Maximizing the productivity of the microalgae Scenedesmus AMDD cultivated in a continuous photobioreactor using an online flow rate control.

In this study, production of the microalga Scenedesmus AMDD in a 300 L continuous flow photobioreactor was maximized using an online flow (dilution rate) control algorithm. To enable online control, biomass concentration was estimated in real time by measuring chlorophyll-related culture fluorescence. A simple microalgae growth model was developed and used to solve the optimization problem aimed at maximizing the photobioreactor productivity. When optimally controlled, Scenedesmus AMDD culture demonstrated an average volumetric biomass productivity of 0.11 g L-1 d-1 over a 25 day cultivation period, equivalent to a 70 % performance improvement compared to the same photobioreactor operated as a turbidostat. The proposed approach for optimizing photobioreactor flow can be adapted to a broad range of microalgae cultivation systems.

Single-cell multi-omics analysis reveals cooperative transcription factors for gene regulation in oligodendrocytes.

Oligodendrocytes are the myelinating cells within the central nervous system. Many oligodendrocyte genes have been associated with brain disorders. However, how transcription factors (TFs) cooperate for gene regulation in oligodendrocytes remains largely uncharacterized. To address this, we integrated scRNA-seq and scATAC-seq data to identify the cooperative TFs that co-regulate the target gene (TG) expression in oligodendrocytes. First, we identified co- binding TF pairs whose binding sites overlapped in oligodendrocyte-specific regulatory regions. Second, we trained a deep learning model to predict the expression level of each TG using the expression levels of co-binding TFs. Third, using the trained models, we computed the TF importance and TF-TF interaction scores for predicting TG expression by the Shapley interaction scores. We found that the co-binding TF pairs involving known important TF pairs for oligodendrocyte differentiation, such as SOX10-TCF12, SOX10-MYRF, and SOX10-OLIG2, exhibited significantly higher Shapley scores than others (t-test, p-value < 1e-4). Furthermore, we identified 153 oligodendrocyte-associated eQTLs that reside in oligodendrocyte-specific enhancers or promoters where their eGenes (TGs) are regulated by cooperative TFs, suggesting potential novel regulatory roles from genetic variants. We also experimentally validated some identified TF pairs such as SOX10-OLIG2 and SOX10-NKX2.2 by co-enrichment analysis, using ChIP-seq data from rat peripheral nerve.

Assessing the Biological Mechanisms Linking Smoking Behavior and Cognitive Function: A Mediation Analysis of Untargeted Metabolomics.

(1) Smoking is the most significant preventable health hazard in the modern world. It increases the risk of vascular problems, which are also risk factors for dementia. In addition, toxins in cigarettes increase oxidative stress and inflammation, which have both been linked to the development of Alzheimer's disease and related dementias (ADRD). This study identified potential mechanisms of the smoking-cognitive function relationship using metabolomics data from the longitudinal Wisconsin Registry for Alzheimer's Prevention (WRAP). (2) 1266 WRAP participants were included to assess the association between smoking status and four cognitive composite scores. Next, untargeted metabolomic data were used to assess the relationships between smoking and metabolites. Metabolites significantly associated with smoking were then tested for association with cognitive composite scores. Total effect models and mediation models were used to explore the role of metabolites in smoking-cognitive function pathways. (3) Plasma N-acetylneuraminate was associated with smoking status Preclinical Alzheimer Cognitive Composite 3 (PACC3) and Immediate Learning (IMM). N-acetylneuraminate mediated 12% of the smoking-PACC3 relationship and 13% of the smoking-IMM relationship. (4) These findings provide links between previous studies that can enhance our understanding of potential biological pathways between smoking and cognitive function.

meQTL mapping in the GENOA study reveals genetic determinants of DNA methylation in African Americans.

Identifying genetic variants that are associated with variation in DNA methylation, an analysis commonly referred to as methylation quantitative trait locus (meQTL) mapping, is an important first step towards understanding the genetic architecture underlying epigenetic variation. Most existing meQTL mapping studies have focused on individuals of European ancestry and are underrepresented in other populations, with a particular absence of large studies in populations with African ancestry. We fill this critical knowledge gap by performing a large-scale cis-meQTL mapping study in 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We identify a total of 4,565,687 cis-acting meQTLs in 320,965 meCpGs. We find that 45% of meCpGs harbor multiple independent meQTLs, suggesting potential polygenic genetic architecture underlying methylation variation. A large percentage of the cis-meQTLs also colocalize with cis-expression QTLs (eQTLs) in the same population. Importantly, the identified cis-meQTLs explain a substantial proportion (median = 24.6%) of methylation variation. In addition, the cis-meQTL associated CpG sites mediate a substantial proportion (median = 24.9%) of SNP effects underlying gene expression. Overall, our results represent an important step toward revealing the co-regulation of methylation and gene expression, facilitating the functional interpretation of epigenetic and gene regulation underlying common diseases in African Americans.

A data value metric for quantifying information content and utility.

Data-driven innovation is propelled by recent scientific advances, rapid technological progress, substantial reductions of manufacturing costs, and significant demands for effective decision support systems. This has led to efforts to collect massive amounts of heterogeneous and multisource data, however, not all data is of equal quality or equally informative. Previous methods to capture and quantify the utility of data include value of information (VoI), quality of information (QoI), and mutual information (MI). This manuscript introduces a new measure to quantify whether larger volumes of increasingly more complex data enhance, degrade, or alter their information content and utility with respect to specific tasks. We present a new information-theoretic measure, called Data Value Metric (DVM), that quantifies the useful information content (energy) of large and heterogeneous datasets. The DVM formulation is based on a regularized model balancing data analytical value (utility) and model complexity. DVM can be used to determine if appending, expanding, or augmenting a dataset may be beneficial in specific application domains. Subject to the choices of data analytic, inferential, or forecasting techniques employed to interrogate the data, DVM quantifies the information boost, or degradation, associated with increasing the data size or expanding the richness of its features. DVM is defined as a mixture of a fidelity and a regularization terms. The fidelity captures the usefulness of the sample data specifically in the context of the inferential task. The regularization term represents the computational complexity of the corresponding inferential method. Inspired by the concept of information bottleneck in deep learning, the fidelity term depends on the performance of the corresponding supervised or unsupervised model. We tested the DVM method for several alternative supervised and unsupervised regression, classification, clustering, and dimensionality reduction tasks. Both real and simulated datasets with weak and strong signal information are used in the experimental validation. Our findings suggest that DVM captures effectively the balance between analytical-value and algorithmic-complexity. Changes in the DVM expose the tradeoffs between algorithmic complexity and data analytical value in terms of the sample-size and the feature-richness of a dataset. DVM values may be used to determine the size and characteristics of the data to optimize the relative utility of various supervised or unsupervised algorithms.

Fluorescence-based real time monitoring and diagnostics of recombinant Pichia pastoris cultivations in a bioreactor.

This study describes the application of the multivariate curve resolution (MCR) analysis technique for real-time analysis of culture fluorescence during recombinant Pichia pastoris cultivation in a bioreactor. Fluorescence spectra were acquired with an on-line dual excitation wavelength fluorometer and then used to develop a real time MCR-based bioprocess monitoring and diagnostics tool. Initial bioreactor experiments using two similar recombinant antibody secreting P. pastoris cell lines showed significant differences in protein production. To distinguish between the contributions of operating conditions and the specific cell line's genetic composition to the observed differences in protein production, the bioreactor experiments were repeated and accompanied by real time MCR analysis. The tests demonstrated high sensitivity of MCR-derived "pure concentration" profiles to growth as well as to initial conditions, thus enabling real-time cultivation process trend diagnostics and fault detection. © 2018 Her Majesty the Queen in Right of Canada © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2761, 2019.

Achieving the daily step goal of 10,000 steps: the experience of a Canadian family attached to pedometers.

BACKGROUND: Health experts recommend daily step goals of 10,000 steps for adults and 12,000 steps for youths to achieve a healthy active living. This article reports the findings of a Canadian family project to investigate whether the recommended daily step goals are achievable in a real life setting, and suggests ways to increase the daily steps to meet the goal. The family project also provides an example to encourage more Canadians to conduct family projects on healthy living. METHODS: This is a pilot feasibility study. A Canadian family was recruited for the study, with 4 volunteers (father, mother, son and daughter). Each volunteer was asked to wear a pedometer and to record daily steps for three time periods of each day during a 2-month period. Both minimal routine steps, and additional steps from special non-routine activities, were recorded at work, school and home. RESULTS: The mean number of daily steps from routine minimal daily activities for the family was 6685 steps in a day (16 hr, approx 400 steps/hr). There was thus a mean deficit of 4315 steps per day, or approximately 30,000 steps per week, from the goal (10,000 steps for adults; 12,000 steps for youths). Special activities that were found to effectively increase the steps above the routine level include: walking at brisk pace, grocery shopping, window shopping in a mall, going to an entertainment centre, and attending parties (such as to celebrate the holiday season and birthdays). DISCUSSION: To increase our daily steps to meet the daily step goal, a new culture is recommended: "get off the chair". By definition, sitting on a chair precludes the opportunity to walk. We encourage people to get off the chair, to go shopping, and to go partying, as a practical and fun way to increase the daily steps. This paper is a call for increased physical activity to meet the daily step goal.

Daily step goal of 10,000 steps: a literature review.

BACKGROUND: This review looks at ways to increase physical activity, by walking and other sports and home activities, to reach the daily 10,000 steps goal. It also looks at a number of issues associated with achieving the daily step goal, such as considerations in walking, step counting and physical activity. METHODS: The review is based on MEDLINE (1982-2006) and Google searches using keywords "pedometer", "daily step goal", "physical activity", "exercise". RESULTS: Research has suggested a daily 10,000 step goal for maintaining a desirable level of physical activity for health. However, this is not normally achievable through routine daily activities. For many, there is a daily deficit of approximately 4000 steps (most from 3000 to 6000 steps), which must be gained from other more rigorous activities. This paper provides information based on the Compendium of Physical Activities, to help people to choose their physical activities to supplement their daily steps, through both sports activities and home activities. It thus helps people to better achieve the goals of Canada's Physical Activity Guide. There are issues to consider in counting steps. A pedometer is not an exact method to measure energy expenditure. Focusing on counting steps may lead to an obsessive attitude toward exercise. Excessive walking and physical activity may lead to certain health problems. DISCUSSION: Walking is a practical and fun way to change our sedentary life style and to improve the health of the nation. When there is a deficit in daily steps, both sports and home activities can be used to supplement the daily steps to reach the daily step goal. The user-friendly table provided in this paper helps people to identify the sports and home activities, and estimate the durations needed, to meet the daily step goal.