scholarly journals Morphology-driven downscaling of Streptomyces lividans to micro-cultivation

2017 ◽  
Author(s):  
Dino van Dissel ◽  
Gilles P. van Wezel
Keyword(s):  
Image Analysis ◽  
High Throughput Screening ◽  
Streptomyces Lividans ◽  
Product Formation ◽  
Mixing Rate ◽  
Mycelial Morphology ◽  
Complex Morphology ◽  
Shake Flasks ◽  
Scaling Down ◽  
Micro Cultivation

ABSTRACTActinobacteria are prolific producers of secondary metabolites and industrially relevant enzymes. Growth of these mycelial microorganisms in small culture volumes is challenging due to their complex morphology. Since morphology and production are typically linked, scaling down culture volumes requires better control over morphogenesis. In larger scale platforms, ranging from shake flasks to bioreactors, the hydrodynamics play an important role in shaping the morphology and determining product formation. Here, we report on the effects of agitation on the mycelial morphology of Streptomyces lividans grown in microtitre plates (MTP). Our work shows that at the proper agitation rates cultures can be scaled down to volumes as small as 100 μl while maintaining the same morphology as seen in larger scale platforms. Using image analysis we compared the morphologies of the cultures; when agitated at 1400 rpm the mycelial morphology in microcultures approached that obtained in shake flasks, while product formation was also maintained. Our study shows that the morphology of actinobacteria in microcultures can be controlled in a similar manner as in larger scale cultures by carefully controlling the mixing rate. This could facilitate high-throughput screening and upscaling.

Application of AFP Nanoprobing Light-Induced Photovoltaic Current in the Failure Analysis

2017 ◽  
Author(s):  
C.Q. Chen ◽  
P.T. Ng ◽  
G.B. Ang ◽  
Francis Rivai ◽  
S.L. Ting ◽  
...  
Keyword(s):  
Image Analysis ◽  
Failure Analysis ◽  
Photovoltaic Effect ◽  
Electrical Characterization ◽  
Fault Isolation ◽  
Device Performance ◽  
Semiconductor Technology ◽  
Current Image ◽  
Scaling Down ◽  
Detailed Circuit

Abstract As semiconductor technology keeps scaling down, failure analysis and device characterizations become more and more challenging. Global fault isolation without detailed circuit information comprises the majority of foundry EFA cases. Certain suspected areas can be isolated, but further narrow-down of transistor and device performance is very important with regards to process monitoring and failure analysis. A nanoprobing methodology is widely applied in advanced failure analysis, especially during device level electrical characterization. It is useful to verify device performance and to prove the problematic structure electrically. But sometimes the EFA spot coverage is too big to do nanoprobing analysis. Then further narrow-down is quite critical to identify the suspected structure before nanoprobing is employed. That means there is a gap between global fault isolation and localized device analysis. Under these kinds of situation, PVC and AFP current image are offen options to identify the suspected structure, but they still have their limitation for many soft defect or marginal fails. As in this case, PVC and AFP current image failed to identify the defect in the spot range. To overcome the shortage of PVC and AFP current image analysis, laser was innovatively applied in our current image analysis in this paper. As is known to all, proper wavelength laser can induce the photovoltaic effect in the device. The photovoltaic effect induced photo current can bring with it some information of the device. If this kind of information was properly interpreted, it can give us some clue of the device performance.

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer

2021 ◽  
pp. jclinpath-2020-207351
Author(s):  
Jenny Fitzgerald ◽  
Debra Higgins ◽  
Claudia Mazo Vargas ◽  
William Watson ◽  
Catherine Mooney ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Patient Outcome ◽  
Image Analysis ◽  
High Throughput Screening ◽  
Personalised Medicine ◽  
Digital Pathology ◽  
Disease Stage ◽  
Breast And Prostate Cancer ◽  
Biomarker Evaluation

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

An Improved Structure Design of Rolling Drum for the Mixture of Different Sizes of Particles

2014 ◽  
Vol 556-562 ◽  
pp. 456-459
Author(s):  
Yuan Lou Gao ◽  
Li Zhou ◽  
Xin Wang
Keyword(s):  
Image Analysis ◽  
Experimental Method ◽  
Chemical Industry ◽  
Food Industry ◽  
Structure Design ◽  
Cement Industry ◽  
Mixing Rate ◽  
Traditional Structure ◽  
The Stability

In many industries, such as chemical industry, food industry and cement industry, rolling drum are widely used for the mixing of solids which in many cases is the mixture of particles of different sizes. The quality of product is directly determined by the mixing uniformity, thus it is desired to guarantee the stability of mixing extent of rolling drum. This paper describes a new structure design for the mixture of different sizes of particles, and an image-analysis-based experimental method to verify the effectiveness of this new structure. Results of the experiment showed that using the new structure would enable a faster mixing rate, and obtaia higher mixing extent relative to the traditional structure.

scholarly journals A 96-multiplex capillary electrophoresis screening platform for product based evolution of P450 BM3

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Anna Gärtner ◽  
Anna Joëlle Ruff ◽  
Ulrich Schwaneberg
Keyword(s):  
Capillary Electrophoresis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Product Formation ◽  
Resonance Spectroscopy ◽  
P450 Bm3 ◽  
Microtiter Plates ◽  
Main Challenge ◽  
Total Turnover ◽  
Screening Systems

Abstract The main challenge that prevents a broader application of directed enzyme evolution is the lack of high-throughput screening systems with universal product analytics. Most directed evolution campaigns employ screening systems based on colorimetric or fluorogenic surrogate substrates or universal quantification methods such as nuclear magnetic resonance spectroscopy or mass spectrometry, which have not been advanced to achieve a high-throughput. Capillary electrophoresis with a universal UV-based product detection is a promising analytical tool to quantify product formation. Usage of a multiplex system allows the simultaneous measurement with 96 capillaries. A 96-multiplexed capillary electrophoresis (MP-CE) enables a throughput that is comparable to traditional direct evolution campaigns employing 96-well microtiter plates. Here, we report for the first time the usage of a MP-CE system for directed P450 BM3 evolution towards increased product formation (oxidation of alpha-isophorone to 4-hydroxy-isophorone; highest reached total turnover number after evolution campaign: 7120 mol4-OH molP450−1). The MP-CE platform was 3.5-fold more efficient in identification of beneficial variants than the standard cofactor (NADPH) screening system.

scholarly journals ZF-AutoML: An Easy Machine-Learning-Based Method to Detect Anomalies in Fluorescent-Labelled Zebrafish

Inventions ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 72
Author(s):  
Ryota Sawaki ◽  
Daisuke Sato ◽  
Hiroko Nakayama ◽  
Yuki Nakagawa ◽  
Yasuhito Shimada
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Fluorescent Protein ◽  
Learning Program ◽  
High Throughput Analysis ◽  
Easy Method ◽  
High Fecundity

Background: Zebrafish are efficient animal models for conducting whole organism drug testing and toxicological evaluation of chemicals. They are frequently used for high-throughput screening owing to their high fecundity. Peripheral experimental equipment and analytical software are required for zebrafish screening, which need to be further developed. Machine learning has emerged as a powerful tool for large-scale image analysis and has been applied in zebrafish research as well. However, its use by individual researchers is restricted due to the cost and the procedure of machine learning for specific research purposes. Methods: We developed a simple and easy method for zebrafish image analysis, particularly fluorescent labelled ones, using the free machine learning program Google AutoML. We performed machine learning using vascular- and macrophage-Enhanced Green Fluorescent Protein (EGFP) fishes under normal and abnormal conditions (treated with anti-angiogenesis drugs or by wounding the caudal fin). Then, we tested the system using a new set of zebrafish images. Results: While machine learning can detect abnormalities in the fish in both strains with more than 95% accuracy, the learning procedure needs image pre-processing for the images of the macrophage-EGFP fishes. In addition, we developed a batch uploading software, ZF-ImageR, for Windows (.exe) and MacOS (.app) to enable high-throughput analysis using AutoML. Conclusions: We established a protocol to utilize conventional machine learning platforms for analyzing zebrafish phenotypes, which enables fluorescence-based, phenotype-driven zebrafish screening.

scholarly journals The metabolic switch can be activated in a recombinant strain of Streptomyces lividans by a low oxygen transfer rate in shake flasks

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Ramsés A. Gamboa-Suasnavart ◽  
Norma A. Valdez-Cruz ◽  
Gerardo Gaytan-Ortega ◽  
Greta I. Reynoso-Cereceda ◽  
Daniel Cabrera-Santos ◽  
...  
Keyword(s):  
Oxygen Transfer ◽  
Transfer Rate ◽  
Recombinant Strain ◽  
Oxygen Transfer Rate ◽  
Streptomyces Lividans ◽  
Low Oxygen ◽  
Metabolic Switch ◽  
Shake Flasks

Image analysis of mycelial morphology in virginiamycin production by batch culture of Streptomyces virginiae

1996 ◽  
Vol 81 (1) ◽  
pp. 7-12 ◽  
Author(s):  
Young Kook Yang ◽  
Masatoshi Morikawa ◽  
Hiroshi Shimizu ◽  
Suteaki Shioya ◽  
Ken-Ichi Suga ◽  
...  
Keyword(s):  
Image Analysis ◽  
Batch Culture ◽  
Mycelial Morphology ◽  
Streptomyces Virginiae

scholarly journals In vitro Assays and Imaging Methods for Drug Discovery for Cardiac Fibrosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Giorgia Palano ◽  
Ariana Foinquinos ◽  
Erik Müllers
Keyword(s):  
Image Analysis ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Large Scale ◽  
Cardiac Fibrosis ◽  
3D Models ◽  
In Vitro Assays ◽  
Stress Injury

As a result of stress, injury, or aging, cardiac fibrosis is characterized by excessive deposition of extracellular matrix (ECM) components resulting in pathological remodeling, tissue stiffening, ventricular dilatation, and cardiac dysfunction that contribute to heart failure (HF) and eventually death. Currently, there are no effective therapies specifically targeting cardiac fibrosis, partially due to limited understanding of the pathological mechanisms and the lack of predictive in vitro models for high-throughput screening of antifibrotic compounds. The use of more relevant cell models, three-dimensional (3D) models, and coculture systems, together with high-content imaging (HCI) and machine learning (ML)-based image analysis, is expected to improve predictivity and throughput of in vitro models for cardiac fibrosis. In this review, we present an overview of available in vitro assays for cardiac fibrosis. We highlight the potential of more physiological 3D cardiac organoids and coculture systems and discuss HCI and automated artificial intelligence (AI)-based image analysis as key methods able to capture the complexity of cardiac fibrosis in vitro. As 3D and coculture models will soon be sufficiently mature for application in large-scale preclinical drug discovery, we expect the combination of more relevant models and high-content analysis to greatly increase translation from in vitro to in vivo models and facilitate the discovery of novel targets and drugs against cardiac fibrosis.

How to Scale‐up while Scaling down: EVOscreen™, a Miniaturized Ultra‐High‐Throughput Screening System

1999 ◽  
pp. 440-461
Author(s):  
Rodney Turner ◽  
Sylvia Sterrer ◽  
Karl‐Heinz Wiesmüller ◽  
Franz‐Josef Meyer‐Almes
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Scale Up ◽  
Screening System ◽  
Scaling Down
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