Operation of an ADR Using Helium Exchange Gas as a Substitute for a Failed Heat Switch

The Soft X-ray Spectrometer (SXS) is one of four instruments on the Japanese Astro-H mission, which is currently planned for launch in late 2015. The SXS will perform imaging spectroscopy in the soft X-ray band (0.3-12 keV) using a 6 6 pixel array of microcalorimeters cooled to 50 mK. The detectors are cooled by a 3-stage adiabatic demagnetization refrigerator (ADR) that rejects heat to either a superfluid helium tank (at 1.2 K) or to a 4.5 K Joule-Thomson (JT) cryocooler. Four gas-gap heat switches are used in the assembly to manage heat flow between the ADR stages and the heat sinks. The engineering model (EM) ADR was assembled and performance tested at NASA/GSFC in November 2011, and subsequently installed in the EM dewar at Sumitomo Heavy Industries, Japan. During the first cooldown in July 2012, a failure of the heat switch that linked the two colder stages of the ADR to the helium tank was observed. Operation of the ADR requires some mechanism for thermally linking the salt pills to the heat sink, and then thermally isolating them. With the failed heat switch unable to perform this function, an alternate plan was devised which used carefully controlled amounts of exchange gas in the dewar's guard vacuum to facilitate heat exchange. The process was successfully demonstrated in November 2012, allowing the ADR to cool the detectors to 50 mK for hold times in excess of 10 h. This paper describes the exchange-gas-assisted recycling process, and the strategies used to avoid helium contamination of the detectors at low temperature

Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS

Background: Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. Results: RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at http://dm.postech.ac.kr/refmed. Conclusions: RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user's feedback and efficiently processes the function to return relevant articles in real time.1114Nsciescopu

Measuring the Heat Load on the Flight ASTRO-H Soft Xray Spectrometer Dewar

The Soft Xray Spectrometer (SXS) instrument on-board the ASTRO-H X-ray mission is based on microcalorimeters operating at 50 mK. Low temperature is achieved by use of an adiabatic demagnetization refrigerator (ADR) cyclically operating up to a heat sink at either 1.2 K or 4.5 K. The 1.2 K heat sink is provided by a 40 liter superfluid helium dewar. The parasitic heat to the helium from supports, plumbing, wires, and radiation, and the cyclic heat dumped by the ADR operation determine the liquid helium lifetime. To measure this lifetime we have used various techniques to rapidly achieve thermal equilibrium and then measure the boil-off rate of the helium. We have measured a parasitic heat of 650 microwatts and a cyclic heat of 100 microwatts for a total of 750 microwatts. This closely matches the predicted heat load. Starting with a fill level at launch of more than 33 liters results in a lifetime of greater than 4 years for the liquid helium. The techniques and accuracy for this measurement will be explained in this paper

Automation of a problem list using natural language processing

BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However, the current problem list is usually incomplete and inaccurate, and is often totally unused. To alleviate this issue, we are building an environment where the problem list can be easily and effectively maintained. METHODS: For this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main components: a background and a foreground application. The background application uses Natural Language Processing (NLP) to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record. These proposed medical problems drive the foreground application designed for management of the problem list. Within this application, the extracted problems are proposed to the physicians for addition to the official problem list. RESULTS: The set of 80 targeted medical problems selected for this project covered about 5% of all possible diagnoses coded in ICD-9-CM in our study population (cardiovascular adult inpatients), but about 64% of all instances of these coded diagnoses. The system contains algorithms to detect first document sections, then sentences within these sections, and finally potential problems within the sentences. The initial evaluation of the section and sentence detection algorithms demonstrated a sensitivity and positive predictive value of 100% when detecting sections, and a sensitivity of 89% and a positive predictive value of 94% when detecting sentences. CONCLUSION: The global aim of our project is to automate the process of creating and maintaining a problem list for hospitalized patients and thereby help to guarantee the timeliness, accuracy and completeness of this information

Linking genes to literature: text mining, information extraction, and retrieval applications for biology

Efficient access to information contained in online scientific literature collections is essential for life science research, playing a crucial role from the initial stage of experiment planning to the final interpretation and communication of the results. The biological literature also constitutes the main information source for manual literature curation used by expert-curated databases. Following the increasing popularity of web-based applications for analyzing biological data, new text-mining and information extraction strategies are being implemented. These systems exploit existing regularities in natural language to extract biologically relevant information from electronic texts automatically. The aim of the BioCreative challenge is to promote the development of such tools and to provide insight into their performance. This review presents a general introduction to the main characteristics and applications of currently available text-mining systems for life sciences in terms of the following: the type of biological information demands being addressed; the level of information granularity of both user queries and results; and the features and methods commonly exploited by these applications. The current trend in biomedical text mining points toward an increasing diversification in terms of application types and techniques, together with integration of domain-specific resources such as ontologies. Additional descriptions of some of the systems discussed here are available on the internet

ImmunoPET imaging of TIGIT in the glioma microenvironment

Abstract Glioblastoma (GBM) is the most common primary malignant brain tumor. Currently, there are few effective treatment options for GBM beyond surgery and chemo-radiation, and even with these interventions, median patient survival remains poor. While immune checkpoint inhibitors (ICIs) have demonstrated therapeutic efficacy against non-central nervous system cancers, ICI trials for GBM have typically had poor outcomes. TIGIT is an immune checkpoint receptor that is expressed on activated T-cells and has a role in the suppression of T-cell and Natural Killer (NK) cell function. As TIGIT expression is reported as both prognostic and a biomarker for anti-TIGIT therapy, we constructed a molecular imaging agent, [89Zr]Zr-DFO-anti-TIGIT (89Zr-αTIGIT), to visualize TIGIT in preclinical GBM by immunoPET imaging. PET imaging and biodistribution analysis of 89Zr-αTIGIT demonstrated uptake in the tumor microenvironment of GBM-bearing mice. Blocking antibody and irrelevant antibody tracer studies demonstrated specificity of 89Zr-αTIGIT with significance at a late time point post-tracer injection. However, the magnitude of 89Zr-αTIGIT uptake in tumor, relative to the IgG tracer was minimal. These findings highlight the features and limitations of using 89Zr-αTIGIT to visualize TIGIT in the GBM microenvironment