Toward optimal control of flat plate photobioreactors: the greenhouse analogy?

Abstract: The cultivation of algae in photo-bioreactors shows similarities to crop cultivation in greenhouses, especially when the reactors are driven by sun light. Advanced methodologies for dynamic optimization and optimal control for greenhouses are known from earlier research. The aim here is to extend these methodologies to microalgae cultivated in a flat plate photo-bioreactor. A one-state space model for the algal biomass in the reactor is presented. The growth rate vs. light curve is parameterized on the basis of experimental evidence. Spatial distribution of light and growth rate between the plates is also considered. The control variable is the dilution rate. Dynamic optimal control trajectories are presented for various choices of goal function and external solar irradiation trajectories over a horizon of 3 days. It was found that the algae present in the reactor at final time represent a value for the future. Numerical and theoretical results suggest that the control is bang-(singular-)bang, with a strong dependence on the weather. The optimal biomass also depends on the available light, and achieving it to reach a new optimal steady cycle after a prolonged change in weather may take several days. A preliminary theoretical analysis suggests a control law that maximizes the effective growth rate. The analysis shows that like in the greenhouse case, the co-state of the algal biomass plays a pivot role in developing on-line controllers

Subnormal short‐latency facial mimicry responses to dynamic emotional facial expressions in male adolescents with disruptive behavior disorders and callous‐unemotional traits

Using still pictures of emotional facial expressions as experimental stimuli, reduced amygdala responses or impaired recognition of basic emotions were repeatedly found in people with psychopathic traits. The amygdala also plays an important role in short‐latency facial mimicry responses. Since dynamic emotional facial expressions may have higher ecological validity than still pictures, we compared short‐latency facial mimicry responses to dynamic and static emotional expressions between adolescents with psychopathic traits and normal controls. Facial EMG responses to videos or still pictures of emotional expressions (happiness, anger, sadness, fear) were measured. Responses to 500‐ms dynamic expressions in videos, as well as the subsequent 1500‐ms phase of maximal (i.e., static) expression, were compared between male adolescents with disruptive behavior disorders and high (n = 14) or low (n = 17) callous‐unemotional (CU) traits, and normal control subjects (n = 32). Responses to still pictures were also compared between groups. EMG responses to dynamic expressions were generally significantly smaller in the high‐CU group than in the other two groups, which generally did not differ. These group differences gradually emerged during the 500‐ms stimulus presentation period but in general they were already seen a few hundred milliseconds after stimulus onset. Group differences were absent during the 1500‐ms phase of maximal expression and during exposure to still pictures. Subnormal short‐latency mimicry responses to dynamic emotional facial expressions in the high‐CU group might have negative consequences for understanding emotional facial expressions of others during daily life when human facial interactions are primarily dynamic

Quantitative modeling and analytic assessment of the transcription dynamics of the XlnR regulon in Aspergillus niger

Background: Transcription of genes coding for xylanolytic and cellulolytic enzymes in Aspergillus niger is controlled by the transactivator XlnR. In this work we analyse and model the transcription dynamics in the XlnR regulon from time-course data of the messenger RNA levels for some XlnR target genes, obtained by reverse transcription quantitative PCR (RT-qPCR). Induction of transcription was achieved using low (1 mM) and high (50 mM) concentrations of D-xylose (Xyl). We investigated the wild type strain (Wt) and a mutant strain with partial loss-of-function of the carbon catabolite repressor CreA (Mt). Results: An improved kinetic differential equation model based on two antagonistic Hill functions was proposed, and fitted to the time-course RT-qPCR data from the Wt and the Mt by numerical optimization of the parameters. We show that perturbing the XlnR regulon with Xyl in low and high concentrations results in different expression levels and transcription dynamics of the target genes. At least four distinct transcription profiles were observed, particularly for the usage of 50 mM Xyl. Higher transcript levels were observed for some genes after induction with 1 mM rather than 50 mM Xyl, especially in the Mt. Grouping the expression profiles of the investigated genes has improved our understanding of induction by Xyl and the according regulatory role of CreA. Conclusions: The model explains for the higher expression levels at 1 mM versus 50 mM in both Wt and Mt. It does not yet fully encapsulate the effect of partial loss-of-function of CreA in the Mt. The model describes the dynamics in most of the data and elucidates the time-dynamics of the two major regulatory mechanisms: i) the activation by XlnR, and ii) the carbon catabolite repression by CreA.</p

Does It Matter Whether You or Your Brain Did It? An Empirical Investigation of the Influence of the Double Subject Fallacy on Moral Responsibility Judgments

Despite progress in cognitive neuroscience, we are still far from understanding the relations between the brain and the conscious self. We previously suggested that some neuroscientific texts that attempt to clarify these relations may in fact make them more difficult to understand. Such texts—ranging from popular science to high-impact scientific publications—position the brain and the conscious self as two independent, interacting subjects, capable of possessing opposite psychological states. We termed such writing ‘Double Subject Fallacy’ (DSF). We further suggested that such DSF language, besides being conceptually confusing and reflecting dualistic intuitions, might affect people’s conceptions of moral responsibility, lessening the perception of guilt over actions. Here, we empirically investigated this proposition with a series of three experiments (pilot and two preregistered replications). Subjects were presented with moral scenarios where the defendant was either (1) clearly guilty, (2) ambiguous, or (3) clearly innocent while the accompanying neuroscientific evidence about the defendant was presented using DSF or non-DSF language. Subjects were instructed to rate the defendant’s guilt in all experiments. Subjects rated the defendant in the clearly guilty scenario as guiltier than in the two other scenarios and the defendant in the ambiguously described scenario as guiltier than in the innocent scenario, as expected. In Experiment 1 (N = 609), an effect was further found for DSF language in the expected direction: subjects rated the defendant less guilty when the neuroscientific evidence was described using DSF language, across all levels of culpability. However, this effect did not replicate in Experiment 2 (N = 1794), which focused on different moral scenario, nor in Experiment 3 (N = 1810), which was an exact replication of Experiment 1. Bayesian analyses yielded strong evidence against the existence of an effect of DSF language on the perception of guilt. Our results thus challenge the claim that DSF language affects subjects’ moral judgments. They further demonstrate the importance of good scientific practice, including preregistration and—most critically—replication, to avoid reaching erroneous conclusions based on false-positive results

A framework for selecting deep learning hyper-parameters

Recent research has found that deep learning architectures show significant improvements over traditional shallow algorithms when mining high dimensional datasets. When the choice of algorithm employed, hyper-parameter setting, number of hidden layers and nodes within a layer are combined, the identification of an optimal configuration can be a lengthy process. Our work provides a framework for building deep learning architectures via a stepwise approach, together with an evaluation methodology to quickly identify poorly performing architectural configurations. Using a dataset with high dimensionality, we illustrate how different architectures perform and how one algorithm configuration can provide input for fine-tuning more complex models

Hybrid Deep Neural Network for Brachial Plexus Nerve Segmentation in Ultrasound Images

Ultrasound-guided regional anesthesia (UGRA) can replace general anesthesia (GA), improving pain control and recovery time. This method can be applied on the brachial plexus (BP) after clavicular surgeries. However, identification of the BP from ultrasound (US) images is difficult, even for trained professionals. To address this problem, convolutional neural networks (CNNs) and more advanced deep neural networks (DNNs) can be used for identification and segmentation of the BP nerve region. In this paper, we propose a hybrid model consisting of a classification model followed by a segmentation model to segment BP nerve regions in ultrasound images. A CNN model is employed as a classifier to precisely select the images with the BP region. Then, a U-net or M-net model is used for the segmentation. Our experimental results indicate that the proposed hybrid model significantly improves the segmentation performance over a single segmentation model.Comment: The first two authors contributed equall

Quantitative perfusion MRI of tumor model in mouse

INTRODUCTION: Perfusion in the body provides valuable information about physiological status and disease progression. Measuring perfusion in tumors is considered important with the recognition of angiogenesis, the process of developing new blood vessels, as a key element in the pathophysiology of tumor growth and metastasis1. Many studies have used Gd contrast agents to evaluate tumor blood flow and vasculature but quantification has been complicated and model/agent dependent. Arterial spin labeling (ASL) is a noninvasive and quantitative technique that measures perfusion by magnetically labeling water as a freely diffusible endogenous tracer. Application of ASL to measure perfusion in tumor is a challenge due to the low perfusion values and artifacts caused by …postprintThe 19th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2011), Montréal, QC., 7-13 May 2011. In Proceedings of the International Society for Magnetic Resonance in Medicine, 2011, v. 19, p. 108

Overdracht met nieuwe combinatie van rechtsvormen

Complexe multifunctionele bedrijven kennen verschillende activiteiten, die bij elkaar een geheel vormen. Landbouw plus zorg, verwerking,verkoop, recreatie of educatie. Als de oorspronkelijke ondernemer wil overdragen, hoe pak je dat dan het beste aan

Reducing dementia risk by targeting modifiable risk factors in mid-life: study protocol for the Innovative midlife intervention for dementia deterrence (In-MINDD) randomised controlled feasibility trial

Background Dementia prevalence is increasing as populations live longer, with no cure and the costs of caring exceeding many other conditions. There is increasing evidence for modifiable risk factors which, if addressed in mid-life, can reduce the risk of developing dementia in later life. These include physical inactivity, low cognitive activity, mid-life obesity, high blood pressure, and high cholesterol. This study aims to assess the acceptability and feasibility and impact of giving those in mid-life, aged between 40 and 60 years, an individualised dementia risk modification score and profile and access to personalised on-line health information and goal setting in order to support the behaviour change required to reduce such dementia risk. A secondary aim is to understand participants’ and practitioners’ views of dementia prevention and explore the acceptability and integration of the Innovative Midlife Intervention for Dementia Deterrence (In-MINDD) intervention into daily life and routine practice. Methods/design In-MINDD is a multi-centre, primary care-based, single-blinded randomised controlled feasibility trial currently being conducted in four European countries (France, Ireland, the Netherlands and the UK). Participants are being recruited from participating general practices. Inclusion criteria will include age between 40 and 60 years; at least one modifiable risk factor for dementia risk (including diabetes, hypertension, obesity, renal dysfunction, current smoker, raised cholesterol, coronary heart disease, current or previous history of depression, self-reported sedentary lifestyle, and self-reported low cognitive activity) access to the Internet. Primary outcome measure will be a change in dementia risk modification score over the timescale of the trial (6 months). A qualitative process evaluation will interview a sample of participants and practitioners about their views on the acceptability and feasibility of the trial and the links between modifiable risk factors and dementia prevention. This work will be underpinned by Normalisation Process Theory. Discussion This study will explore the feasibility and acceptability of a risk profiler and on-line support environment to help individuals in mid-life assess their risk of developing dementia in later life and to take steps to alleviate that risk by tackling health-related behaviour change. Testing the intervention in a robust and theoretically informed manner will inform the development of a future, full-scale randomised controlled trial