7,426 research outputs found
Orphan crops and sustainability transitions in agri-food systems: Towards a multidimensional and multilevel transition framework
Neglected and underutilized species (NUS i.e. orphan crops) are widely claimed to contribute to sustainable development. However, the relationship between NUS and sustainable agri-food systems is still unclear. Therefore, this paper analyses the role of
NUS in the transition towards sustainable and resilient agri-food systems and identifies actions needed and levers of change. It draws upon a systematic review of 35 articles identified through a search performed in July 2022 on the Web of Science. The analysis of the literature was conducted following the Multi-Level Perspective on socio-technical transitions (MLP) and its three elements viz. niches, sociotechnical regime and sociotechnical landscape. The review suggests that the transition dynamics and success depend not only on the features of the niche NUS (cf. strengths and weaknesses), regime (cf. barriers to change and competitiveness of major crops with NUS) and landscape (cf. macro-trends and policies) but also on the interactions among them. The levers of change lie in the areas of policy, market and finance, technology, culture, and science and innovation. Further research is needed to elucidate the mechanisms leading to the mainstreaming of NUS into agri-food systems as well as the dynamics of interaction between niche NUS and commercial, staple crops
Study of neural circuits using multielectrode arrays in movement disorders
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Rodríguez Allué, Manuel JoséNeurodegenerative movement-related disorders are characterized by a progressive degeneration and loss of neurons, which lead to motor control impairment. Although the precise mechanisms underlying these conditions are still unknown, an increasing number of studies point towards the analysis of neural networks and functional connectivity to unravel novel insights. The main objective of this work is to understand cellular mechanisms related to dysregulated motor control symptoms in movement disorders, such as Chorea-Acanthocytosis (ChAc), by employing multielectrode arrays to analyze the electrical activity of neuronal networks in mouse models. We found no notable differences in cell viability between neurons with and without VPS13A knockdown, that is the only gene known to be implicated in the disease, suggesting that the absence of VPS13A in neurons may be partially compensated by other proteins. The MEA setup used to capture the electrical activity from neuron primary cultures is described in detail, pointing out its specific characteristics. At last, we present the alternative backup approach implemented to overcome the challenges faced during the research process and to explore the advanced algorithms for signal processing and analysis.
In this report, we present a thorough account of the conception and implementation of our research, outlining the multiple limitations that have been encountered all along the course of the project. We provide a detailed analysis on the project’s economical and technical feasibility, as well as a comprehensive overview of the ethical and legal aspects considered during the execution
Using machine learning to predict pathogenicity of genomic variants throughout the human genome
Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität.
Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores.
Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt.
Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity.
Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants.
The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency.
In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org
Quantum Computing and IS - Harnessing the Opportunities of Emerging Technologies
Emerging technologies have high potential for impact and are worthy of attention by the Information Systems (IS) community. To date, IS has not been able to lead the research and teaching of emerging technologies in their early stages, arguably because: (1) IS researchers often lack knowledge of the foundational principles of such emerging technologies, and (2) during the emerging phase, there is insufficient data on adoption, use, and impact of these technologies. To overcome these challenges, the IS discipline must be willing to break its own disciplinary research boundaries to go beyond software applications and their related management issues and start studying emerging technologies before they are massively adopted by industry. In this paper, we use quantum computing as an exemplar emerging technology and outline a research and education agenda for IS to harness its opportunities. We propose that IS researchers may conduct rigorous research in emergent technologies through collaboration with researchers from other disciplines. We also see a role for IS researchers in the scholarship of emerging technologies that is of introducing emerging technology in IS curricula
Pea breeding for resistance to rhizospheric pathogens
Pea (Pisum sativum L.) is a grain legume widely cultivated in temperate climates. It is important in the race for food security owing to its multipurpose low-input requirement and environmental promoting traits. Pea is key in nitrogen fixation, biodiversity preservation, and nutritional functions as food and feed. Unfortunately, like most crops, pea production is constrained by several pests and diseases, of which rhizosphere disease dwellers are the most critical due to their long-term persistence in the soil and difficulty to manage. Understanding the rhizosphere environment can improve host plant root microbial association to increase yield stability and facilitate improved crop performance through breeding. Thus, the use of various germplasm and genomic resources combined with scientific collaborative efforts has contributed to improving pea resistance/cultivation against rhizospheric diseases. This improvement has been achieved through robust phenotyping, genotyping, agronomic practices, and resistance breeding. Nonetheless, resistance to rhizospheric diseases is still limited, while biological and chemical-based control strategies are unrealistic and unfavourable to the environment, respectively. Hence, there is a need to consistently scout for host plant resistance to resolve these bottlenecks. Herein, in view of these challenges, we reflect on pea breeding for resistance to diseases caused by rhizospheric pathogens, including fusarium wilt, root rots, nematode complex, and parasitic broomrape. Here, we will attempt to appraise and harmonise historical and contemporary knowledge that contributes to pea resistance breeding for soilborne disease management and discuss the way forward
2023-2024 Boise State University Undergraduate Catalog
This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State
Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems
In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources
Leveraging a machine learning based predictive framework to study brain-phenotype relationships
An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the overarching question of how to best structure and run experiments ambiguous. In this work, I cover two explicit pieces of this larger question, the relationship between data representation and predictive performance and a case study on issues related to data collected from disparate sites and cohorts. I then present the Brain Predictability toolbox, a soft- ware package to explicitly codify and make more broadly accessible to researchers the recommended steps in performing a predictive experiment, everything from framing a question to reporting results. This unique perspective ultimately offers recommen- dations, explicit analytical strategies, and example applications for using machine learning to study the brain
EXAMINING PROTEIN CONFORMATIONAL DYNAMICS USING COMPUTATIONAL TECHNIQUES: STUDIES ON PHOSPHATIDYLINOSITOL-3-KINASE AND THE SODIUM-IODIDE SYMPORTER
Experimental biophysics techniques used to study proteins, polymers of amino acids that comprise most therapeutic targets of human disease, face limitations in their ability to interrogate the continual structural fluctuations exhibited by these macromolecules in the context of their myriad cellular functions. This dissertation aims to illustrate case studies that demonstrate how protein conformational dynamics can be characterized using computational methods, yielding novel insights into their functional regulation and activity. Towards this end, the work presented here describes two specific membrane proteins of therapeutic relevance: Phosphoinositide 3-kinase (PI3Kα), and the Na+/I- symporter (NIS).
The PI3KCA gene, encoding the catalytic subunit of the PI3Kα protein that phosphorylates phosphatidylinositol-4,5-bisphosphate (PIP2) to generate phosphatidylinositol-3,4,5-triphosphate (PIP3), is highly mutated in human cancer. As such, a deeper mechanistic understanding of PI3Kα could facilitate the development of novel chemotherapeutic approaches. The second chapter of this dissertation describes molecular dynamics (MD) simulations that were conducted to determine how PI3Kα conformations are influenced by physiological effectors and the nSH2 domain of a regulatory subunit, p85. The results reported here suggest that dynamic allostery plays a role in populating the catalytically competent conformation of PI3Kα.
NIS, a thirteen-helix transmembrane protein found in the thyroid and other tissues, transports iodide, a required constituent of thyroid hormones T3 and T4. Despite extensive experimental information and clinical data, many mechanistic details about NIS remain unresolved. The third chapter of this dissertation describes the results of unbiased and enhanced-sampling MD simulations of inwardly and outwardly open models of bound NIS under an enforced ion gradient. Simulations of NIS in the absence or presence of perchlorate are also described. The work presented in this dissertation aims to add to our mechanistic understanding of NIS ion transport and elucidate conformational states that occur between the inward and outward transitions of NIS in the absence and presence of bound Na+ and I- ions, which can provide valuable insight into its physiological activity and inform therapeutic interventions.
Taken together, these case studies demonstrate the ability of computational techniques to provide novel insights into the impact of structural dynamics on the functional regulation of therapeutically important biological macromolecules
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