An experimental study of the viscometric and volumetric properties of octane-pentadecane n-alkane binary and ternary systems at several temperatures.

The viscosities and densities of five ternary C\sb8-C\sb{15} n-alkane liquid systems and their corresponding eight binary subsystems have been measured at 293.15, 298.15, 308.15 and 313.15 K over the entire composition range. The experimental and literature viscosity data were used to test and modify some existing viscosity predictive models. The technique proposed earlier by Asfour et al. (1991) for the prediction of the dependence of viscosities of binary n-alkane systems on composition has been extended to cover ternary mixtures. A pseudo-binary mixture model has been developed in this study to modify the Generalized Corresponding States Principle (GCSP) proposed earlier by Teja and Rice (1981) for either prediction or correlation of the viscosity of liquid mixtures. The proposed modification of the GCSP method has the following advantages for n-alkane liquid mixtures having more than two components: (i) for the prediction of mixture viscosities, it does not require the selection procedure of reference fluids, thus eliminating the possible significant errors resulting from such a selection; (ii) for the correlation of mixture viscosities, it reduces the number of the binary interaction coefficients to one, no matter how many components are in a system. Obviously, this results in substantial cost and time savings. Two equations are proposed in this study as supplements to the original GCSP to make it more efficient; viz. (i) the first equation is concerned with the prediction of the interaction coefficient of the binary n-alkane systems from the pure component properties, (ii) the second equation provides a technique for the appropriate reference fluid selection when the original GCSP method is used for the viscosity prediction of ternary n-alkane liquid systems. Some literature excess property models have also been subjected to testing by using the excess property data calculated from the experimental viscometric and volumetric properties obtained in this study.Dept. of Chemistry and Biochemistry. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis1992 .W955. Source: Dissertation Abstracts International, Volume: 53-12, Section: B, page: 6430. Supervisor: Abdul-Fattah A. Asfour. Thesis (Ph.D.)--University of Windsor (Canada), 1992

Towards Open Vocabulary Learning: A Survey

In the field of visual scene understanding, deep neural networks have made impressive advancements in various core tasks like segmentation, tracking, and detection. However, most approaches operate on the close-set assumption, meaning that the model can only identify pre-defined categories that are present in the training set. Recently, open vocabulary settings were proposed due to the rapid progress of vision language pre-training. These new approaches seek to locate and recognize categories beyond the annotated label space. The open vocabulary approach is more general, practical, and effective compared to weakly supervised and zero-shot settings. This paper provides a thorough review of open vocabulary learning, summarizing and analyzing recent developments in the field. In particular, we begin by comparing it to related concepts such as zero-shot learning, open-set recognition, and out-of-distribution detection. Then, we review several closely related tasks in the case of segmentation and detection, including long-tail problems, few-shot, and zero-shot settings. For the method survey, we first present the basic knowledge of detection and segmentation in close-set as the preliminary knowledge. Next, we examine various scenarios in which open vocabulary learning is used, identifying common design elements and core ideas. Then, we compare the recent detection and segmentation approaches in commonly used datasets and benchmarks. Finally, we conclude with insights, issues, and discussions regarding future research directions. To our knowledge, this is the first comprehensive literature review of open vocabulary learning. We keep tracing related works at https://github.com/jianzongwu/Awesome-Open-Vocabulary.Comment: Project page at https://github.com/jianzongwu/Awesome-Open-Vocabular

The Effects of ATIR Blocker on the Severity of COVID-19 in Hypertensive Inpatients and Virulence of SARS-CoV-2 in Hypertensive hACE2 Transgenic Mice.

Angiotensin-converting enzyme 2 (ACE2) is required for the cellular entry of the severe acute respiratory syndrome coronavirus 2. ACE2, via the Ang-(1-7)-Mas-R axis, is part of the antihypertensive and cardioprotective effects of the renin-angiotensin system. We studied hospitalized COVID-19 patients with hypertension and hypertensive human(h) ACE2 transgenic mice to determine the outcome of COVID-19 with or without AT1 receptor (AT1R) blocker treatment. The severity of the illness and the levels of serum cardiac biomarkers (CK, CK-BM, cTnI), as well as the inflammation markers (IL-1, IL-6, CRP), were lesser in hypertensive COVID-19 patients treated with AT1R blockers than those treated with other antihypertensive drugs. Hypertensive hACE2 transgenic mice, pretreated with AT1R blocker, had increased ACE2 expression and SARS-CoV-2 in the kidney and heart, 1 day post-infection. We conclude that those hypertensive patients treated with AT1R blocker may be at higher risk for SARS-CoV-2 infection. However, AT1R blockers had no effect on the severity of the illness but instead may have protected COVID-19 patients from heart injury, via the ACE2-angiotensin1-7-Mas receptor axis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12265-021-10147-3

Fur in Magnetospirillum gryphiswaldense Influences Magnetosomes Formation and Directly Regulates the Genes Involved in Iron and Oxygen Metabolism

Magnetospirillum gryphiswaldense strain MSR-1 has the unique capability of taking up large amounts of iron and synthesizing magnetosomes (intracellular magnetic particles composed of Fe3O4). The unusual high iron content of MSR-1 makes it a useful model for studying biological mechanisms of iron uptake and homeostasis. The ferric uptake regulator (Fur) protein plays a key role in maintaining iron homeostasis in many bacteria. We identified and characterized a fur-homologous gene (MGR_1314) in MSR-1. MGR_1314 was able to complement a fur mutant of E. coli in iron-responsive manner in vivo. We constructed a fur mutant strain of MSR-1. In comparison to wild-type MSR-1, the mutant strain had lower magnetosome formation, and was more sensitive to hydrogen peroxide and streptonigrin, indicating higher intracellular free iron content. Quantitative real-time RT-PCR and chromatin immunoprecipitation analyses indicated that Fur protein directly regulates expression of several key genes involved in iron transport and oxygen metabolism, in addition it also functions in magnetosome formation in M. gryphiswaldense

Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities

BACKGROUND: Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures. METHODS: We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach. RESULTS: We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-μg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively. CONCLUSIONS: Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies

TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the Cα-N bond (Phi) and the Cα-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8° and 44.6°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value<1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/TANGLE/

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

A Specialized Search Engine for City Classification Information Retrieval

This paper describes the process of constructing a specialized search engine for city classification information retrieval. Compared with the general search engine, it can provide more precise information and domain-specific knowledge for users by means of some effective approaches. The forward maximum matching algorithm is used for Chinese segmentation. The query expansion method is conducted to the given queries based up on the synonymy lexicon. And the query recommendation approach is proposed in terms of user logs. The experimental results show where the proposed approaches are tested on corpora of significant scale, showing clear improvements with respect to conventional keyword-based search.The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.htmlProceedings of KSS'2007 : The Eighth International Symposium on Knowledge and Systems Sciences : November 5-7, 2007, [Ishikawa High-Tech Conference Center, Nomi, Ishikawa, JAPAN]Organized by: Japan Advanced Institute of Science and Technolog

Inducing fuzzy reasoning rules from numerical data

published_or_final_versionMechanical EngineeringDoctoralDoctor of Philosoph

Topic Map and Its Application to Document Retrieval

As computer and network technologies develop, information on line becomes flooded. Under this condition searching requested documents is really a difficult task. People are always seeking for an efficient searching tool and Topic Map is one of them. Topic Map is a new tool proposed by the International Organization for Standardization (ISO) to solve problems about knowledge representation and knowledge management. It has been widely used in many knowledge fields and in this paper we mainly make research on its application to information (document) retrieval. We firstly address some issues related to Topic Map: topic, association, occurrence as well as identity, facet and scope. By means of the current technologies for Topic Map developing, such as TMCL, TMQL, XML, SGML and so on, we propose a multi-layer Topic Map-based document retrieval model (TMDRM). TMRDM is based on Topic Map’s richly cross-linked structure and capabilities of topics used to group together objects that relate to a single abstract concept. This model helps people narrow the search scope step by step in order to facilitate them to proper documents.The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.htmlIFSR 2005 : Proceedings of the First World Congress of the International Federation for Systems Research : The New Roles of Systems Sciences For a Knowledge-based Society : Nov. 14-17, 2134, Kobe, JapanSymposium 6, Session 5 : Vision of Knowledge Civilization Future Computataion