Connecting high‐throughput biodiversity inventories: Opportunities for a site‐based genomic framework for global integration and synthesis

Abstract Objective Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed 10 phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Data Sciences and Informatics (OHDSI) network. Materials and Methods We constructed classifiers using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE) R-package, an open-source framework for learning phenotype classifiers using datasets in the Observational Medical Outcomes Partnership Common Data Model. We labeled training data based on the presence of multiple mentions of disease-specific codes. Performance was evaluated on cohorts derived using rule-based definitions and real-world disease prevalence. Classifiers were developed and evaluated across 3 medical centers, including 1 international site. Results Compared to the multiple mentions labeling heuristic, classifiers showed a mean recall boost of 0.43 with a mean precision loss of 0.17. Performance decreased slightly when classifiers were shared across medical centers, with mean recall and precision decreasing by 0.08 and 0.01, respectively, at a site within the USA, and by 0.18 and 0.10, respectively, at an international site. Discussion and Conclusion We demonstrate a high-throughput pipeline for constructing and sharing phenotype classifiers across sites within the OHDSI network using APHRODITE. Classifiers exhibit good portability between sites within the USA, however limited portability internationally, indicating that classifier generalizability may have geographic limitations, and, consequently, sharing the classifier-building recipe, rather than the pretrained classifiers, may be more useful for facilitating collaborative observational research.

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High-throughput developability assays enable library-scale identification of producible protein scaffold variants

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2026658118 ◽

2021 ◽

Vol 118 (23) ◽

pp. e2026658118

Author(s):

Alexander W. Golinski ◽

Katelynn M. Mischler ◽

Sidharth Laxminarayan ◽

Nicole L. Neurock ◽

Matthew Fossing ◽

...

Keyword(s):

High Throughput ◽

Cross Validation ◽

Recombinant Expression ◽

Sequence Information ◽

Biotechnological Applications ◽

Predictive Utility ◽

Protein Scaffold ◽

Assay Performance ◽

A Site ◽

Machine Learning Models

Proteins require high developability—quantified by expression, solubility, and stability—for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low throughput in nature, often slowing the developmental pipeline. We evaluated the ability of 10 variations of three high-throughput developability assays to predict the bacterial recombinant expression of paratope variants of the protein scaffold Gp2. Enabled by a phenotype/genotype linkage, assay performance for 105 variants was calculated via deep sequencing of populations sorted by proxied developability. We identified the most informative assay combination via cross-validation accuracy and correlation feature selection and demonstrated the ability of machine learning models to exploit nonlinear mutual information to increase the assays’ predictive utility. We trained a random forest model that predicts expression from assay performance that is 35% closer to the experimental variance and trains 80% more efficiently than a model predicting from sequence information alone. Utilizing the predicted expression, we performed a site-wise analysis and predicted mutations consistent with enhanced developability. The validated assays offer the ability to identify developable proteins at unprecedented scales, reducing the bottleneck of protein commercialization.

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Design an active verification mechanism for certificates revocation in OCSP for internet authentication

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i4.pp4208-4216 ◽

2020 ◽

Vol 10 (4) ◽

pp. 4208

Author(s):

Khalid Fazaa Mahmmod ◽

Mohammed Muzahem Azeez ◽

Zeyad Hashem Ismael

Keyword(s):

High Throughput ◽

Data Security ◽

Hash Function ◽

Social Implications ◽

Standard Protocol ◽

Storage Space ◽

Gate Array ◽

A Site

No doubt that data security online is crucial. Therefore, great attention has been paid to that aspect by companies and organizations given its economic and social implications. Thus, online certificate status protocol (OCSP) is considered one of the most prominent protocol functioning in this field, which offers a prompt support for certificates online. In this research, a model designed based on field programable gate array (FPGA) using Merkel’s tree has been proposed to overcome the delay that might have occurred in sorting and authentication of certificates. Having adopted this model and with the assistance of Hash function algorithm, more than 50% of certificates have been processed in comparison with standard protocol. Moreover, certificates have been provided with substantial storage space with high throughput. Basically, Hash function algorithm has been designed to arrange and specify a site of verified or denied certificates within time of validity to protect servers from intrusion and clients from using applications with harmful contents.

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High Throughput Screening of Materials for Solid Oxide Fuel Cells

MRS Proceedings ◽

10.1557/proc-804-jj2.10 ◽

2003 ◽

Vol 804 ◽

Cited By ~ 2

Author(s):

John P. Lemmon ◽

Venkatesan Manivannan ◽

Tracey Jordan ◽

Lamyaa Hassib ◽

Oltea Siclovan ◽

...

Keyword(s):

Fuel Cells ◽

Solid Oxide Fuel Cells ◽

Cathode Material ◽

High Throughput ◽

High Throughput Screening ◽

Vacancy Concentration ◽

Performance Testing ◽

Solid Oxide ◽

Oxide Fuel ◽

A Site

ABSTRACTState of the art commercial cathodes for solid oxide fuel cells (SOFC) include LaMnO3 with a zirconia-based electrolyte. However, the vacancy concentration in A site doped LaMnO3 is low, thus ionic conductivity is also very low (10−7 – 10−8 S/cm at 800 °C). The surface path dominates the reaction rate of the LaMnO3 cathode; therefore the optimized electrode is a porous composite material of both the cathode material and electrolyte and relies on triple-point boundaries for performance. The electrical conductivity and thermal expansion properties of this cathode material and other A3+B3+O3 perovskites can be tuned by substitution at the A and/or B site. The numerous combinations of composition, processing and microstructure needed for improved cathode performance is well suited for a high throughput screening (HTS) approach towards optimization and discovery. We present here a high throughput discovery process that includes, synthesis, performance testing and characterization techniques directed towards new low temperature SOFC cathode materials.

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New Approaches in Sensing and Targeting Bacterial rRNA A-site

Medicinal Chemistry ◽

10.2174/1573406415666191011160035 ◽

2019 ◽

Vol 15 ◽

Author(s):

Preethi Parameswaran ◽

Nihar Ranjan ◽

S.J.S. Flora

Keyword(s):

Antibiotic Resistance ◽

Life Cycle ◽

High Throughput ◽

Antibacterial Agents ◽

Lead Compounds ◽

New Class ◽

Chemical Agents ◽

Direct Sensing ◽

A Site ◽

Fluorescent Molecules

: New chemical agents that could combat increasing antibiotic resistance are urgently needed. In this mini-review, an old but highly relevant RNA sequence which is crucial for the continuation of bacterial life-cycle is covered. Some of the most significant advances of the last decade in sensing and targeting the bacterial rRNA A-site: a well-validated binding site of proverbially known aminoglycoside antibiotics is described. Some of the major advances in direct sensing of the bacterial decoding side (A-site) are described and also new fluorescent molecules that are capable of detecting lead compounds through high-throughput assays by displacement of fluorescent probe molecules are highlighted. Lastly, a focus on some of the recently discovered non-aminoglycoside small molecule binders of bacterial rRNA A-site as a new class of molecules that could provide future scaffolds and molecules for developing new antibacterial agents has been discussed.1

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Development and Validation of Phenotype Classifiers across Multiple Sites in the Observational Health Sciences and Informatics (OHDSI) Network

10.1101/673418 ◽

2019 ◽

Author(s):

Mehr Kashyap ◽

Martin Seneviratne ◽

Juan M Banda ◽

Thomas Falconer ◽

Borim Ryu ◽

...

Keyword(s):

High Throughput ◽

R Package ◽

Health Sciences ◽

Training Data ◽

Supervised Machine Learning ◽

Observational Research ◽

Medical Centers ◽

The Usa ◽

A Site ◽

Development And Validation

ABSTRACTObjectiveAccurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed ten phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Sciences and Informatics (OHDSI) network.Materials and MethodsWe constructed classifiers using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE) R-package, an open-source framework for learning phenotype classifiers using datasets in the OMOP CDM. We labeled training data based on the presence of multiple mentions of disease-specific codes. Performance was evaluated on cohorts derived using rule-based definitions and real-world disease prevalence. Classifiers were developed and evaluated across three medical centers, including one international site.ResultsCompared to the multiple mentions labeling heuristic, classifiers showed a mean recall boost of 0.43 with a mean precision loss of 0.17. Performance decreased slightly when classifiers were shared across medical centers, with mean recall and precision decreasing by 0.08 and 0.01, respectively, at a site within the USA, and by 0.18 and 0.10, respectively, at an international site.Discussion and ConclusionWe demonstrate a high-throughput pipeline for constructing and sharing phenotype classifiers across multiple sites within the OHDSI network using APHRODITE. Classifiers exhibit good portability between sites within the USA, however limited portability internationally, indicating that classifier generalizability may have geographic limitations, and consequently, sharing the classifier-building recipe, rather than the pre-trained classifiers, may be more useful for facilitating collaborative observational research.

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Eels Under Standing Wave Conditions

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100054911 ◽

1982 ◽

Vol 40 ◽

pp. 492-495

Author(s):

O.L. Krivanek ◽

J. TaftØ

Keyword(s):

Standing Wave ◽

Wave Conditions ◽

Set Up ◽

A Site ◽

Complex Crystal

It is well known that a standing electron wavefield can be set up in a crystal such that its intensity peaks at the atomic sites or between the sites or in the case of more complex crystal, at one or another type of a site. The effect is usually referred to as channelling but this term is not entirely appropriate; by analogy with the more established particle channelling, electrons would have to be described as channelling either through the channels or through the channel walls, depending on the diffraction conditions.

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Head Size and DNA Content in Bacteriophage T4

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100094255 ◽

1972 ◽

Vol 30 ◽

pp. 200-201

Author(s):

Fred Eiserling ◽

A. H. Doermann ◽

Linde Boehner

Keyword(s):

Electron Microscopy ◽

Dna Content ◽

Bacteriophage T4 ◽

Head Size ◽

Virus Particles ◽

Altered Protein ◽

A Cell ◽

Bacterial Viruses ◽

A Site ◽

Protein Shell

The control of form or shape inheritance can be approached by studying the morphogenesis of bacterial viruses. Shape variants of bacteriophage T4 with altered protein shell (capsid) size and nucleic acid (DNA) content have been found by electron microscopy, and a mutant (E920g in gene 66) controlling head size has been described. This mutant produces short-headed particles which contain 2/3 the normal DNA content and which are non-viable when only one particle infects a cell (Fig. 1).We report here the isolation of a new mutant (191c) which also appears to be in gene 66 but at a site distinct from E920g. The most striking phenotype of the mutant is the production of about 10% of the phage yield as “giant” virus particles, from 3 to 8 times longer than normal phage (Fig. 2).

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High Throughput Screening Methods

10.1039/9781782626770 ◽

2016 ◽

Cited By ~ 4

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Screening Methods

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