Synthesis and Biological Study of Adenylyl Cyclase Inhibitors

Adenylyl cyclases (AC) is a critical family of enzymes which modulates the dynamic cellular level of cAMP, cyclic adenosine monophosphate. The study of cAMP showed that it is indispensable for the signal transduction cascades during many physiological processes, such as immune responses and metabolism which highly relate to cancers. Previous studies of AC inhibitors have been limited due to a lack of isoform-selective small molecule modulators. Selectivity of the molecules is imperative to the activation of only the desired AC inhibitor. The design of the described project was to test the structure activity relationship (SAR) by synthesizing a class of AC I inhibitors and then use the results to develop a small molecule with maximum selectivity for therapeutic targeting. Multi-step synthesis featured with epoxide ring-opening reaction followed by the Friedel–Crafts reaction. Compounds were differentiated by changing substituents on the nitrogen atom. The synthetic molecules have been tested via SAR of AC I inhibitor and IC50. Once synthesized, the compounds were tested for their inhibition rate and the results showed that the majority of scaffolds had great SAR rates at 40 µM and two also had impressive rates as low as 4 µM. Further investigation with IC50 studies is on-going. The results suggest that the current synthetic compounds are potentially great AC I inhibitors and further study will continue which will contribute to cancer research

Learning to Solve Tasks with Exploring Prior Behaviour

Postprin

Dual Effects of the US-China Trade War and COVID-19 on United States Imports: Transfer of China's industrial chain?

The trade tension between the U.S. and China since 2018 has caused a steady decoupling of the world's two largest economies. The pandemic outbreak in 2020 complicated this process and had numerous unanticipated repercussions. This paper investigates how U.S. importers reacted to the trade war and worldwide lockdowns due to the COVID-19 pandemic. We examine the effects of the two incidents on U.S. imports separately and collectively, with various economic scopes. Our findings uncover intricate trading dynamics among the U.S., China, and Southeast Asia, through which businesses relocated portions of their global supply chain away from China to avoid high tariffs. Our analysis indicates that increased tariffs cause the U.S. to import less from China. Meanwhile, Southeast Asian exporters have integrated more into value chains centered on Chinese suppliers by participating more in assembling and completing products. However, the worldwide lockdowns over pandemic have reversed this trend as, over this period, the U.S. effectively imported more goods directly from China and indirectly through Southeast Asian exporters that imported from China.Comment: 30 pages, 6 figure

Learning to Solve Tasks with Exploring Prior Behaviours

Demonstrations are widely used in Deep Reinforcement Learning (DRL) for facilitating solving tasks with sparse rewards. However, the tasks in real-world scenarios can often have varied initial conditions from the demonstration, which would require additional prior behaviours. For example, consider we are given the demonstration for the task of \emph{picking up an object from an open drawer}, but the drawer is closed in the training. Without acquiring the prior behaviours of opening the drawer, the robot is unlikely to solve the task. To address this, in this paper we propose an Intrinsic Rewards Driven Example-based Control \textbf{(IRDEC)}. Our method can endow agents with the ability to explore and acquire the required prior behaviours and then connect to the task-specific behaviours in the demonstration to solve sparse-reward tasks without requiring additional demonstration of the prior behaviours. The performance of our method outperforms other baselines on three navigation tasks and one robotic manipulation task with sparse rewards. Codes are available at https://github.com/Ricky-Zhu/IRDEC

MEK Guards Proteome Stability and Inhibits Tumor-Suppressive Amyloidogenesis via HSF1

SummarySignaling through RAS/MAP kinase pathway is central to biology. ERK has long been perceived as the only substrate for MEK. Here, we report that HSF1, the master regulator of the proteotoxic stress response, is a new MEK substrate. Beyond mediating cell-environment interactions, the MEK-HSF1 regulation impacts malignancy. In tumor cells, MEK blockade inactivates HSF1 and thereby provokes proteomic chaos, presented as protein destabilization, aggregation, and, strikingly, amyloidogenesis. Unlike their non-transformed counterparts, tumor cells are particularly susceptible to proteomic perturbation and amyloid induction. Amyloidogenesis is tumor suppressive, reducing in vivo melanoma growth and contributing to the potent anti-neoplastic effects of proteotoxic stressors. Our findings unveil a key biological function of the oncogenic RAS-MEK signaling in guarding proteostasis and suppressing amyloidogenesis. Thus, proteomic instability is an intrinsic feature of malignant state, and disrupting the fragile tumor proteostasis to promote amyloidogenesis may be a feasible therapeutic strategy

Editing Language Model-based Knowledge Graph Embeddings

Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deployed as static artifacts, which are challenging to modify without re-training after deployment. To address this issue, we propose a new task of editing language model-based KG embeddings in this paper. The proposed task aims to enable data-efficient and fast updates to KG embeddings without damaging the performance of the rest. We build four new datasets: E-FB15k237, A-FB15k237, E-WN18RR, and A-WN18RR, and evaluate several knowledge editing baselines demonstrating the limited ability of previous models to handle the proposed challenging task. We further propose a simple yet strong baseline dubbed KGEditor, which utilizes additional parametric layers of the hyper network to edit/add facts. Comprehensive experimental results demonstrate that KGEditor can perform better when updating specific facts while not affecting the rest with low training resources. Code and datasets will be available in https://github.com/zjunlp/PromptKG/tree/main/deltaKG.Comment: Work in progress and the project website is https://zjunlp.github.io/project/KGE_Editing

Toward Enhanced Metadata Quality of Large-Scale Digital Libraries: Estimating Volume Time Range

In large-scale digital libraries, it is not uncommon that some bibliographic fields in metadata records are incomplete or missing. Adding to the incomplete or missing metadata can greatly facilitate users' search and access to digital library resources. Temporal information, such as publication date, is a key descriptor of digital resources. In this study, we investigate text mining methods to automatically resolve missing publication dates for the HathiTrust corpora, a large collection of documents digitized by optical character recognition (OCR). In comparison with previous approaches using only unigrams as features, our experiment results show that methods incorporating higher order n-gram features, e.g., bigrams and trigrams, can more effectively classify a document into discrete temporal intervals or "chronons". Our approach can be generalized to classify volumes within other digital libraries.ye

Exciton Assisted Deeply Subwavelength Nano-Photonics

The wave nature of light sets a fundamental diffraction limit that challenges confinement and control of light in nanoscale structures with dimensions significantly smaller than the wavelength. Here, we demonstrate van der Waals MoS_2 nano-photonic devices with dimensions as small as ~ \lambda/16 (~60 nm at 1000 nm excitation wavelength). This deep subwavelength light confinement is achieved by exploiting the coupling between MoS_2 excitons and photons. We validate deep subwavelength light control via far- and near-field measurements. Our near-field measurements reveal detailed imaging of excitation, evolution, and guidance of fields in MoS_2 nanodevices, whereas our far-field study examines highly confined integrated photonics. Exciton-driven nano-photonics at a fraction of a wavelength demonstrated here could dramatically reduce the size of integrated photonic devices and opto-electronic circuits with potential applications in optical information science and engineering.Comment: 39 pages, 32 figure

Primary and potential secondary risks of landslide outburst floods

Outburst floods triggered by breaching of landslide dams may cause severe loss of life and property downstream. Accurate identification and assessment of such floods, especially when leading to secondary impacts, are critical. In 2018, the Baige landslide in the Tibetan Plateau twice blocked the Jinsha River, eventually resulting in a severe outburst flood. The Baige landslide remains active, and it is possible that a breach happens again. Based on numerical simulation using a hydrodynamic model, remote sensing, and field investigation, we reproduce the outburst flood process and assess the hazard associated with future floods. The results show that the hydrodynamic model could accurately simulate the outburst flood process, with overall accuracy and Kappa accuracy for the flood extent of 0.956 and 0.911. Three future dam break scenarios were considered with landslide dams of heights 30 m, 35 m, and 51 m. The potential storage capacity and length of upstream flow back up in the upstream valley for these heights were 142 × 106m3/32 km, 182 × 106m3/40 km, and 331 × 106m3/50 km. Failure of these three dams leads to maximum inundation extents of 0.18 km2, 0.34 km2, and 0.43 km2, which is significant out-of-bank flow and serious infrastructure impacts. These results demonstrate the seriousness of secondary hazards associated with this region