Materials designed to degrade: structure, properties, processing, and performance relationships in polyhydroxyalkanoate biopolymers.

Conventional plastics pose significant environmental and health risks across their life cycle, driving intense interest in sustainable alternatives. Among these, polyhydroxyalkanoates (PHAs) stand out for their biocompatibility, degradation characteristics, and diverse applications. Yet, challenges like production cost, scalability, and limited chemical variety hinder their widespread adoption, impacting material selection and design.

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.

Biodegradation Studies of Polyhydroxybutyrate and Polyhydroxybutyrate--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--polyhydroxyvalerate (PHBV; 8 wt.

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.

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.

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).

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.

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, , of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers.

Inhibition of DNA Methylation in 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.

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.

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.

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

A high-quality draft genome sequence of the microalgal species 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.

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.

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.

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.

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.

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.

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.

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.

Probing the kinetics of SYTOX Orange stain binding to double-stranded DNA with implications for DNA analysis.

Rapid binding kinetics of SYTOX Orange stain with double-stranded DNA (dsDNA) was revealed on the DNA fragment sizing flow cytometer. We demonstrated for the first time that the dye molecules could be adsorbed onto the capillary surface and native DNA fragments can be dynamically stained while passing through the capillary. High-quality burst size distribution histograms were obtained for DNA samples analyzed immediately after staining, dilution, or mixing.

Fingerprinting of single viral genomes.

We demonstrate the use of technology developed for optical mapping to acquire DNA fingerprints from single genomes for the purpose of discrimination and identification of bacteria and viruses. Single genome fingerprinting (SGF) provides not only the size but also the order of the restriction fragments, which adds another dimension to the information that can be used for discrimination. Analysis of single organisms may eliminate the need to culture cells and thereby significantly reduce analysis time.

Performance assessment of DNA fragment sizing by high-sensitivity flow cytometry and pulsed-field gel electrophoresis.

The sizing of restriction fragments is the chief analytical technique utilized in the production of DNA fingerprints. Few techniques have been able to compete with pulsed-field gel electrophoresis (PFGE), which is capable of discriminating among bacteria at species and strain levels by resolving restriction fragments. However, an ultrasensitive flow cytometer (FCM) developed in our lab has also demonstrated the ability to discriminate bacteria at species and strain levels.

Microsphere-based duplexed immunoassay for influenza virus typing by flow cytometry.

We have developed a rapid, duplexed microsphere-based immunoassay for the characterization of influenza virus types that has the potential to overcome many of the limitations of current detection methods. The assay uses microspheres of two sizes, each coupled to an influenza type A- or type B-specific monoclonal antibody (MAb), to capture influenza viruses in the sample. A cocktail of fluorescently labeled, influenza-specific polyclonal antibodies then binds the captured viruses.