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1. High-throughput double emulsion-based microfluidic production of hydrogel microspheres with tunable chemical functionalities toward biomolecular conjugation

   https://doi.org/10.1039/c7lc01088e  

   Liu, Eric Y. ; Jung, Sukwon ; Weitz, David A. ; Yi, Hyunmin ; Choi, Chang-Hyung ( January 2018 , Lab on a Chip)

   Chemically functional hydrogel microspheres hold significant potential in a range of applications including biosensing, drug delivery, and tissue engineering due to their high degree of flexibility in imparting a range of functions. In this work, we present a simple, efficient, and high-throughput capillary microfluidic approach for controlled fabrication of monodisperse and chemically functional hydrogel microspheres via formation of double emulsion drops with an ultra-thin oil shell as a sacrificial template. This method utilizes spontaneous dewetting of the oil phase upon polymerization and transfer into aqueous solution, resulting in poly(ethylene glycol) (PEG)-based microspheres containing primary amines (chitosan, CS) or carboxylates (acrylicmore »acid, AA) for chemical functionality. Simple fluorescent labelling of the as-prepared microspheres shows the presence of abundant, uniformly distributed and readily tunable functional groups throughout the microspheres. Furthermore, we show the utility of chitosan's primary amine as an efficient conjugation handle at physiological pH due to its low pKa by direct comparison with other primary amines. We also report the utility of these microspheres in biomolecular conjugation using model fluorescent proteins, R-phycoerythrin (R-PE) and green fluorescent protein (GFPuv), via tetrazine– trans -cyclooctene (Tz–TCO) ligation for CS-PEG microspheres and carbodiimide chemistry for AA-PEG microspheres, respectively. The results show rapid coupling of R-PE with the microspheres' functional groups with minimal non-specific adsorption. In-depth protein conjugation kinetics studies with our microspheres highlight the differences in reaction and diffusion of R-PE with CS-PEG and AA-PEG microspheres. Finally, we demonstrate orthogonal one-pot protein conjugation of R-PE and GFPuv with CS-PEG and AA-PEG microspheres via simple size-based encoding. Combined, these results represent a significant advancement in the rapid and reliable fabrication of monodisperse and chemically functional hydrogel microspheres with tunable properties.« less
2. Proxy Calibrations in the Cold-Water Coral Desmophyllum dianthus: Nutrient Concentrations in Antarctic Intermediate Water

   Ko, S.: Sherrell ; Pelejero, C. ; Martínez-Dios, A. ; Lucas, A.: Calvo ; Kozdon, R. ; Bostock, H. ; Tracey, D.M. ( December 2020 , AGU Fall Meeting 2020)

   Element-calcium ratios in the skeleton of cold-water coral Desmophyllum dianthus represent potential archives for paleo-reconstruction of several ocean properties including temperature and nutrient concentrations. However, relatively large uncertainties in these proxy calibrations and heterogeneity in the skeletal composition have limited its application to date. We address these issues by analyzing corals cultured under systematically varied seawater conditions (phosphate, barium, temperature, pH, feeding frequency) over a two-year period, and refine the calibration of P/Ca, Ba/Ca, U/Ca, and Li/Mg proxies for seawater phosphate, barium, carbonate ion concentration, and temperature, respectively. Composition of the corals is determined using laser-ablation ICPMS, with robust plasmamore »conditions established using the Normalized Argon Index [1], and proxy element incorporation is evaluated for influences of temperature, pH, and feeding frequency. The aragonite precipitated during the stages of the culturing experiment is identified using fluorescent and geochemical labelling of the skeleton through calcein and lead isotopes, respectively. This approach allows us to resolve monthly and annual increments in these slow growing (1-2mm/year) organisms, and also to evaluate the influence of calcification rate on the composition. We address the issue of heterogeneity by adapting methods for LA-ICPMS imaging to create macroscale images to reveal the full pattern of skeletogenesis and related compositional variability of D. dianthus. Preliminary images suggest that heterogeneity stems from the asymmetric precipitation of aragonite, and from centers of calcification (also known as early mineralization zones) that complicate the interpretation of elemental signals throughout the skeleton, but also help to identify new skeletal regions suitable for proxy measurement. Finally, we also discuss the role of endolithic organisms in some of these specimens. [1] Fietzke, J. & Frische, M. (2016), J. Anal. At. Spectrom. 31, 234–244.« less
3. Micropropagation of Rare Scutellaria havanensis Jacq. and Preliminary Studies on Antioxidant Capacity and Anti-Cancer Potential

   https://doi.org/10.3390/molecules26195813  

   Irvin, Lani ; Zavala Ortiz, Yarelia ; Rivera, Kamila Rivera ; Nanda Vaidya, Brajesh ; Sherman, Samantha H ; Batista, Rosalinda Aybar ; Negrón Berríos, Juan A. ; Joshee, Nirmal ; Arun, Alok ( October 2021 , Molecules)

   We report the development of in vitro propagation protocols through an adventitious shoot induction pathway for a rare and medicinal Scutellaria havanensis. In vitro propagation studies using nodal explants showed MS medium supplemented with 10 µM 6-Benzylaminopurine induced the highest number of adventitious shoots in a time-dependent manner. A ten-day incubation was optimum for shoot bud induction as longer exposures resulted in hyperhydricity of the explants and shoots induced. We also report preliminary evidence of Agrobacterium tumefaciens EHA105-mediated gene transfer transiently expressing the green fluorescent protein in this species. Transformation studies exhibited amenability of various explant tissues, internode being themore »most receptive. As the plant has medicinal value, research was carried out to evaluate its potential antioxidant capacity and the efficacy of methanolic leaf extracts in curbing the viability of human colorectal cancer cell line HCT116. Comparative total polyphenol and flavonoid content measurement of fresh and air-dried leaf extract revealed that the fresh leaf extracts contain higher total polyphenol and flavonoid content. The HCT 116 cell viability was assessed by colorimetric assay using a 3-(4,5-dimethyl-thiazol-2-yl)-2,5-diphenyltetrazolium bromide, showed a steady growth inhibition after 24 h of incubation. Scanning electron microscopy of leaf surface revealed a high density of glandular and non-glandular trichomes. This research provides a basis for the conservation of this rare plant and future phytochemical screening and clinical research.« less
4. Development and Application of Nanoparticle-Nanopolymer Composite Spheres for the Study of Environmental Processes

   https://doi.org/10.3389/ftox.2021.752296  

   Rauschendorfer, Robert J. ; Whitham, Kyle M. ; Summer, Star ; Patrick, Samantha A. ; Pierce, Aliandra E. ; Sefi-Cyr, Haley ; Tadjiki, Soheyl ; Kraft, Michael D. ; Emory, Steven R. ; Rider, David A. ; et al ( December 2021 , Frontiers in Toxicology)

   Plastics have long been an environmental contaminant of concern as both large-scale plastic debris and as micro- and nano-plastics with demonstrated wide-scale ubiquity. Research in the past decade has focused on the potential toxicological risks posed by microplastics, as well as their unique fate and transport brought on by their colloidal nature. These efforts have been slowed by the lack of analytical techniques with sufficient sensitivity and selectivity to adequately detect and characterize these contaminants in environmental and biological matrices. To improve analytical analyses, microplastic tracers are developed with recognizable isotopic, metallic, or fluorescent signatures capable of being identified amidstmore »a complex background. Here we describe the synthesis, characterization, and application of a novel synthetic copolymer nanoplastic based on polystyrene (PS) and poly(2-vinylpyridine) (P2VP) intercalated with gold, platinum or palladium nanoparticles that can be capped with different polymeric shells meant to mimic the intended microplastic. In this work, particles with PS and polymethylmethacrylate (PMMA) shells are used to examine the behavior of microplastic particles in estuarine sediment and coastal waters. The micro- and nanoplastic tracers, with sizes between 300 and 500 nm in diameter, were characterized using multiple physical, chemical, and colloidal analysis techniques. The metallic signatures of the tracers allow for quantification by both bulk and single-particle inductively-coupled plasma mass spectrometry (ICP-MS and spICP-MS, respectively). As a demonstration of environmental applicability, the tracers were equilibrated with sediment collected from Bellingham Bay, WA, United States to determine the degree to which microplastics bind and sink in an estuary based of grain size and organic carbon parameters. In these experiments, between 80 and 95% of particles were found to associate with the sediment, demonstrative of estuaries being a major anticipated sink for these contaminants. These materials show considerable promise in their versatility, potential for multiplexing, and utility in studying micro- and nano-plastic transport in real-world environments.« less
5. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmetmore »is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge.« less