An application study on the stochastic effect of EUV photons (Conference Presentation)

Abstract Background Morphology, being one of the key factors influencing productivity of filamentous fungi, is of great interest during bioprocess development. With increasing demand of high-throughput phenotyping technologies for fungi due to the emergence of novel time-efficient genetic engineering technologies, workflows for automated liquid handling combined with high-throughput morphology analysis have to be developed. Results In this study, a protocol allowing for 48 parallel microbioreactor cultivations of Aspergillus carbonarius with non-invasive online signals of backscatter and dissolved oxygen was established. To handle the increased cultivation throughput, the utilized microbioreactor is integrated into a liquid handling platform. During cultivation of filamentous fungi, cell suspensions result in either viscous broths or form pellets with varying size throughout the process. Therefore, tailor-made liquid handling parameters such as aspiration/dispense height, velocity and mixing steps were optimized and validated. Development and utilization of a novel injection station enabled a workflow, where biomass samples are automatically transferred into a flow through chamber fixed under a light microscope. In combination with an automated image analysis concept, this enabled an automated morphology analysis pipeline. The workflow was tested in a first application study, where the projected biomass area was determined at two different cultivation temperatures and compared to the microbioreactor online signals. Conclusions A novel and robust workflow starting from microbioreactor cultivation, automated sample harvest and processing via liquid handling robots up to automated morphology analysis was developed. This protocol enables the determination of projected biomass areas for filamentous fungi in an automated and high-throughput manner. This measurement of morphology can be applied to describe overall pellet size distribution and heterogeneity.

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Fleet Resilience: Evaluating Maintenance Strategies in Critical Equipment

Applied Sciences ◽

10.3390/app11010038 ◽

2020 ◽

Vol 11 (1) ◽

pp. 38

Author(s):

Orlando Durán ◽

Javier Aguilar ◽

Andrea Capaldo ◽

Adolfo Arata

Keyword(s):

Application Study ◽

System Availability ◽

Maintenance Strategies ◽

Resilience Index ◽

Reliability Block Diagrams ◽

Maintenance Policies ◽

Definition Of ◽

The Impact ◽

Important System ◽

Intrinsic Characteristic

Resilience is an intrinsic characteristic of systems. Through it, the capacity of a system to react to the existence of disruptive events is expressed. A series of metrics to represent systems’ resilience have been proposed, however, only one indicator relates the availability of the system to this characteristic. With such a metric, it is possible to relate the topological aspects of a system and the resources available in order to be able to promptly respond to the loss of performance as a result of unexpected events. This work proposes the adaptation and application of such a resilience index to assess the influence of different maintenance strategies and topologies in fleets’ resilience. In addition, an application study considering an actual mining fleet is provided. A set of critical assets was identified and represented using reliability block diagrams. Monte Carlo simulation experiments were conducted and the system availability data were extracted. Resilience indexes were obtained in order to carry out the definition of the best maintenance policies in critical equipment and the assessment of the impact of modifying system redundancies. The main results of this work lead to the overall conclusion that redundancy is an important system attribute in order to improve resiliency along time.

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Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

Reproductive Biology and Endocrinology ◽

10.1186/s12958-021-00734-z ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Qingsong Xi ◽

Qiyu Yang ◽

Meng Wang ◽

Bo Huang ◽

Bo Zhang ◽

...

Keyword(s):

Machine Learning ◽

In Vitro Fertilization ◽

Endometrial Thickness ◽

Learning System ◽

Embryo Selection ◽

Selection Strategy ◽

Application Study ◽

Vitro Fertilization ◽

The Impact

Abstract Background To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing. Methods This was an application study including 9211 patients with 10,076 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes. Results For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1 + P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1 × P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree. Conclusion Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

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