SAMSTARplus: An Automatic Tool for Generating Multi- Dimensional Schemas from an Entity-Relationship Diagram

This paper presents a tool that automatically generates multidimensional schemas for data warehouses from OLTP entity-relationship diagrams (ERDs). Based on user’s input parameters, it generates star schemas, snowflake schemas, or a fact constellation schema by taking advantage of only structural information of input ERDs. Hence, SAMSTARplus can help users reduce efforts for designing data warehouses and aids decision making

Comparison between Kidney and Hemoperfusion for Paraquat Elimination

The mortality rate of acute paraquat (PQ) poisoning depends on the PQ concentration in the blood. It has been shown that the kidneys eliminate PQ effectively. However, early renal function deterioration is frequently observed in acute PQ intoxication. This study is designed to compare the efficacy of PQ elimination with hemoperfusion (HP) and kidneys, taking into account the functional deterioration of the kidneys. The amount of renal and HP excretion of PQ were measured during the procedure of HP in patients with acute PQ intoxication. The PQ clearance and the actual amount of PQ elimination by the HP cartridge during the HP procedure were 111±11 mL/min (range; 13.2-162.2 mL/min) and 251.4±506.3 mg (range; 4.6-1,655.7) each. While, the renal clearance and actual amount of renal elimination of PQ was 79.8±56.0 mL/min (range; 9.7-177.0) and 75.4±73.6 mg (range; 4.9-245.8). As the creatinine clearance decreased, the PQ elimination by HP was as effective as or more effective than the renal elimination. In conclusion, early HP must be provided for life saving treatment in patients with acute PQ intoxication

A novel sphingosylphosphorylcholine and sphingosine-1-phosphate receptor 1 antagonist, KRO-105714, for alleviating atopic dermatitis

Background Atopic dermatitis (eczema) is a type of inflammation of the skin, which presents with itchy, red, swollen, and cracked skin. The high global incidence of atopic dermatitis makes it one of the major skin diseases threatening public health. Sphingosylphosphorylcholine (SPC) and sphingosine-1-phosphate (S1P) act as pro-inflammatory mediators, as an angiogenesis factor and a mitogen in skin fibroblasts, respectively, both of which are important biological responses to atopic dermatitis. The SPC level is known to be elevated in atopic dermatitis, resulting from abnormal expression of sphingomyelin (SM) deacylase, accompanied by a deficiency in ceramide. Also, S1P and its receptor, sphingosine-1-phosphate receptor 1 (S1P1) are important targets in treating atopic dermatitis. Results In this study, we found a novel antagonist of SPC and S1P1, KRO-105714, by screening 10,000 compounds. To screen the compounds, we used an SPC-induced cell proliferation assay based on a high-throughput screening (HTS) system and a human S1P1 protein-based [35S]-GTPγS binding assay. In addition, we confirmed the inhibitory effects of KRO-105714 on atopic dermatitis through related cell-based assays, including a tube formation assay, a cell migration assay, and an ELISA assay on inflammatory cytokines. Finally, we confirmed that KRO-105714 alleviates atopic dermatitis symptoms in a series of mouse models. Conclusions Taken together, our data suggest that SPC and S1P1 antagonist KRO-105714 has the potential to alleviate atopic dermatitis.This work was supported by a grant from the Korea Research Council for Industrial Science and Technology (KK-1933-20) to HC, under the industrial infrastructure program for fundamental technologies and Korea Institute for Advancement of Technology through the Inter-ER Cooperation Projects (R0002017) which are funded by the Ministry of Trade, Industry & Energy, Korea to YDG

Prognostic impact of AJCC response criteria for neoadjuvant chemotherapy in stage II/III breast cancer patients: breast cancer subtype analyses

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Background Neoadjuvant chemotherapy (NAC) is a standard treatment for stage II/III breast cancer patients, and response to NAC is a useful prognostic marker. Since its introduction, 6–8 cycles of NAC has become the standard regimen to improve the outcome of these patients. The purpose of this study is to evaluate the prognostic impact of the American Joint Committee on Cancer (AJCC) response criteria and this tools usefulness in four different breast cancer subtypes. Methods We conducted a retrospective cohort study of clinical stage II/III breast cancer patients who received NAC of more than 6 cycles. Response after NAC and the clinicopathological factors were reviewed. AJCC response criteria for NAC were adopted from the AJCC Manual, 7th edition: complete response (CR), partial response (PR), and no response (NR). Results A total of 183 patients were enrolled; 22 (12.0 %), 123 (67.2 %), and 38 (20.8 %) patients showed CR, PR, and NR, respectively. The AJCC response was significantly associated with relapse-free survival (RFS) (P < 0.001), whereas pathologic CR (pCR), the current gold standard for response evaluation for NAC, was not (P = 0.140). AJCC response was a significant prognostic factor for RFS in all four breast cancer subtypes, namely luminal A (P = 0.006), luminal B (P = 0.001), HER-2 enriched (P = 0.039), and triple-negative breast cancer (P = 0.035). Conclusions The AJCC response criteria represent a simple and easily reproducible tool for response evaluation of NAC patients and a useful clinical prognostic marker for RFS. These criteria also have a prognostic impact in all four breast cancer subtypes, including luminal A in which pCR has a limited role

Efficient Time-Series Subsequence Matching Using Duality in Constructing Windows

In this paper, we propose a new subsequence matching method, Dual Match. Dual Match exploits duality in constructing windows and significantly improves performance. Dual Match divides data sequences into disjoint windows and the query sequence into sliding windows, and thus, is a dual approach of the one by Faloutsos et al. (Proceedings of the ACM SIGMOD International Conference on Management of Data, Seattle, Washington, 1994, pp. 419--429.) (FRM in short), which divides data sequences into sliding windows and the query sequence into disjoint windows. FRM causes a lot of false alarms (i.e., candidates that do not qualify) by storing minimum bounding rectangles rather than individual points representing windows to save storage space for the index. Dual Match solves this problem by directly storing points without incurring excessive storage overhead. Experimental results show that, in most cases, Dual Match provides large improvement both in false alarms and performance over FRM given the same amount of storage space. In particular, for low selectivities (less than 10^-4), Dual Match significantly improves performance up to 430-fold. On the other hand, for high selectivities (more than 10^-2), it shows a very minor degradation (less than 29%). For selectivities in between (10^-4 - 10^-2), Dual Match shows performance slightly better than that of FRM. Overall, these results indicate that our approach provides a new paradigm in subsequence matching that improves performance significantly in large database applications

TruMuzic: A Deep Learning and Data Provenance-Based Approach to Evaluating the Authenticity of Music

The digitalization of music has led to increased availability of music globally, and this spread has further raised the possibility of plagiarism. Numerous methods have been proposed to analyze the similarity between two pieces of music. However, these traditional methods are either focused on good processing speed at the expense of accuracy or they are not able to properly identify the correct features and the related feature weights needed for achieving accurate comparison results. Therefore, to overcome these issues, we introduce a novel model for detecting plagiarism between two given pieces of music. The model does this with a focus on the accuracy of the similarity comparison. In this paper, we make the following three contributions. First, we propose the use of provenance data along with musical data to improve the accuracy of the model’s similarity comparison results. Second, we propose a deep learning-based method to classify the similarity level of a given pair of songs. Finally, using linear regression, we find the optimized weights of extracted features following the ground truth data provided by music experts. We used the main dataset, containing 3800 pieces of music, to evaluate the proposed method’s accuracy; we also developed several additional datasets with their own established ground truths. The experimental results show that our method, which we call ‘TruMuzic’, improves the overall accuracy of music similarity comparison by 10% compared to the other state-of-the-art methods from recent literature