From high- to one-dimensional dynamics of decision making: testing simplifications in attractor models.

Computational models introduce simplifications that need to be understood and validated. For attractor models of decision making, the main simplification is the high-level representation of different sub-processes of the complex decision system in one dynamic description of the overall process dynamics. This simplification implies that the overall process dynamics of the decision system are independent from specific values handled in different sub-processes. Here, we test the validity of this simplification empirically by investigating choice perseveration in a nonverbal, value-based decision task. Specifically, we tested whether choice perseveration occurred irrespectively of the attribute dimension as suggested by a simulation of the computational model. We find evidence supporting the validity of the simplification. We conclude that the simplification might capture mechanistic aspects of decision-making processes, and that the summation of the overall process dynamics of decision systems into one single variable is a valid approach in computational modeling. Supplement materials such as empirical data, analysis scripts, and the computational model are publicly available at the Open Science Framework (osf.io/7fb5q).

Early stress-induced impaired microglial pruning of excitatory synapses on immature CRH-expressing neurons provokes aberrant adult stress responses.

Several mental illnesses, characterized by aberrant stress reactivity, often arise after early-life adversity (ELA). However, it is unclear how ELA affects stress-related brain circuit maturation, provoking these enduring vulnerabilities. We find that ELA increases functional excitatory synapses onto stress-sensitive hypothalamic corticotropin-releasing hormone (CRH)-expressing neurons, resulting from disrupted developmental synapse pruning by adjacent microglia. Microglial process dynamics and synaptic element engulfment were attenuated in ELA mice, associated with deficient signaling of the microglial phagocytic receptor MerTK. Accordingly, selective chronic chemogenetic activation of ELA microglia increased microglial process dynamics and reduced excitatory synapse density to control levels. Notably, selective early-life activation of ELA microglia normalized adult acute and chronic stress responses, including stress-induced hormone secretion and behavioral threat responses, as well as chronic adrenal hypertrophy of ELA mice. Thus, microglial actions during development are powerful contributors to mechanisms by which ELA sculpts the connectivity of stress-regulating neurons, promoting vulnerability to stress and stress-related mental illnesses.

Using source data to aid and build variational state-space autoencoders with sparse target data for process monitoring.

In industrial processes, different operating conditions and ratios of ingredients are used to produce multi-grade products in the same production line. Yet, the production grade changes so quickly as the demand from customers varies from time to time. As a result, the process data collected in certain operating regions are often scarce. Process dynamics, nonlinearity, and process uncertainty increase the hardship in developing a reliable model to monitor the process status. In this paper, the source-aided variational state-space autoencoder (SA-VSSAE) is proposed. It integrates variational state-space autoencoder with the Gaussian mixture. With the additional information from the source grades, SA-VSSAE can be used for monitoring processes with sparse target data by performing information sharing to enhance the reliability of the target model. Unlike the past works which perform information sharing and modeling in a two-step procedure, the proposed model is designed for information sharing and modeling in a one-step procedure without causing information loss. In contrast to the traditional state-space model, which is linear and deterministic, the variational state-space autoencoder (VSSAE) extracts the dynamic and nonlinear features in the process variables using neural networks. Also, by taking process uncertainty into consideration, VSSAE describes the features in a probabilistic form. Probability density estimates of the residual and latent variables are given to design the monitoring indices for fault detection. A numerical example and an industrial polyvinyl chloride drying process are presented to show the advantages of the proposed method over the comparative methods.

An Experimentally Aided Operational Virtual Prototyping to Obtain the Best Spindle Speed during Face Milling of Large-Size Structures.

The paper presents an original method concerning the problem of vibration reduction in the general case while milling large-size and geometrically complex details with the use of an innovative approach to the selection of spindle speed. A computational model is obtained by applying the so-called operational approach to identify the parameters of the workpiece modal model. Thanks to the experimental modal analysis results, modal subsystem identification was performed and reliable process data for simulation studies were obtained. Next, simulations of the milling process, for successive values of the spindle speed, are repeated until the best vibration state of the workpiece is obtained. For this purpose, the root mean square values of the time plots of vibration displacements are examined. The effectiveness of the approach proposed for reducing vibrations in the process of face milling is verified on the basis of the results of appropriate experimental investigations. The economic profitability of the implementation of the operational technique in the production practice of enterprises dealing with mechanical processing is demonstrated as well.

From powder to tablets: Investigation of residence time distributions in a continuous manufacturing process train as basis for continuous process verification.

The essence of Continuous Manufacturing (CM) resides in the fact that continuous process units are directly connected to each other forming a continuous process train. The thorough understanding of material flow in this train based on suitable sensors, including on-line process analytical technologies and other sensors, is key in understanding the time-domain behavior of the system and the process. This real-time monitoring correlated with the time domain material flow behavior could be used to close control-loops. In practical terms, the implementation of such a control strategy is only feasible, if the overlying control system knows precisely what material is when and where at all times. Consequently, thorough knowledge of the residence time distribution (RTD) of the material throughout the whole manufacturing network needs to be established early on in development. Once RTD is well understood, its constant observation could also be used for continuous process verification purposes hinging on the argument that the flow pattern of the material is unchanged. As continuous processes that run over extended periods of time are susceptible to unforeseen incidents like equipment wear-out or clogging, drifts or shifts in RTD could indicate such issues early on. The presented work aims to demonstrate this proposed concept for an integrated wet-granulation CM process. To achieve this aim, three steps were completed: First, thorough RTD knowledge was generated, by inducing endogenous step-tests in active pharmaceutical ingredient (API) content in the range of ±30% at varying process conditions, and analyzing the material RTDs via NIRS analysis at four different locations in the line. Second, it was demonstrated that also low-level step tests of ±5% and even ±3% are sufficient for accurate RTD determination. This validated the possibility of continuous RTD assessment during (pre-)validation trials or even commercial manufacturing, as the drug product would comply with required quality characteristics (content uniformity, assay). In the third step, it was then demonstrated that recurring low-level step testing during routine manufacturing could be used as a way to determine the current system health, as observed changes in RTD indicated blockages and accidental material hold-up in the line. While deliberate changes in API content during commercial production might seem counter intuitive, they would actually aid in ensuring the production of quality product in a better way, than running at constant process settings over an extended period of time without the constant assessment of system health.

[Unified form of liquid chromatography plate height equation].

The effects of radial diffusion and mobile phase heating on the column efficiency during chromatographic separation were investigated. Starting from the heat transfer equation, an equation of plate height for liquid chromatography was derived using the principle of chromatographic dynamics, with consideration of the mobile phase friction and electric heat generation:H=2γDm/u+2λdpu1/3/u1/3+ω(Dm/dp)1/3+2ku/(1+k)2(1+κ0)kd+θ(κ0+κ0k+k)2dp2u/30Dmκ0(1+κ0)2(1+k)2+κi(κ0+κ0k+k)2dp5/3u2/3/3κ0ΩDm2/3(1+κ0)2(1+k)2+r02(κ0+κ0k+k)u/4Dr(1+k)exp(-Kr02α)This equation summarized the relationship between plate heights for high performance liquid chromatography (HPLC), ultra performance liquid chromatography (UPLC), capillary electrochromatography (CEC), and eliminate stagnant fluid layer chromatography (ESFLC) and various factors. The last term in the equation represented the contribution of radial diffusion and column heating to the plate height. When the linear velocity of the mobile phase was low and the column diameter was fine, the contribution of the frictional heat generation of the mobile phase to the plate height approached zero, and the plate height equation reduced to the Horvath and Lin equation. When the linear velocity of the mobile phase was too high, friction heat was generated in the column system. The temperature difference between the axis and the edge of the column increased, resulting in a decrease in the column efficiency. The temperature difference between the axis and the edge of the column was proportional to the square of the velocity of the mobile phase. The authors clearly point out that the column efficiency in liquid chromatography is closely related to the inner diameter of the column. The use of a column with a small inner diameter is conducive to high analytical speed and high efficiency, a very high mobile phase line velocity would serious degrade the column efficiency.

Choice perseveration in value-based decision making: The impact of inter-trial interval and mood.

In a series of decisions, people tend to show choice perseveration, that is, they repeat their choices. This choice perseveration is assumed to emerge due to residual activity from the previous decision. Here, we use a computational model with attractor dynamics to describe this process and to predict how choice perseveration can be modulated. We derive two qualitative predictions: Choice perseveration should decrease under longer (vs. shorter) inter-trial intervals and positive (vs. negative) mood. We test these predictions in a dynamic decision task where we modulate decisions across trials via sequentially manipulated reward options. Our findings replicate our previous study in showing choice perseveration in value-based decision making. Furthermore, choice perseveration decreased with increasing inter-trial interval as predicted by the model. However, we did not find clear evidence supporting mood effects on choice perseveration. We discuss how integrating decision process dynamics by the means of applying the neural attractor model can increase our understanding of the evolution of decision outcomes and therefore complement the psychophysical perspective on decision making.

Using State Space Grids for Modeling Temporal Team Dynamics.

We outline the potential of dynamics systems theory for researching team processes and highlight how state space grids, as a methodological application rooted in the dynamic systems perspective, can help build new knowledge about temporal team dynamics. Specifically, state space grids visualize the relationship between two categorical variables that are synchronized in time, allowing the (team) researcher to track and capture the emerging structure of social processes. In addition to being a visualization tool, state space grids offer various quantifications of the dynamic properties of the team system. These measures tap into both the content and the structure of the dynamic team system. We highlight the implications of the state space grid technique for team science and discuss research areas that could benefit most from the method. To illustrate the various opportunities of state space grids, we provide an application example based on coded team interaction data. Moreover, we provide a step-by-step tutorial for researchers interested in using the state space grid technique and provide an overview of current software options. We close with a discussion of how researchers and practitioners can use state space grids for team training and team development.

The dynamics of an anaerobic digestion of crop substrates with an unfavourable carbon to nitrogen ratio.

The purpose of this study was to investigate the effects of the characteristics of basic crop substrates, such as the carbon, nitrogen, ash and volatile fatty acids contents, on the dynamics of the anaerobic digestion process. For this purpose, the stepwise anaerobic digestion of silage from six different plant species was carried out. Scaled probability distributions (log-normal, log-logistic, logistic, Weibull and Gompertz) were used to approximate the cumulative methane production curves obtained. The results indicated that the Gompertz distribution best fit the process. The hazard function of the Gompertz distribution was used to describe the process change dynamics. Ridge regression models were made and tested to clarify the impact of the crop properties on the distribution parameters. The analysis results indicated that the initial rate of the process depended on the reactor acidity and that the nitrogen content of the substrate was a key factor that affected the process dynamics.

Measuring Process Dynamics and Nuclear Migration for Clones of Neural Progenitor Cells.

Neural stem and progenitor cells (NPCs) generate processes that extend from the cell body in a dynamic manner. The NPC nucleus migrates along these processes with patterns believed to be tightly coupled to mechanisms of cell cycle regulation and cell fate determination. Here, we describe a new segmentation and tracking approach that allows NPC processes and nuclei to be reliably tracked across multiple rounds of cell division in phase-contrast microscopy images. Results are presented for mouse adult and embryonic NPCs from hundreds of clones, or lineage trees, containing tens of thousands of cells and millions of segmentations. New visualization approaches allow the NPC nuclear and process features to be effectively visualized for an entire clone. Significant differences in process and nuclear dynamics were found among type A and type C adult NPCs, and also between embryonic NPCs cultured from the anterior and posterior cerebral cortex.