10 Years of GWAS in intraocular pressure

Intraocular pressure (IOP) is the only modifiable risk factor for glaucoma, the leading cause of irreversible blindness worldwide. In this review, we summarize the findings of genome-wide association studies (GWASs) of IOP published in the past 10 years and prior to December 2022. Over 190 genetic loci and candidate genes associated with IOP have been uncovered through GWASs, although most of these studies were conducted in subjects of European and Asian ancestries. We also discuss how these common variants have been used to derive polygenic risk scores for predicting IOP and glaucoma, and to infer causal relationship with other traits and conditions through Mendelian randomization. Additionally, we summarize the findings from a recent large-scale exome-wide association study (ExWAS) that identified rare variants associated with IOP in 40 novel genes, six of which are drug targets for clinical treatment or are being evaluated in clinical trials. Finally, we discuss the need for future genetic studies of IOP to include individuals from understudied populations, including Latinos and Africans, in order to fully characterize the genetic architecture of IOP

The effect of the ion beam energy on the properties of indium tin oxide thin films prepared by ion beam assisted deposition

Indium tin oxide (ITO) thin films have been deposited onto polycarbonate substrates by ion beam assisted deposition technique at room temperature. The structural, optical and electrical properties of the films have been characterized by X-ray diffraction, atomic force microscopy, optical transmittance, ellipsometric and Hall effect measurements. The effect of the ion beam energy on the properties of the films has been studied. The optical parameters have been obtained by fitting the ellipsometric spectra. It has been found that high quality ITO film (low electrical resistivity and high optical transmittance) can be obtained at low ion beam energy. In addition, the ITO film prepared at low ion beam energy gives a high reflectance in IR region which is useful for some electromagnetic wave shielding applications.Fundação Calouste Gulbenkia

DeepFake detection based on high-frequency enhancement network for highly compressed content

The DeepFake, which generates synthetic content, has sparked a revolution in the fight against deception and forgery. However, most existing DeepFake detection methods mainly focus on improving detection performance with high-quality data while ignoring low-quality synthetic content that suffers from high compression. To address this issue, we propose a novel High-Frequency Enhancement framework, which leverages a learnable adaptive high-frequency enhancement network to enrich weak high-frequency information in compressed content without uncompressed data supervision. The framework consists of three branches, i.e., the Basic branch with RGB domain, the Local High-Frequency Enhancement branch with Block-wise Discrete Cosine Transform, and the Global High-Frequency Enhancement branch with Multi-level Discrete Wavelet Transform. Among them, the local branch utilizes the Discrete Cosine Transform coefficient and channel attention mechanism to indirectly achieve adaptive frequency-aware multi-spatial attention, while the global branch supplements the high-frequency information by extracting coarse-to-fine multi-scale high-frequency cues and cascade-residual-based multi-level fusion by Discrete Wavelet Transform coefficients. In addition, we design a Two-Stage Cross-Fusion module to effectively integrate all information, thereby greatly enhancing weak high-frequency information in low-quality data. Experimental results on FaceForensics++, Celeb-DF, and OpenForensics datasets show that the proposed method outperforms the existing state-of-the-art methods and can effectively improve the detection performance of DeepFakes, especially on low-quality data. The code is available here

Texture and artifact decomposition for improving generalization in deep-learning-based deepfake detection

The harmful utilization of DeepFake technology poses a significant threat to public welfare, precipitating a crisis in public opinion. Existing detection methodologies, predominantly relying on convolutional neural networks and deep learning paradigms, focus on achieving high in-domain recognition accuracy amidst many forgery techniques. However, overseeing the intricate interplay between textures and artifacts results in compromised performance across diverse forgery scenarios. This paper introduces a groundbreaking framework, denoted as Texture and Artifact Detector (TAD), to mitigate the challenge posed by the limited generalization ability stemming from the mutual neglect of textures and artifacts. Specifically, our approach delves into the similarities among disparate forged datasets, discerning synthetic content based on the consistency of textures and the presence of artifacts. Furthermore, we use a model ensemble learning strategy to judiciously aggregate texture disparities and artifact patterns inherent in various forgery types, thereby enabling the model’s generalization ability. Our comprehensive experimental analysis, encompassing extensive intra-dataset and cross-dataset validations along with evaluations on both video sequences and individual frames, confirms the effectiveness of TAD. The results from four benchmark datasets highlight the significant impact of the synergistic consideration of texture and artifact information, leading to a marked improvement in detection capabilities

Exploiting genetics and genomics to improve the understanding of eye diseases [Editorial]

Editorial on the Research Topic Exploiting genetics and genomics to improve the understanding of eye disease

Longitudinal trends in the association of metabolic syndrome with 550 k single-nucleotide polymorphisms in the Framingham Heart Study

We investigated the association of metabolic syndrome (MetS) with a 500 k and a 50 k single-nucleotide polymorphism (SNP) gene chip in the Framingham Heart Study. We cross-sectionally evaluated the MetS longitudinal trends. Data analyzed were from the Offspring Cohort (four exams: first (n = 2,441), third (n = 2,185), fifth (n = 2,308), and seventh (n = 2,328)) and the Generation 3 Cohort (one exam: the first exam (n = 3,997)). The prevalence of MetS was determined using the National Cholesterol Education Program Adult Treatment Panel III diagnostic criteria, modified with a newly developed correction for medication use. The association test between an SNP and MetS was performed with a generalized estimating equations method under the additive genetic model. Multiple-testing corrections were also performed. The prevalence of MetS in the offspring cohort increased from one visit to the next, and reached the highest point by the seventh exam comparable with the prevalence reported for the general US population. The pattern of the MetS prevalence over time also reflected itself in the association tests, in which the highest significances were seen in the fifth and seventh exams. The association tests showed that SNPs within genes PRDM16, CETP, PTHB1, PAPPA, and FBN3, and also some SNPs not in genes were significant or close to significance at the genome-wide thresholds. These findings are important in terms of eventually identifying with the causal loci for MetS