MetaAge: Meta-Learning Personalized Age Estimators

Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods suffer from the lack of large-scale datasets due to the high-level requirements: identity labels and enough samples for each person to form a long-term aging pattern. In this paper, we aim to learn personalized age estimators without the above requirements and propose a meta-learning method named MetaAge for age estimation. Unlike most existing personalized methods that learn the parameters of a personalized estimator for each person in the training set, our method learns the mapping from identity information to age estimator parameters. Specifically, we introduce a personalized estimator meta-learner, which takes identity features as the input and outputs the parameters of customized estimators. In this way, our method learns the meta knowledge without the above requirements and seamlessly transfers the learned meta knowledge to the test set, which enables us to leverage the existing large-scale age datasets without any additional annotations. Extensive experimental results on three benchmark datasets including MORPH II, ChaLearn LAP 2015 and ChaLearn LAP 2016 databases demonstrate that our MetaAge significantly boosts the performance of existing personalized methods and outperforms the state-of-the-art approaches.Comment: Accepted by IEEE Transactions on Image Processing (TIP

Association of Common Variants in LOX with Keratoconus: A Meta-Analysis

Background: Several case-control studies have been performed to examine the association of genetic variants in lysyl oxidase (LOX) with keratoconus. However, the results remained inconclusive and great heterogeneity might exist across populations. Method A comprehensive literature search for studies that published up to June 25, 2015 was performed. Summary odds ratios (OR) and 95% confidence intervals (CI) of each single nucleotide polymorphism (SNP) were estimated with fixed effects model when I250%. Publication bias was evaluated using funnel plots and Egger’s test. Results: A total of four studies including 1,467 keratoconus cases and 4,490 controls were involved in this meta-analysis. SNPs rs2956540 and rs10519694 showed significant association with keratoconus, with ORs of 0.71 (95% CI: 0.63–0.80, P = 1.43E-08) and 0.77 (95% CI: 0.61–0.97, P = 0.026), respectively. In contrast, our study lacked sufficient evidences to support the association of rs1800449/rs2288393 with keratoconus across populations. Conclusion: This meta-analysis suggested that two LOX variants, rs2956540 and rs10519694, may affect individual susceptibility to keratoconus, while distinct heterogeneity existed within this locus. Larger-scale and multi-ethnic genetic studies on keratoconus are required to further validate the results

Impact of SARS-CoV-2 on Ambient Air Quality in Northwest China (NWC)

SARS-CoV-2 was discovered in Wuhan (Hubei) in late 2019 and covered the globe by March 2020. To prevent the spread of the SARS-CoV-2 outbreak, China imposed a countrywide lockdown that significantly improved the air quality. To investigate the collective effect of SARS-CoV-2 on air quality, we analyzed the ambient air quality in five provinces of northwest China (NWC): Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX) and Qinghai (QH), from January 2019 to December 2020. For this purpose, fine particulate matter (PM2.5), coarse particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) were obtained from the China National Environmental Monitoring Center (CNEMC). In 2020, PM2.5, PM10, SO2, NO2, CO, and O3 improved by 2.72%, 5.31%, 7.93%, 8.40%, 8.47%, and 2.15%, respectively, as compared with 2019. The PM2.5 failed to comply in SN and XJ; PM10 failed to comply in SN, XJ, and NX with CAAQS Grade II standards (35 µg/m3, 70 µg/m3, annual mean). In a seasonal variation, all the pollutants experienced significant spatial and temporal distribution, e.g., highest in winter and lowest in summer, except O3. Moreover, the average air quality index (AQI) improved by 4.70%, with the highest improvement in SN followed by QH, GS, XJ, and NX. AQI improved in all seasons; significant improvement occurred in winter (December to February) and spring (March to May) when lockdowns, industrial closure etc. were at their peak. The proportion of air quality Class I improved by 32.14%, and the number of days with PM2.5, SO2, and NO2 as primary pollutants decreased while they increased for PM10, CO, and O3 in 2020. This study indicates a significant association between air quality improvement and the prevalence of SARS-CoV-2 in 2020.The National Natural Science Foundation of China (No. 21667026) and the Social Science Foundation of Xinjiang Production and Construction Corps (No. 18YB13) funded this work

Component-based Feature Saliency for Clustering

Simultaneous feature selection and clustering is a major challenge in unsupervised learning. In particular, there has been significant research into saliency measures for features that result in good clustering. However, as datasets become larger and more complex, there is a need to adopt a finer-grained approach to saliency by measuring it in relation to a part of a model. Another issue is learning the feature saliency and advanced model parameters. We address the first by presenting a novel Gaussian mixture model, which explicitly models the dependency of individual mixture components on each feature giving a new component-based feature saliency measure. For the second, we use Markov Chain Monte Carlo sampling to estimate the model and hidden variables. Using a synthetic dataset, we demonstrate the superiority of our approach, in terms of clustering accuracy and model parameter estimation, over an approach using a model-based feature saliency with expectation maximisation. We performed an evaluation of our approach with six synthetic trajectory datasets. To demonstrate the generality of our approach, we applied it to a network traffic flow dataset for intrusion detection. Finally, we performed a comparison with state-of-the-art clustering techniques using three real-world trajectory datasets of vehicle traffic

In vitro anti-angiogenic properties of LGD1069, a selective retinoid X-receptor agonist through down-regulating Runx2 expression on Human endothelial cells

<p>Abstract</p> <p>Background</p> <p>LGD1069 (Targretin^®) is a selective retinoid X receptor (RXR) ligand, which is used in patients for cutaneous T-cell lymphoma. Our published study reported that LGD1069 inhibited tumor-induced angiogenesis in non-small cell lung cancer. In present study, we found that LGD1069 suppressed the proliferation, adhesion, invasion and migration of endothelial cells directly, and affected the expression of vegf and some matrix genes.</p> <p>Methods</p> <p>Human umbilical vein endothelial cells (HUVECs) were used for <it>in vitro </it>study. MTT assay and Sulforhodamine B assay were used for cell viability assay; the tube formation assay was used to investigate the effect of LGD1069 on angiogenesis <it>in vitro</it>. <it>In vitro </it>adhesion, migration and invasion of HUVEC cells were analyzed by Matrigel adhesion, migration and invasion assay. Gene expressions were measured by RT-PCR and Western blot analysis.</p> <p>Results</p> <p>Our data showed here that LGD1069 inhibited the activation of TGF-β/Smad pathway significantly. Furthermore, it was demonstrated that expression of Runx2 was suppressed pronouncedly during incubation with LGD1069. Runx2 is a DNA-binding transcription factor which plays a master role in tumor-induced angiogenesis and cancer cells metastasis by interaction with the TGF-β/Smad pathway of transcriptional modulators.</p> <p>Conclusions</p> <p>Our results suggested that LGD1069 may impair angiogenic and metastatic potential induced by tumor cells through suppressing expression of Runx2 directly on human endothelial cells, which may point out new pathway through which LGD1069 display anti-angiogenic properties, and provide new molecular evidence to support LGD1069 as a potent anti-metastatic agent in cancer therapy.</p