Subunit Selective Degradation of WIZ, a Lenalidomide- and Pomalidomide-Dependent Substrate of E3 Ubiquitin Ligase CRL4CRBN

This dissertation is focused on identifying novel targets of immunomodulatory(IMiD) drugs. IMiDs are a class of drugs that are used to treat multiple myeloma.The first chapter is an introduction to the clinical use of IMiDs, as well as the proteincereblon (CRBN), the primary target of IMiDs. The second chapter describes worktowards the identification of a novel IMiD target, WIZ, that is regulated by CRBNin an IMiD dependent manner. Mass spectrometry was performed to identify novelbinding partners, and IMiD dependent regulation by CRBN was validated usingchemical and genetic methods. Understanding how these drugs work will informthe production of more potent and more selective drugs.</p

Planets: Integrated Services for Digital Preservation

The Planets Project is developing services and technology to address core challenges in digital preservation. This article introduces the motivation for this work, describes the extensible technical architecture and places the Planets approach into the context of the Open Archival Information System (OAIS) Reference Model. It also provides a scenario demonstrating Planets’ usefulness in solving real-life digital preservation problems and an overview of the project’s progress to date

Distinct regions of the Swi5 and Ace2 transcription factors are required for specific gene activation

Swi5 and Ace2 are cell cycle-regulated transcription factors that activate expression of early G1-specific genes in Saccharomyces cerevisiae. Swi5 and Ace2 have zinc finger DNA-binding domains that are highly conserved, and the two proteins bind to the same DNA sequences in vitro. Despite this similarity in DNA binding, Swi5 and Ace2 activate different genes in vivo, with Swi5 activating the HO gene and Ace2 activating CTS1 expression. In this report we have used chimeric fusions between Swi5 and Ace2 to determine what regions of these proteins are necessary for promoter-specific activation of HO and CTS1. We have identified specific regions of Swi5 and Ace2 that are required for activation of HO and CTS1, respectively. The Swi5 protein binds HO promoter DNA cooperatively with the Pho2 homeodomain protein, and the HO specificity region of Swi5 identified in the chimeric analysis coincides with the region of Swi5 previously identified that interacts with Pho2 in vitro. Swi5 and Ace2 also activate expression of a number of other genes expressed in G1 phase of the cell cycle, including ASH1, CDC6, EGT2, PCL2, PCL9, RME1, and SIC1. Analysis of the Swi5/Ace2 chimeras shows that distinct regions of Swi5 and Ace2 contribute to the transcriptional activation of some of these other G1-regulated genes

Weakly-supervised Caricature Face Parsing through Domain Adaptation

A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step that provides a complete facial structure understanding. However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive. For real photos, there are numerous labeled datasets for face parsing. Thus, we formulate caricature face parsing as a domain adaptation problem, where real photos play the role of the source domain, adapting to the target caricatures. Specifically, we first leverage a spatial transformer based network to enable shape domain shifts. A feed-forward style transfer network is then utilized to capture texture-level domain gaps. With these two steps, we synthesize face caricatures from real photos, and thus we can use parsing ground truths of the original photos to learn the parsing model. Experimental results on the synthetic and real caricatures demonstrate the effectiveness of the proposed domain adaptation algorithm. Code is available at: https://github.com/ZJULearning/CariFaceParsing .Comment: Accepted in ICIP 2019, code and model are available at https://github.com/ZJULearning/CariFaceParsin

The Autonomy of Chinese Migrants Despite Structural and Social Determinants

China is currently undergoing one of the largest domestic migration movements in its history, as hundreds of millions of its citizens move out of their countryside homes into urban areas to seek work in the wake of the nation’s rapid globalization. This paper examines the lives of these migrants – how much agency they have over their decisions and their destinies while simultaneously subject to overarching controls set onto them by economic circumstance, government laws, and cultural traditions. It explores how they subvert tradition and former government policies by leaving home, and how they respond when confronted with discrimination in the cities. It also examines how migrant workers of the Banli Electrical Appliance Factory in Yuyao, Zhejiang find ways to reconstruct their human identities and exercise independent decision-making despite being valued solely for their labor, using research conducted at this factory through guided conversation from six key informants and participative observation living in the factory and working on the assembly line from May 4 to May 20. Finally, it explores the connection that migrants have to their homes, through memories and money, and their decisions about returning

Incentivizing the sharing of healthcare data in the AI Era

This article contributes to the policy dialogue about how to govern healthcare data in the AI era and how to incentivize patients to share their data. Existing approaches to data-sharing restrict the flow of data. Yet, as healthcare AI technologies rely on data in enhancing their scope, such lack of data hinders the creation of future applications and diminishes the need for data to furnish them. We shift attention to a GDPR based policy that does not restrict data flows and argue that the existing experience in monetizing digitalized copyright material such as music can offer a practical and well tested solution

FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors

Face Super-Resolution (SR) is a domain-specific super-resolution problem. The specific facial prior knowledge could be leveraged for better super-resolving face images. We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes full use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR) face images without well-aligned requirement. Specifically, we first construct a coarse SR network to recover a coarse high-resolution (HR) image. Then, the coarse HR image is sent to two branches: a fine SR encoder and a prior information estimation network, which extracts the image features, and estimates landmark heatmaps/parsing maps respectively. Both image features and prior information are sent to a fine SR decoder to recover the HR image. To further generate realistic faces, we propose the Face Super-Resolution Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss into FSRNet. Moreover, we introduce two related tasks, face alignment and parsing, as the new evaluation metrics for face SR, which address the inconsistency of classic metrics w.r.t. visual perception. Extensive benchmark experiments show that FSRNet and FSRGAN significantly outperforms state of the arts for very LR face SR, both quantitatively and qualitatively. Code will be made available upon publication.Comment: Chen and Tai contributed equally to this pape