Ultraviolet Resonant Nanogap Antennas with Rhodium Nanocube Dimers for Enhancing Protein Intrinsic Autofluorescence

Plasmonic optical nanoantennas offer compelling solutions for enhancing light-matter interactions at the nanoscale. However, until now, their focus has been mainly limited to the visible and near-infrared regions, overlooking the immense potential of the ultraviolet (UV) range, where molecules exhibit their strongest absorption. Here, we present the realization of UV resonant nanogap antennas constructed from paired rhodium nanocubes. Rhodium emerges as a robust alternative to aluminum, offering enhanced stability in wet environments and ensuring reliable performance in the UV range. Our results showcase the nanoantenna ability to enhance the UV autofluorescence of label-free streptavidin and hemoglobin proteins. We achieve significant enhancements of the autofluorescence brightness per protein by up to 120-fold, and reach zeptoliter detection volumes enabling UV autofluorescence correlation spectroscopy (UV-FCS) at high concentrations of several tens of micromolar. We investigate the modulation of fluorescence photokinetic rates and report excellent agreement between experimental results and numerical simulations. This work expands the applicability of plasmonic nanoantennas into the deep UV range, unlocking the investigation of label-free proteins at physiological concentrations

So, You Want to Use Next Generation Sequencing In Marine Systems? Insight from the Pan Pacific Advanced Studies Institute

The emerging field of next-generation sequencing (NGS) is rapidly expanding capabilities for cutting edge genomic research, with applications that can help meet marine conservation challenges of food security, biodiversity loss, and climate change. Navigating the use of these tools, however, is complex at best. Furthermore, applications of marine genomic questions are limited in developing nations where both marine biodiversity and threats to marine biodiversity are most concentrated. This is particularly true in Southeast Asia. The first Pan-Pacific Advanced Studies Institute (PacASI) entitled Genomic Applications to Marine Science and Resource Management in Southeast Asia was held in July 2012 in Dumaguete, Philippines, with the intent to draw together leading scientists from both sides of the Pacific Ocean to understand the potential of NGS in helping address the aforementioned challenges. Here we synthesize discussions held during the PacASI to provide perspectives and guidance to help scientists new to NGS choose among the variety of available advanced genomic methodologies specifically for marine science questions

So, You Want to Use Next-Generation Sequencing in Marine Systems? Insight from the Pan-Pacific Advanced Studies Institute

The emerging field of next-generation sequencing (NGS) is rapidly expanding capabilities for cutting edge genomic research, with applications that can help meet marine conservation challenges of food security, biodiversity loss, and climate change. Navigating the use of these tools, however, is complex at best. Furthermore, applications of marine genomic questions are limited in developing nations where both marine biodiversity and threats to marine biodiversity are most concentrated. This is particularly true in Southeast Asia. The first Pan-Pacific Advanced Studies Institute (PacASI) entitled “Genomic Applications to Marine Science and Resource Management in Southeast Asia” was held in July 2012 in Dumaguete, Philippines, with the intent to draw together leading scientists from both sides of the Pacific Ocean to understand the potential of NGS in helping address the aforementioned challenges. Here we synthesize discussions held during the PacASI to provide perspectives and guidance to help scientists new to NGS choose among the variety of available advanced genomic methodologies specifically for marine science questions

Towards more complete metagenomic analyses through circularized genomes and conjugative elements

Advancements in sequencing technologies have revolutionized biological sciences and led to the emergence of a number of fields of research. One such field of research is metagenomics, which is the study of the genomic content of complex communities of bacteria. The goal of this thesis was to contribute computational methodology that can maximize the data generated in these studies and to apply these protocols human and environmental metagenomic samples. Standard metagenomic analyses include a step for binning of assembled contigs, which has previously been shown to exclude mobile genetic elements, and I demonstrated that this phenomenon extends to all conjugative elements, which are a subset of mobile genetic elements. I proposed two separate methodologies that could detect contigs that are potential conjugative elements: a curated set of profile hidden Markov models that are very efficient to run, or annotation using the full UniRef90 database, a slower but more sensitive method. I then applied this framework to a large population-based cohort and to a study examining the association of the maternal human gut microbiota and the development of spina bifida. Broadly, the composition and abundances of conjugative elements were discriminatory between the age and geographic cohorts. In the spina bifida cohort, there was an enrichment of Campylobacter hominis and a conjugative element belonging to Campylobacter hominis, which was excluded from the metagenomic bins. Next, I characterized a novel species belonging to the recently discovered manganese-oxidizing genus Manganitrophus growing on oil refinery carbon filters. I successfully circularized the genomes of three strains and got quality assemblies for the remaining two samples. Furthermore, I identified a previously uncharacterized conjugative plasmid belonging to the species using my framework developed in chapter 2. Finally, I developed an assembly pipeline to perform a secondary assembly on binned assemblies using long reads. The secondary assemblies yielded a number of additional circularized sequences that would be useful as scaffolds in future metatranscriptomic, variation analysis, and community dynamic studies. The methodologies and applications in this thesis provide a framework for more complete metagenomic analyses going forward that will aid in our understanding of microbial ecology

A study of mRNA translation with computational analysis of ribosome profiling datasets

Ribosome profiling is based on capturing and sequencing of the mRNA fragments enclosed within the translating ribosome and it thereby provides a “snapshot” of ribosome positions at the transcriptome wide level. The approach was developed in 2009 and was a significant advancement towards the better understanding of the regulation of protein synthesis. I this thesis I describe my analysis of ribosome profiling data. In Chapter 1, I present a review of the recent developments of understanding obtained with ribosome profiling as well as discussing the implications of artifacts on the interpretation of its data. Chapters 2 and 3 details using ribosome profiling to examine the translational response to eIF2 repression and to the deprivation of oxygen and glucose respectively. Chapter 4 details how the interaction of the Shine Dalgarno with the ribosome rRNA alters the length of the mRNA protected fragments. In Chapter 5, I present analysis at identifying the relative impact of mRNA features on local ribosome profiling read density

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

The MGX framework for microbial community analysis

Jaenicke S. The MGX framework for microbial community analysis. Bielefeld: Universität Bielefeld; 2020