Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data.

Freshwater cyanobacterial harmful algal blooms (cyanoHABs) are a worldwide problem resulting in substantial economic losses, due to harm to drinking water supplies, commercial fishing, wildlife, property values, recreation, and tourism. Moreover, toxins produced from some cyanoHABs threaten human and animal health. Climate warming can affect the distribution of cyanoHABs, where rising temperatures facilitate more intense blooms and a greater distribution of cyanoHABs in inland freshwater. Nutrient runoff from adjacent watersheds is also a major driver of cyanoHAB formation. While some of the physicochemical factors behind cyanoHAB dynamics are known, there are still major gaps in our understanding of the conditions that trigger and sustain cyanoHABs over time. In this perspective, we suggest that sufficient data sets, as well as machine learning (ML) and artificial intelligence (AI) tools, are available to build a comprehensive model of cyanoHAB dynamics based on integrated environmental/climate, nutrient/water chemistry, and cyanoHAB microbiome and 'omics data to identify key factors contributing to HAB formation, intensity, and toxicity. By taking a holistic approach to the analysis of all available data, including the rapidly growing number of biological data sets, we can provide the foundational knowledge needed to address the increasing threat of cyanoHABs to the security of our water resources.

Biodegradation Studies of Polyhydroxybutyrate and Polyhydroxybutyrate-co-Polyhydroxyvalerate Films in Soil.

Due to increased environmental pressures, significant research has focused on finding suitable biodegradable plastics to replace ubiquitous petrochemical-derived polymers. Polyhydroxyalkanoates (PHAs) are a class of polymers that can be synthesized by microorganisms and are biodegradable, making them suitable candidates. The present study looks at the degradation properties of two PHA polymers: polyhydroxybutyrate (PHB) and polyhydroxybutyrate-co-polyhydroxyvalerate (PHBV; 8 wt.% valerate), in two different soil conditions: soil fully saturated with water (100% relative humidity, RH) and soil with 40% RH. The degradation was evaluated by observing the changes in appearance, chemical signatures, mechanical properties, and molecular weight of samples. Both PHB and PHBV were degraded completely after two weeks in 100% RH soil conditions and showed significant reductions in mechanical properties after just three days. The samples in 40% RH soil, however, showed minimal changes in mechanical properties, melting temperatures/crystallinity, and molecular weight over six weeks. By observing the degradation behavior for different soil conditions, these results can pave the way for identifying situations where the current use of plastics can be replaced with biodegradable alternatives.

Predicting the Mechanical Response of Polyhydroxyalkanoate Biopolymers Using Molecular Dynamics Simulations.

Polyhydroxyalkanoates (PHAs) have emerged as a promising class of biosynthesizable, biocompatible, and biodegradable polymers to replace petroleum-based plastics for addressing the global plastic pollution problem. Although PHAs offer a wide range of chemical diversity, the structure-property relationships in this class of polymers remain poorly established. In particular, the available experimental data on the mechanical properties is scarce. In this contribution, we have used molecular dynamics simulations employing a recently developed forcefield to predict chemical trends in mechanical properties of PHAs. Specifically, we make predictions for Young's modulus, and yield stress for a wide range of PHAs that exhibit varying lengths of backbone and side chains as well as different side chain functional groups. Deformation simulations were performed at six different strain rates and six different temperatures to elucidate their influence on the mechanical properties. Our results indicate that Young's modulus and yield stress decrease systematically with increase in the number of carbon atoms in the side chain as well as in the polymer backbone. In addition, we find that the mechanical properties were strongly correlated with the chemical nature of the functional group. The functional groups that enhance the interchain interactions lead to an enhancement in both the Young's modulus and yield stress. Finally, we applied the developed methodology to study composition-dependence of the mechanical properties for a selected set of binary and ternary copolymers. Overall, our work not only provides insights into rational design rules for tailoring mechanical properties in PHAs, but also opens up avenues for future high throughput atomistic simulation studies geared towards identifying functional PHA polymer candidates for targeted applications.

Machine Learning for Melting Temperature Predictions and Design in Polyhydroxyalkanoate-Based Biopolymers.

Diminishing fossil fuel-based resources and ever-growing environmental concerns related to plastic pollution demand for the development of sustainable and biodegradable polymeric material alternatives. Polyhydroxyalkanoates (PHAs) represent an eco-friendly and economically viable class of polymers with a wide range of applications. However, the chemical diversity combined with tunable physical properties available within PHAs poses discovery and optimization challenges with respect to identifying optimal application-specific chemical compositions. Here we use an example of melting temperature (Tm) prediction to demonstrate the promise of machine learning (ML)-based techniques for establishing efficient structure-property mappings in PHA-based chemical space. We employ a manually curated data set of experimentally measured Tm values for a wide range of PHA homo- and copolymer chemistries along with their reported polymer molecular weights and polydispersity indices. Descriptors based on topology, shape, and charge/polarity of specific motifs forming the polymer backbone were then used to numerically represent the polymers. The ML models developed by using available data were used to rapidly predict the property of multicomponent PHA-based copolymers, while estimating uncertainties underlying the predictions. Combined with a previously developed glass transition temperature (Tg) prediction model and an evolutionary algorithm-based search strategy, the approach is demonstrated to address polymer design with multiobjective optimization challenges.

Characterization of Polyhydroxybutyrate-Based Composites Prepared by Injection Molding.

The waste generated by single-use plastics is often non-recyclable and non-biodegradable, inevitably ending up in our landfills, ecosystems, and food chain. Through the introduction of biodegradable polymers as substitutes for common plastics, we can decrease our impact on the planet. In this study, we evaluate the changes in mechanical and thermal properties of polyhydroxybutyrate-based composites with various additives: Microspheres, carbon fibers or polyethylene glycol (2000, 10,000, and 20,000 MW). The mixtures were injection molded using an in-house mold attached to a commercial extruder. The resulting samples were characterized using microscopy and a series of spectroscopic, thermal, and mechanical techniques. We have shown that the addition of carbon fibers and microspheres had minimal impact on thermal stability, whereas polyethylene glycol showed slight improvements at higher molecular weights. All of the composite samples showed a decrease in hardness and compressibility. The findings described in this study will improve our understanding of polyhydroxybutyrate-based composites prepared by injection molding, enabling advancements in integrating biodegradable plastics into everyday products.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering.

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers.

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure-property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

Inhibition of DNA Methylation in Picochlorum soloecismus Alters Algae Productivity.

Eukaryotic organisms regulate the organization, structure, and accessibility of their genomes through chromatin remodeling that can be inherited as epigenetic modifications. These DNA and histone protein modifications are ultimately responsible for an organism's molecular adaptation to the environment, resulting in distinctive phenotypes. Epigenetic manipulation of algae holds yet untapped potential for the optimization of biofuel production and bioproduct formation; however, epigenetic machinery and modes-of-action have not been well characterized in algae. We sought to determine the extent to which the biofuel platform species Picochlorum soloecismus utilizes DNA methylation to regulate its genome. We found candidate genes with domains for DNA methylation in the P. soloecismus genome. Whole-genome bisulfite sequencing revealed DNA methylation in all three cytosine contexts (CpG, CHH, and CHG). While global DNA methylation is low overall (∼1.15%), it occurs in appreciable quantities (12.1%) in CpG dinucleotides in a bimodal distribution in all genomic contexts, though terminators contain the greatest number of CpG sites per kilobase. The P. soloecismus genome becomes hypomethylated during the growth cycle in response to nitrogen starvation. Algae cultures were treated daily across the growth cycle with 20 µM 5-aza-2'-deoxycytidine (5AZA) to inhibit propagation of DNA methylation in daughter cells. 5AZA treatment significantly increased optical density and forward and side scatter of cells across the growth cycle (16 days). This increase in cell size and complexity correlated with a significant increase (∼66%) in lipid accumulation. Site specific CpG DNA methylation was significantly altered with 5AZA treatment over the time course, though nitrogen starvation itself induced significant hypomethylation in CpG contexts. Genes involved in several biological processes, including fatty acid synthesis, had altered methylation ratios in response to 5AZA; we hypothesize that these changes are potentially responsible for the phenotype of early induction of carbon storage as lipids. This is the first report to utilize epigenetic manipulation strategies to alter algal physiology and phenotype. Collectively, these data suggest these strategies can be utilized to fine-tune metabolic responses, alter growth, and enhance environmental adaption of microalgae for desired outcomes.

Molecular dynamics simulations for glass transition temperature predictions of polyhydroxyalkanoate biopolymers.

Polyhydroxyalkanoates (PHAs) represent an emerging class of biosynthetic and biodegradable polyesters that exhibit considerable potential to replace petroleum-based plastics towards a sustainable future. Despite the promise, general structure-property mappings within this class of polymers remain largely unexplored. An efficient exploration of this vast chemical space calls for the development and validation of predictive methods for accurate estimation of a diverse range of properties for PHA-based polymers. Towards this aim, here we present and validate the results of our molecular dynamics (MD) simulation based approach aimed at predicting glass transition temperatures (Tg) of PHA-based polymers. Since generally available and widely used polymer forcefields exhibit a relatively poor performance for Tg predictions, we have developed a new forcefield by modifying the polymer consistent force field (PCFF) via refining a selected set of torsion potentials of the polymer backbone using accurate density functional theory (DFT) computations. After carefully assessing the dependence of critical simulation parameters, such as, polymer chain length, number of polymer chains, supercell size, and thermal quenching rate used in the simulation, the applicability and transferability of the modified PCFF (mPCFF) is demonstrated by directly comparing the computed Tg predictions of various polymers with different chemistries, polymer side chain lengths and functional groups forming the polymer side chains against the respective experimentally measured values. Furthermore, the transport properties such as self-diffusion coefficient and viscosity are computationally determined and their well-known correlation with the target properties is demonstrated. Lastly, we have employed the developed approach to predict Tg values for a number of yet-to-be-synthesized PHA-based polymers with a diverse set of functional groups in the polymer side chains. The results are further rationalized by correlating the predicted Tg values with the inter-chain H-bond formation tendencies of the different side chain functional groups. This work represents an important first step towards computationally guided design of PHA-based functional polymers and opens up new directions for a systematic investigation of composition- and configuration-dependent structure-property relationships in more complex binary and ternary copolymer systems.

Machine-Learning-Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homopolymers and Copolymers.

Polyhydroxyalkanoate-based polymers-being ecofriendly, biosynthesizable, and economically viable and possessing a broad range of tunable properties-are currently being actively pursued as promising alternatives for petroleum-based plastics. The vast chemical complexity accessible within this class of polymers gives rise to challenges in the rational discovery of novel polymer chemistries for specific applications. The burgeoning field of polymer informatics addresses this challenge via providing tools and strategies for accelerated property prediction and materials design via surrogate machine-learning models built on reliable past data. In this contribution, we use glass transition temperature Tg as an example target property to demonstrate promise of the data-enabled route to accelerated learning of accurate structure-property mappings in PHA-based polymers. Our analysis uses a data set of experimentally measured Tg values, polymer molecular weights, and a polydispersity index for PHA-based homo- and copolymers that was carefully assembled from the literature. A fingerprinting scheme that captures key properties based on topology, shape, and charge/polarity of specific chemical units or motifs forming the polymer backbone was devised to numerically represent the polymers. A validated statistical learning model is then developed to allow for a mapping of the polymer fingerprints onto the property space in a physically meaningful and reliable manner. Once developed, the model can not only rapidly predict the property of new PHA polymers but also provide uncertainties underlying the predictions. The model is further combined with an evolutionary-algorithm-based search strategy to efficiently identify multicomponent polymer compositions with a prespecified Tg. While the present contribution is focused specifically on Tg, the surrogate model development approach put forward here is general and can, in principle, be extended to a range of other properties.

High-Quality Draft Genome Sequence of the Green Alga Tetraselmis striata (Chlorophyta) Generated from PacBio Sequencing.

A high-quality draft genome sequence of the microalgal species Tetraselmis striata was generated using PacBio sequencing. The assembled genome is 228 Mb, derived from 3,613 polished contigs at 84× coverage depth. This genome contains an average GC content of 57.9% and 48,906 predicted genes.

Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation.

Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the method's accuracy to predict lipid content in algal cells (Picochlorum soloecismus) during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.

Separation of Escherichia coli bacteria from peripheral blood mononuclear cells using standing surface acoustic waves.

A microfluidic device was developed to separate heterogeneous particle or cell mixtures in a continuous flow using acoustophoresis. In this device, two identical surface acoustic waves (SAWs) generated by interdigital transducers (IDTs) propagated toward a microchannel, which accordingly built up a standing surface acoustic wave (SSAW) field across the channel. A numerical model, coupling a piezoelectric effect in the solid substrate and acoustic pressure in the fluid, was developed to provide a better understanding of SSAW-based particle manipulation. It was found that the pressure nodes across the channel were individual planes perpendicular to the solid substrate. In the separation experiments, two side sheath flows hydrodynamically focused the injected particle or cell mixtures into a very narrow stream along the centerline. Particles flowing through the SSAW field experienced an acoustic radiation force that highly depends on the particle properties. As a result, dissimilar particles or cells were laterally attracted toward the pressure nodes at different magnitudes, and were eventually switched to different outlets. Two types of fluorescent microspheres with different sizes were successfully separated using the developed device. In addition, Escherichia coli bacteria premixed in peripheral blood mononuclear cells (PBMCs) were also efficiently isolated using the SSAW-base separation technique. Flow cytometric analysis on the collected samples found that the purity of separated E. coli bacteria was 95.65%.

Analysis of beryllium to biomolecule binding using a metal specific fluorescent probe and competitive assay.

Studying metal-biomolecule interactions is critical to the elucidation of the molecular basis of the biological functions and toxicity of metals. In the present study, a competitive fluorimetric approach has been developed to measure the apparent affinity of biomolecules for Be(2+) by using a Be(2+)-specific fluorigenic probe (10-hydroxybenzo[h]quinoline-7-sulfonate, HBQS). Under physiological conditions, HBQS coordinates with Be(2+) in a molar ratio of 1:1 and results in a fluorescence shift from 580 nm for HBQS to 480 nm for the Be-HBQS complex associated with significant fluorescence enhancement. When a beryllium ligand is present in the mixture of Be(2+) and HBQS, the competition of ligand against HBQS for beryllium ion binding results in dissociation and thus a fluorescence decrease of the Be-HBQS complex. By titrating ligand and monitoring the dose-dependent decrease of Be-HBQS complex fluorescence at 480 nm, the apparent affinity between ligand and Be(2+) can be derived. Applying this simple approach, the apparent affinities of various nucleotides and the iron-storage protein ferritin for beryllium ion have been determined. In particular, the apparent dissociation constant of Be(2+) and adenosine 5'-triphosphate (ATP) was also validated by an electrospray ionization mass spectrometric (ESI-MS) method. The general applicability of the proposed competition assay was further demonstrated using FluoZin-1, a zinc fluorescent indicator, in a binding study for Zn(2+) and bovine serum albumin. This newly developed competitive fluorimetric assay provides a sensitive, simple, and generic approach for affinity estimation of metal and biomolecule binding.

Immunophenotyping of chicken peripheral blood lymphocyte subpopulations: individual variability and repeatability.

T-cell lymphocyte populations can be delineated into subsets based on expression of cell surface proteins that can be measured in peripheral blood by monoclonal antibodies and flow cytometry percentages of the lymphocyte subpopulations. In order to accurately assess immunocompetence in birds, natural variability in both avian immune function and the methodology must be understood. Our objectives were to (1) further develop flow cytometry for estimating subpopulations of lymphocytes in peripheral blood from poultry, (2) estimate repeatability and variability in the methodology with respect to poultry in a free-range and environmentally diverse situation, and (3) estimate the best antibody and cell marker combination for estimating lymphocyte subpopulations. This work demonstrated the repeatability of using flow cytometry for measurements of peripheral blood in chickens using anti-chicken antibodies for lymphocyte subpopulations. Immunofluorescence staining of cells isolated from peripheral blood revealed that the CD3(+) antibodies reacted with an average of approximately 12-24% of the lymphoid cells in the blood, depending on the fluorescence type. The CD4(+) and CD8(+) molecules were expressed in a range of 4-31% and 1-10% of the lymphoid cells in the blood, respectively. Both fluorescence label and antibody company contribute to the variability of results and should be considered in future flow cytometry studies in poultry.

Petrobactin is produced by both pathogenic and non-pathogenic isolates of the Bacillus cereus group of bacteria.

Petrobactin is the primary siderophore synthesized by Bacillus anthracis str Sterne and is required for virulence of this organism in a mouse model. The siderophore's biosynthetic machinery was recently defined and gene homologues of this operon exist in several other Bacillus strains known to be mammalian pathogens, but are absent in several known to be harmless such as B. subtilis and B. lichenformis. Thus, a common hypothesis regarding siderophore production in Bacillus species is that petrobactin production is exclusive to pathogenic isolates. In order to test this hypothesis, siderophores produced by 106 strains of an in-house library of the Bacillus cereus sensu lato group were isolated and identified using a MALDI-TOF-MS assay. Strains were selected from a previously defined phylogenetic tree of this group in order to include both known pathogens and innocuous strains. Petrobactin is produced by pathogenic strains and innocuous isolates alike, and thus is not itself indicative of virulence.

Differentiation of Bacillus anthracis, B. cereus, and B. thuringiensis by using pulsed-field gel electrophoresis.

A pulsed-field gel electrophoresis (PFGE) method was developed for discriminating Bacillus anthracis from B. cereus and B. thuringiensis. A worldwide collection of 25 B. anthracis isolates showed high-profile homology, and these isolates were unambiguously distinguished from B. cereus and B. thuringiensis isolates by cluster analysis of the whole-genome macrorestriction enzyme digestion patterns generated by NotI.

Proteomic analysis of beryllium-induced genotoxicity in an Escherichia coli mutant model system.

Beryllium is the second lightest metal, has a high melting point and high strength-to-weight ratio, and is chemically stable. These unique chemical characteristics make beryllium metal an ideal choice as a component material for a wide variety of applications in aerospace, defense, nuclear weapons, and industry. However, inhalation of beryllium dust or fumes induces significant health effects, including chronic beryllium disease and lung cancer. In this study, the mutagenicity of beryllium sulfate (BeSO(4)) and the comutagenicity of beryllium with a known mutagen 1-methyl-3-nitro-1-nitrosoguanidine (MNNG) were evaluated using a forward mutant detection system developed in Escherichia coli. In this system, BeSO(4) was shown to be weakly mutagenic alone and significantly enhanced the mutagenicity of MNNG up to 3.5-fold over MNNG alone. Based on these results a proteomic study was conducted to identify the proteins regulated by BeSO(4). Using the techniques of 2-DE and oMALDI-TOF MS, we successfully identified 32 proteins being differentially regulated by beryllium and/or MNNG in the E. coli test system. This is the first study to describe the proteins regulated by beryllium in vitro, and the results suggest several potential pathways for the focus of further research into the mechanisms underlying beryllium-induced genotoxicity.