Recent Application of Zebrafish Models in Atherosclerosis Research

Atherosclerotic cardiovascular disease is one of the leading causes of death worldwide. Establishing animal models of atherosclerosis is of great benefit for studying its complicated pathogenesis and screening and evaluating related drugs. Although researchers have generated a variety of models for atherosclerosis study in rabbits, mice and rats, the limitations of these models make it difficult to monitor the development of atherosclerosis, and these models are unsuitable for large scale screening of potential therapeutic targets. On the contrast, zebrafish can fulfill these purposes thanks to their fecundity, rapid development ex utero, embryonic transparency, and conserved lipid metabolism process. Thus, zebrafish have become a popular alternative animal model for atherosclerosis research. In this mini review, we summarize different zebrafish models used to study atherosclerosis, focusing on the latest applications of these models to the dynamic monitoring of atherosclerosis progression, mechanistic study of therapeutic intervention and drug screening, and assessment of the impacts of other risk factors.

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Key-Frame Detection and Super-Resolution of Hyperspectral Video via Sparse-Based Cumulative Tensor Factorization

Mathematical Problems in Engineering ◽

10.1155/2020/9548749 ◽

2020 ◽

Vol 2020 ◽

pp. 1-20

Author(s):

Ruofei Zhou ◽

Gang Wang ◽

Bo Li ◽

Jinlong Wang ◽

Tianzhu Liu ◽

...

Keyword(s):

Large Scale ◽

Rapid Development ◽

Super Resolution ◽

Three Dimensions ◽

Dynamic Monitoring ◽

Tensor Factorization ◽

Data Set ◽

Key Frame ◽

The Core ◽

Sr Method

Thanks to the rapid development of hyperspectral sensors, hyperspectral videos (HSV) can now be collected with high temporal and spectral resolutions and utilized to handle invisible dynamic monitoring missions, such as chemical gas plume tracking. However, using such sequential large-scale data effectively is challenged, because the direct process of these data requires huge demands in terms of computational loads and memory. This paper presents a key-frame and target-detecting algorithm based on cumulative tensor CANDECOMP/PARAFAC (CP) factorization (CTCF) to select the frames where the target shows up, and a novel super-resolution (SR) method using sparse-based tensor Tucker factorization (STTF) is used to improve the spatial resolution. In the CTCF method, the HSV sequence is seen as cumulative tensors and the correlation of adjacent frames is exploited by applying CP tensor approximation. In the proposed STTF-based SR method, we consider the HSV frame as a third-order tensor; then, HSV frame super-resolution problem is transformed into estimations of the dictionaries along three dimensions and estimation of the core tensor. In order to promote sparse core tensors, a regularizer is incorporated to model the high spatial-spectral correlations. The estimations of the core tensor and the dictionaries along three dimensions are formulated as sparse-based Tucker factorizations of each HSV frame. Experimental results on real HSV data set demonstrate the superiority of the proposed CTCF and STTF algorithms over the comparative state-of-the-art target detection and SR approaches.

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Training Aides to Screen Children for Speech and Language Problems

Language Speech and Hearing Services in Schools ◽

10.1044/0161-1461.0704.236 ◽

1976 ◽

Vol 7 (4) ◽

pp. 236-241 ◽

Cited By ~ 5

Author(s):

Marisue Pickering ◽

William R. Dopheide

Keyword(s):

Rural Areas ◽

Large Scale ◽

School Personnel ◽

School Age Children ◽

Speech And Language ◽

Elementary School Age ◽

Evaluation Procedures ◽

Language Problems ◽

Competency Based ◽

Large Scale Screening

This report deals with an effort to begin the process of effectively identifying children in rural areas with speech and language problems using existing school personnel. A two-day competency-based workshop for the purpose of training aides to conduct a large-scale screening of speech and language problems in elementary-school-age children is described. Training strategies, implementation, and evaluation procedures are discussed.

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Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666190122151634 ◽

2019 ◽

Vol 19 (1) ◽

pp. 4-16 ◽

Cited By ~ 6

Author(s):

Qihui Wu ◽

Hanzhong Ke ◽

Dongli Li ◽

Qi Wang ◽

Jiansong Fang ◽

...

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Large Scale ◽

Recent Progress ◽

High Specificity ◽

Learning Approaches ◽

Anticancer Peptides ◽

The Past ◽

Traditional Approaches ◽

Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

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Analytical Validation and Clinical Application of Rapid Serological Tests for SARS-CoV-2 Suitable for Large-Scale Screening

Diagnostics ◽

10.3390/diagnostics11050869 ◽

2021 ◽

Vol 11 (5) ◽

pp. 869

Author(s):

Amedeo De Nicolò ◽

Valeria Avataneo ◽

Jessica Cusato ◽

Alice Palermiti ◽

Jacopo Mula ◽

...

Keyword(s):

Early Diagnosis ◽

Real Time Pcr ◽

Large Scale ◽

High Sensitivity ◽

Analytical Validation ◽

Pcr Assay ◽

Serological Tests ◽

Flow Test ◽

Lateral Flow Test ◽

Large Scale Screening

Recently, large-scale screening for COVID-19 has presented a major challenge, limiting timely countermeasures. Therefore, the application of suitable rapid serological tests could provide useful information, however, little evidence regarding their robustness is currently available. In this work, we evaluated and compared the analytical performance of a rapid lateral-flow test (LFA) and a fast semiquantitative fluorescent immunoassay (FIA) for anti-nucleocapsid (anti-NC) antibodies, with the reverse transcriptase real-time PCR assay as the reference. In 222 patients, LFA showed poor sensitivity (55.9%) within two weeks from PCR, while later testing was more reliable (sensitivity of 85.7% and specificity of 93.1%). Moreover, in a subset of 100 patients, FIA showed high sensitivity (89.1%) and specificity (94.1%) after two weeks from PCR. The coupled application for the screening of 183 patients showed satisfactory concordance (K = 0.858). In conclusion, rapid serological tests were largely not useful for early diagnosis, but they showed good performance in later stages of infection. These could be useful for back-tracing and/or to identify potentially immune subjects.

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BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space

International Journal of Molecular Sciences ◽

10.3390/ijms22157773 ◽

2021 ◽

Vol 22 (15) ◽

pp. 7773

Author(s):

Neann Mathai ◽

Conrad Stork ◽

Johannes Kirchmair

Keyword(s):

Large Scale ◽

Computational Approach ◽

Large Sets ◽

Compound Libraries ◽

Wide Range ◽

Protein Space ◽

High Chance ◽

Large Scale Screening ◽

Early Drug ◽

Selection Of

Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the “fitness” of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle (“BonMOLière”).

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Large-scale screening of natural genetic resource in the hydrocarbon-producing microalga Botrycoccus braunii identified novel fast-growing strains

Scientific Reports ◽

10.1038/s41598-021-86760-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Koji Kawamura ◽

Suzune Nishikawa ◽

Kotaro Hirano ◽

Ardianor Ardianor ◽

Rudy Agung Nugroho ◽

...

Keyword(s):

Large Scale ◽

Biomass Productivity ◽

Botryococcus Braunii ◽

Algal Biofuel ◽

Fast Growing ◽

Wild Strains ◽

Glass Tubes ◽

The Tropics ◽

Large Scale Screening ◽

Hydrocarbon Productivity

AbstractAlgal biofuel research aims to make a renewable, carbon–neutral biofuel by using oil-producing microalgae. The freshwater microalga Botryococcus braunii has received much attention due to its ability to accumulate large amounts of petroleum-like hydrocarbons but suffers from slow growth. We performed a large-scale screening of fast-growing strains with 180 strains isolated from 22 ponds located in a wide geographic range from the tropics to cool-temperate. A fast-growing strain, Showa, which recorded the highest productivities of algal hydrocarbons to date, was used as a benchmark. The initial screening was performed by monitoring optical densities in glass tubes and identified 9 wild strains with faster or equivalent growth rates to Showa. The biomass-based assessments showed that biomass and hydrocarbon productivities of these strains were 12–37% and 11–88% higher than that of Showa, respectively. One strain, OIT-678 established a new record of the fastest growth rate in the race B strains with a doubling time of 1.2 days. The OIT-678 had 36% higher biomass productivity, 34% higher hydrocarbon productivity, and 20% higher biomass density than Showa at the same cultivation conditions, suggesting the potential of the new strain to break the record for the highest productivities of hydrocarbons.

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High-Rate Transition Metal-based Cathode Materials for Battery-Supercapacitor Hybrid Devices

Nanoscale Advances ◽

10.1039/d1na00523e ◽

2021 ◽

Author(s):

Cong Wang ◽

Zehao Song ◽

Pei Shi ◽

Lin Lv ◽

Houzhao Wan ◽

...

Keyword(s):

Large Scale ◽

Rapid Development ◽

Electronic Devices ◽

High Rate ◽

Specific Power ◽

Energy Storage Devices ◽

Storage Devices ◽

Electrochemical Devices ◽

Hybrid Devices ◽

Scale Grid

With the rapid development of portable electronic devices, electric vehicles and large-scale grid energy storage devices, it needs to reinforce specific energy and specific power of related electrochemical devices meeting...

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Identification of Six Loci in Which Mutations Partially Restore Peroxisome Biogenesis and/or Alleviate the Metabolic Defect of pex2 Mutants in Podospora

Genetics ◽

10.1093/genetics/161.3.1089 ◽

2002 ◽

Vol 161 (3) ◽

pp. 1089-1099

Author(s):

Gwenaël Ruprich-Robert ◽

Véronique Berteaux-Lecellier ◽

Denise Zickler ◽

Arlette Panvier-Adoutte ◽

Marguerite Picard

Keyword(s):

Sole Carbon Source ◽

Large Scale ◽

Podospora Anserina ◽

Developmental Defect ◽

Dominant Allele ◽

Peroxisome Biogenesis ◽

Metabolic Defects ◽

Metabolic Defect ◽

Mutant Strains ◽

Large Scale Screening

Abstract Peroxins (PEX) are proteins required for peroxisome biogenesis. Mutations in PEX genes cause lethal diseases in humans, metabolic defects in yeasts, and developmental disfunctions in plants and filamentous fungi. Here we describe the first large-scale screening for suppressors of a pex mutation. In Podospora anserina, pex2 mutants exhibit a metabolic defect [inability to grow on medium containing oleic acid (OA medium) as sole carbon source] and a developmental defect (inability to differentiate asci in homozygous crosses). Sixty-three mutations able to restore growth of pex2 mutants on OA medium have been analyzed. They fall in six loci (suo1 to suo6) and act as dominant, allele-nonspecific suppressors. Most suo mutations have pleiotropic effects in a pex2+ background: formation of unripe ascospores (all loci except suo5 and suo6), impaired growth on OA medium (all loci except suo4 and suo6), or sexual defects (suo4). Using immunofluorescence and GFP staining, we show that peroxisome biogenesis is partially restored along with a low level of ascus differentiation in pex2 mutant strains carrying either the suo5 or the suo6 mutations. The data are discussed with respect to β-oxidation of fatty acids, peroxisome biogenesis, and cell differentiation.

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