-based CF scheme to focus more on mining hard negatives and distinguishing false negatives from the vast unlabeled user-item interactions, for informative contrast signals. Worse still, there is limited understanding of contrastive loss in CF methods, especially w.r.t. its generalization ability. To bridge the gap, we delve into the reasons underpinning the success of contrastive loss in CF, and propose a principled Adversarial InfoNCE loss (AdvInfoNCE), which is a variant of InfoNCE, specially tailored for CF methods. AdvInfoNCE adaptively explores and assigns hardness to each negative instance in an adversarial fashion and further utilizes a fine-grained hardness-aware ranking criterion to empower the recommender's generalization ability. Training CF models with AdvInfoNCE, we validate the effectiveness of AdvInfoNCE on both synthetic and real-world benchmark datasets, thus showing its generalization ability to mitigate out-of-distribution problems. Given the theoretical guarantees and empirical superiority of AdvInfoNCE over most contrastive loss functions, we advocate its adoption as a standard loss in recommender systems, particularly for the out-of-distribution tasks. Codes are available at https://github.com/LehengTHU/AdvInfoNCE.Comment: Accepted to NeurIPS 202

The reinforcing influence of recommendations on global diversification

Recommender systems are promising ways to filter the overabundant information in modern society. Their algorithms help individuals to explore decent items, but it is unclear how they allocate popularity among items. In this paper, we simulate successive recommendations and measure their influence on the dispersion of item popularity by Gini coefficient. Our result indicates that local diffusion and collaborative filtering reinforce the popularity of hot items, widening the popularity dispersion. On the other hand, the heat conduction algorithm increases the popularity of the niche items and generates smaller dispersion of item popularity. Simulations are compared to mean-field predictions. Our results suggest that recommender systems have reinforcing influence on global diversification.Comment: 6 pages, 6 figure

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term