2,324 research outputs found
Perspectives on the COVID-19 Pandemic: Truth and Trust
When one examines life in various countries of the world regardless of type of government, health system, or even culture, one sees the great diversity of the world but also large areas of commonality among all people and all places. The twentieth century’s history is one of almost constant war, two of those wars having affected the majority of the global population, but only one event parallels today’s pandemic in impact on almost every individual on planet Earth: the great pandemic of 1918, which by various accounts took between 20 to 100 million lives over a three-year period
Sweat, Cigs and A Ball of Noise
Corduroy Jones is contacted by a local promoter to reunite his band Streetwise Preacher. Over a three week span, Corduroy and his bandmates Tony and Johnny go through a series of events leading up to their Halloween Night reunion show
How party polarization makes the legislative process even slower when government is divided
Since 2010, the U.S. has experienced divided government, with the Democrats holding the White House, and The Republican Party controlling the House of Representatives, and since January, the Senate. In new research which analyses the passage of 2,200 bills from 1949 to 2010, Tyler Hughes and Deven Carlson find that divided government slows down the legislative process by 60 days, on average. This delay is made even worse when the level of party polarization is higher, which makes partisan compromises even more difficult
Modeling reactivity to biological macromolecules with a deep multitask network
Most
small-molecule drug candidates fail before entering the market,
frequently because of unexpected toxicity. Often, toxicity is detected
only late in drug development, because many types of toxicities, especially
idiosyncratic adverse drug reactions (IADRs), are particularly hard
to predict and detect. Moreover, drug-induced liver injury (DILI)
is the most frequent reason drugs are withdrawn from the market and
causes 50% of acute liver failure cases in the United States. A common
mechanism often underlies many types of drug toxicities, including
both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes
into reactive metabolites, which then conjugate to sites in proteins
or DNA to form adducts. DNA adducts are often mutagenic and may alter
the reading and copying of genes and their regulatory elements, causing
gene dysregulation and even triggering cancer. Similarly, protein
adducts can disrupt their normal biological functions and induce harmful
immune responses. Unfortunately, reactive metabolites are not reliably
detected by experiments, and it is also expensive to test drug candidates
for potential to form DNA or protein adducts during the early stages
of drug development. In contrast, computational methods have the potential
to quickly screen for covalent binding potential, thereby flagging
problematic molecules and reducing the total number of necessary experiments.
Here, we train a deep convolution neural networkthe XenoSite
reactivity modelusing literature data to accurately predict
both sites and probability of reactivity for molecules with glutathione,
cyanide, protein, and DNA. On the site level, cross-validated predictions
had area under the curve (AUC) performances of 89.8% for DNA and 94.4%
for protein. Furthermore, the model separated molecules electrophilically
reactive with DNA and protein from nonreactive molecules with cross-validated
AUC performances of 78.7% and 79.8%, respectively. On both the site-
and molecule-level, the model’s performances significantly
outperformed reactivity indices derived from quantum simulations that
are reported in the literature. Moreover, we developed and applied
a selectivity score to assess preferential reactions with the macromolecules
as opposed to the common screening traps. For the entire data set
of 2803 molecules, this approach yielded totals of 257 (9.2%) and
227 (8.1%) molecules predicted to be reactive only with DNA and protein,
respectively, and hence those that would be missed by standard reactivity
screening experiments. Site of reactivity data is an underutilized
resource that can be used to not only predict if molecules are reactive,
but also show where they might be modified to reduce toxicity while
retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity
Privacy-preserving scoring of tree ensembles : a novel framework for AI in healthcare
Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacy-preserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacy-preserving machine learning for healthcare
- …