17 research outputs found
Molecular weight and end capping effects on the optoelectronic properties of structurally related ‘heavy atom’ donor–acceptor polymers
No full textTwo donor–acceptor polymers containing either Si or Ge in the donor and Se in the acceptor, poly[(4,4′-bis(2-ethylhexyl)dithieno[3,2-b:2′,3′-d]silole)-2,6-diyl-alt-(2,1,3-benzoselenadiazole)-4,7-diyl] and poly[(4,4′-bis(2-ethylhexyl)dithieno[3,2-b:2′,3′-d]germole)-2,6-diyl-alt-(2,1,3-benzoselena diazole)-4,7-diyl], were synthesized by microwave assisted polymerization. These polymer structures are attractive because they combine the red light absorption characteristics of the Se acceptor, with high charge carrier mobility inherent to the Si- or Ge-containing donor. Here we study the effects of molecular weight and end capping on the photophysical, morphological, and photovoltaic properties. The solution and film absorption profiles and solution onset are dictated by molecular weight, whereas the subtler heteroatom effect dictates the absorption onset in the polymer films. Molecular weight appears to affect polymer absorption to the greatest degree in a medium molecular weight regime and these effects have a significant aggregation component. Highlighting the red-light absorption of the Se-acceptor monomer, both Si-donor and Ge-donor polymer devices display improved photon harvesting beyond 850 nm relative to their S-acceptor analogues. Higher hole mobility relative to the C-donor/Se-acceptor polymer analogue indicates successful integration of heavy atom donor properties with the 2,1,3-benzoselenadiazole acceptor. Molecular weight invokes the greatest change on polymer/fullerene blend morphology, followed by phenyl end capping, and finally by the Si or Ge heteroatom.Peer reviewedFinal article publishe
Reply to Lee and Howden
No full textGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Pattern Recognition and Bioinformatic
Cryptosporidium and Giardia in locally harvested clams in Iqaluit, Nunavut
No full textHigh prevalences of Cryptosporidium and Giardia were recently found in enteric illness patients in the Qikiqtaaluk region of Nunavut, Canada, with a foodborne, waterborne or animal source of parasites suspected. Clams (Mya truncata) are a commonly consumed, culturally important and nutritious country food in Iqaluit; however, shellfish may concentrate protozoan pathogens from contaminated waters. The goal of this work was to investigate clams as a potential source of Cryptosporidium and Giardia infections in residents in Iqaluit, Nunavut. The objectives were to estimate the prevalence and genetically characterize Cryptosporidium and Giardia in locally harvested clams. Clams (n = 404) were collected from Iqaluit harvesters in September 2016. Haemolymph (n = 328) and digestive gland (n = 390) samples were screened for Cryptosporidium and Giardia via PCR, and amplified products were further processed for sequence analyses for definitive confirmation. Giardia DNA was found in haemolymph from 2 clams, while Cryptosporidium was not detected. The two Giardia sequences were identified as zoonotic Giardia enterica assemblage B. The overall prevalence of Giardia in clams near Iqaluit was low (0.6%) compared with other studies in southern Canada and elsewhere. The presence of Giardia DNA in clams suggests human or animal faecal contamination of coastal habitat around Iqaluit in shellfish harvesting waters. Results from this study are intended to inform public health practice and planning in Inuit Nunangat
A modeling framework for characterizing root exudation-driven geochemical dynamics in the Critical Zone
No full textLand use change and intensive agricultural practices have induced significant shifts in the transport and transformation of water, carbon, and nutrients across landscapes. The long-term implications of such changes on soil health and water chemistry remain an open challenge, and the mechanisms that drive changes in solute chemistry and eventually stream water chemistry are not completely understood. Vegetation plays a central role in driving
Critical Zone (CZ) hydrobiogeochemistry through root exudation, the process by which plant roots respond to their environment and release reactive carbon (C) into the soil to influence soil microbial symbionts to their advantage. Although previous studies have demonstrated that such root processes promote soil weathering, there are no existing models capable of describing the interaction of root exudation with temporally-variable processes ranging from mineral dissolution to energy and moisture fluxes in the soil column in a single framework.
The goal of this thesis is to address gaps in the numerical simulation of hydrobiogeochemical dynamics in the shallow subsurface CZ through the development of a modeling framework that links root-microbe-soil-water interactions and feedbacks with above-ground natural and anthropogenic forcings and below-ground influences of soil parent material. This framework was achieved through the development of (1) the root exudation model REWT (Root Exudation in Watershed-scale Transport) which explicitly describes root exudation and associated feedbacks with the soil microbiome; and (2) the model CrunchREWT, which couples REWT with the existing reactive transport model CrunchFlow to incorporate fluid-mineral interactivity, acid/base chemistry, solute complexation, and other geochemical processes to link root-microbe feedbacks to the broader soil environment. These models are driven by a oneway coupling with the existing multi-layer canopy-root ecohydrologic model MLCan, which vertically resolves canopy- and root-system moisture and temperature gradients and fluxes, and plant uptake of moisture and nutrients for various plant species in both natural and intensively managed ecosystems.
We present REWT and CrunchREWT simulations for an intensively managed site in Bondville, Illinois, USA which undergoes corn-corn-soybean rotation. REWT simulations indicate that rates of nitrification and respiration are substantially altered due to the explicit consideration of root exudation. CrunchREWT results show that root-sourced reactive C inputs can lead to the augmentation or reduction of solute concentrations in the soil by several orders of magnitude. Silicate weathering products illustrate episodic leaching patterns, and calcium simulations reveal the development of a stable weathering front consistent with observations. Aluminum concentrations are particularly responsive to geochemical transformations driven by root-sourced reactive C, and analysis of leaching concentration vs leaching flux indicates hysteresis behavior. This work demonstrates the importance of systematically incorporating root exudates into hydrobiogeochemical models and can serve to inform experimental design for shallow subsurface CZ processes.
Although many insights can be gleaned from the simulations presented, several challenges impede our ability to directly compare results with observational data, including the difficulty of procuring the broad array of data necessary to parameterize and validate CrunchREWT. We envision the management-induced reactive zone (MIRZ) flux monitoring system, designed to provide the depth-resolved MIRZ biogeochemical data CrunchREWT requires. We also outline further model improvements that will allow for validation with observational data, including the representation of multi-phase gas diffusion through the soil, tile drain water fluxes and injection of gases in the soil through the tile line, exudation of organic acids, and expansion to a three-dimensional simulation framework.
The work presented here lays the foundation to explore the role of vegetation in soil development and landscape co-evolution. It contributes to a more integrated representation of the interconnected physical, chemical, and biological processes that govern ecosystem functioning.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-05-01The student, Susana Roque-Malo, accepted the attached license on 2021-04-16 at 12:03.The student, Susana Roque-Malo, submitted this Dissertation for approval on 2021-04-16 at 12:05.This Dissertation was approved for publication on 2021-04-19 at 15:40.DSpace SAF Submission Ingestion Package generated from Vireo submission #16232 on 2021-09-16 at 20:08:01Made available in DSpace on 2021-09-17T04:04:07Z (GMT). No. of bitstreams: 3
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SAFPred: Synteny-aware gene function prediction for bacteria using protein embeddings
No full textMotivationToday, we know the function of only a small fraction of the protein sequences predicted from genomic data. This problem is even more salient for bacteria, which represent some of the most phylogenetically and metabolically diverse taxa on Earth. This low rate of bacterial gene annotation is compounded by the fact that most function prediction algorithms have focused on eukaryotes, and conventional annotation approaches rely on the presence of similar sequences in existing databases. However, often there are no such sequences for novel bacterial proteins. Thus, we need improved gene function prediction methods tailored for bacteria. Recently, transformer-based language models—adopted from the natural language processing field—have been used to obtain new representations of proteins, to replace amino acid sequences. These representations, referred to as protein embeddings, have shown promise for improving annotation of eukaryotes, but there have been only limited applications on bacterial genomes.ResultsTo predict gene functions in bacteria, we developed SAFPred, a novel synteny-aware gene function prediction tool based on protein embeddings from state-of-the-art protein language models. SAFpred also leverages the unique operon structure of bacteria through conserved synteny. SAFPred outperformed both conventional sequence-based annotation methods and state-of-the-art methods on multiple bacterial species, including for distant homolog detection, where the sequence similarity to the proteins in the training set was as low as 40%. Using SAFPred to identify gene functions across diverse enterococci, of which some species are major clinical threats, we identified 11 previously unrecognized putative novel toxins, with potential significance to human and animal health.Availability and implementationhttps://github.com/AbeelLab/safpred.Pattern Recognition and Bioinformatic
Deciphering drug resistance in Mycobacterium tuberculosis using whole-genome sequencing: Progress, promise, and challenges
No full textTuberculosis (TB) is a global infectious threat that is intensified by an increasing incidence of highly drug-resistant disease. Whole-genome sequencing (WGS) studies of Mycobacterium tuberculosis, the causative agent of TB, have greatly increased our understanding of this pathogen. Since the first M. tuberculosis genome was published in 1998, WGS has provided a more complete account of the genomic features that cause resistance in populations of M. tuberculosis, has helped to fill gaps in our knowledge of how both classical and new antitubercular drugs work, and has identified specific mutations that allow M. tuberculosis to escape the effects of these drugs. WGS studies have also revealed how resistance evolves both within an individual patient and within patient populations, including the important roles of de novo acquisition of resistance and clonal spread. These findings have informed decisions about which drug-resistance mutations should be included on extended diagnostic panels. From its origins as a basic science technique, WGS of M. tuberculosis is becoming part of the modern clinical microbiology laboratory, promising rapid and improved detection of drug resistance, and detailed and real-time epidemiology of TB outbreaks. We review the successes and highlight the challenges that remain in applying WGS to improve the control of drug-resistant TB through monitoring its evolution and spread, and to inform more rapid and effective diagnostic and therapeutic strategies.Pattern Recognition and Bioinformatic
Computational Methods for Strain-Level Microbial Detection in Colony and Metagenome Sequencing Data
No full textMetagenomic sequencing is a powerful tool for examining the diversity and complexity of microbial communities. Most widely used tools for taxonomic profiling of metagenomic sequence data allow for a species-level overview of the composition. However, individual strains within a species can differ greatly in key genotypic and phenotypic characteristics, such as drug resistance, virulence and growth rate. Therefore, the ability to resolve microbial communities down to the level of individual strains within a species is critical to interpreting metagenomic data for clinical and environmental applications, where identifying a particular strain, or tracking a particular strain across a set of samples, can help aid in clinical diagnosis and treatment, or in characterizing yet unstudied strains across novel environmental locations. Recently published approaches have begun to tackle the problem of resolving strains within a particular species in metagenomic samples. In this review, we present an overview of these new algorithms and their uses, including methods based on assembly reconstruction and methods operating with or without a reference database. While existing metagenomic analysis methods show reasonable performance at the species and higher taxonomic levels, identifying closely related strains within a species presents a bigger challenge, due to the diversity of databases, genetic relatedness, and goals when conducting these analyses. Selection of which metagenomic tool to employ for a specific application should be performed on a case-by case basis as these tools have strengths and weaknesses that affect their performance on specific tasks. A comprehensive benchmark across different use case scenarios is vital to validate performance of these tools on microbial samples. Because strain-level metagenomic analysis is still in its infancy, development of more fine-grained, high-resolution algorithms will continue to be in demand for the future.Pattern Recognition and Bioinformatic
SynerClust: A highly scalable, synteny-aware orthologue clustering tool
No full textAccurate orthologue identification is a vital component of bacterial comparative genomic studies, but many popular sequence-similarity-based approaches do not scale well to the large numbers of genomes that are now generated routinely. Furthermore, most approaches do not take gene synteny into account, which is useful information for disentangling paralogues. Here, we present SynerClust, a user-friendly synteny-aware tool based on synergy that can process thousands of genomes. SynerClust was designed to analyse genomes with high levels of local synteny, particularly prokaryotes, which have operon structure. SynerClust’s run-time is optimized by selecting cluster representatives at each node in the phylogeny; thus, avoiding the need for exhaustive pairwise similarity searches. In benchmarking against Roary, Hieranoid2, PanX and Reciprocal Best Hit, SynerClust was able to more completely identify sets of core genes for datasets that included diverse strains, while using substantially less memory, and with scalability comparable to the fastest tools. Due to its scalability, ease of installation and use, and suitability for a variety of computing environments, orthogroup clustering using SynerClust will enable many large-scale prokaryotic comparative genomics efforts.Pattern Recognition and Bioinformatic
Mycobacterium tuberculosis Whole Genome Sequences From Southern India Suggest Novel Resistance Mechanisms and the Need for Region-Specific Diagnostics
No full textBackground.India is home to 25% of all tuberculosis cases and the second highest number of multidrug resistant cases worldwide. However, little is known about the genetic diversity and resistance determinants of Indian Mycobacterium tuberculosis, particularly for the primary lineages found in India, lineages 1 and 3.Methods.We whole genome sequenced 223 randomly selected M. tuberculosis strains from 196 patients within the Tiruvallur and Madurai districts of Tamil Nadu in Southern India. Using comparative genomics, we examined genetic diversity, transmission patterns, and evolution of resistance.Results.Genomic analyses revealed (1) prevalence of strains from lineages 1 and 3, (2) recent transmission of strains among patients from the same treatment centers, (3) emergence of drug resistance within patients over time, (4) resistance gained in an order typical of strains from different lineages and geographies, (5) underperformance of known resistance-conferring mutations to explain phenotypic resistance in Indian strains relative to studies focused on other geographies, and (6) the possibility that resistance arose through mutations not previously implicated in resistance, or through infections with multiple strains that confound genotype-based prediction of resistance.Conclusions.In addition to substantially expanding the genomic perspectives of lineages 1 and 3, sequencing and analysis of M. tuberculosis whole genomes from Southern India highlight challenges of infection control and rapid diagnosis of resistant tuberculosis using current technologies. Further studies are needed to fully explore the complement of diversity and resistance determinants within endemic M. tuberculosis populations.Pattern Recognition and Bioinformatic
