1,048 research outputs found
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FRET-based dynamic structural biology: Challenges, perspectives and an appeal for open-science practices
Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current ‘state of the art’ from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of ‘soft recommendations’ about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage ‘open science’ practices
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Digital agriculture: research, development and innovation in production chains.
Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
Cryo-Electron Microscopy to Investigate Molecular Dynamics and Conformational Changes in Protein Complexes
Stress can be considered as one of the most fundamental aspects in life, and all living organisms are constantly exposed to a variety of different stress situations. Thus, efficient stress sensing and reaction mechanisms are crucial for their survival. Stress response mechanisms are as diverse as the causative stimuli and oftentimes cross-linked forming a versatile reaction network, to ensure the cells’ survival under critical situations. Notably, stress response mechanisms play a major role in pathogenicity, virulence and disease. Pathogenic Bacteria are permanently facing environmental pressure originating from the host’s defense systems or drug treatments, while mutations in eukaryotic stress response systems have been shown to cause a large number of severe human diseases such as diabetes, cancer or Parkinson’s and Alzheimer’s disease. A profound molecular knowledge on the respective mechanisms is thus the inevitable prerequisite towards a global understanding of this fundamental aspect of life, paving the way for the development of new drugs or therapeutic approaches.
Within this thesis, various aspects of stress response mechanisms in three different systems were investigated using state-of-the-art electron microscopy techniques. First, I set out to solve the structure of the Vibrio vulnificus stressosome complex, a key player in the bacterial environmental stress response. Currently, there is no structural data available for any gram-negative stressosome. A medium-resolution cryo-electron microscopy (cryo-EM) structure of the minimal complex could be obtained, which features an exceptional symmetry break originating from its unique, regulatory stoichiometry. Based on the structural data, it was possible to propose an activation mechanism and to pinpoint a number of significant differences in comparison to gram-positive stressosome complexes. Undoubtedly, the structure contributes a major piece of information necessary to understand stress sensing and signal transduction in this human pathogen. This study was complemented by a number of physiological and phylogenetic experiments contributed by our co-workers, and published recently (VIII. PUBLICATION 1).
The second project focused on the gram-positive soil bacterium Corynebacterium glutamicum, a prime model organism for investigations of the bacterial osmostress response. Sensing of hyper-osmotic stress and regulation of the respective stress response in C. glutamicum are simultaneously performed by BetP, a conformationally asymmetric-trimeric secondary active transporter able to import the compatible solute betaine. Two stimuli are identified to initiate the full osmostress response in BetP, namely an elevated cytoplasmic K+ concentration and a loosely defined ‘membrane stimulus’. Despite the availability of functional data on BetP regulation, structural information especially of the down-regulated state and the subsequent transition events are absent. Using single particle cryo-EM analysis, I was able to provide high-resolution structures of the down-regulated and a transition state, which elucidated a number of important structural features not described so far. It could be shown that down-regulated BetP adopts a symmetric arrangement stabilized by antight cytoplasmic interaction network of the sensory domain, further strengthened by Cardiolipin molecules located at regulatory lipid binding sites. These constraints are released upon stress sensing, as demonstrated by fourier transform infrared (FTIR) spectroscopy and molecular dynamics simulation (MD) data contributed by our co-workers, resulting in the well-established, asymmetric-trimeric structures previously known. The wealth of new data on the down-regulated state allowed to propose a detailed regulation mechanism and to further sharpen the previously vague picture of the membrane stimulus. The data are summarized and presented in IX. PREPRINT 1.
A third topic of this thesis was the three dimensional investigation via dual-axis scanning transmission electron microscopy (STEM) tomography of crystalloid-ER structures we identified before in human embryonic kidney (HEK) cells upon over-expression of polycystin-2 (PC-2). In this study presented in X. MANUSCRIPT 1, I was further able to proof the presence of ER whorls, and to obtain high-resolution three-dimensional (3D) reconstructions of the two different ER morphotypes. These data provided unmatched insights into the cellular ER interaction partners and clearly demonstrated the dynamic nature of the organelle even under stress situations. A detailed discussion of the identified morphological features in their respective cellular context finally allowed for the description of the organellar membrane architecture at a high level of detail.
Lastly, the discussion addresses the electron microscopy techniques and instruments used and contains an outlook on further perspectives for the projects. Overall, this thesis yielded intriguing mechanistic insights into the versatile bacterial and eukaryotic stress response mechanisms, reflecting their manifold nature ultimately converging to a common outcome
Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability
The integration of Artificial Intelligence (AI) into the field of drug
discovery has been a growing area of interdisciplinary scientific research.
However, conventional AI models are heavily limited in handling complex
biomedical structures (such as 2D or 3D protein and molecule structures) and
providing interpretations for outputs, which hinders their practical
application. As of late, Graph Machine Learning (GML) has gained considerable
attention for its exceptional ability to model graph-structured biomedical data
and investigate their properties and functional relationships. Despite
extensive efforts, GML methods still suffer from several deficiencies, such as
the limited ability to handle supervision sparsity and provide interpretability
in learning and inference processes, and their ineffectiveness in utilising
relevant domain knowledge. In response, recent studies have proposed
integrating external biomedical knowledge into the GML pipeline to realise more
precise and interpretable drug discovery with limited training instances.
However, a systematic definition for this burgeoning research direction is yet
to be established. This survey presents a comprehensive overview of
long-standing drug discovery principles, provides the foundational concepts and
cutting-edge techniques for graph-structured data and knowledge databases, and
formally summarises Knowledge-augmented Graph Machine Learning (KaGML) for drug
discovery. A thorough review of related KaGML works, collected following a
carefully designed search methodology, are organised into four categories
following a novel-defined taxonomy. To facilitate research in this promptly
emerging field, we also share collected practical resources that are valuable
for intelligent drug discovery and provide an in-depth discussion of the
potential avenues for future advancements
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Operational modal analysis and prediction of remaining useful life for rotating machinery
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe significance of rotating machinery spans areas from household items to vital industry sectors, such as aerospace, automotive, railway, sea transport, resource extraction, and manufacturing. Hence, our technologised society depends on efficient and reliable operation of rotating machinery. To contribute to this aim, this thesis leverages measurable quantities during its operation for structural-mechanical evaluation employing Operational Modal Analysis (OMA) and the prediction of Remaining Useful Life (RUL). Modal parameters determined by OMA are central for the design, test, and validation of rotating machinery. This thesis introduces the first open parametric simulation dataset of rotating machinery during an acceleration run. As there is a lack of similar open datasets suitable for OMA, it lays a foundation for improved reproducibility and comparability of future research. Based on this, the Averaged Order-Based Modal Analysis (AOBMA) method is developed. The novel addition of scaling and weighted averaging of individual machine orders in AOBMA alleviates the analysis effort of the existing Order-Based Modal Analysis (OBMA) method by providing a unified set of modal parameters with higher accuracy. As such, AOBMA showed a lower mean absolute relative error of 0.03 for damping ratio estimations across compared modes while OBMA provided an error value of 0.32 depending on the processed order. At excitation with high harmonic contributions, AOBMA also resulted in the highest number of accurately identified modes among the compared methods. At a harmonic ratio of 0.8, for example, AOBMA identified an average of 11.9 modes per estimation, while OBMA and baseline OMA followed with 9.5 and 9 modes, respectively. Moreover, it is the first study, which systematically evaluates the impact of excitation conditions on the compared methods and finds an advantage of OBMA and AOBMA over traditional OMA regarding mode shape estimation accuracy. While OMA can be used to evaluate significant structural changes, Machine Learning (ML) methods have seen substantially greater success in condition monitoring, including RUL prediction. However, as these methods often require large amounts of time and cost-
intensive training data, a novel data-efficient RUL prediction methodology is introduced, taking advantage of distinct healthy and faulty condition data. When the number of training sequences from an open dataset is reduced to 5%, an average prediction Root Mean Square Error (RMSE) of 24.9 operation cycles is achieved, outperforming the baseline method with an RMSE of 28.1. Motivated by environmental considerations, the impact of data reduction on the training duration of several method variants is quantified. When the full training set is
utilised, the most resource-saving variant of the proposed approach achieves an average training duration of 8.9% compared to the baseline method
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