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Compound-specific isotope analysis of amino acids as a new tool to uncover trophic chains in soil food webs
Food webs in soil differ fundamentally from those aboveground; they are based on inputs from both living plants via root exudates, and from detritus, which is a complex mixture of fungi, bacteria, and dead plant remains. Trophic relationships are difficult to disentangle due to the cryptic lifestyle of soil animals and inevitable microbial contributions to their diet. Compound-specific isotope analysis of amino acids (AAs) is increasingly used to explore complex food webs. The combined use of AA 𝛿13C and 𝛿15N values is a promising new approach to disentangle trophic relationships since it provides independent but complementary information on basal resources, as well as the trophic position of consumers. We conducted a controlled feeding study in which we reconstructed trophic chains from main basal resources (bacteria, fungi, plants) to primary consumers (springtails, oribatid mites) and predators (gamasid mites, spiders). We analyzed dual compound-specific isotope AA values of both resources and consumers. By applying an approach termed \"stable isotope (13C) fingerprinting\" we identified basal resources, and concomitantly calculated trophic positions using 15N values of trophic and source AAs in consumers. In the 13C fingerprinting analysis, consumers in general grouped close to their basal resources. However, higher than usual offsets in AA 𝛿13C between diet and consumers suggest either gut microbial supplementation or the utilization of specific resource fractions. Identification of trophic position crucially depends on correct estimates of the trophic discrimination factor (TDFGlu-Phe), which was close to the commonly applied value of 7.6‰ in primary consumers feeding on microbial resources, but considerably lower in arachnid predators (~2.4‰), presumably due to higher diet quality, excretion of guanine, and fluid feeding. While our feeding study demonstrates that dual compound-specific AA analyses hold great promise in delineating trophic linkages among soildwelling consumers and their resources, it also highlights that a \"one-size-fits-all\" approach to TDFGlu-Phe does not apply to soil food webs.
Journal Article
polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end machine-driven polymer informatics pipeline that can search this space for suitable candidates at unprecedented speed and accuracy. This pipeline includes a polymer chemical fingerprinting capability called polyBERT (inspired by Natural Language Processing concepts), and a multitask learning approach that maps the polyBERT fingerprints to a host of properties. polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language. The present approach outstrips the best presently available concepts for polymer property prediction based on handcrafted fingerprint schemes in speed by two orders of magnitude while preserving accuracy, thus making it a strong candidate for deployment in scalable architectures including cloud infrastructures.
The polymer universe is gigantic. Searching this space effectively requires ultrafast high-fidelity property prediction methods. Here, the authors present a chemical language model that can probe this space at unprecedented speed and accuracy.
Journal Article
DNA : the story of the genetic revolution
\"James D. Watson, the Nobel laureate whose pioneering work helped unlock the mystery of DNA's structure, charts the greatest scientific journey of our time, from the discovery of the double helix to today's controversies to what the future may hold. [This edition has been] updated to include new findings in gene editing, epigenetics, agricultural chemistry, as well as two entirely new chapters on personal genomics and cancer research\"--Provided by publisher.
BOOK
Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes
PurposeThis review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science.MethodsWeb of Science and Google Scholar were used to review published papers spanning the period 2013–2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018–2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017.ScopeAreas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing.ConclusionsThe popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach.
Journal Article
DNA testing and privacy
\"Home DNA testing companies, such as 23 and Me and AncestryDNA, are at peak popularity, fulfilling our desires to know where we come from and what our future might look like. But questions have arisen about who owns test results and whether testing companies have the right to sell customers' data to pharmaceutical companies and other outlets. Yet home DNA tests have been credited with catching criminals, such as the Golden State Killer. Containing viewpoints from diverse voices in the field, this volume examines the controversies surrounding home DNA tests.\"-- Provided by publisher.
Book
Imaging-based molecular barcoding with pixelated dielectric metasurfaces
Although mid-infrared (mid-IR) spectroscopy is a mainstay of molecular fingerprinting, its sensitivity is diminished somewhat when looking at small volumes of sample. Nanophotonics provides a platform to enhance the detection capability. Tittl et al. built a mid-IR nanophotonic sensor based on reflection from an all-dielectric metasurface array of specially designed scattering elements. The scattering elements could be tuned via geometry across a broad range of wavelengths in the mid-IR. The approach successfully detected and differentiated the absorption fingerprints of various molecules. The technique offers the prospect of on-chip molecular fingerprinting without the need for spectrometry, frequency scanning, or moving mechanical parts. Science , this issue p. 1105 A pixelated dielectric metasurface is used for the mid-infrared detection of molecular fingerprints. Metasurfaces provide opportunities for wavefront control, flat optics, and subwavelength light focusing. We developed an imaging-based nanophotonic method for detecting mid-infrared molecular fingerprints and implemented it for the chemical identification and compositional analysis of surface-bound analytes. Our technique features a two-dimensional pixelated dielectric metasurface with a range of ultrasharp resonances, each tuned to a discrete frequency; this enables molecular absorption signatures to be read out at multiple spectral points, and the resulting information is then translated into a barcode-like spatial absorption map for imaging. The signatures of biological, polymer, and pesticide molecules can be detected with high sensitivity, covering applications such as biosensing and environmental monitoring. Our chemically specific technique can resolve absorption fingerprints without the need for spectrometry, frequency scanning, or moving mechanical parts, thereby paving the way toward sensitive and versatile miniaturized mid-infrared spectroscopy devices.
Journal Article
Genetics, crime and justice
As our understanding of genetics increases, its application to criminal justice becomes more significant. This timely book examines the use of genetic information both in criminal investigations and during the trial process. It discusses current scientific understanding and considers some potential legal, ethical and sociological issues with the use of genetic information. The author draws together debates from scientists, ethicists, sociologists and lawyers in order to understand how the criminal justice system currently reacts, and ought to react, to the new challenges presented by genetic evidence. She asks the important question of where priorities should lie: whether with society's desire to be protected from crime, or with an individual's desire to be protected from an unwanted intrusion into his or her genome. Topics include rights of privacy and consent in obtaining DNA samples, evidentiary issues in court, the impact of genetic evidence on punishment theory and sentencing, and genetic discrimination. This book will be of use to criminal and medical law students, along with academics, practitioners and policymakers interested in exploring the various criminal law issues in relation to genetics. It will also be of interest to criminal justice, philosophy, ethics, sociology and psychology students and academics looking explore the legal issues involved in such a topic.-- Source other than Library of Congress.
Book
VarCoNet: A Variability‐Aware Self‐Supervised Framework for Functional Connectome Extraction From Resting‐State fMRI
Accounting for interindividual variability in brain function is key to precision medicine. Here, by considering functional interindividual variability as meaningful data rather than noise, we introduce VarCoNet, an enhanced self‐supervised framework for robust functional connectome (FC) extraction from resting‐state fMRI (rs‐fMRI) data. VarCoNet employs self‐supervised contrastive learning to exploit inherent functional interindividual variability, serving as a brain function encoder that generates FC embeddings readily applicable to downstream tasks even in the absence of labeled data. Contrastive learning is facilitated by a novel augmentation strategy based on segmenting rs‐fMRI signals. At its core, VarCoNet integrates a 1D‐convolutional neural network (CNN) with a Transformer encoder for advanced time‐series processing, enhanced with robust Bayesian hyperparameter optimization. Our VarCoNet framework is evaluated on two downstream tasks: (i) subject fingerprinting, using rs‐fMRI data from the Human Connectome Project (2117 recordings), and (ii) autism spectrum disorder (ASD) classification, using rs‐fMRI data from the Autism Brain Imaging Data Exchange (ABIDE) I (995 recordings) and II (730 recordings) datasets. Using different brain parcellations, our extensive testing against state‐of‐the‐art methods, including 13 deep learning methods, demonstrates VarCoNet's superiority, robustness, interpretability, and generalizability, achieving up to 98% subject fingerprinting accuracy and an area under the curve (AUC) of 72.6% for ASD classification. Overall, VarCoNet provides a versatile and robust framework for FC analysis in rs‐fMRI. VarCoNet is a robust framework for functional connectome estimation from rs‐fMRI. Using contrastive learning, it trains a 1D‐CNN–Transformer encoder to improve the intra‐ to intersubject variability ratio. VarCoNet achieves state‐of‐the‐art subject fingerprinting and outperforms existing methods in ASD classification.
Journal Article