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

OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression

This paper presents a language-powered paradigm for ordinal regression. Existing methods usually treat each rank as a category and employ a set of weights to learn these concepts. These methods are easy to overfit and usually attain unsatisfactory performance as the learned concepts are mainly derived from the training set. Recent large pre-trained vision-language models like CLIP have shown impressive performance on various visual tasks. In this paper, we propose to learn the rank concepts from the rich semantic CLIP latent space. Specifically, we reformulate this task as an image-language matching problem with a contrastive objective, which regards labels as text and obtains a language prototype from a text encoder for each rank. While prompt engineering for CLIP is extremely time-consuming, we propose OrdinalCLIP, a differentiable prompting method for adapting CLIP for ordinal regression. OrdinalCLIP consists of learnable context tokens and learnable rank embeddings; The learnable rank embeddings are constructed by explicitly modeling numerical continuity, resulting in well-ordered, compact language prototypes in the CLIP space. Once learned, we can only save the language prototypes and discard the huge language model, resulting in zero additional computational overhead compared with the linear head counterpart. Experimental results show that our paradigm achieves competitive performance in general ordinal regression tasks, and gains improvements in few-shot and distribution shift settings for age estimation. The code is available at https://github.com/xk-huang/OrdinalCLIP.Comment: Accepted by NeurIPS2022. Code is available at https://github.com/xk-huang/OrdinalCLI

DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation

Talking head synthesis is a promising approach for the video production industry. Recently, a lot of effort has been devoted in this research area to improve the generation quality or enhance the model generalization. However, there are few works able to address both issues simultaneously, which is essential for practical applications. To this end, in this paper, we turn attention to the emerging powerful Latent Diffusion Models, and model the Talking head generation as an audio-driven temporally coherent denoising process (DiffTalk). More specifically, instead of employing audio signals as the single driving factor, we investigate the control mechanism of the talking face, and incorporate reference face images and landmarks as conditions for personality-aware generalized synthesis. In this way, the proposed DiffTalk is capable of producing high-quality talking head videos in synchronization with the source audio, and more importantly, it can be naturally generalized across different identities without any further fine-tuning. Additionally, our DiffTalk can be gracefully tailored for higher-resolution synthesis with negligible extra computational cost. Extensive experiments show that the proposed DiffTalk efficiently synthesizes high-fidelity audio-driven talking head videos for generalized novel identities. For more video results, please refer to \url{https://sstzal.github.io/DiffTalk/}.Comment: Project page https://sstzal.github.io/DiffTalk

A rapid and sensitive method for measuring cell adhesion

We have adapted the CyQuant® assay to provide a simple, rapid, sensitive and highly reproducible method for measuring cell adhesion. The modified CyQuant® assay eliminates the requirement for labour intensive fluorescent labelling protocols prior to experimentation and has the sensitivity to measure small numbers (>1000) of adherent cells

TNFR2 ligation in human T regulatory cells enhances IL2-induced cell proliferation through the non-canonical NF-κB pathway.

Human T regulatory cells (T regs) express high levels of TNF receptor 2 (TNFR2). Ligation of TNFR2 with TNF, which can recognise both TNFR1 and TNFR2, or with a TNFR2-selective binding molecule, DARPin 18 (D18) activates canonical NF-κB signalling, assessed by IκBα degradation, and the magnitude of the response correlates with the level of TNFR2 expression. RNA-seq analysis of TNF- or D18-treated human T regs revealed that TNFR2 ligation induces transcription of NFKB2 and RELB, encoding proteins that form the non-canonical NF-κB transcription factor. In combination with IL2, D18 treatment is specific for T regs in (1) stabilising NF-κB-inducing kinase protein, the activator of non-canonical NF-κB signalling, (2) inducing translocation of RelB from cytosol to nucleus, (3) increasing cell cycle entry, and (4) increasing cell numbers. However, the regulatory function of the expanded T regs is unaltered. Inhibition of RelB nuclear translocation blocks the proliferative response. We conclude that ligation of TNFR2 by D18 enhances IL2-induced T regs proliferation and expansion in cell number through the non-canonical NF-κB pathway

Clonal hematopoiesis and therapy-related myeloid neoplasms following neuroblastoma treatment.

Therapy-related myeloid neoplasms (TMN) constitute one of the most challengingcomplications of cancer treatment.1 Whilst understanding of TMN pathogenesis remains fragmentary, genomic studies in adults have thus far refuted the notion that TMN simply result from cytotoxin-induced DNA damage.2–4 Analysis of the preclinical evolution of a limited number of adult TMN have retraced the majority of cases to clonal haematopoiesis (CH) that predates cytotoxic treatment and lacks the mutational footprint of genotoxic therapies.2–6 Balanced translocations, generally attributed to treatment with topoisomerase II inhibitors, are implicated in a minority of TMN.1 TMN is a leading cause of premature death in childhood cancer survivors, and affects 7-11% of children treated for high-risk neuroblastoma and sarcoma.7,8 However, the origin of pediatric TMN remains unclear. Targeted sequencing of known cancer genes detects CH in ~4% of children following cytotoxic treatment,6,9 whereas CH is vanishingly rare in young individuals in the general population.10,11 Moreover, to our knowledge, no cases of childhood TMN have been retraced to pretreatment CH. In light of these observations, we asked whether a broader driver landscape had eluded targeted CH screens in pediatric cancer patients and/or whether therapy-induced mutagenesis may be an under-recognised catalyst of CH and TMN in this patient group

CCN3: a key growth regulator in Chronic Myeloid Leukaemia

Chronic Myeloid Leukaemia (CML) is characterized by expression of the constitutively active Bcr-Abl tyrosine kinase. We have shown previously that the negative growth regulator, CCN3, is down-regulated as a result of Bcr-Abl kinase activity and that CCN3 has a reciprocal relationship of expression with BCR-ABL. We now show that CCN3 confers growth regulation in CML cells by causing growth inhibition and regaining sensitivity to the induction of apoptosis. The mode of CCN3 induced growth regulation was investigated in K562 CML cells using gene transfection and treatment with recombinant CCN3. Both strategies showed CCN3 regulated CML cell growth by reducing colony formation capacity, increasing apoptosis and reducing ERK phosphorylation. K562 cells stably transfected to express CCN3 showed enhanced apoptosis in response to treatment with the tyrosine kinase inhibitor, imatinib. Whilst CCN3 expression was low or undetectable in CML stem cells, primary CD34+ CML progenitors were responsive to treatment with recombinant CCN3. This study shows that CCN3 is an important growth regulator in haematopoiesis, abrogation of CCN3 expression enhances BCR-ABL dependent leukaemogenesis. CCN3 restores growth regulation, regains sensitivity to the induction of apoptosis and enhances imatinib cell kill in CML cells. CCN3 may provide an additional therapeutic strategy in the management of CML