Supervised contrastive learning with identity-label embeddings for facial action unit recognition

Facial expression analysis is a crucial area of research for understanding human emotions. One important approach to this is the automatic detection of facial action units (AUs), which are small, visible changes in facial appearance. Despite extensive research, automatic AU detection remains a challenging computer vision problem. This paper addresses two central difficulties: the first is the inherent differences in facial behaviour and appearance across individuals, which leads current AU recognition models to overfit subjects in the training set and generalize poorly to unseen subjects; the second is representing the complex interactions among different AUs. In this paper, we propose a novel two-stage training framework, called CL-ILE, to address these long-standing challenges. In the first stage of CL-ILE, we introduce identity-label embeddings (ILEs) to train an ID feature encoder capable of generating person-specific feature embeddings for input face images. In the second stage, we present a data-driven method that implicitly models the relationships among AUs using contrastive loss in a supervised setting while eliminating the person-specific features generated by the first stage to enhance generalizability. By removing the ID feature encoder and ILEs from the first stage after training, CL-ILE becomes more lightweight and readily applicable to real-world applications than models using graph-based structures. We evaluate our approach on two widely-used AU recognition datasets, BP4D and DISFA, demonstrating that CL-ILE can achieve state-of-the-art performance on the F1 score

A review of applications of artificial intelligence in veterinary medicine

Artificial intelligence is a newer concept in veterinary medicine than human medicine, but its existing benefits illustrate the significant potential it may also have in this field. This article reviews the application of artificial intelligence to various fields of veterinary medicine. Successful integration of different artificial intelligence strategies can offer practical solutions to issues, such as time pressure, in practice. Several databases were searched to identify literature on the application of artificial intelligence in veterinary medicine. Exclusion and inclusion criteria were applied to obtain relevant papers. There was evidence for an acceleration of artificial intelligence research in recent years, particularly for diagnostics and imaging. Some of the benefits of using artificial intelligence included standardisation, increased efficiency, and a reduction in the need for expertise in particular fields. However, limitations identified in the literature included a requirement for ideal situations for artificial intelligence to achieve accuracy and other inherent, unresolved issues. Ethical considerations and a hesitancy to engage with artificial intelligence, by both the public and veterinarians, are further barriers that must be addressed for artificial intelligence to be fully integrated in daily practice. The rapid growth in artificial intelligence research substantiates its potential to improve veterinary practice

STAT1-cooperative DNA binding distinguishes type 1 from type 2 interferon signaling

STAT1 is an indispensable component of a heterotrimer (ISGF3) and a STAT1 homodimer (GAF) that function as transcription regulators in type 1 and type 2 interferon signaling, respectively. To investigate the importance of STAT1-cooperative DNA binding, we generated gene-targeted mice expressing cooperativity-deficient STAT1 with alanine substituted for Phe77. Neither ISGF3 nor GAF bound DNA cooperatively in the STAT1F77A mouse strain, but type 1 and type 2 interferon responses were affected differently. Type 2 interferon–mediated transcription and antibacterial immunity essentially disappeared owing to defective promoter recruitment of GAF. In contrast, STAT1 recruitment to ISGF3 binding sites and type 1 interferon–dependent responses, including antiviral protection, remained intact. We conclude that STAT1 cooperativity is essential for its biological activity and underlies the cellular responses to type 2, but not type 1 interferon

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

MER41 Repeat Sequences Contain Inducible STAT1 Binding Sites

Chromatin immunoprecipitation combined with massively parallel sequencing methods (ChIP-seq) is becoming the standard approach to study interactions of transcription factors (TF) with genomic sequences. At the example of public STAT1 ChIP-seq data sets, we present novel approaches for the interpretation of ChIP-seq data

Stat3 Activates the Receptor Tyrosine Kinase Like Orphan Receptor-1 Gene in Chronic Lymphocytic Leukemia Cells

BACKGROUND: The receptor tyrosine kinase like orphan receptor (ROR)-1 gene is overexpressed in chronic lymphocytic leukemia (CLL). Because Stat3 is constitutively activated in CLL and sequence analysis revealed that the ROR1 promoter harbors gamma-interferon activation sequence-like elements typically activated by Stat3, we hypothesized that Stat3 activates ROR1. METHODOLOGY/PRINCIPAL FINDINGS: Because IL-6 induced Stat3 phosphorylation and upregulated Ror1 protein levels in MM1 cells, we used these cells as a model. We transfected MM1 cells with truncated ROR1 promoter luciferase reporter constructs and found that IL-6 induced luciferase activity of ROR1-195 and upstream constructs. Co-transfection with Stat3 siRNA reduced the IL-6-induced luciferase activity, suggesting that IL-6 induced luciferase activity by activating Stat3. EMSA and the ChIP assay confirmed that Stat3 binds ROR1, and EMSA studies identified two Stat3 binding sites. In CLL cells, EMSA and ChIP studies determined that phosphorylated Stat3 bound to the ROR1 promoter at those two ROR1 promoter sites, and ChIP analysis showed that Stat3 co-immunoprecipitated DNA of STAT3, ROR1, and several Stat3-regulated genes. Finally, like STAT3-siRNA in MM1 cells, STAT3-shRNA downregulated STAT3, ROR1, and STAT3-regulated genes and Stat3 and Ror1 protein levels in CLL cells. CONCLUSION/SIGNIFICANCE: Our data suggest that constitutively activated Stat3 binds to the ROR1 promoter and activates ROR1 in CLL cells