SASMU: boost the performance of generalized recognition model using synthetic face dataset

Nowadays, deploying a robust face recognition product becomes easy with the development of face recognition techniques for decades. Not only profile image verification but also the state-of-the-art method can handle the in-the-wild image almost perfectly. However, the concern of privacy issues raise rapidly since mainstream research results are powered by tons of web-crawled data, which faces the privacy invasion issue. The community tries to escape this predicament completely by training the face recognition model with synthetic data but faces severe domain gap issues, which still need to access real images and identity labels to fine-tune the model. In this paper, we propose SASMU, a simple, novel, and effective method for face recognition using a synthetic dataset. Our proposed method consists of spatial data augmentation (SA) and spectrum mixup (SMU). We first analyze the existing synthetic datasets for developing a face recognition system. Then, we reveal that heavy data augmentation is helpful for boosting performance when using synthetic data. By analyzing the previous frequency mixup studies, we proposed a novel method for domain generalization. Extensive experimental results have demonstrated the effectiveness of SASMU, achieving state-of-the-art performance on several common benchmarks, such as LFW, AgeDB-30, CA-LFW, CFP-FP, and CP-LFW.Comment: under revie

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

<p>Abstract</p> <p>Background</p> <p>Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.</p> <p>Results</p> <p>We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes.</p> <p>Conclusion</p> <p>The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. SpecificDB provides comprehensive information and a user-friendly interface.</p

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

<p>Abstract</p> <p>Background</p> <p>Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.</p> <p>Results</p> <p>We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes.</p> <p>Conclusion</p> <p>The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. SpecificDB provides comprehensive information and a user-friendly interface.</p

Association Between Platelet Count and Components of Metabolic Syndrome in Geriatric Taiwanese Women

SummaryBackgroundThe growing elderly population in Taiwan, as in many other countries, has resulted in increased importance of the metabolic syndrome (MetS). Although it has been reported in different age groups, the relationship between platelets and MetS remains unknown in geriatric patients.Patients and MethodsWe enrolled 1460 women >65 years old. Women with a known history of diabetes, hyperlipidemia or hypertension or those taking medication for these conditions were all excluded. The women were further divided into quartiles arbitrarily according to platelet count (PC) (PC1–PC4, lowest to highest accordingly).ResultsAmong the MetS components, body mass index (BMI), total cholesterol, low-density lipoprotein cholesterol (LDL-C) and log transformation triglyceride (Log TG) were all significantly higher in the PC4 group (p < 0.05), and they were also positively correlated with PC. However, in multiple regression, BMI became nonsignificant. Both LDL-C and Log TG were the only two factors that remained positively and independently correlated with PC. Compared to PC1, all the other three groups had significantly higher odds ratios for having MetS (2.013, 1.473–2.751; 1.486, 1.081–2.042; 1.537, 1.117–2.114; odds ratios and 95% confidence intervals for PC4, PC3 and PC2, respectively).ConclusionElderly women with MetS had higher PC. Among the five components, TG was positively correlated with PC. There was a positive correlation between PC and LDL-C but not high-density lipoprotein cholesterol. The importance of both lipids might be re-evaluated in the future in older women

HPV infection and p53 inactivation in pterygium

PurposeOur recent report indicated that tumor suppressor gene (p53) mutations and protein aberrant expression were detected in pterygium. Inactivation of p53 by Human papillomavirus (HPV) 16/18 E6 plays a crucial role in cervical tumorigenesis. In this study, we further speculate that p53 inactivation may be linked with HPV infection in pterygium pathogenesis. To investigate the involvement of HPV 16/18 E6 in p53 inactivation in pterygium, the association between HPV 16 or HPV 18 infection, the HPV E6 oncoprotein, and p53 protein expression was analyzed in this study.MethodsHPV 16/18 infection was detected by nested-polymerase chain reaction (nested-PCR), the p53 mutation was detected by direct sequencing, and the p53 and the HPV 16/18 E6 proteins were studied using immunohistochemistry on 129 pterygial specimens and 20 normal conjunctivas.ResultsThe HPV 16/18 was detected in 24% of the pterygium tissues (31 of 129) but not in the normal conjunctiva, and the HPV16/18 E6 oncoprotein was detected in 48.3% of HPV 16/18 DNA-positive pterygium tissues (15 of 31). In addition, p53 protein negative expression in pterygium was correlated with HPV16/18 E6 oncoprotein expression but not with a p53 mutation.ConclusionsHPV 16/18 E6 contributes to HPV-mediated pterygium pathogenesis as it is partly involved in p53 inactivation and is expressed in HPV DNA-positive pterygium