TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering

Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-world applications, we need to periodically train a collaborative filtering algorithm to extract user/item embedding vectors and therefore, a time-series of embedding vectors can be naturally defined. We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e.g., every month, iii) trains our time-series forecasting model with the extracted time-series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time-series data, i.e., neural controlled differential equations (NCDEs). Our experiments with four real-world benchmark datasets show that the proposed time-series forecasting-based upgrade kit can significantly enhance existing popular collaborative filtering algorithms.Comment: Accepted at IEEE BigData 202

Polymorphisms in Genes That Regulate Cyclosporine Metabolism Affect Cyclosporine Blood Levels and Clinical Outcomes in Patients Who Receive Allogeneic Hematopoietic Stem Cell Transplantation

In patients who received allogeneic hematopoietic stem cell transplantation (HSCT), we investigated the correlations between single nucleotide polymorphisms (SNPs) in genes that regulate cyclosporine metabolism and clinical outcomes. All patients received sibling-matched HSCT. DNA samples of patients and donors were analyzed for 4 SNPs: MDR1 +1236C>T (rs1128503), +2677G>T>A (rs2032582), +3435C>T (rs1045642), and CYP3A5 +6986G>A (rs776746). A total of 156 patients (median age 40 years) were analyzed. Nineteen patients received HSCT for nonmalignant disease. The CYP3A5 +6986AA genotype was associated with a high cyclosporine blood level after transplantation. However, this genotype was not related to any particular clinical outcome. In contrast, the MDR1 +1236C>T SNP was correlated with specific clinical outcomes. When neither the donor nor the recipient had the CC genotype of MDR1 +1236, patients had lower creatinine levels (P < .001) and less transplantation-related mortality (TRM) (P = .012). These patients also showed longer overall survival (OS) in both univariate (P = .003) and multivariate (P = .003) analyses. Although the CYP3A5 +6986AA genotype was correlated with a high blood cyclosporine concentration, lack of the MDR1 +1236CC genotype in both the donor and recipient was correlated with less TRM and a longer OS in patients who received allogeneic HSCT

Metal recovery from electroplating wastewater using acidophilic iron oxidizing bacteria: Pilot-scale feasibility test

金沢大学理工研究域機械工学系Wastewater from electroplating plants contains several valuable metallic ions such as iron, nickel, and zinc. In general, neutralization followed by sedimentation has been used for the treatment of electroplating wastewater because of low treatment cost and high stability of treated water quality. However, this method results in the production of large amounts of heavy metal sludge that may cause secondary pollution and additional cost. In addition, the recovery of valuable metallic contents from the wastewater sludge has not been technically feasible. It would be highly desirable economically as well as environmentally if a metal recovery process from the wastewater is developed. In the present work, we developed a biological process for metal recovery from electroplating wastewater. Wastewater from electroplating plants contains iron in the form of ferrous ion together with other metal ions. To add economic value to the chemical sludge, iron should be separated from other metals such as nickel and zinc in the wastewater. The iron could be separated from the mixture of metal ions in wastewater by using biological oxidation of ferrous ion into ferric ion followed by stepwise chemical precipitation with hydroxide ion since ferric ion begins to precipitate around pH 4 while ferrous ion precipitates around pH 7 similarly to the other metal ions (nickel and zinc). To improve the biological oxidation, an immobilized bioreactor using polyurethane foam as support media was developed. The bioreactor system showed a very good performance and worked stably over a long period of time. © 2005 American Chemical Society