The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

/pdf/the-uk-biobank-resource-with-deep-phenotyping-and-genomic-2fs3jmksld.pdf

The UK Biobank resource with deep phenotyping and genomic data

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

https://spiral.imperial.ac.uk/bitstream/10044/1/71213/5/1-s2.0-S1074761318301213-main.pdf

The Immune Landscape of Cancer

Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.

10 Years of GWAS Discovery: Biology, Function, and Translation

Christian Fuchsberger, Goncalo Abecasis and colleagues describe a new web-based imputation service that enables rapid imputation of large numbers of samples and allows convenient access to large reference panels of sequenced individuals. Their state space reduction provides a computationally efficient solution for genotype imputation with no loss in imputation accuracy.

Next-generation genotype imputation service and methods.

We present a novel approach for the detection of deepfake videos using a pair of vision transformers pre-trained by a self-supervised masked autoencoding setup. Our method consists of two distinct components, one of which focuses on learning spatial information from individual RGB frames of the video, while the other learns temporal consistency information from optical flow fields generated from consecutive frames. Unlike most approaches where pre-training is performed on a generic large corpus of images, we show that by pre-training on smaller face-related datasets, namely Celeb-A (for the spatial learning component) and YouTube Faces (for the temporal learning component), strong results can be obtained. We perform various experiments to evaluate the performance of our method on commonly used datasets namely FaceForensics++ (Low Quality and High Quality, along with a new highly compressed version named Very Low Quality) and Celeb-DFv2 datasets. Our experiments show that our method sets a new state-of-the-art on FaceForensics++ (LQ, HQ, and VLQ), and obtains competitive results on Celeb-DFv2. Moreover, our method outperforms other methods in the area in a cross-dataset setup where we fine-tune our model on FaceForensics++ and test on CelebDFv2, pointing to its strong cross-dataset generalization ability.

Unmasking Deepfakes: Masked Autoencoding Spatiotemporal Transformers for Enhanced Video Forgery Detection

Aberration is one of the contributors to edge placement error (EPE) variation and overlay error in the scanner system. In the EUV system, aberration-induced wavefront errors can have a much bigger impact than the in a DUV system due to significantly reduced wavelength and heating effect from illumination energy loss through reflective optics. Although in-situ metrology of the scanner system can extract wavefront information accurately and it can be feedbacked for aberration control, dynamic heating effects during long-time full-field exposures and tool-to-tool performance gap by aberration variation in high volume manufacturing (HVM) should be directly monitored on the wafer patterning result for reliable process control. This study aims to identify the feasibility of aberration monitoring on the wafer by using an e-beam metrology tool. Simulation work is performed first for source adjustment and the selection of generic designs of aberration-sensitive patterns of dark field Ta-based EUV masks. For quantitative analysis, the wavefront phase error is directly modulated (dialed-in) in the EUV system, and a wafer is exposed for each modulation condition. The wavefront error induced by the modulated aberration is extracted from measured CD data, using an optical simulation model. We finally discuss the correlation between the extracted aberration and the modulated wavefront error. Based on the result in this work, we investigate the feasibility of the potential application of e-beam metrology on dynamic aberration variation in HVM.

E-beam metrology-based EUVL aberration monitoring

We indicate the possible expansion of the domain of optical beam shits to demonstrate exotic physical effects, such as, demonstration of joint correlation of the beam shifts, and mimicking PT symmetric systems.

Emerging Aspects of Optical Beam Shifts

Abstract. We present an automated application for defect detection and classification from ZEISS multibeam scanning electron microscope (MultiSEM®) images, based on machine learning (ML) technology. We acquire MultiSEM images of a semiconductor wafer suited for process window characterization at the imec iN5 logic node and use a dedicated application to train ML models for defect detection and classification. We show the user flow for training and execution, and the resulting capture and nuisance rates. Due to straightforward parallelization, the application is designed for the large amounts of data generated rapidly by the MultiSEM.

Defect detection and classification on imec iN5 node BEoL test vehicle with multibeam scanning electron microscope

Breast cancer is considered as a leading cause of cancer-related death worldwide. The development of chemotherapeutic resistance and the production of undesirable side effects steer the search for new potential candidates with therapeutic implications. Lantana camara has been reported to cure a number of ailments, with few studies showing its cytotoxic effect on breast cancer cell lines. However, the impact of Lantana camara on triple negative breast cancer cells is largely obscure to date. The present study investigated the effect of ethanolic extract of Lantana camara leaves on the triple negative breast cancer cell line, MDA-MB-231. We found that Lantana camara leaf extract induced cytomorphological changes and exhibited a growth inhibitory effect on MDA-MB-231 cells in a dose-dependent manner. The extract was observed to induce G0/G1 cell cycle arrest. Nuclear staining of the cells exposed to Lantana camara leaf extract suggested the presence of condensed nuclei and the result of flow cytometry analysis confirmed cell death by apoptosis. Lantana camara leaf extract also reduced the migration of MDA-MB-231 cells as evidenced by the wound healing assay results. In summary, this study demonstrates the efficacy of the extract to produce growth-inhibitory and anti-migratory effects on MDA-MB-231 cells. Our results thus suggest that Lantana camara leaf extract can be considered a potent source of chemotherapeutic agents for triple-negative breast cancer treatment.

/pdf/effect-of-lantana-camara-ethanolic-leaf-extract-on-survival-2q3w4wzf.pdf

Sayantan Das

Papers

Unmasking Deepfakes: Masked Autoencoding Spatiotemporal Transformers for Enhanced Video Forgery Detection

E-beam metrology-based EUVL aberration monitoring

Emerging Aspects of Optical Beam Shifts

Defect detection and classification on imec iN5 node BEoL test vehicle with multibeam scanning electron microscope

Effect of Lantana camara ethanolic leaf extract on survival and migration of MDA-MB-231 triple negative breast cancer cell line