Metabolomic analysis for scale-down model improvement

The use of scale-down models is essential for the development, characterization and continuous improvement of commercial cell culture processes. A model demonstrated to be predictive of large-scale performance is advantageous as small-scale data can be used to support process investigations and changes, reduce costs and consequently accelerate new therapies to market. Aligning geometric aspect ratios and dimensionless engineering parameters improved the overall scale-down model representation for key process outputs such as pCO2 accumulation and cell viability. Despite these changes, the revised scale-down model did not fully match the large-scale process lactate accumulation. Interestingly, it was demonstrated that pCO2 accumulation at small-scale reduced net lactate consumption, however pCO2 accumulation alone was not sufficient to fully match the absence of metabolic shift observed at large-scale. Small-scale pH ranging studies demonstrated that at settings above the large-scale process set point, the scale-down reactors had lactate profiles more closely matching the commercial process. Conversely, a lower pH set point in the commercial-scale reactor combined with an improved sparging strategy to remove CO2 had a transient increase in net lactate consumption, falling short of a metabolic shift. Combined, these data suggest that physicochemical differences between small and large scale bioreactors are contributing to the differences in metabolic behavior seen in this process. A metabolomic approach was used to elucidate the root cause of metabolic difference between scales. A range of pH settings from 6.8-7.3 were evaluated in the 5L scale down model of the commercial process. Samples from the commercial process operated at different pH set points were also obtained and included in the study. Results of metabolic profiling and hypothesis explaining the metabolic differences observed between scales will be presented

The Role of the Direct and Indirect Basal Ganglia Pathways in the Learning, Performance, and Goal-Directed Control of Action Sequences

Animals engage in intricately woven action sequences that are constructed from trial-and-error learning, but the mechanisms by which the brain links together individual actions which are later recalled as fluid chains of behavior are not fully understood. The aim of this dissertation is to investigate the learning and goal-directed control of action sequences in rats. Experiment 1 addresses a question that comes out of a reinforcement learning model of action sequencing: how does the extent of training change how the performance of an action sequence is impacted by reward devaluation. The data show that action sequences remain goal-directed overall regardless of the extent of training, but the locus of goal-directed control shifts over training. The subsequent experiments address how the direct and indirect basal ganglia pathways contribute to the learning and goal-directed control of action sequences. Experiments 2 through 5 make use of the same action sequence task used in Experiment 1 while also introducing chemogenetic manipulations during and/or after training. Manipulations are targeted to either D1 receptor-expressing neurons in the dorsomedial or dorsolateral striatum (Experiments 2 and 3, respectively) or D2 neurons in the dorsomedial or dorsolateral striatum (Experiments 4 and 5, respectively). While chemogenetic-mediated inhibition spared goal-directed control at the level of sequence rates across all experiments, the completion and initiation of sequences were compromised by D1 and D2 neuronal inhibition in the dorsomedial striatum, respectively. In addition, inhibiting D2 neurons in the dorsolateral striatum compromised action sequence learning and performance during training

Metabolomics process modeling: A systems biology approach to understand variability in commercial biologics cell culture processes

The biopharmaceutical industry strives to develop and operate efficient, robust, reproducible commercial biologics processes. A major challenge of industrial biologics processes is optimization of cell culture conditions to increase productivity while maintaining consistent product quality. The cell culture operations, which involve the use of live cell hosts, have historically introduced significant variability to the overall process. Technological improvements which include the implementation of advanced cell line engineering, chemically defined media, quality by design (QbD) development approaches, and in-line and at-line monitoring, have significantly reduced process variability. Nonetheless, performance variability remains a challenge for many commercial programs. This variability in turn can impact both product yields and product quality. Even small performance differences can become significant in low-yield processes with large campaign sizes, or processes manufactured at multiple sites. The ability to understand and eliminate sources of variability is greatly enhanced by augmenting the quality and quantity of data available from commercial campaigns. Metabolomics Process Monitoring (MPM) is a data-driven approach to understand sources of manufacturing variability on a cellular level. Here we present a case study of MPM implementation in a legacy commercial biologics program. First, we describe how the MPM workflow was successfully integrated into a commercial manufacturing process. Second, we discuss novel data normalization techniques developed to enable long term trending. Third, we describe the selection of an orthogonal projections to latent structures (OPLS) model to link systems biology and process data. Finally, we share key mechanistic insights obtained from the case study, and provide a vision for how MPM can enhance commercial biologics capabilities going forward

A prospective cohort study of the impact of outpatient Intensive Cardiac Rehabilitation on depression and cardiac self-efficacy

STUDY OBJECTIVE: To evaluate whether an Intensive Cardiac Rehabilitation (ICR) program improves depression and cardiac self-efficacy among patients with a qualifying cardiac diagnosis. DESIGN: Prospective, longitudinal cohort design. SETTING: Single-center, tertiary referral, outpatient cardiac rehabilitation center. PARTICIPANTS: Patients with a qualifying diagnosis for ICR. INTERVENTIONS: Outpatient ICR. MAIN OUTCOME MEASURES: Mental health, as assessed using the Patient Health Questionnaire-9 (PHQ-9) and cardiac self-efficacy using the Cardiac Self-Efficacy (CSE) scale. RESULTS: Of the 268 patients included (median age 69 y, 73% men), 70% had no depressive symptoms at baseline (PHQ-9 score \u3c5). PHQ-9 scores improved in the overall sample (p \u3c 0.0001), with greater improvements among patients with mild depressive symptoms at baseline (-4 points, p \u3c 0.001) and those with moderate to severe depressive symptoms at baseline (-5.5 points, p \u3c 0.001). Cardiac self-efficacy improved overall, and the two subsections of the cardiac self-efficacy questionnaire titled, maintain function and control symptoms improved (all p \u3c 0.001). CONCLUSIONS: Participation in an outpatient ICR program is associated with fewer depressive symptoms and greater cardiac self-efficacy among patients with CVD who qualify for ICR. The improvement in depression was greatest for those with moderate to severe depressive symptoms

Contributions of the basal ganglia to action sequence learning and performance

Animals engage in intricately woven and choreographed action sequences that are constructed from trial-and-error learning. The mechanisms by which the brain links together individual actions which are later recalled as fluid chains of behavior are not fully understood, but there is broad consensus that the basal ganglia play a crucial role in this process. This paper presents a comprehensive review of the role of the basal ganglia in action sequencing, with a focus on whether the computational framework of reinforcement learning can capture key behavioral features of sequencing and the neural mechanisms that underlie them. While a simple neurocomputational model of reinforcement learning can capture key features of action sequence learning, this model is not sufficient to capture goal-directed control of sequences or their hierarchical representation. The hierarchical structure of action sequences, in particular, poses a challenge for building better models of action sequencing, and it is in this regard that further investigations into basal ganglia information processing may be informative

Goal-directed control on interval schedules does not depend on the action-outcome correlation

When an organism’s action is based on an anticipation of its consequences, that action is said to be goal-directed. It has long been thought that goal-directed control is made possible by experiencing a strong correlation between response rates and reward rates (Dickinson, 1985). To test this idea, we designed a set of experiments to determine whether the response rate-reward rate correlation is a reliable predictor of goal-directed control on interval schedules. In Experiment 1, rats were trained on random interval (RI) schedules in which the response rate-reward rate correlation was manipulated across groups. In tests of reward devaluation, rats behaved in a goal-directed manner regardless of the experienced correlation. In Experiment 2, rats once again experienced either a strong or weak correlation, but on RI schedules with lower overall reward densities. This time, behavior appeared habitual regardless of the experienced correlation. Experiment 3 confirmed that the density of the RI schedule influences goal-directed control, and also revealed that extensive training on these schedules resulted in goal-directed action. Finally, in Experiment 4 goal-directed responding was greater and emerged sooner on fixed than random interval schedules, but, again, was manifest after extensive training on the RI schedule. Taken together, our data suggest that goal-directed and habitual control are not determined by the correlation between response rates and reward rates. We discuss the importance of temporal uncertainty, action-outcome contiguity, and reinforcement probability in goal-directed control

Maintained goal-directed control with overtraining on ratio schedules

It is thought that goal-directed control of actions weakens or becomes masked by habits over time. We tested the opposing hypothesis that goal-directed control becomes stronger over time, and that this growth is modulated by the overall action-outcome contiguity. Despite group differences in action-outcome contiguity early in training, rats trained under random and fixed ratio schedules showed equivalent goal-directed control of lever pressing that appeared to grow over time. We confirmed that goal-directed control was maintained after extended training under another type of ratio schedule—continuous reinforcement—using specific satiety and taste aversion devaluation methods. These results add to the growing literature showing that extensive training does not reliably weaken goal-directed control and that it may strengthen it, or at least maintain it

Mesostriatal dopamine is sensitive to changes in specific cue-reward contingencies.

Learning causal relationships relies on understanding how often one event precedes another. To investigate how dopamine neuron activity and neurotransmitter release change when a retrospective relationship is degraded for a specific pair of events, we used outcome-selective Pavlovian contingency degradation in rats. Conditioned responding was attenuated for the cue-reward contingency that was degraded, as was dopamine neuron activity in the midbrain and dopamine release in the ventral striatum in response to the cue and subsequent reward. Contingency degradation also abolished the trial-by-trial history dependence of the dopamine responses at the time of trial outcome. This profile of changes in cue- and reward-evoked responding is not easily explained by a standard reinforcement learning model. An alternative model based on learning causal relationships was better able to capture dopamine responses during contingency degradation, as well as conditioned behavior following optogenetic manipulations of dopamine during noncontingent rewards. Our results suggest that mesostriatal dopamine encodes the contingencies between meaningful events during learning

The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data.

The Center for Hydrometeorology and Remote Sensing (CHRS) has created the CHRS Data Portal to facilitate easy access to the three open data licensed satellite-based precipitation datasets generated by our Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system: PERSIANN, PERSIANN-Cloud Classification System (CCS), and PERSIANN-Climate Data Record (CDR). These datasets have the potential for widespread use by various researchers, professionals including engineers, city planners, and so forth, as well as the community at large. Researchers at CHRS created the CHRS Data Portal with an emphasis on simplicity and the intention of fostering synergistic relationships with scientists and experts from around the world. The following paper presents an outline of the hosted datasets and features available on the CHRS Data Portal, an examination of the necessity of easily accessible public data, a comprehensive overview of the PERSIANN algorithms and datasets, and a walk-through of the procedure to access and obtain the data