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The combination of physical equations with deep learning is becoming a promising methodology for bioprocess digitalization. In this paper, we investigate for the first time the combination of long short‐term memory (LSTM) networks with first principles equations in a hybrid workflow to describe human embryonic kidney 293 (HEK293) culture dynamics. Experimental data of 27 extracellular state variables in 20 fed‐batch HEK293 cultures were collected in a parallel high throughput 250 mL cultivation system in an industrial process development setting. The adaptive moment estimation method with stochastic regularization and cross‐validation were employed for deep learning. A total of 784 hybrid models with varying deep neural network architectures, depths, layers sizes and node activation functions were compared. In most scenarios, hybrid LSTM models outperformed classical hybrid Feedforward Neural Network (FFNN) models in terms of training and testing error. Hybrid LSTM models revealed to be less sensitive to data resampling than FFNN hybrid models. As disadvantages, Hybrid LSTM models are in general more complex (higher number of parameters) and have a higher computation cost than FFNN hybrid models. The hybrid model with the highest prediction accuracy consisted in a LSTM network with seven internal states connected in series with dynamic material balance equations. This hybrid model correctly predicted the dynamics of the 27 state variables (R2 = 0.93 in the test data set), including biomass, key substrates, amino acids and metabolic by‐products for around 10 cultivation days. [ABSTRACT FROM AUTHOR]

The literature suggests that cannabis use and schizotypal traits both constitute risk factors for the later development of schizophrenia. However, their interrelationships remain to be evaluated. The present study examined the association between cannabis use and schizotypal traits in 232 healthy students who ranged in age from 18 to 25 years. All the students had completed the Schizotypal Personality Questionnaire and four of the Chapman Psychosis Proneness Scales: the Magical Ideation Scale; the Perceptual Aberration Scale; the Revised Physical Anhedonia Scale; and the Revised Social Anhedonia Scale. Subjects were divided into three groups according to cannabis use typology: those who had never used cannabis, those who were past or occasional users, and those who were regular users. Higher scores on the Schizotypal Personality Questionnaire and the Magical Ideation Scale characterized the regular and past or occasional users compared with those who had never used cannabis. The co-occurrence of cannabis use and schizotypal traits appeared to be independent of anxiety and depression dimensions. These data suggest that cannabis use and schizotypal traits have to be jointly considered in further longitudinal studies of schizophrenia risk factors. [Copyright &y& Elsevier]

Background: Flux analyses, such as Metabolic Flux Analysis (MFA), Flux Balance Analysis (FBA), Flux Variability Analysis (FVA) or similar methods, can provide insights into the cellular metabolism, especially in combination with experimental data. The most common integration of extracellular concentration data requires the estimation of the specific fluxes (/rates) from the measured concentrations. This is a time-consuming, mathematically ill-conditioned inverse problem, raising high requirements for the quality and quantity of data. Method: In this contribution, a time integrated flux analysis approach is proposed which avoids the error-prone estimation of specific flux values. The approach is adopted for a Metabolic time integrated Flux Analysis and (sparse) time integrated Flux Balance/Variability Analysis. The proposed approach is applied to three case studies: (1) a simulated bioprocess case studying the impact of the number of samples (experimental points) and measurements' noise on the performance; (2) a simulation case to understand the impact of network redundancies and reaction irreversibility; and (3) an experimental bioprocess case study, showing its relevance for practical applications. Results: It is observed that this method can successfully estimate the time integrated flux values, even with relatively low numbers of samples and significant noise levels. In addition, the method allows the integration of additional constraints (e.g., bounds on the estimated concentrations) and since it eliminates the need for estimating fluxes from measured concentrations, it significantly reduces the workload while providing about the same level of insight into the metabolism as classic flux analysis methods. [ABSTRACT FROM AUTHOR]