Insulin signaling and pharmacology in humans and in corals.

Once thought to be a unique capability of the Langerhans islets in the pancreas of mammals, insulin (INS) signaling is now recognized as an evolutionarily ancient function going back to prokaryotes. INS is ubiquitously present not only in humans but also in unicellular eukaryotes, fungi, worms, and Drosophila. Remote homologue identification also supports the presence of INS and INS receptor in corals where the availability of glucose is largely dependent on the photosynthetic activity of the symbiotic algae. The cnidarian animal host of corals operates together with a 20,000-sized microbiome, in direct analogy to the human gut microbiome. In humans, aberrant INS signaling is the hallmark of metabolic disease, and is thought to play a major role in aging, and age-related diseases, such as Alzheimer's disease. We here would like to argue that a broader view of INS beyond its human homeostasis function may help us understand other organisms, and in turn, studying those non-model organisms may enable a novel view of the human INS signaling system. To this end, we here review INS signaling from a new angle, by drawing analogies between humans and corals at the molecular level.

High-frequency imagery to capture coral tissue (Montipora capricornis) response to environmental stress, a pilot study.

Environment stress is a major threat to the existence of coral reefs and has generated a lot of interest in the coral research community. Under the environmental stress, corals can experience tissue loss and/or the breakdown of symbiosis between the cnidarian host and its symbiotic algae causing the coral tissue to appear white as the skeleton can be seen by transparency. Image analysis is a common method used to assess tissue response under the environmental stress. However, the traditional approach is limited by the dynamic nature of the coral-algae symbiosis. Here, we observed coral tissue response in the scleractinian coral, Montipora capricornis, using high frequency image analysis throughout the experiment, as opposed to the typical start/end point assessment method. Color analysis reveals that the process can be divided into five stages with two critical stages according to coral tissue morphology and color ratio. We further explore changes to the morphology of individual polyps by means of the Pearson correlation coefficient and recurrence plots, where the quasi-periodic and nonstationary dynamics can be identified. The recurrence quantification analysis also allows the comparison between the different polyps. Our research provides a detailed visual and mathematical analysis of coral tissue response to environmental stress, which potentially shows universal applicability. Moreover, our approach provides a robust quantitative advancement for improving our insight into a suite of biotic responses in the perspective of coral health evaluation and fate prediction.

Transfer of knowledge from model organisms to evolutionarily distant non-model organisms: The coral Pocillopora damicornis membrane signaling receptome.

With the ease of gene sequencing and the technology available to study and manipulate non-model organisms, the extension of the methodological toolbox required to translate our understanding of model organisms to non-model organisms has become an urgent problem. For example, mining of large coral and their symbiont sequence data is a challenge, but also provides an opportunity for understanding functionality and evolution of these and other non-model organisms. Much more information than for any other eukaryotic species is available for humans, especially related to signal transduction and diseases. However, the coral cnidarian host and human have diverged over 700 million years ago and homologies between proteins in the two species are therefore often in the gray zone, or at least often undetectable with traditional BLAST searches. We introduce a two-stage approach to identifying putative coral homologues of human proteins. First, through remote homology detection using Hidden Markov Models, we identify candidate human homologues in the cnidarian genome. However, for many proteins, the human genome alone contains multiple family members with similar or even more divergence in sequence. In the second stage, therefore, we filter the remote homology results based on the functional and structural plausibility of each coral candidate, shortlisting the coral proteins likely to have conserved some of the functions of the human proteins. We demonstrate our approach with a pipeline for mapping membrane receptors in humans to membrane receptors in corals, with specific focus on the stony coral, P. damicornis. More than 1000 human membrane receptors mapped to 335 coral receptors, including 151 G protein coupled receptors (GPCRs). To validate specific sub-families, we chose opsin proteins, representative GPCRs that confer light sensitivity, and Toll-like receptors, representative non-GPCRs, which function in the immune response, and their ability to communicate with microorganisms. Through detailed structure-function analysis of their ligand-binding pockets and downstream signaling cascades, we selected those candidate remote homologues likely to carry out related functions in the corals. This pipeline may prove generally useful for other non-model organisms, such as to support the growing field of synthetic biology.

Evaluation of fluorescence-based viability stains in cells dissociated from scleractinian coral Pocillopora damicornis.

The application of established cell viability assays such as the commonly used trypan blue staining method to coral cells is not straightforward due to different culture parameters and different cellular features specific to mammalian cells compared to marine invertebrates. Using Pocillopora damicornis as a model, we characterized the autofluorescence and tested different fluorescent dye pair combinations to identify alternative viability indicators. The cytotoxicity of different representative molecules, namely small organic molecules, proteins and nanoparticles (NP), was measured after 24 h of exposure using the fluorescent dye pair Hoechst 33342 and SYTOX orange. Our results show that this dye pair can be distinctly measured in the presence of fluorescent proteins plus chlorophyll. P. damicornis cells exposed for 24 h to Triton-X100, insulin or titanium dioxide (TiO2) NPs, respectively, at concentrations ranging from 0.5 to 100 µg/mL, revealed a LC50 of 0.46 µg/mL for Triton-X100, 6.21 µg/mL for TiO2 NPs and 33.9 µg/mL for insulin. This work presents the approach used to customize dye pairs for membrane integrity-based cell viability assays considering the species- and genotype-specific autofluorescence of scleractinian corals, namely: endogenous fluorescence characterization followed by the selection of dyes that do not overlap with endogenous signals.

Real-time monitoring of cellular oxidative stress during aerosol sampling: a proof of concept study.

The Portable In Vitro Exposure Cassette (PIVEC) was developed for on-site air quality testing using lung cells. Here, we describe the incorporation of a sensor within the PIVEC for real time monitoring of cellular oxidative stress during exposure to contaminated air. An electrochemical, enzymatic biosensor based on cytochrome c (cyt c) was selected to measure reactive oxygen species (ROS), including hydrogen peroxide and super oxides, due to the stability of signal over time. Human A549 lung cells were grown at the air-liquid interface and exposed within the PIVEC to dry 40 nm copper nanoparticle aerosols for 10 minutes. The generation of ROS compounds was measured during exposure and post-exposure for one hour using the biosensor and compared to intracellular ROS determined using the 2',7'-dichlorodihydrofluoroscein diacetate (DCFH-DA) assay. A similar increase in oxidative stress upon aerosol exposure was measured using both the cyt c biosensor and DCFH-DA assay. The incorporation of a biosensor within the PIVEC is a unique, first-of-its-kind system designed to monitor the real-time effect of aerosols.

Extracting experimental parameter entities from scientific articles.

Systematic reviews are labor-intensive processes to combine all knowledge about a given topic into a coherent summary. Despite the high labor investment, they are necessary to create an exhaustive overview of current evidence relevant to a research question. In this work, we evaluate three state-of-the-art supervised multi-label sequence classification systems to automatically identify 24 different experimental design factors for the categories of Animal, Dose, Exposure, and Endpoint from journal articles describing the experiments related to toxicity and health effects of environmental agents. We then present an in depth analysis of the results evaluating the lexical diversity of the design parameters with respect to model performance, evaluating the impact of tokenization and non-contiguous mentions, and finally evaluating the dependencies between entities within the category entities. We demonstrate that in general, algorithms that use embedded representations of the sequences out-perform statistical algorithms, but that even these algorithms struggle with lexically diverse entities.

Machine Assisted Experimentation of Extrusion-Based Bioprinting Systems.

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time- and resource-intensive and not easily translatable to other laboratories. This study approaches EBB parameter optimization through machine learning (ML) models trained using data collected from the published literature. We investigated regression-based and classification-based ML models and their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite bioinks. In addition, we interrogated if regression-based models can predict suitable extrusion pressure given the desired cell viability when keeping other experimental parameters constant. We also compared models trained across data from general literature to models trained across data from one literature source that utilized alginate and gelatin bioinks. The results indicate that models trained on large amounts of data can impart physical trends on cell viability, filament diameter, and extrusion pressure seen in past literature. Regression models trained on the larger dataset also predict cell viability closer to experimental values for material concentration combinations not seen in training data of the single-paper-based regression models. While the best performing classification models for cell viability can achieve an average prediction accuracy of 70%, the cell viability predictions remained constant despite altering input parameter combinations. Our trained models on bioprinting literature data show the potential usage of applying ML models to bioprinting experimental design.

Identifying Chemical Reactions and Their Associated Attributes in Patents.

Chemical patents are an essential source of information about novel chemicals and chemical reactions. However, with the increasing volume of such patents, mining information about these chemicals and chemical reactions has become a time-intensive and laborious endeavor. In this study, we present a system to extract chemical reaction events from patents automatically. Our approach consists of two steps: 1) named entity recognition (NER)-the automatic identification of chemical reaction parameters from the corresponding text, and 2) event extraction (EE)-the automatic classifying and linking of entities based on their relationships to each other. For our NER system, we evaluate bidirectional long short-term memory (BiLSTM)-based and bidirectional encoder representations from transformer (BERT)-based methods. For our EE system, we evaluate BERT-based, convolutional neural network (CNN)-based, and rule-based methods. We evaluate our NER and EE components independently and as an end-to-end system, reporting the precision, recall, and F 1 score. Our results show that the BiLSTM-based method performed best at identifying the entities, and the CNN-based method performed best at extracting events.

Applying model approaches in non-model systems: A review and case study on coral cell culture.

Model systems approaches search for commonality in patterns underlying biological diversity and complexity led by common evolutionary paths. The success of the approach does not rest on the species chosen but on the scalability of the model and methods used to develop the model and engage research. Fine-tuning approaches to improve coral cell cultures will provide a robust platform for studying symbiosis breakdown, the calcification mechanism and its disruption, protein interactions, micronutrient transport/exchange, and the toxicity of nanoparticles, among other key biological aspects, with the added advantage of minimizing the ethical conundrum of repeated testing on ecologically threatened organisms. The work presented here aimed to lay the foundation towards development of effective methods to sort and culture reef-building coral cells with the ultimate goal of obtaining immortal cell lines for the study of bleaching, disease and toxicity at the cellular and polyp levels. To achieve this objective, the team conducted a thorough review and tested the available methods (i.e. cell dissociation, isolation, sorting, attachment and proliferation). The most effective and reproducible techniques were combined to consolidate culture methods and generate uncontaminated coral cell cultures for ~7 days (10 days maximum). The tests were conducted on scleractinian corals Pocillopora acuta of the same genotype to harmonize results and reduce variation linked to genetic diversity. The development of cell separation and identification methods in conjunction with further investigations into coral cell-type specific metabolic requirements will allow us to tailor growth media for optimized monocultures as a tool for studying essential reef-building coral traits such as symbiosis, wound healing and calcification at multiple scales.

Digital image processing to detect subtle motion in stony coral.

Coral reef ecosystems support significant biological activities and harbor huge diversity, but they are facing a severe crisis driven by anthropogenic activities and climate change. An important behavioral trait of the coral holobiont is coral motion, which may play an essential role in feeding, competition, reproduction, and thus survival and fitness. Therefore, characterizing coral behavior through motion analysis will aid our understanding of basic biological and physical coral functions. However, tissue motion in the stony scleractinian corals that contribute most to coral reef construction are subtle and may be imperceptible to both the human eye and commonly used imaging techniques. Here we propose and apply a systematic approach to quantify and visualize subtle coral motion across a series of light and dark cycles in the scleractinian coral Montipora capricornis. We use digital image correlation and optical flow techniques to quantify and characterize minute coral motions under different light conditions. In addition, as a visualization tool, motion magnification algorithm magnifies coral motions in different frequencies, which explicitly displays the distinctive dynamic modes of coral movement. Specifically, our assessment of displacement, strain, optical flow, and mode shape quantify coral motion under different light conditions, and they all show that M. capricornis exhibits more active motions at night compared to day. Our approach provides an unprecedented insight into micro-scale coral movement and behavior through macro-scale digital imaging, thus offering a useful empirical toolset for the coral research community.

3D Printing of Antibacterial Polymer Devices Based on Nitric Oxide Release from Embedded S-Nitrosothiol Crystals.

Controlled release of drugs from medical implants is an effective approach to reducing foreign body reactions and infections. We report here on a one-step 3D printing strategy to create drug-eluting polymer devices with a drug-loaded bulk and a drug-free coating. The spontaneously formed drug-free coating dramatically reduces the surface roughness of the implantable devices and serves as a protective layer to suppress the burst release of drugs. A high viscosity liquid silicone that can be extruded based on its shear-thinning property and quickly vulcanize upon exposure to ambient moisture is used as the ink for 3D printing. S-Nitrosothiol type nitric oxide (NO) donors in their crystalline forms are selected as model drugs because of the potent antimicrobial, antithrombotic, and anti-inflammatory properties of NO. Direct ink writing of the homogenized polymer-drug mixtures generates rough and ill-defined device surfaces because of the exposed S-nitrosothiol microparticles. When a low-viscosity silicone (polydimethylsiloxane) is added into the ink, this silicone diffuses outward upon deposition to form a drug-free outermost layer without compromising the integrity of the printed structures. S-Nitrosoglutathione (GSNO) or S-nitroso-N-acetylpenicillamine (SNAP) embedded in the printed silicone matrix releases NO under physiological conditions from days to about one month. The microsized drug crystals are well-preserved in the ink preparation and printing processes, which is one reason for the sustained NO release. Biofilm and cytotoxicity experiments confirmed the antibacterial property and safety of the printed NO-releasing devices. This additive manufacturing platform does not require dissolution of drugs and involves no thermal or UV processes and, therefore, offers unique opportunities to produce drug-eluting silicone devices in a customized manner.

Improving Quality in Nanoparticle-Induced Cytotoxicity Testing by a Tiered Inter-Laboratory Comparison Study.

The quality and relevance of nanosafety studies constitute major challenges to ensure their key role as a supporting tool in sustainable innovation, and subsequent competitive economic advantage. However, the number of apparently contradictory and inconclusive research results has increased in the past few years, indicating the need to introduce harmonized protocols and good practices in the nanosafety research community. Therefore, we aimed to evaluate if best-practice training and inter-laboratory comparison (ILC) of performance of the 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay for the cytotoxicity assessment of nanomaterials among 15 European laboratories can improve quality in nanosafety testing. We used two well-described model nanoparticles, 40-nm carboxylated polystyrene (PS-COOH) and 50-nm amino-modified polystyrene (PS-NH2). We followed a tiered approach using well-developed standard operating procedures (SOPs) and sharing the same cells, serum and nanoparticles. We started with determination of the cell growth rate (tier 1), followed by a method transfer phase, in which all laboratories performed the first ILC on the MTS assay (tier 2). Based on the outcome of tier 2 and a survey of laboratory practices, specific training was organized, and the MTS assay SOP was refined. This led to largely improved intra- and inter-laboratory reproducibility in tier 3. In addition, we confirmed that PS-COOH and PS-NH2 are suitable negative and positive control nanoparticles, respectively, to evaluate impact of nanomaterials on cell viability using the MTS assay. Overall, we have demonstrated that the tiered process followed here, with the use of SOPs and representative control nanomaterials, is necessary and makes it possible to achieve good inter-laboratory reproducibility, and therefore high-quality nanotoxicological data.

Analysis of Inter-Domain and Cross-Domain Drug Review Polarity Classification.

Individuals increasingly rely on social media to discuss health-related issues. One way to provide easier access to relevant in- formation is through sentiment analysis - classifying text into polarity classes such as positive and negative. In this paper, we generated freely available datasets of WebMD.com drug reviews and star ratings for Common, Cancer, Depression, Diabetes, and Hypertension drugs. We explored four supervised learning models: Naive Bayes, Random Forests, Support Vector Machines, and Convolutional Neural Networks for the purpose of determining the polarity of drug reviews. We conducted inter-domain and cross-domain evaluations. We found that SVM obtained the highest f-measure on average and that cross-domain training produced similar or higher results to models trained directly on their respective datasets.

Comparative analysis of ventilation efficiency on ultrafine particle removal in university MakerSpaces.

The proliferation of 3D printing MakerSpaces in university settings has led to an increased risk of student and technician exposure to ultrafine particles. New MakerSpaces do not have standardized specifications to aid in the design of the space; therefore, a need exists to characterize the impacts of different engineering controls on MakerSpace air quality. This study compares three university MakerSpaces: a library MakerSpace operating ≤4 devices under typical office space ventilation with no engineering controls, a laboratory MakerSpace operating 29 printers inside grated cabinets, with laboratory-grade ventilation, and a center MakerSpace operating ≤4 devices with neither engineering controls nor internal ventilation. All MakerSpaces were studied under both controlled (using a standard print design) and uncontrolled (real-time user operation) conditions measuring emitted particle concentrations in the near-field. Additionally, volatile organic emissions and the difference between near-field and far-field particle concentrations were investigated in multiple MakerSpaces. The center MakerSpace had the greatest net increase in mean particle number concentration (+1378.9% relative to background during a print campaign using polylactic acid (PLA) filament in a MakerBot (MakerBot-PLA)). The number-weighted mean diameter had the greatest change relative to background during the library campaign, +37.1% for the Lulzbot-PLA and -56.1% for the Ultimaker-PLA studies. For the standard NIST design with MakerBot-PLA, the laboratory's particle removal ratio was 30 times greater than in the library with open cabinets and 54 times greater when the cabinet doors were closed. The average particle removal rate from the center MakerSpace was up to 2.5 times less efficient than that of the library for the same MakerBot-PLA combination. These results suggest ventilation as a key priority in the design of a new university MakerSpace.

A New Portable In Vitro Exposure Cassette for Aerosol Sampling.

This protocol introduces a new in vitro exposure system, capable of being worn, including its characterization and performance. Air-liquid interface (ALI) in vitro exposure systems are often large and bulky, making transport to the field and operation at the source of emission or within the breathing zone difficult. Through miniaturization of these systems, the lab can be brought to the field, expediting processing time and providing a more appropriate exposure method that does not alter the aerosol prior to contacting the cells. The Portable In vitro Exposure Cassette (PIVEC) adapts a 37 mm filter cassette to allow for in vitro toxicity testing outside of a traditional laboratory setting. The PIVEC was characterized using three sizes of copper nanoparticles to determine deposition efficiency based on gravimetric and particle number concentration analysis. Initial cytotoxicity experiments were performed with exposed lung cells to determine the ability of the system to deposit particles while maintaining cell viability. The PIVEC provides a similar or increased deposition efficiency when comparing to available perpendicular flow in vitro exposure devices. Despite the lower sample throughput, the small size gives some advantages to the current in vitro ALI exposure systems. These include the ability to be worn for personal monitoring, mobility from the laboratory to the source of emission, and the option to set-up multiple systems for spatial resolution while maintaining a lower user cost. The PIVEC is a system capable of collecting aerosols in the field and within the breathing zone onto an air-interfaced, in vitro model.

Influence of ZnO thin film crystallinity on in vitro biocompatibility.

This study evaluated the cytocompatibility of single- and poly-crystalline ZnO thin films using extract and direct contact methods. Exposure to poly-crystalline ZnO extract resulted in reduced cell viability, on average 82%/70% as measured by MTS/LDH assays, respectively. Direct exposure to both single- and poly-crystalline ZnO thin films resulted in reduced cell viability, which was attributed to anoikis due to inhibition of cell adhesion to the substrate by zinc. Intracellular zinc imaging suggests that single crystalline ZnO thin films do not result in a significant change in intracellular zinc concentrations. Overall, the results suggest that single-crystalline ZnO thin films have better short-term (24 h) cytocompatibility and support their potential to serve as a biocompatible sensor material.

Integration among databases and data sets to support productive nanotechnology: Challenges and recommendations.

Many groups within the broad field of nanoinformatics are already developing data repositories and analytical tools driven by their individual organizational goals. Integrating these data resources across disciplines and with non-nanotechnology resources can support multiple objectives by enabling the reuse of the same information. Integration can also serve as the impetus for novel scientific discoveries by providing the framework to support deeper data analyses. This article discusses current data integration practices in nanoinformatics and in comparable mature fields, and nanotechnology-specific challenges impacting data integration. Based on results from a nanoinformatics-community-wide survey, recommendations for achieving integration of existing operational nanotechnology resources are presented. Nanotechnology-specific data integration challenges, if effectively resolved, can foster the application and validation of nanotechnology within and across disciplines. This paper is one of a series of articles by the Nanomaterial Data Curation Initiative that address data issues such as data curation workflows, data completeness and quality, curator responsibilities, and metadata.

An annotated corpus with nanomedicine and pharmacokinetic parameters.

A vast amount of data on nanomedicines is being generated and published, and natural language processing (NLP) approaches can automate the extraction of unstructured text-based data. Annotated corpora are a key resource for NLP and information extraction methods which employ machine learning. Although corpora are available for pharmaceuticals, resources for nanomedicines and nanotechnology are still limited. To foster nanotechnology text mining (NanoNLP) efforts, we have constructed a corpus of annotated drug product inserts taken from the US Food and Drug Administration's Drugs@FDA online database. In this work, we present the development of the Engineered Nanomedicine Database corpus to support the evaluation of nanomedicine entity extraction. The data were manually annotated for 21 entity mentions consisting of nanomedicine physicochemical characterization, exposure, and biologic response information of 41 Food and Drug Administration-approved nanomedicines. We evaluate the reliability of the manual annotations and demonstrate the use of the corpus by evaluating two state-of-the-art named entity extraction systems, OpenNLP and Stanford NER. The annotated corpus is available open source and, based on these results, guidelines and suggestions for future development of additional nanomedicine corpora are provided.

Effectiveness of hand washing on the removal of iron oxide nanoparticles from human skin ex vivo.

In this study, the effectiveness of washing with soap and water in removing nanoparticles from exposed skin was investigated. Dry, nanoscale hematite (α-Fe2O3) or maghemite (γ-Fe2O3) powder, with primary particle diameters between 20-30 nm, were applied to two samples each of fresh and frozen ex vivo human skin in two independent experiments. The permeation of nanoparticles through skin, and the removal of nanoparticles after washing with soap and water were investigated. Bare iron oxide nanoparticles remained primarily on the surface of the skin, without penetrating beyond the stratum corneum. Skin exposed to iron oxide nanoparticles for 1 and 20 hr resulted in removal of 85% and 90%, respectively, of the original dose after washing. In the event of dermal exposure to chemicals, removal is essential to avoid potential local irritation or permeation across skin. Although manufactured at an industrial scale and used extensively in laboratory experiments, limited data are available on the removal of engineered nanoparticles after skin contact. Our finding raises questions about the potential consequences of nanoparticles remaining on the skin and whether alternative washing methods should be proposed. Further studies on skin decontamination beyond use of soap and water are needed to improve the understanding of the potential health consequences of dermal exposure to nanoparticles.