DrugCentral (http://drugcentral.org) is an open-access online drug compendium. DrugCentral integrates structure, bioactivity, regulatory, pharmacologic actions and indications for active pharmaceutical ingredients approved by FDA and other regulatory agencies. Monitoring of regulatory agencies for new drugs approvals ensures the resource is up-to-date. DrugCentral integrates content for active ingredients with pharmaceutical formulations, indexing drugs and drug label annotations, complementing similar resources available online. Its complementarity with other online resources is facilitated by cross referencing to external resources. At the molecular level, DrugCentral bridges drug-target interactions with pharmacological action and indications. The integration with FDA drug labels enables text mining applications for drug adverse events and clinical trial information. Chemical structure overlap between DrugCentral and five online drug resources, and the overlap between DrugCentral FDA-approved drugs and their presence in four different chemical collections, are discussed. DrugCentral can be accessed via the web application or downloaded in relational database format.

DrugCentral is a public resource (http://drugcentral.org) that serves the scientific community by providing up-to-date drug information, as described in previous papers. The current release includes 109 newly approved (October 2018 through March 2020) active pharmaceutical ingredients in the US, Europe, Japan and other countries; and two molecular entities (e.g. mefuparib) of interest for COVID19. New additions include a set of pharmacokinetic properties for ∼1000 drugs, and a sex-based separation of side effects, processed from FAERS (FDA Adverse Event Reporting System); as well as a drug repositioning prioritization scheme based on the market availability and intellectual property rights forFDA approved drugs. In the context of the COVID19 pandemic, we also incorporated REDIAL-2020, a machine learning platform that estimates anti-SARS-CoV-2 activities, as well as the ‘drugs in news’ feature offers a brief enumeration of the most interesting drugs at the present moment. The full database dump and data files are available for download from the DrugCentral web portal.

We describe a method for remotely detecting intentional packet drops on the Internet via side channel inferences. That is, given two arbitrary IP addresses on the Internet that meet some simple requirements, our proposed technique can discover packet drops (e.g., due to censorship) between the two remote machines, as well as infer in which direction the packet drops are occurring. The only major requirements for our approach are a client with a global IP Identifier (IPID) and a target server with an open port. We require no special access to the client or server. Our method is robust to noise because we apply intervention analysis based on an autoregressive-moving-average (ARMA) model. In a measurement study using our method featuring clients from multiple continents, we observed that, of all measured client connections to Tor directory servers that were censored, 98% of those were from China, and only 0.63% of measured client connections from China to Tor directory servers were not censored. This is congruent with current understandings about global Internet censorship, leading us to conclude that our method is effective.

Abstract This paper examines the relationship between political events and information control on WeChat through a longitudinal analysis of keyword censorship related to China's 19th National Communist Party Congress (NCPC19). We use a novel method to track censorship on WeChat before, during and after the NCPC19 to probe the following questions. Does censorship change after an event is over? What roles do the government and private companies play in information control in China? Our findings show that the system of information control in China can trigger blunt reactions to political events. In addition to critical content around the Congress and leaders, WeChat also censored neutral and potentially positive references to government policies and ideological concepts. The decision making behind this censorship is a product of the interaction between the government, which influences actions through directives, and the companies, which ultimately implement controls on their platforms. While this system is effective in compelling companies to implement censorship, the intermingling of the state and private companies can lead to outcomes that may not align with government strategies. We call for a deeper understanding of the role of private companies in censorship and a more nuanced assessment of the government's capacity to control social media.

In this paper, we present an analysis of over one year and a half of data from tracking the censorship and surveillance keyword lists of two instant messaging programs used in China. Through reverse engineering of TOM-Skype and Sina UC, we were able to obtain the URLs and encryption keys for various versions of these two programs and have been downloading the keyword blacklists daily. This paper examines the social and political contexts behind the contents of these lists, and analyzes those times when the list has been updated, including correlations with current events.