Science Concierge: A fast content-based recommendation system for scientific publications

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.Comment: 12 pages, 5 figure

A high-reproducibility and high-accuracy method for automated topic classification

Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent search, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state-of-the-art in topic classification. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results which are not accurate in inferring the most suitable model parameters. Adapting approaches for community detection in networks, we propose a new algorithm which displays high-reproducibility and high-accuracy, and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure. Our algorithm promises to make "big data" text analysis systems more reliable.Comment: 23 pages, 24 figure

Effects of Pulsatile Exercise-Induced Shear Stress on eNOS, SOD, VCAM-1, and ICAM-1 mRNA Expression of Human Carotid Artery Endothelial Cells

Exercise-induced endothelial shear stress (ESS) has been proposed as a molecular mechanism that regulates the expression of genes involved in the protection against atherosclerosis. However, research on this topic has not considered the pulsatile nature of blood flow for in vivo ESS estimations. PURPOSE: to analyze the effect of pulsatile exercise-induced ESS on endothelial nitric oxide synthase (eNOS), superoxide dismutase (SOD), vascular cell adhesion molecule 1 (VCAM-1), and intercellular adhesion molecule 1 (ICAM-1) mRNA expression of human carotid artery endothelial cells. METHODS: A reverse translational approach was employed for this study. First, an in vivo assessment, a total of 24 apparently healthy young subjects (14 females and 10 males) were recruited to perform two exercise tests on a cycle ergometer. The first test was a maximal incremental test which established the workloads for the next session, according to lactate levels. The second one, performed at least 48 hours after the first exercise test, was a steady-state test at lactate levels of \u3c2 mmol/L for 5 minutes. Left common carotid artery diameters and velocities were recorded through Doppler ultrasound. Microhematocrit measurement was used to determine blood density (ρ) and viscosity (μ). ESS was calculated by Womersley’s approximation, ESS = μ * 2K * Velocity/Diameter, where K is a function of Womersley’s parameter (α). Thereafter, in an in vitro experiment, commercially available human carotid artery endothelial cells were cultured on 6 slides until 95-100% confluence and were randomly assigned to no ESS exposure or were exposed to anterograde pulsatile flow (OsciFlow®) in a flow chamber (Streamer®) for 35 minutes, simulating exercise-induced ESS from the previous assessments. Finally, eNOS, SOD, VCAM-1, and ICAM-1 mRNA expression were compared between both groups, using GAPDH as the housekeeping gene. RESULTS: Exercise-induced ESS for lactate \u3c2 mmol was on average 56.32 (14.82) dynes/cm2. A significant increment on eNOS mRNA expression (P\u3c0.05) and a significant reduction on SOD mRNA expression (P\u3c0.05) were observed on those cells exposed to exercise-induced ESS compared to the group without ESS exposure. No significant differences were detected on mRNA expression of VCAM-1 and ICAM-1 between both groups. CONCLUSION: Pulsatile ESS generated during 35 minutes of low-intensity cycling might favor the upregulation of eNOS and the downregulation of SOD which in turn could provide a molecular explanation of the beneficial effects of exercise on atherosclerosis

Strengthening the primary health care response to COVID-19: an operational tool for policymakers

Aims: The aim of this paper is to introduce an operational checklist to serve as a tool for policymakers in the WHO European Region to strengthen primary health care (PHC) services and address the COVID-19 pandemic more effectively and to present the results from piloting the tool in Armenia. Backgrounds: PHC has the potential to play a fundamental role in countries’ responses to COVID-19. However, this potential remains unrealized in many countries. To assist countries, the WHO Regional Office for Europe developed a guidance document – Strengthening the Health Systems Response to COVID-19: Adapting Primary Health Care Services to more Effectively Address COVID-19 – that identifies strategic actions countries can take to strengthen their PHC response to the pandemic. Based on this guidance document, an operational checklist was developed to serve as a tool for policymakers to operationalize the recommended actions. Methods: The operational checklist was developed by transforming key points in the guidance document into questions in order to identify potentially modifiable factors to strengthen PHC in response to COVID-19. The operational checklist was then piloted in Armenia in June 2020 as part of a WHO mission to provide technical advice on strengthening Armenia’s PHC response to COVID-19. Two WHO experts performed semi-structured, face-to-face interviews with nine key informants (both facility managers and clinical staff) in three PHC facilities (two in a rural and one in an urban area). The data collected were analyzed to identify underlying challenges limiting PHC providers’ ability to effectively and efficiently respond to COVID-19 and maintain essential health services. Findings: The paper finds that making adjustments only to health services delivery will be insufficient to address most of the challenges identified by PHC providers in the context of COVID-19 in Armenia. In particular, strategic responses to the pandemic were missed, due, in part, to the absence of COVID-19 management teams at the facility level. Furthermore, the absence of PHC experts in Armenia’s national pandemic response team meant that health system issues identified at the facility level could not easily be communicated to or addressed by policymakers. The checklist therefore helps policymakers identify critical challenges – at both the facility and health system level – that need to be addressed to strengthen the PHC response to the COVID-19 pandemic

Scalable-resolution structured illumination microscopy

Structured illumination microscopy suffers from the need of sophisticated instrumentation and precise calibration. This makes structured illumination microscopes costly and skill-dependent. We present a novel approach to realize super-resolution structured illumination microscopy using an alignment non-critical illumination system and a reconstruction algorithm that does not need illumination information. The optical system is designed to encode higher order frequency components of the specimen by projecting PSF-modulated binary patterns for illuminating the sample plane, which do not have clean Fourier peaks conventionally used in structured illumination microscopy. These patterns fold high frequency content of sample into the measurements in an obfuscated manner, which are de-obfuscated using multiple signal classification algorithm. This algorithm eliminates the need of clean peaks in illumination and the knowledge of illumination patterns, which makes instrumentation simple and flexible for use with a variety of microscope objective lenses. We present a variety of experimental results on beads and cell samples to demonstrate resolution enhancement by a factor of 2.6 to 3.4 times, which is better than the enhancement supported by the conventional linear structure illumination microscopy where the same objective lens is used for structured illumination as well as collection of light. We show that the same system can be used in SIM configuration with different collection objective lenses without any careful re-calibration or realignment, thereby supporting a range of resolutions with the same system

Finding datasets in publications: the Syracuse University approach

Datasets are critical for scientific research, playing a role in replication, reproducibility, and efficiency. Researchers have recently shown that datasets are becoming more important for science to function properly, even serving as artifacts of study themselves. However, citing datasets is not a common or standard practice in spite of recent efforts by data repositories and funding agencies. This greatly affects our ability to track their usage and importance. A potential solution to this problem is to automatically extract dataset mentions from scientific articles. In this work, we propose to achieve such extraction by using a neural network based on a BiLSTM-CRF architecture. Our method achieves F1=0.885 in social science articles released as part of the Rich Context Dataset. We discuss future improvements to the model and applications beyond social sciences