9,047 research outputs found

    Decoding spatial location of attended audio-visual stimulus with EEG and fNIRS

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    When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location in the presence of background noises and irrelevant visual objects. The ability to decode the attended spatial location would facilitate brain computer interfaces (BCI) for complex scene analysis. Here, we tested two different neuroimaging technologies and investigated their capability to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. For functional near-infrared spectroscopy (fNIRS), we targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. We found that fNIRS provides robust decoding of attended spatial locations for most participants and correlates with behavioral performance. Moreover, we found that FEF makes a large contribution to decoding performance. Surprisingly, the performance was significantly above chance level 1s after cue onset, which is well before the peak of the fNIRS response. For electroencephalography (EEG), while there are several successful EEG-based algorithms, to date, all of them focused exclusively on auditory modality where eye-related artifacts are minimized or controlled. Successful integration into a more ecological typical usage requires careful consideration for eye-related artifacts which are inevitable. We showed that fast and reliable decoding can be done with or without ocular-removal algorithm. Our results show that EEG and fNIRS are promising platforms for compact, wearable technologies that could be applied to decode attended spatial location and reveal contributions of specific brain regions during complex scene analysis

    Relations between soil organic carbon, soil structure and physical processes in an agricultural topsoil : The role of soil mineral constituents

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    A better understanding of the interactions between soil organic carbon (SOC) and mineral constituents (e.g. clay and reactive oxide phases) and their consequences for soil structure and physical processes is important for assessing the potential for, and benefits of, carbon sequestration in arable soils. This thesis investigated the factors determining topsoil SOC content at the field scale for an arable field with large var-iations in soil properties. Relationships between SOC, soil pore size distributions, macropore network characteristics, water flow and solute transport were also exam-ined using intact soil samples from the field. The spatial variation in SOC content at the Bjertorp field was mainly explained by the oxalate-extractable aluminum (Alox) content followed by carbon input from crops that was estimated from crop yield. In contrast, clay and oxalate-extractable iron (Feox) seemed not to play a major role in SOC stabilization/accumulation, pos-sibly due to the occurrence of stagnant water in soils with larger clay contents. It was concluded that reactive Al phases may be important for physico-chemical stabiliza-tion of SOC for arable topsoils in humid continental climates. Multiple linear regression analysis revealed that an increase of SOC was associ-ated with relatively large increases of porosities in the 0.2–5 µm and 480–720 µm diameter classes, which can contribute to enhancing both water supply to crops and water flow rates. The degree of preferential solute transport under steady state near-saturated conditions was reduced with larger volumes of small macropores (240–480 µm diameter) and mesopores (30–100 µm diameter), whereas it was not corre-lated with measures of macropore connectivity. The statistical analysis indicated that SOC had only limited effects on the degree of preferential transport, being overshad-owed by the large variation in clay content across the field

    Genomik-basierte Verbesserung des heimischen Sojazuchtmaterials und Etablierung eines molekularen Screeningsystems für Soja-Pathogene

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    Zur Einleitung von Pflanzenschutzmaßnahmen bei Soja ist es essentiell, Informationen über vorhandene Pathogene zu gewinnen. Basierend auf der quantitativen Real Time-PCR wurden Einzelnachweise für die Hauptpathogene der Sojabohne in Deutschland entwickelt. Die Pathogenität der Krankheitserreger wurde geprüft und das Inokulationsverfahren für weitere Untersuchungen etabliert. Erste Mehrfach-Nachweise für verschiedene Erreger und Probentypen wurden entwickelt, die für eine frühzeitige Detektion von Erregern in Soja-Proben (Saatgut, Pflanze, Boden) verwendet werden können. Im Rahmen des Projekts wurde zudem durch Kombination von aktuellen und zukünftigen Klimaparametern sowie Genotypisierungs- und Phänotypisierungsdaten eine Kernkollektion von Akzessionen erstellt, die sich durch eine hohe Diversität auszeichnet und sich für den Anbau unter den Bedingungen von Zentraleuropa eignet. Dabei wurden neue Gene für Umweltadaptation identifiziert, für die molekulare Marker für die Züchtung entwickelt werden, und geeignete Akzessionen für die Einkreuzung in aktuelle Züchtungsprogramme wurden selektiert. Die Ergebnisse zeigen außerdem klar, dass hochertragreiche Linien gezüchtet werden können, um die Sojaanbauregion nach Norden zu erweitern. Zudem bieten die gewonnenen Varianzkomponenten, Heritabilitäten und Merkmalskorrelationen eine solide Grundlage für die Gestaltung von Zuchtprogrammen, insbesondere auch von Speed-Breeding Programmen, die zukünftig mit genomischer und phänomischer Selektion beschleunigt werden können. Es wurden bereits erste Linien an die private Pflanzenzüchtung abgegeben, die in weiteren Prüfungen ermitteln, ob die Linien als Sorten zugelassen werden können. Gerade in kühleren Lagen, wie z.B. in Norddeutschland, ist die Gefahr von Ertragseinbußen auf Grund von Kühlestress groß. In diesem Projekt wurden Linien von Kreuzungsnachkommenschaften und ein diverses Set aus Genbankakzessionen hinsichtlich ihres Hülsenansatzes nach einer Kühlestressphase charakterisiert. Das Testsystem führte zu reproduzierbaren Ergebnissen und es wurden erste QTL für den Hülsenansatz unter Stressbedingungen ermittelt

    Annals [...].

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    Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin

    Omics measures of ageing and disease susceptibility

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    While genomics has been a major field of study for decades due to relatively inexpensive genotyping arrays, the recent advancement of technology has also allowed the measure and study of various “omics”. There are now numerous methods and platforms available that allow high throughput and high dimensional quantification of many types of biological molecules. Traditional genomics and transcriptomics are now joined by proteomics, metabolomics, glycomics, lipidomics and epigenomics. I was lucky to have access to a unique resource in the Orkney Complex Disease Study (ORCADES), a cohort of individuals from the Orkney Islands that are extremely deeply annotated. Approximately 1000 individuals in ORCADES have genomics, proteomics, lipidomics, glycomics, metabolomics, epigenomics, clinical risk factors and disease phenotypes, as well as body composition measurements from whole body scans. In addition to these cross-sectional omics and health related measures, these individuals also have linked electronic health records (EHR) available, allowing the assessment of the effect of these omics measures on incident disease over a ~10-year follow up period. In this thesis I use this phenotype rich resource to investigate the relationship between multiple types of omics measures and both ageing and health outcomes. First, I used the ORCADES data to construct measures of biological age (BA). The idea that there is an underlying rate at which the body deteriorates with age that varies between individuals of the same chronological age, this biological age, would be more indicative of health status, functional capacity and risk of age-related diseases than chronological age. Previous models estimating BA (ageing clocks) have predominantly been built using a single type of omics assay and comparison between different omics ageing clocks has been limited. I performed the most exhaustive comparison of different omics ageing clocks yet, with eleven clocks spanning nine different omics assays. I show that different omics clocks overlap in the information they provide about age, that some omics clocks track more generalised ageing while others track specific disease risk factors and that omics ageing clocks are prognostic of incident disease over and above chronological age. Second, I assessed whether individually or in multivariable models, omics measures are associated with health-related risk factors or prognostic of incident disease over 10 years post-assessment. I show that 2,686 single omics biomarkers are associated with 10 risk factors and 44 subsequent incident diseases. I also show that models built using multiple biomarkers from whole body scans, metabolomics, proteomics and clinical risk factors are prognostic of subsequent diabetes mellitus and that clinical risk factors are prognostic of incident hypertensive disorders, obesity, ischaemic heart disease and Framingham risk score. Third, I investigated the genetic architecture of a subset of the proteomics measures available in ORCADES, specifically 184 cardiovascular-related proteins. Combining genome-wide association (GWAS) summary statistics from ORCADES and 17 other cohorts from the SCALLOP Consortium, giving a maximum sample size of 26,494 individuals, I performed 184 genome-wide association meta-analyses (GWAMAs) on the levels of these proteins circulating in plasma. I discovered 592 independent significant loci associated with the levels of at least one protein. I found that between 8-37% of these significant loci colocalise with known expression quantitative trait loci (eQTL). I also find evidence of causal associations between 11 plasma protein levels and disease susceptibility using Mendelian randomisation, highlighting potential candidate drug targets

    Antibody Targeting of HIV-1 Env: A Structural Perspective

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    A key component of contemporary efforts toward a human immunodeficiency virus 1 (HIV-1) vaccine is the use of structural biology to understand the structural characteristics of antibodies elicited both from human patients and animals immunized with engineered 'immunogens,' or early vaccine candidates. This thesis will report on projects characterizing both types of antibodies against HIV-1. Chapter 1 will introduce relevant topics, including the reasons HIV-1 is particularly capable of evading the immune system in natural infection and after vaccination, the 20+ year history of unsuccessful HIV-1 vaccine large-scale efficacy trials, an introduction to broadly neutralizing antibodies (bNAbs), and a review of common strategies utilized in HIV-1 immunogen design today. Chapter 2 describes the isolation, high-resolution structural characterization, and in vitro resistance profile of a new bNAb, 1-18, that is both very broad and potent, as well as able to restrict HIV-1 escape in vivo. Chapter 3 reports the results of an epitope-focusing immunogen design and immunization experiment carried out in wild type mice, rabbits, and non-human primates where it was shown that B cells targeting the desired epitope were expanded after a single prime immunization with immunogen RC1 or a variant, RC1-4fill. Chapter 4 describes Ab1245, an off-target non-neutralizing monoclonal antibody isolated in a macaque that had been immunized with a series of sequential immunogens after the prime immunization reported in Chapter 3. The antibody structure describes a specific type of distracting response as it binds in a way that causes a large structural change in Env, resulting in the destruction of the neutralizing fusion peptide epitope. Chapter 5 is adapted from a review about how antibodies differentially recognize the viruses HIV-1, SARS-CoV-2, and Zika virus. This review serves as an introduction to the virus SARS-CoV-2, which is the topic of the final chapter, Chapter 6. In this chapter, structures of many neutralizing antibodies isolated from SARS-CoV-2 patients were used to define potentially therapeutic classes of neutralizing receptor-binding domain (RBD) antibodies based on their epitopes and binding profiles

    DEEP REINFORCEMENT LEARNING AND MODEL PREDICTIVE CONTROL APPROACHES FOR THE SCHEDULED OPERATION OF DOMESTIC REFRIGERATORS

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    Excess capacity of the UK’s national grid is widely quoted to be reducing to around 4% over the coming years as a consequence of increased economic growth (and hence power usage) and reductions in power generation plants. There is concern that short term variations in power demand could lead to serious wide-scale disruption on a national scale. This is therefore spawning greater attention on augmenting traditional generation plants with renewable and localized energy storage technologies, and consideration of improved demand side responses (DSR), where power consumers are incentivized to switch off assets when the grid is under pressure. It is estimated, for instance, that refrigeration/HVAC systems alone could account for ~14% of the total UK energy usage, with refrigeration and water heating/cooling systems, in particular, being able to act as real-time ‘buffer’ technologies that can be demand-managed to accommodate transient demands by being switched-off for short periods without damaging their outputs. Large populations of thermostatically controlled loads (TCLs) hold significant potential for performing ancillary services in power systems since they are well-established and widely distributed around the power network. In the domestic sector, refrigerators and freezers collectively constitute a very large electrical load since they are continuously connected and are present in almost most households. The rapid proliferation of the ‘Internet of Things’ (IoT) now affords the opportunity to monitor and visualise smart buildings appliances performance and specifically, schedule the operation of the widely distributed domestic refrigerator and freezers to collectively improve energy efficiency and reduce peak power consumption on the electrical grid. To accomplish this, this research proposes the real-time estimation of the thermal mass of individual refrigerators in a network using on-line parameter identification, and the co-ordinated (ON-OFF) scheduling of the refrigerator compressors to maintain their respective temperatures within specified hysteresis bands—commensurate with accommodating food safety standards. Custom Model Predictive Control (MPC) schemes and a Machine Learning algorithm (Reinforcement Learning) are researched to realize an appropriate scheduling methodology which is implemented through COTS IoT hardware. Benefits afforded by the proposed schemes are investigated through experimental trials which show that the co-ordinated operation of domestic refrigerators can 1) reduce the peak power consumption as seen from the perspective of the electrical power grid (i.e. peak power shaving), 2) can adaptively control the temperature hysteresis band of individual refrigerators to increase operational efficiency, and 3) contribute to a widely distributed aggregated load shed for Demand Side Response purposes in order to aid grid stability. Comparative studies of measurements from experimental trials show that the co-ordinated scheduling of refrigerators allows energy savings of between 19% and 29% compared to their traditional isolated (non-co-operative) operation. Moreover, by adaptively changing the hysteresis bands of individual fridges in response to changes in thermal behaviour, a further 20% of savings in energy are possible at local refrigerator level, thereby providing benefits to both network supplier and individual consumer

    Algorithms to estimate Shapley value feature attributions

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    Feature attributions based on the Shapley value are popular for explaining machine learning models; however, their estimation is complex from both a theoretical and computational standpoint. We disentangle this complexity into two factors: (1)~the approach to removing feature information, and (2)~the tractable estimation strategy. These two factors provide a natural lens through which we can better understand and compare 24 distinct algorithms. Based on the various feature removal approaches, we describe the multiple types of Shapley value feature attributions and methods to calculate each one. Then, based on the tractable estimation strategies, we characterize two distinct families of approaches: model-agnostic and model-specific approximations. For the model-agnostic approximations, we benchmark a wide class of estimation approaches and tie them to alternative yet equivalent characterizations of the Shapley value. For the model-specific approximations, we clarify the assumptions crucial to each method's tractability for linear, tree, and deep models. Finally, we identify gaps in the literature and promising future research directions

    Evidence gathering in support of sustainable Scottish inshore fisheries: work package (4) final report: a pilot study to define the footprint and activities of Scottish inshore fisheries by identifying target fisheries, habitats and associated fish stocks

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    [Extract from Executive Summary] This work was conducted under Work package 4 of the European Fisheries Funded program “Evidence Gathering in Support of Sustainable Scottish Inshore Fisheries”. The overall aim of the program was to work in partnership with Marine Scotland Fisheries Policy and with the Scottish Inshore Fisheries Groups to help develop inshore fisheries management. Specifically the program aims were to establish the location of fishing activities within inshore areas; to identify catch composition and associated fishery impacts; to define the environmental footprint and availability of stocks; to develop economic value within local fisheries and; to establish an information resource base to assist the development of inshore fisheries management provisions.Publisher PD
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