Targeting Sec61α by Ipomoeassin F Leads to Highly Cytotoxic Effect

Ipomoeassin F is a flagship congener of a resin glycoside family that inhibits growth of many tumor cell lines with only single-digital nanomolar IC50 values. However, biological and pharmacological mechanisms of ipomoeassin F have been undefined. To facilitate exploration of the biological and pharmacological properties, we performed sophisticate SAR (Structure–activity relationship) studies of ipomoeassin F to understand its pharmacophore and structure properties so that we can design favorable probes for further biological investigation. By applying appropriate deviates that possess fluorescent groups and similar bio-activity, the target protein was found to be localized in endoplasmic reticulum (ER). Through biotin affinity pull down and proteomics studies, the target protein Sec61α (protein transport protein Sec61 subunit alpha isoform 1) was successfully isolated and confirmed. The isolated protein validation by Western blot provides convincing evidence to support the conclusion that Sec61α is the primary molecular target. Subsequently, the binding mode between ipomoeassin F and Sec61α was proved to be noncovalent or reversible covalent binding with the help of reverse/delayed competition. Based on competition result between ipomoeassin F and the derivatives with different activities, future applications of those derivatives to search for detect Sec61α-specific bio-active compounds by high throughput screening (HTS) are proposed. As a novel plant-derived carbohydrate-based macrocyclic molecule, ipomoeassin F was proved to have an exclusive mode of action. The successfully identification of its target protein Sec61α provides a new molecular tool to further understand the Sec61α biological properties and its potential to be a new therapeutic target for drug discovery. Most importantly, the information about this distinct mode of action would lead to a new way to design and develop more effective anti-cancer drugs

The Patterns of Stationary Activities during COVID-19 Distancing Relaxation: The elevated pedestrian network of Mong Kok, Hong Kong

COVID-19 is expected to impact the low-income groups' use of public space and related quality of life beyond the current pandemic outbreak. To what extent may the current pandemic affect the use of public space once some restrictions will be lifted? This study focuses on the migrant domestic workers’ spatio-temporal changes in the patterns of public space use during social distancing relaxation period in Hong Kong. The findings highlight increase of individual leisure activities, decrease of density around informal food-production and of gathering group, comparatively to the pre-pandemic situation. Keywords: Covid-19, public space, migrant domestic workers, behavioural mapping eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5i15.246

SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments

Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving and the generalization of learning-based algorithms on different environmental effects is still an open problem. Although monocular depth prediction has been well studied recently, there is few work focusing on the robust learning-based depth prediction across different environments, e.g. changing illumination and seasons, owing to the lack of such a multi-environment real-world dataset and benchmark. To this end, the first cross-season monocular depth prediction dataset and benchmark SeasonDepth is built based on CMU Visual Localization dataset. To benchmark the depth estimation performance under different environments, we investigate representative and recent state-of-the-art open-source supervised, self-supervised and domain adaptation depth prediction methods from KITTI benchmark using several newly-formulated metrics. Through extensive experimental evaluation on the proposed dataset, the influence of multiple environments on performance and robustness is analyzed qualitatively and quantitatively, showing that the long-term monocular depth prediction is still challenging even with fine-tuning. We further give promising avenues that self-supervised training and stereo geometry constraint help to enhance the robustness to changing environments. The dataset is available on https://seasondepth.github.io, and benchmark toolkit is available on https://github.com/SeasonDepth/SeasonDepth.Comment: 19 pages, 13 figure

Modeling Three-dimensional Invasive Solid Tumor Growth in Heterogeneous Microenvironment under Chemotherapy

A systematic understanding of the evolution and growth dynamics of invasive solid tumors in response to different chemotherapy strategies is crucial for the development of individually optimized oncotherapy. Here, we develop a hybrid three-dimensional (3D) computational model that integrates pharmacokinetic model, continuum diffusion-reaction model and discrete cell automaton model to investigate 3D invasive solid tumor growth in heterogeneous microenvironment under chemotherapy. Specifically, we consider the effects of heterogeneous environment on drug diffusion, tumor growth, invasion and the drug-tumor interaction on individual cell level. We employ the hybrid model to investigate the evolution and growth dynamics of avascular invasive solid tumors under different chemotherapy strategies. Our simulations reproduce the well-established observation that constant dosing is generally more effective in suppressing primary tumor growth than periodic dosing, due to the resulting continuous high drug concentration. In highly heterogeneous microenvironment, the malignancy of the tumor is significantly enhanced, leading to inefficiency of chemotherapies. The effects of geometrically-confined microenvironment and non-uniform drug dosing are also investigated. Our computational model, when supplemented with sufficient clinical data, could eventually lead to the development of efficient in silico tools for prognosis and treatment strategy optimization.Comment: 41 pages, 8 figure

A Multi-Market-Driven Approach to Energy Scheduling of Smart Microgrids in Distribution Networks

In order to coordinate the economic desire of microgrid (MG) owners and the stability operation requirement of the distribution system operator (DSO), a multi-market participation framework is proposed to stimulate the energy transaction potential of MGs through distributed and centralized ways. Firstly, an MG equipped with storage can contribute to the stability improvement at special nodes of the distribution grid where the uncertain factors (such as intermittent renewable sources and electric vehicles) exist. The DSO is thus interested in encouraging specified MGs to provide voltage stability services by creating a distribution grid service market (DGSM), where the dynamic production-price auction is used to capture the competition of the distributed MGs. Moreover, an aggregator, serving as a broker and controller for MGs, is considered to participate in the day-ahead wholesale market. A Stackelberg game is modeled accordingly to solve the price and quantity package allocation between aggregator and MGs. Finally, the modified IEEE-33 bus distribution test system is used to demonstrate the applicability and effectiveness of the proposed multi-market mechanism. The results under this framework improve both MGs and utility

Economic power schedule and transactive energy through an intelligent centralized energy management system for a DC residential distribution system

Direct current (DC) residential distribution systems (RDS) consisting of DC living homes will be a significant integral part of future green transmission. Meanwhile, the increasing number of distributed resources and intelligent devices will change the power flow between the main grid and the demand side. The utilization of distributed generation (DG) requires an economic operation, stability, and an environmentally friendly approach in the whole DC system. This paper not only presents an optimization schedule and transactive energy (TE) approach through a centralized energy management system (CEMS), but also a control approach to implement and ensure DG output voltages to various DC buses in a DC RDS. Based on data collection, prediction and a certain objectives, the expert system in a CEMS can work out the optimization schedule, after this, the voltage droop control for steady voltage is aligned with the command of the unit power schedule. In this work, a DC RDS is used as a case study to demonstrate the process, the RDS is associated with unit economic models, and a cost minimization objective is proposed that is to be achieved based on the real-time electrical price. The results show that the proposed framework and methods will help the targeted DC residential system to reduce the total cost and reach stability and efficiency