Reliable Medical Recommendation Based on Privacy-Preserving Collaborative Filtering

Collaborative filtering (CF) methods are widely adopted by existing medical recommendation systems, which can help clinicians perform their work by seeking and recommending appropriate medical advice. However, privacy issue arises in this process as sensitive patient private data are collected by the recommendation server. Recently proposed privacy-preserving collaborative filtering methods, using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service. The aim of this study is to address the privacy issues in the context of neighborhood-based CF methods by proposing a Privacy Preserving Medical Recommendation (PPMR) algorithm, which can protect patients’ treatment information and demographic information during online recommendation process without compromising recommendation accuracy and efficiency. The proposed algorithm includes two privacy preserving operations: Private Neighbor Selection and Neighborhood-based Differential Privacy Recommendation. Private Neighbor Selection is conducted on the basis of the notion of k-anonymity method, meaning that neighbors are privately selected for the target user according to his/her similarities with others. Neighborhood-based Differential Privacy Recommendation and a differential privacy mechanism are introduced in this operation to enhance the performance of recommendation. Our algorithm is evaluated using the real-world hospital EMRs dataset. Experimental results demonstrate that the proposed method achieves stable recommendation accuracy while providing comprehensive privacy for individual patients

Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks

We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs). Two main challenges are focused in this paper for online disease diagnosis: (1) serving cold-start users via graph convolutional networks and (2) handling scarce clinical description via a symptom retrieval system. To this end, we first organize the EHR data into a heterogeneous graph that is capable of modeling complex interactions among users, symptoms and diseases, and tailor the graph representation learning towards disease diagnosis with an inductive learning paradigm. Then, we build a disease self-diagnosis system with a corresponding EHR Graph-based Symptom Retrieval System (GraphRet) that can search and provide a list of relevant alternative symptoms by tracing the predefined meta-paths. GraphRet helps enrich the seed symptom set through the EHR graph when confronting users with scarce descriptions, hence yield better diagnosis accuracy. At last, we validate the superiority of our model on a large-scale EHR dataset

The hope and reality of long‐acting hemophilia products

Recombinant DNA technology and protein engineering are creating hope that we can address ongoing challenges in hemophilia care such as reducing the costs of therapy, increasing the availability to the developing world, and improving the functional properties of these proteins. Technological advances to improve the half‐life of recombinant clotting factors have brought long‐acting clotting factors for hemophilia replacement therapy closer to reality. Preclinical and clinical trial results are reviewed as well as the potential benefits and risks of these novel therapies. Am. J. Hematol. 2012. © 2012 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91179/1/23146_ftp.pd

Identification of functional SNP associated with sperm quality in porcine ANXA5 that contributes to the growth of immature Sertoli cell

AnnexinA5 (ANXA5) has been identified as a positional candidate gene for reproduction and fertility traits in boars, but its role in testicular tissue development, as well as genetic variations remain unclear. The aim of this study was to explore the effect of ANXA5 in the growth of swine Sertoli cells and identify its functional variations. Firstly, the expression of porcine ANXA5 in different tissues was detected by semi-quantitative RT-PCR and its effect on the proliferation of Sertoli cells was evaluated by CCK8, EdU, flow cytometry analyses and qRT-PCR. Then, putative causative variants were screened by integrating in silico analysis and DNA sequencing, and the subsequent association analysis was performed in Largewhite boars. Lastly, dual luciferase reporter assay was used to clarify the effect of specific SNP or ESR1 on ANXA5 transcription. The results showed that ANXA5 expressed in all the detected tissues, promoted proliferation of Sertoli cells by advancing cell cycle progression from the G1 to S phase and encouraging expression of PCNA. Putative causative variants, including two ns-SNPs within the coding region, and three closely linked SNPs in the promoter region were identified. Statistical analysis showed that the frequency of the T allele at g.-676 T > C, A allele at g.-674C > A, and T allele at g.-105G > T were each 0.75, the heterozygotes of Yorkshire boars had greater sperm motility as compared to TT, AA, and TT animals (p < 0.05). Luciferase reporter analysis suggested g.-105G > T and ESR1 modulated ANXA5 transcription. Taken together, this study demonstrated ANXA5 affected swine immature Sertoli cells growth and g.-105G > T was a candidate genetic marker for reproductive trait of boar

Identification of mutations in porcine STAT5A that contributes to the transcription of CISH

Identification of causative genes or genetic variants associated with phenotype traits benefits the genetic improvement of animals. CISH plays a role in immunity and growth, however, the upstream transcriptional factors of porcine CISH and the genetic variations in these factors remain unclear. In this study, we firstly identified the minimal core promoter of porcine CISH and confirmed the existence of STATx binding sites. Overexpression and RT-qPCR demonstrated STAT5A increased CISH transcriptional activity (P < 0.01) and mRNA expression (P < 0.01), while GATA1 inhibited CISH transcriptional activity (P < 0.01) and the following mRNA expression (P < 0.05 or P < 0.01). Then, the putative functional genetic variations of porcine STAT5A were screened and a PCR-SSCP was established for genotype g.508A>C and g.566C>T. Population genetic analysis showed the A allele frequency of g.508A>C and C allele frequency of g.566C>T was 0.61 and 0.94 in Min pigs, respectively, while these two alleles were fixed in the Landrace population. Statistical analysis showed that Min piglets with CC genotype at g.566C>T or Hap1: AC had higher 28-day body weight, 35-day body weight, and ADG than TC or Hap3: CT animals (P < 0.05, P < 0.05). Further luciferase activity assay demonstrated that the activity of g.508A>C in the C allele was lower than the A allele (P < 0.05). Collectively, the present study demonstrated that STAT5A positively regulated porcine CISH transcription, and SNP g.566C>T in the STAT5A was associated with the Min piglet growth trait

Three problems in digital photography: Image sharpness, image interpolation, and image restoration

We present three research topics related to digital photography:image sharpness, image interpolation, and image restoration. In Chapter 1, we propose three device-independent reference-free sharpness metrics to measure the perceived sharpness of a digital image: \u27\u27Laplacian of Gaussian Contrast (LoGC)\u27\u27, \u27\u27Average Edge Transition Slope (AETS)\u27\u27, and \u27\u27Average Edge Transition Width (AETW)\u27\u27. Results from psychophysical experiments show that our proposed metrics agree well with perceived sharpness. In order to compute the AETW and AETS, we develop an algorithm that can accurately extract edge normal profiles from any complex images. We also design and perform psychophysical tests to study sharpness detection threshold as well as sharpness preference. Our major conclusions are: 1) the sharpness detection threshold is relatively consistent across image contents, while the sharpness preference strongly depends on the image content; 2) the sharpness preference is consistently higher than the detection threshold across image contents, which implies that the average observer prefers a sharpened image to the original image. In Chapter 2, we set out to improve an existing image interpolation algorithm--the Resolution Synthsis (ResSynth) algorithm. ResSynth interpolates sharper images than do the commonly used Bilinear and Bicubic interpolation; and it is computationally efficient. However, it has some deficiencies: 1) aggravated noise and JPEG artifacts; 2) halos; 3) occasional pixel errors around the edges. To overcome these problems, we modify ResSynth with three major procedures. We demonstrate that our New ResSynth algorithm significantly improves the image quality over ResSynth for a wide range of images. In Chapter 3, we present an adaptive bilateral filter (ABF) for sharpness enhancement and noise removal. ABF sharpens an image by increasing the slope of the edges without producing overshoot or undershoot. Our new approach to slope restoration significantly differs from the previous slope restoration algorithms in that ABF does not involve detecting edges. Compared with the bilateral filter, ABF restored images are significantly sharper. Compared with an unsharp mask (USM) based sharpening method — the Optimal USM (OUM), ABF restored edges are as sharp as those rendered by the OUM, but without halo. ABF also outperforms the bilateral filter and the OUM in noise removal