PCAF interacts with XBP-1S and mediates XBP-1S-dependent transcription

X-box binding protein 1 (XBP-1) is a key regulator required for cellular unfolded protein response (UPR) and plasma cell differentiation. In addition, involvement of XBP-1 in host cell–virus interaction and transcriptional regulation of viruses, such as human T-lymphotropic virus type 1 (HTLV-1), has been revealed recently. Two XBP-1 isoforms, XBP-1U and XBP-1S, which share an identical N-terminal domain, are present in cells. XBP-1S is a transcription activator while XBP-1U is the inactive isoform. Although the transactivation domain of XBP-1S has been identified within the XBP-1S-specific C-terminus, molecular mechanism of the transcriptional activation by XBP-1S still remains unknown. Here we report the interaction between p300/CBP-associated factor (PCAF) and XBP-1S through the C-terminal domain of XBP-1S. No binding between XBP-1U and PCAF is detected. In a cell-based reporter assay, overexpression of PCAF further stimulates the XBP-1S-mediated cellular and HTLV-1 transcription while knockdown of PCAF exhibits the opposite effect. Expression of endogenous XBP-1S cellular target genes, such as BiP and CHOP, is significantly inhibited when PCAF is knocked down. Furthermore, PCAF is recruited to the promoters of XBP-1S target genes in vivo, in a XBP-1S-dependent manner. Collectively, our results demonstrate that PCAF mediates the XBP-1S-dependent transcription through the interaction with XBP-1S

NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks

Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently the Google Brain's team demonstrated the ability of Capsule Networks (CapsNets) to encode and learn spatial correlations between different input features, thereby obtaining superior learning capabilities compared to traditional (i.e., non-capsule based) DNNs. However, designing CapsNets using conventional methods is a tedious job and incurs significant training effort. Recent studies have shown that powerful methods to automatically select the best/optimal DNN model configuration for a given set of applications and a training dataset are based on the Neural Architecture Search (NAS) algorithms. Moreover, due to their extreme computational and memory requirements, DNNs are employed using the specialized hardware accelerators in IoT-Edge/CPS devices. In this paper, we propose NASCaps, an automated framework for the hardware-aware NAS of different types of DNNs, covering both traditional convolutional DNNs and CapsNets. We study the efficacy of deploying a multi-objective Genetic Algorithm (e.g., based on the NSGA-II algorithm). The proposed framework can jointly optimize the network accuracy and the corresponding hardware efficiency, expressed in terms of energy, memory, and latency of a given hardware accelerator executing the DNN inference. Besides supporting the traditional DNN layers, our framework is the first to model and supports the specialized capsule layers and dynamic routing in the NAS-flow. We evaluate our framework on different datasets, generating different network configurations, and demonstrate the tradeoffs between the different output metrics. We will open-source the complete framework and configurations of the Pareto-optimal architectures at https://github.com/ehw-fit/nascaps.Comment: To appear at the IEEE/ACM International Conference on Computer-Aided Design (ICCAD '20), November 2-5, 2020, Virtual Event, US

Transcriptomic convergence despite genomic divergence drive field cancerization in synchronous squamous tumors

IntroductionField cancerization is suggested to arise from imbalanced differentiation in individual basal progenitor cells leading to clonal expansion of mutant cells that eventually replace the epithelium, although without evidence.MethodsWe performed deep sequencing analyses to characterize the genomic and transcriptomic landscapes of field change in two patients with synchronous aerodigestive tract tumors.ResultsOur data support the emergence of numerous genetic alterations in cancer-associated genes but refutes the hypothesis that founder mutation(s) underpin this phenomenon. Mutational signature analysis identified defective homologous recombination as a common underlying mutational process unique to synchronous tumors.DiscussionOur analyses suggest a common etiologic factor defined by mutational signatures and/or transcriptomic convergence, which could provide a therapeutic opportunity

Incremental Object Detection via Meta-Learning

In a real-world setting, object instances from new classes can be continuously encountered by object detectors. When existing object detectors are applied to such scenarios, their performance on old classes deteriorates significantly. A few efforts have been reported to address this limitation, all of which apply variants of knowledge distillation to avoid catastrophic forgetting. We note that although distillation helps to retain previous learning, it obstructs fast adaptability to new tasks, which is a critical requirement for incremental learning. In this pursuit, we propose a meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared. This ensures a seamless information transfer via a meta-learned gradient preconditioning that minimizes forgetting and maximizes knowledge transfer. In comparison to existing meta-learning methods, our approach is task-agnostic, allows incremental addition of new-classes and scales to high-capacity models for object detection. We evaluate our approach on a variety of incremental learning settings defined on PASCAL-VOC and MS COCO datasets, where our approach performs favourably well against state-of-the-art methods.Comment: Published in IEEE Transactions on Pattern Analysis & Machine Intelligence, Nov 2021. Code is available in https://github.com/JosephKJ/iO

Magnetic iron nanoparticles for in vivo targeted delivery and as biocompatible contrast agents

Iron nanoparticles (NPs) of size less than 20 nm were synthesized using an in-house developed cryomill. These NPs exhibit values of saturation magnetisation (similar to 180 emu g(-1)) close to that of pure iron. The particles were found to be nontoxic at concentrations required for MRI imaging as indicated by MTT assay. In vivo studies demonstrated the suitability of using these particles as contrast agents for MRI. The iron NPs were bio-capped with TRITC-dextran and injected into mice to study the transport behavior of the NPs under the influence of an external magnetic field. The iron NPs showed enhanced aggregation and contrast when a bar magnet was placed on the mice as observed by whole body fluorescence imaging

Whole-exome sequencing of breast cancer, malignant peripheral nerve sheath tumor and neurofibroma from a patient with neurofibromatosis type 1

10.1002/cam4.551Cancer Medicine4121871-187

JAK-STAT and G-protein-coupled receptor signaling pathways are frequently altered in epitheliotropic intestinal T-cell lymphoma

10.1038/leu.2016.13LEUKEMIA3061311-1319UNITED KINGDO

HEXIM1 Induces Differentiation of Human Pluripotent Stem Cells

10.1371/journal.pone.0072823PLoS ONE88-POLN

Exome sequencing of liver flukeg-associated cholangiocarcinoma

10.1038/ng.2273Nature Genetics446690-693NGEN