RTG/science instrument radiation interactions for deep space probes, phase 2, 3, and 4

Assessment of interference to scientific instruments onboard RTG powered spacecraft caused by radiation emanating from RTG unit with application to Pioneer F/G space probe

GEMRec: A graph-based emotion-aware music recommendation approach

© Springer International Publishing AG 2016. Music recommendation has gained substantial attention in recent times. As one of the most important context features,user emotion has great potential to improve recommendations,but this has not yet been sufficiently explored due to the difficulty of emotion acquisition and incorporation. This paper proposes a graph-based emotion-aware music recommendation approach (GEMRec) by simultaneously taking a user’s music listening history and emotion into consideration. The proposed approach models the relations between user,music,and emotion as a three-element tuple (user,music,emotion),upon which an Emotion Aware Graph (EAG) is built,and then a relevance propagation algorithm based on random walk is devised to rank the relevance of music items for recommendation. Evaluation experiments are conducted based on a real dataset collected from a Chinese microblog service in comparison to baselines. The results show that the emotional context from a user’s microblogs contributes to improving the performance of music recommendation in terms of hitrate,precision,recall,and F1 score

The cryogenic system for the SLAC E158 experiment

E158 is a fixed target experiment at SLAC in which high energy (up to 48 GeV) polarized electrons are scattered off the unpolarized electrons in a 1.5 m long liquid hydrogen target. The total volume of liquid hydrogen in the system is 47.1. The beam can deposit as much as 700 W into the liquid hydrogen. Among the requirements for the system are: that density fluctuations in the liquid hydrogen be kept to a minimum, that the target can be moved out of the beam line while cold and replaced to within 2 mm and that the target survive lifetime radiation doses of up to 1×106 Gy. The cryogenic system for the experiment consists of the target itself, the cryostat containing the target, a refurbished CTI 4000 refrigerator providing more than 1 kW of cooling at 20 K and associated transfer lines and valve boxes. This paper discusses the requirements, design, construction, testing and operation of the cryogenic system. The unique features of the design associated with hydrogen safety and the high radiation field in which the target resides are also covered

Liquid Hydrogen Target Experience at SLAC

Liquid hydrogen targets have played a vital role in the physics program at SLAC for the past 40 years. These targets have ranged from small "beer can" targets to the 1.5 m long E158 target that was capable of absorbing up to 800 W without any significant density changes. Successful use of these targets has required the development of thin-wall designs, liquid hydrogen pumps, remote positioning and alignment systems, safety systems, control and data acquisition systems, cryogenic cooling circuits and heat exchangers. Detailed operating procedures have been created to ensure safety and operational reliability.This paper surveys the evolution of liquid hydrogen targets at SLAC and discusses advances in several of the enabling technologies that made these targets possible

Tumour regression and improved gastrointestinal tolerability from controlled release of SN-38 from novel polyoxazoline-modified dendrimers

Irinotecan is used clinically for the treatment of colorectal cancer; however, its utility is limited by its narrow therapeutic index. We describe the use of a generation 5 l-lysine dendrimer that has been part-modified with a polyoxazoline as a drug delivery vehicle for improving the therapeutic index of SN-38, the active metabolite of irinotecan. By conjugating SN-38 to the dendrimer via different linker technologies we sought to vary the release rate of the drug to generate diverse pharmacokinetic profiles. Three conjugates with plasma release half-lives of 2.5 h, 21 h, and 72 h were tested for efficacy and toxicity using a mouse SW620 xenograft model. In this model, the linker with a plasma release half-life of 21 h achieved sustained SN-38 exposure in blood, above the target concentration. Control over the release rate of the drug from the linker, combined with prolonged circulation of the dendrimer, enabled administration of an efficacious dose of SN-38, achieving significant regression of the SW620 tumours. The conjugates with 2.5 and 72 h release half-lives did not achieve an anti-tumour effect. Intraperitoneal dosing of the clinically used prodrug irinotecan produces high initial and local concentrations of SN-38, which are associated with gastrointestinal toxicity. Administration of the 21 h release dendrimer conjugate did not produce a high initial Cmax of SN-38. Consequently, a marked reduction in gastrointestinal toxicity was observed relative to irinotecan treatment. Additional studies investigating the dose concentrations and dose scheduling showed that a weekly dosing schedule of 4 mg SN-38/kg was the most efficacious regimen. After 4 doses at weekly intervals, the survival period of the mice extended beyond 70 days following the final dose. These extensive studies have allowed us to identify a linker, dose and dosing regimen for SN-38 conjugated to polyoxazoline-modified dendrimer that maximised efficacy and minimised adverse side effects

Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

yesDrug vehicles are chemical carriers that provide beneficial aid to the drugs they bear. Taking advantage of their favourable properties can potentially allow the safer use of drugs that are considered highly toxic. A means for vehicle selection without experimental trial would therefore be of benefit in saving time and money for the industry. Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug’s toxicity. Using data acquired from the National Institute of Health’s (NIH) Developmental Therapeutics Program (DTP) we propose a methodology using an area under a curve (AUC) approach that allows us to distinguish which vehicle provides the best toxicity profile for a drug and build classification models based on this knowledge. Our results show that we can achieve prediction accuracies of 80 % using random forest models whilst the decision tree models produce accuracies in the 70 % region. We consider our methodology widely applicable within the scientific domain and beyond for comprehensively building classification models for the comparison of functional relationships between two variables

Structure Analysis of Entamoeba histolytica DNMT2 (EhMeth)

In eukaryotes, DNA methylation is an important epigenetic modification that is generally involved in gene regulation. Methyltransferases (MTases) of the DNMT2 family have been shown to have a dual substrate specificity acting on DNA as well as on three specific tRNAs (tRNAAsp, tRNAVal, tRNAGly). Entamoeba histolytica is a major human pathogen, and expresses a single DNA MTase (EhMeth) that belongs to the DNMT2 family and shows high homology to the human enzyme as well as to the bacterial DNA MTase M.HhaI. The molecular basis for the recognition of the substrate tRNAs and discrimination of non-cognate tRNAs is unknown. Here we present the crystal structure of the cytosine-5-methyltransferase EhMeth at a resolution of 2.15 Å, in complex with its reaction product S-adenosyl-L-homocysteine, revealing all parts of a DNMT2 MTase, including the active site loop. Mobility shift assays show that in vitro the full length tRNA is required for stable complex formation with EhMeth

Synthetic Nanoparticles for Vaccines and Immunotherapy

The immune system plays a critical role in our health. No other component of human physiology plays a decisive role in as diverse an array of maladies, from deadly diseases with which we are all familiar to equally terrible esoteric conditions: HIV, malaria, pneumococcal and influenza infections; cancer; atherosclerosis; autoimmune diseases such as lupus, diabetes, and multiple sclerosis. The importance of understanding the function of the immune system and learning how to modulate immunity to protect against or treat disease thus cannot be overstated. Fortunately, we are entering an exciting era where the science of immunology is defining pathways for the rational manipulation of the immune system at the cellular and molecular level, and this understanding is leading to dramatic advances in the clinic that are transforming the future of medicine.1,2 These initial advances are being made primarily through biologic drugs– recombinant proteins (especially antibodies) or patient-derived cell therapies– but exciting data from preclinical studies suggest that a marriage of approaches based in biotechnology with the materials science and chemistry of nanomaterials, especially nanoparticles, could enable more effective and safer immune engineering strategies. This review will examine these nanoparticle-based strategies to immune modulation in detail, and discuss the promise and outstanding challenges facing the field of immune engineering from a chemical biology/materials engineering perspectiveNational Institutes of Health (U.S.) (Grants AI111860, CA174795, CA172164, AI091693, and AI095109)United States. Department of Defense (W911NF-13-D-0001 and Awards W911NF-07-D-0004