A Single Molecule Scaffold for the Maize Genome

About 85% of the maize genome consists of highly repetitive sequences that are interspersed by low-copy, gene-coding sequences. The maize community has dealt with this genomic complexity by the construction of an integrated genetic and physical map (iMap), but this resource alone was not sufficient for ensuring the quality of the current sequence build. For this purpose, we constructed a genome-wide, high-resolution optical map of the maize inbred line B73 genome containing >91,000 restriction sites (averaging 1 site/∼23 kb) accrued from mapping genomic DNA molecules. Our optical map comprises 66 contigs, averaging 31.88 Mb in size and spanning 91.5% (2,103.93 Mb/∼2,300 Mb) of the maize genome. A new algorithm was created that considered both optical map and unfinished BAC sequence data for placing 60/66 (2,032.42 Mb) optical map contigs onto the maize iMap. The alignment of optical maps against numerous data sources yielded comprehensive results that proved revealing and productive. For example, gaps were uncovered and characterized within the iMap, the FPC (fingerprinted contigs) map, and the chromosome-wide pseudomolecules. Such alignments also suggested amended placements of FPC contigs on the maize genetic map and proactively guided the assembly of chromosome-wide pseudomolecules, especially within complex genomic regions. Lastly, we think that the full integration of B73 optical maps with the maize iMap would greatly facilitate maize sequence finishing efforts that would make it a valuable reference for comparative studies among cereals, or other maize inbred lines and cultivars

Diesel soot aging in urban plumes within hours under cold dark and humid conditions

Fresh and aged diesel soot particles have different impacts on climate and human health. While fresh diesel soot particles are highly aspherical and non-hygroscopic, aged particles are spherical and hygroscopic. Aging and its effect on water uptake also controls the dispersion of diesel soot in the atmosphere. Understanding the timescales on which diesel soot ages in the atmosphere is thus important, yet knowledge thereof is lacking. We show that under cold, dark and humid conditions the atmospheric transformation from fresh to aged soot occurs on a timescale of less than five hours. Under dry conditions in the laboratory, diesel soot transformation is much less efficient. While photochemistry drives soot aging, our data show it is not always a limiting factor. Field observations together with aerosol process model simulations show that the rapid ambient diesel soot aging in urban plumes is caused by coupled ammonium nitrate formation and water uptake.Peer reviewe

Joint analysis of quantitative trait loci and major-effect causative mutations affecting meat quality and carcass composition traits in pigs

Background: Detection of quantitative trait loci (QTLs) affecting meat quality traits in pigs is crucial for the design of efficient marker-assisted selection programs and to initiate efforts toward the identification of underlying polymorphisms. The RYR1 and PRKAG3 causative mutations, originally identified from major effects on meat characteristics, can be used both as controls for an overall QTL detection strategy for diversely affected traits and as a scale for detected QTL effects. We report on a microsatellite-based QTL detection scan including all autosomes for pig meat quality and carcass composition traits in an F2 population of 1,000 females and barrows resulting from an intercross between a Pietrain and a Large White-Hampshire-Duroc synthetic sire line. Our QTL detection design allowed side-by-side comparison of the RYR1 and PRKAG3 mutation effects seen as QTLs when segregating at low frequencies (0.03-0.08), with independent QTL effects detected from most of the same population, excluding any carrier of these mutations.[br/] Results: Large QTL effects were detected in the absence of the RYR1 and PRKGA3 mutations, accounting for 12.7% of phenotypic variation in loin colour redness CIE-a* on SSC6 and 15% of phenotypic variation in glycolytic potential on SSC1. We detected 8 significant QTLs with effects on meat quality traits and 20 significant QTLs for carcass composition and growth traits under these conditions. In control analyses including mutation carriers, RYR1 and PRKAG3 mutations were detected as QTLs, from highly significant to suggestive, and explained 53% to 5% of the phenotypic variance according to the trait.[br/] Conclusions: Our results suggest that part of muscle development and backfat thickness effects commonly attributed to the RYR1 mutation may be a consequence of linkage with independent QTLs affecting those traits. The proportion of variation explained by the most significant QTLs detected in this work is close to the influence of major-effect mutations on the least affected traits, but is one order of magnitude lower than effect on variance of traits primarily affected by these causative mutations. This suggests that uncovering physiological traits directly affected by genetic polymorphisms would be an appropriate approach for further characterization of QTLs

Development of Neural Network Prediction Models for the Energy Producibility of a Parabolic Dish: A Comparison with the Analytical Approach

Solar energy is one of the most widely exploited renewable/sustainable resources for electricity generation, with photovoltaic and concentrating solar power technologies at the forefront of research. This study focuses on the development of a neural network prediction model aimed at assessing the energy producibility of dish–Stirling systems, testing the methodology and offering a useful tool to support the design and sizing phases of the system at different installation sites. Employing the open-source platform TensorFlow, two different classes of feedforward neural networks were developed and validated (multilayer perceptron and radial basis function). The absolute novelty of this approach is the use of real data for the training phase and not predictions coming from another analytical/numerical model. Several neural networks were investigated by varying the level of depth, the number of neurons, and the computing resources involved for two different sets of input variables. The best of all the tested neural networks resulted in a coefficient of determination of 0.98 by comparing the predicted electrical output power values with those measured experimentally. The results confirmed the high reliability of the neural models, and the use of only open-source IT tools guarantees maximum transparency and replicability of the models

Monte-Carlo simulation for calculating phakic supplementary lenses based on a thick and thin lens model using anterior segment OCT data

Background: Phakic lenses (PIOLs, the most common and only disclosed type being the implantable collamer lens, ICL) are used in patients with large or excessive ametropia in cases where laser refractive surgery is contraindicated. The purpose of this study was to present a strategy based on anterior segment OCT data for calculating the refraction correction (REF) and the change in lateral magnification (ΔM) with ICL implantation. Methods: Based on a dataset (N = 3659) containing Casia 2 measurements, we developed a vergence-based calculation scheme to derive the REF and gain or loss in ΔM on implantation of a PIOL having power PIOLP. The calculation concept is based on either a thick or thin lens model for the cornea and the PIOL. In a Monte-Carlo simulation considering, all PIOL steps listed in the US patent 5,913,898, nonlinear regression models for REF and ΔM were defined for each PIOL datapoint. Results: The calculation shows that simplifying the PIOL to a thin lens could cause some inaccuracies in REF (up to ½ dpt) and ΔM for PIOLs with high positive power. The full range of listed ICL powers (- 17 to 17 dpt) could correct REF in a range from - 17 to 12 dpt with a change in ΔM from 17 to - 25%. The linear regression considering anterior segment biometric data and the PIOLP was not capable of properly characterizing REF and ΔM, whereas the nonlinear model with a quadratic term for the PIOLP showed a good performance for both REF and ΔM prediction. Conclusion: Where PIOL design data are available, the calculation concept should consider the PIOL as thick lens model. For daily use, a nonlinear regression model can properly predict REF and ΔM for the entire range of PIOL steps if a vergence calculation is unavailable

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Obscuration-dependent evolution of Active Galactic Nuclei

We aim to constrain the evolution of AGN as a function of obscuration using an X-ray selected sample of $\sim2000$ AGN from a multi-tiered survey including the CDFS, AEGIS-XD, COSMOS and XMM-XXL fields. The spectra of individual X-ray sources are analysed using a Bayesian methodology with a physically realistic model to infer the posterior distribution of the hydrogen column density and intrinsic X-ray luminosity. We develop a novel non-parametric method which allows us to robustly infer the distribution of the AGN population in X-ray luminosity, redshift and obscuring column density, relying only on minimal smoothness assumptions. Our analysis properly incorporates uncertainties from low count spectra, photometric redshift measurements, association incompleteness and the limited sample size. We find that obscured AGN with $N_{H}>{\rm 10^{22}\, cm^{-2}}$ account for ${77}^{+4}_{-5}\%$ of the number density and luminosity density of the accretion SMBH population with $L_{{\rm X}}>10^{43}\text{ erg/s}$, averaged over cosmic time. Compton-thick AGN account for approximately half the number and luminosity density of the obscured population, and ${38}^{+8}_{-7}\%$ of the total. We also find evidence that the evolution is obscuration-dependent, with the strongest evolution around $N_{H}\thickapprox10^{23}\text{ cm}^{-2}$. We highlight this by measuring the obscured fraction in Compton-thin AGN, which increases towards $z\sim3$, where it is $25\%$ higher than the local value. In contrast the fraction of Compton-thick AGN is consistent with being constant at $\approx35\%$, independent of redshift and accretion luminosity. We discuss our findings in the context of existing models and conclude that the observed evolution is to first order a side-effect of anti-hierarchical growth.Comment: Published in Ap

Graph pangenome captures missing heritability and empowers tomato breeding

Missing heritability in genome-wide association studies defines a major problem in genetic analyses of complex biological traits(1,2). The solution to this problem is to identify all causal genetic variants and to measure their individual contributions(3,4). Here we report a graph pangenome of tomato constructed by precisely cataloguing more than 19 million variants from 838 genomes, including 32 new reference-level genome assemblies. This graph pangenome was used forgenome-wide association study analyses and heritability estimation of 20,323 gene-expression and metabolite traits. The average estimated trait heritability is 0.41 compared with 0.33 when using the single linear reference genome. This 24% increase in estimated heritability is largely due to resolving incomplete linkage disequilibrium through the inclusion of additional causal structural variants identified using the graph pangenome. Moreover, by resolving allelic and locus heterogeneity, structural variants improve the power to identify genetic factors underlying agronomically important traits leading to, for example, the identification of two new genes potentially contributing to soluble solid content. The newly identified structural variants will facilitate genetic improvement of tomato through both marker-assisted selection and genomic selection. Our study advances the understanding of the heritability of complex traits and demonstrates the power of the graph pangenome in crop breeding

A Study of The Deep Learning-based Monitoring and Efficient Numerical Modeling Methodologies for Crystallization Processes

Driven by the increasing demands of producing consistent and high-quality crystals for high value-added products such as pharmaceutical ingredients, the operation and design of a crystallization process have phased from an empirical trial-and-error approach to the modern frameworks powered by the online process analytical technologies (PATs) and model-based process optimization techniques. The one-dimensional crystal size distribution (CSD) measured by the well-established PATs is inadequate due to the missing particle morphology information. A major contribution of this thesis is to develop an image analysis-based PAT powered by the deep learning image processing techniques, whose accuracy and functionality outperformed the traditional PATs and other image analysis techniques. The PAT was deployed to monitor and study the slurry mixture of glass beads and catalyst particles as well as a taurine-water batch crystallization process. The results confirmed the superb accuracy of two-dimensional size and shape characterization in a challengingly high solids concentration. The classification capability enabled unparalleled functionalities including quantification of agglomeration level and characterization of different polymorphs based on their distinct appearances. A computerized crystallization platform was built with the developed PAT, which could automate the time-consuming experiments for determining the metastable zone width (MSZW) and induction time of a crystallization system. The application of the PAT revealed the potential to simplify and speed up the research and development stage of a crystallization process. The rich two-dimensional crystal size and shape information provided by our PAT enabled more descriptive multi-dimensional modeling for the better prediction of the crystallization process. The novel population array (PA) solver developed in this thesis could solve the multi-dimensional crystallization population balance equation (PBE) more computationally efficient than the existing discretization-based numerical methods without compromising the accuracy. The PA solver could accurately model the complex phenomena including agglomeration, breakage, and size-dependent growth. The efficient computation enables solving the complex multi-dimensional PBE for crystal morphology modeling. The combination of the innovative PAT and modeling technique is a significant contribution to the crystallization field that enables better understanding and more effective control of a crystallization process

Development of Original and Notable Techniques for Protein Analysis, Native Identification, and Characterisation

This thesis is a compilation of multiple techniques developed to characterise solutions of proteins, or proteins themselves. Proteins are the building blocks of life, and thus they are interesting to study for a deeper understanding of the mechanisms of life but also for developing diagnostic tools as well as medical therapies. The first chapter is a general overview of the thesis with an explanation of the different points developed further in the thesis such as proteins, protein aggregation, microfluidics, and optics. The second chapter describes a microfluidic platform for performing a 2D characterisation, separating the particles using capillary electrophoresis and diffusional sizing. This allowed to characterise the components of heterogenous solutions in function of their size and the mobility of the proteins. The third chapter is looking at existing well-known microfluidic techniques, like free-flow electrophoresis and diffusional sizing, combined with confocal microscopy. The single molecule detection allows to distinguish particles in function of their brightness, breaking the homogenous limitation of these microfluidic techniques under classical epifluorescence microscopy. The fourth chapter shows an elegantly simple way to characterise an exponential decrease of the residence time distribution within a nanocavity trap. The explanation is based only on a random walk assumption without requesting potential interactions. The fifth chapter describes a new method to size particles in solution based on interferometry scattering microscopy by looking at the correlation of the intensity of the interferometric term. The last chapter before the overall conclusion is based on a study of SARS-COV2 seroprevalence in the swiss canton of Zürich between December 2019 and January 2021. This study was conducted on two cohorts, the University Hospital patients, and the blood donation services of Zürich.EPSRC Centre for Doctoral Training (CDT) in Sensor Technologies and Application; EPSRC Centre for Doctoral Training (CDT) in Sensor Technologies for a Healthy and Sustainable Future; Fluidic Analytics Lt