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Data processing

Sarah Kasran, Sean Hughes, Jan De Houwer · OSF Preprints (OSF Preprints)

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» Data processing

Sergey A. Kozlov, Vassili N. Lazarev, Elena S. Kostryukova et al. · 2014

A comprehensive transcriptome analysis of an expressed sequence tag (EST) database of the spider Dolomedes fimbriatus venom glands using single-residue distribution analysis (SRDA) identified 7,169 unique sequences. Mature chains of 163 different toxin-like polypeptides were predicted on the basis of well-established methodology. The number of protein precursors of these polypeptides was appreciably numerous than the number of mature polypeptides. A total of 451 different polypeptide precursors, translated from 795 unique nucleotide sequences, were deduced. A homology search divided the 163 mature polypeptide sequences into 16 superfamilies and 19 singletons. The number of mature toxins in a superfamily ranged from 2 to 49, whereas the diversity of the original nucleotide sequences was greater (2–261 variants). We observed a predominance of inhibitor cysteine knot toxin-like polypeptides among the cysteine-containing structures in the analyzed transcriptome bank. Uncommon spatial folds were also found.

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Data processing

Lynn Tao-ning Wang A, Liang-teck Pang, Andrew R. Neureuther et al. · 2000

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Data processing.

Pranav Goel (17014588), Nikolay Malkin (17014591), SoRelle W. Gaynor (17014594) et al. · Figshare · 2023

<div><p>Campaign contributions are a staple of congressional life. Yet, the search for tangible effects of congressional donations often focuses on the association between contributions and votes on congressional bills. We present an alternative approach by considering the relationship between money and legislators’ speech. Floor speeches are an important component of congressional behavior, and reflect a legislator’s policy priorities and positions in a way that voting cannot. Our research provides the first comprehensive analysis of the association between a legislator’s campaign donors and the policy issues they prioritize with congressional speech. Ultimately, we find a robust relationship between donors and speech, indicating a more pervasive role of money in politics than previously assumed. We use a machine learning framework on a new dataset that brings together legislator metadata for all representatives in the US House between 1995 and 2018, including committee assignments, legislative speech, donation records, and information about Political Action Committees. We compare information about donations against other potential explanatory variables, such as party affiliation, home state, and committee assignments, and find that donors consistently have the strongest association with legislators’ issue-attention. We further contribute a procedure for identifying speech and donation events that occur in close proximity to one another and share meaningful connections, identifying the proverbial needles in the haystack of speech and donation activity in Congress which may be cases of interest for investigative journalism. Taken together, our framework, data, and findings can help increase the transparency of the role of money in politics.</p></div>

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data processing

Khadem, Shafiuzzaman, Trivedi, Rohit, Bahloul, Mohamed et al. · Figshare · 2024

Python file for data processing

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Data processing.

Claire A. Richards (18259315), Solmaz Amiri (11588913), Von P. Walden (8367204) et al. · Figshare · 2024

<div><p>Major power outages have risen over the last two decades, largely due to more extreme weather conditions. However, there is a lack of knowledge on the distribution of power outages and its relationship to social vulnerability and co-occurring hazards. We examined the associations between localized outages and social vulnerability factors (demographic characteristics), controlling for environmental factors (weather), in Washington State between 2018–2021. We additionally analyzed the validity of PowerOutage.us data compared to federal datasets. The population included 27 counties served by 14 electric utilities. We developed a continuous measure of daily outage burden using PowerOutage.us data and operationalized social vulnerability using four factors: poverty level, unemployment, disability, and limited English proficiency. We applied zero-altered lognormal generalized additive mixed-effects models to characterize the relationship between social vulnerability and daily power outage burden, controlling for daily minimum temperature, maximum wind speed, and precipitation, from 2018 to 2021 in Washington State. We found that social vulnerability factors have non-linear relationships with outages. Wind and precipitation are consistent drivers of outage occurrence and duration. There are seasonal effects that vary by county-utility area. Both PowerOutage.us and federal datasets have missing and inaccurate outage data. This is the first study evaluating differential exposure to localized outages as related to social vulnerability that has accounted for weather and temporal correlation. There is a lack of transparency into power outage distribution for those most vulnerable to climate impacts, despite known contributions by electric utilities to climate change. For effective public health surveillance of power outages and transparency, outage data should be made available at finer spatial resolution and temporal scales and/or utilities should be required to report differential exposure to power outages for socially vulnerable populations.</p></div>

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Data processing.

Mous, P.J. · Data Archiving and Networked Services (DANS) · 1995

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Data Processing

Vincent Manzanilla · OSF Preprints (OSF Preprints) · 2020

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data processing

Khadem, Shafiuzzaman, Trivedi, Rohit, Bahloul, Mohamed et al. · Figshare · 2024

Python file for data processing

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Data Processing

National Cancer Institute · Definitions · 2020

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Data processing

Bernhard Hofmann-Wellenhof, Herbert Lichtenegger, James Collins · 1994

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Data processing

Sarah Kasran, Sean Hughes, Jan De Houwer · OSF Preprints (OSF Preprints)

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Data Processing

Svenja Fischer, Andreas Schumann · Water science and technology library · 2023

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Data processing

Michael G. Rossmann, C. G. van Beek · Acta Crystallographica Section D Biological Crystallography · 1999

X-ray diffraction data processing proceeds through indexing, pre-refinement of camera parameters and crystal orientation, intensity integration, post-refinement and scaling. The DENZO program has set new standards for autoindexing, but no publication has appeared which describes the algorithm. In the development of the new Data Processing Suite (DPS), one of the first aims has been the development of an autoindexing procedure at least as powerful as that used by DENZO. The resultant algorithm will be described. Another major problem which has arisen in recent years is scaling and post-refinement of data from different images when there are few, if any, full reflections. This occurs when the mosaic spread approaches or exceeds the angle of oscillation, as is usually the case for frozen crystals. A procedure which is able to obtain satisfactory results for such a situation will be described.

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Data processing.

Riitta Mieronkoski (9105816), Elise Syrjälä (9105819), Mingzhe Jiang (5509499) et al. · Figshare · 2020

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Data processing.

Felipe Liporaci (18476078), Danilo Carlotti (18476081), Ana Carlotti (6566078) · Figshare · 2024

<div><p>Bloodstream infection (BSI) is associated with increased morbidity and mortality in the pediatric intensive care unit (PICU) and high healthcare costs. Early detection and appropriate treatment of BSI may improve patient’s outcome. Data on machine-learning models to predict BSI in pediatric patients are limited and neither study included time series data. We aimed to develop a machine learning model to predict an early diagnosis of BSI in patients admitted to the PICU. This was a retrospective cohort study of patients who had at least one positive blood culture result during stay at a PICU of a tertiary-care university hospital, from January 1<sup>st</sup> to December 31<sup>st</sup> 2019. Patients with positive blood culture results with growth of contaminants and those with incomplete data were excluded. Models were developed using demographic, clinical and laboratory data collected from the electronic medical record. Laboratory data (complete blood cell counts with differential and C-reactive protein) and vital signs (heart rate, respiratory rate, blood pressure, temperature, oxygen saturation) were obtained 72 hours before and on the day of blood culture collection. A total of 8816 data from 76 patients were processed by the models. The machine committee was the best-performing model, showing accuracy of 99.33%, precision of 98.89%, sensitivity of 100% and specificity of 98.46%. Hence, we developed a model using demographic, clinical and laboratory data collected on a routine basis that was able to detect BSI with excellent accuracy and precision, and high sensitivity and specificity. The inclusion of vital signs and laboratory data variation over time allowed the model to identify temporal changes that could be suggestive of the diagnosis of BSI. Our model might help the medical team in clinical-decision making by creating an alert in the electronic medical record, which may allow early antimicrobial initiation and better outcomes.</p></div>

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Data processing

CERN Bulletin · 1974

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Data Processing

Hannah Snyder, Benjamin Hankin, Alyssa Fassett-Carman · OSF Preprints (OSF Preprints) · 2018

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Data processing.

Erol Orel (12191296), Rachel Esra (12191299), Janne Estill (378310) et al. · Figshare · 2022

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DATA PROCESSING

Purdue E-pubs, Hans Hauska · 1980

This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact epubs@purdue.edu for additional information. Hauska, Hans, "The Lulea Image Processing System (LIPS)- a Versatile Approach to Earth Resources Data Processing " (1980). LARS Symposia. Paper 325.

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