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At least 685 records · Page 38

Nonpigmented PsbR is involved in the integrity of excitation landscape in higher plant photosystem II, a case study in Arabidopsis thaliana and a mutant

PsbR is a nonpigmented 10 kDa protein in Photosystem II (PSII) in algae and plants. A recent structural study clarified its enigmatic structural location in a Photosystem II megacomplex that has baffled the community for more than four decades. Our current study interrogates whether absence of PsbR affects the overall dynamics of excitation energy migration within light harvesting complexes (LHC) and PSII super assemblies using highly-active PSII membrane particles, so-called BBY particles, isolated from a PsbR deletion mutant (ΔPsbR) of Arabidopsis thaliana. A femto-second (fs)-time-resolved transient absorption experimentation recorded at 77 K with selective excitation of Chl b which is exclusively present in LHCs enabled us to resolve the temporal differences in LHC→LHC and LHC→PSII excitation energy transfer steps. By applying specific target spectro-kinetic models to the transient absorption datasets, we demonstrated that the time constants of Chl a LHC → Chl a LHC excitation transfer significantly elongates in the ΔPsbR LHC-PSII particles, suggestive of the decreased aggregation level of photosynthetic proteins in the mutant. These findings highlight excitation energy transfer integrity in LHC-PSII assembly is not only determined by the pigmented light-harvesting complexes, but also synergistically by the nonpigmented PSII components. Furthermore, the disturbed integrity in dynamics of excitation energy transfer pathway within LHC-PSII supercomplex is discussed in the context of the altered LHC-PSII megacomplexes type I and II architectures which result from the absence of the PsbR protein in higher plant PSII.

Chlorophyll↗

Enhancing the Range and Reliability of the Spacer Layer Imaging Method

The spacer layer imaging method (SLIM) is widely used to measure the thickness of additive and lubricant films, in lubricant development and evaluation, and for fundamental research into elastohydrodynamic lubrication and tribofilm formation mechanisms. The film thickness measurement, as implemented on several popular tribometers, provides powerful, non-destructive in-situ mapping of film topography with nanometre-scale height sensitivity. However, the results can be highly sensitive to experimental procedure, machine condition, and image analysis, in some cases reporting unphysical film thickness trends. The prevailing image analysis techniques make it challenging to interrogate these errors, often hiding their multivariate nonlinear behaviour from the user by spatial averaging. Herein, several common ‘silent errors’ in the SLIM measurement, including colour matching to incorrect fringe orders, and colour drift due to the optical properties of the system or film itself, are discussed, with examples. A robust suite of novel a priori and a posteriori methods to address these issues, and to improve the accuracy and reliability of the measurement, are also presented, including a novel, computationally inexpensive circle-finding algorithm for automated image processing. In combination, these methods allow reliable mapping of films up to at least 800 nm in thickness, representing a significant milestone for the utility of SLIM applied to elastohydrodynamic contact.

EHL film geometry↗

Phase Stability Through Machine Learning

Understanding the phase stability of a chemical system constitutes the foundation of materials science. Knowledge of the equilibrium state of a system under arbitrary thermodynamic conditions provides valuable information about the types of phases that are likely to be synthesized and how to get there. Accessing the phase diagram in a materials system provides one with the information necessary to design materials and microstructures with optimal properties. While the materials science community has long been focused on exploiting this knowledge to navigate the materials space, recent advances in machine learning (ML) and artificial intelligence (AI) have provided the community with novel ways of interrogating the materials thermodynamics space. Furthermore, this work presents some of the most recent advances in ML/AI applied to phase stability and thermodynamics of materials. Prof. John Morral always had a passion for understanding and teaching the fundamental characteristics of phase diagrams. This review is written to honor his memory.

36 MATERIALS SCIENCE↗

Materials Applications of Photoelectron Emission Microscopy

Photoelectron emission microscopy (PEEM) is a versatile technique that can image a variety of materials including metals, semiconductors and even insulators. Under favorable conditions the most advanced aberration corrected instruments have a spatial resolution approaching 2 nm. Although PEEM cannot compete with transmission or scanning electron microscopies for ultimate resolution, the technique is much more gentle and has the unique advantage of imaging structure as well as electronic and magnetic states on the nanoscale. Since the image contrast is derived from spatial variations in electron photoemission intensity, PEEM is ideal for interrogating both static and dynamic electronic properties of complex nanostructured materials. PEEM can be performed using a variety of photoexcitation sources including synchrotron emission, femtosecond laser pulses and conventional UV lamp emission. Each source has advantages, for example, fs laser excitation enables time-resolved imaging for study of ultrafast dynamics of surface intermediate states while tunable synchrotron sources allow chemically specific excitation. Even more detail can be extracted from energy resolved PEEM. Here, we review the key principles and contrast mechanisms of PEEM and briefly summarize materials applications of PEEM with examples of a thermally-induced structural phase transformation in barium titanate, inter-diffusion between thin metal copper and ruthenium layers, and multiphoton imaging of polystyrene nanoparticles on a silver coated substrate.

43 PARTICLE ACCELERATORS↗

Magnetic properties of friction stir processed composite

There are many existing inspection systems each with their own advantages and drawbacks. These usually comprise of semi-remote sensors which frequently causes difficulty in reaching complex areas of a component. This study proposes to overcome that difficulty by developing embedded functional composites. Through this route, embedding can be achieved in virtually any component part and can be periodically interrogated by a reading device. The “reinforcement rich” processed areas can then be utilized to record properties like strain, temperature, stress state etc. depending on the reinforcement material. In this work, friction stir processing (FSP) was utilized to fabricate a magnetostrictive composite by embedding galfenol particles into a nonmagnetic aluminum (Al) matrix. It targets to develop a composite that produces strain in a varying magnetic field. Reinforcements were observed to be distributed uniformly in the matrix. Magnetization curves were studied using a vibrating sample magnetometer (VSM). A simple and cheap setup was developed to measure the magnetostrictive strain of the composites. Important factors affecting the magnetic properties were identified and ways to improve the magnetic properties discussed.

Das, Shamiparna↗

The Materials Data Facility: Data Services to Advance Materials Science Research

With increasingly strict data management requirements from funding agencies and institutions, expanding focus on the challenges of research replicability, and growing data sizes and heterogeneity, new data needs are emerging in the materials community. The materials data facility (MDF) operates two cloudhosted services, data publication and data discovery, with features to promote open data sharing, self-service data publication and curation, and encourage data reuse, layered with powerful data discovery tools. The data publication service simplifies the process of copying data to a secure storage location, assigning data a citable persistent identifier, and recording custom (e.g., material, technique, or instrument specific)and automatically-extractedmetadata in a registrywhile the data discovery service will provide advanced search capabilities (e.g., faceting, free text range querying, and full text search) against the registered data and metadata. TheMDF services empower individual researchers, research projects, and institutions to (I) publish research datasets, regardless of size, from local storage, institutional data stores, or cloud storage, without involvement of thirdparty publishers; (II) build, share, and enforce extensible domain-specific custom metadata schemas; (III) interact with published data and metadata via representational state transfer (REST) application program interfaces (APIs) to facilitate automation, analysis, and feedback; and (IV) access a data discovery model that allows researchers to search, interrogate, and eventually build on existing published data. We describe MDF’s design, current status, and future plans.

data management↗

Advances in Multimodal Characterization of Structural Materials

The myriad detectors and instruments now available for materials characterization provide researchers with an ever-growing suite of tools to probe material behavior. Progress in the development of instrumentation and workflows that enable the collection, and leverage the potential, of various data modalities have provided novel insights into material behavior. Using data across multiple length scales, or performing complementary analyses of in situ and ex situ data, can help reveal a more complete picture of dynamic processes or material structure. However, the accurate combination, or fusion, of these disparate data modalities presents new challenges. Differences in resolution, as well as the varying length scales at which physical phenomena are exploited to generate these data, necessitate novel approaches to accurately interpret and combine these data. Furthermore, the papers within this special topic focus on the collection and fusion of multimodal data to better understand structural materials. From new frameworks and workflows for data segmentation and analysis, process monitoring, enhancing simulations, or interrogating mechanical response, these papers reveal the potential benefits of utilizing multimodal data.

36 MATERIALS SCIENCE↗

Emerging imaging targets for infiltrative cardiomyopathy: Inflammation and fibrosis

Molecular imaging in infiltrative cardiomyopathies is increasingly penetrating the clinical arena. Current approaches target the infiltrate directly, or its metabolic, physiologic, or functional consequences. Inflammation may not just play a role as the infiltrative mechanism itself. It is also thought to play a key role in the development and progression of heart failure in general, because it promotes the development of tissue fibrosis. The cascade leading from tissue damage to inflammation and further to fibrosis and loss of function has emerged as a therapeutic target. This review focuses (1) on novel tracers of inflammation, which are on the brink of clinical applicability and may be more specific than the gross metabolic marker F-18 deoxyglucose; and (2) on novel biologic imaging targets in fibrosis, which may be exploited for interrogation of the crosstalk between inflammation and loss of contractile function. Ultimately, the success of any novel molecular imaging assay will depend on whether it can be used for successful guidance of novel, targeted therapies aiming at tissue regeneration.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Static, mixed-array total evaporation for improved quantitation of plutonium minor isotopes in small samples

Actinide isotope measurements are a critical signature capability in the modern nuclear forensics “toolbox”, especially when interrogating anthropogenic constituents in real-world scenarios. Unfortunately, established methodologies, such as traditional total evaporation via thermal ionization mass spectrometry, struggle to confidently measure low abundance isotope ratios (<10 -6 ) within already limited quantities of sample. Herein, we investigate the application of static, mixed array total evaporation techniques as a straightforward means of improving plutonium minor isotope measurements, which have been resistant to enhancement in recent years because of elevated radiologic concerns. Furthermore, results are presented for small sample (~20 ng) applications involving a well-known plutonium isotope reference material, CRM-126a, and compared with traditional total evaporation methods.

07 ISOTOPE AND RADIATION SOURCES↗

Front-End Electron Transfer Dissociation Coupled to a 21 Tesla FT-ICR Mass Spectrometer for Intact Protein Sequence Analysis

High resolution mass spectrometry is a key technology for in-depth protein characterization. High-field Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) enables high-level interrogation of intact proteins in the most detail to date. However, an appropriate complement of fragmentation technologies must be paired with FTMS to provide comprehensive sequence coverage, as well as characterization of sequence variants, and post-translational modifications. Here we describe the integration of front-end electron transfer dissociation (FETD) with a custom-built 21 tesla FT-ICR mass spectrometer, which yields unprecedented sequence coverage for proteins ranging from 2.8 to 29 kDa, without the need for extensive spectral averaging (e.g., ~60% sequence coverage for apo-myoglobin with four averaged acquisitions). The system is equipped with a multipole storage device separate from the ETD reaction device, which allows accumulation of multiple ETD fragment ion fills. Consequently, an optimally large product ion population is accumulated prior to transfer to the ICR cell for mass analysis, which improves mass spectral signal-to-noise ratio, dynamic range, and scan rate. We find a linear relationship between protein molecular weight and minimum number of ETD reaction fills to achieve optimum sequence coverage, thereby enabling more efficient use of instrument data acquisition time. Finally, real-time scaling of the number of ETD reactions fills during method-based acquisition is shown, and the implications for LC-MS/MS top-down analysis are discussed. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Quantitative Proteomic Analysis of Optimal Cutting Temperature (OCT) Embedded Core-Needle Biopsy of Lung Cancer

With recent advances in understanding the genomic underpinnings and oncogenic drivers of pathogenesis in different subtypes, it is increasingly clear that proper pretreatment diagnostics are essential for the choice of appropriate treatment options for non-small cell lung cancer (NSCLC). Tumor tissue preservation in optimal cutting temperature (OCT) compound is commonly used in the surgical suite. However, proteins recovered from OCT-embedded specimens pose a challenge for LC-MS/MS experiments, due to the large amounts of polymers present in OCT. Here we present a simple workflow for whole proteome analysis of OCT-embedded NSCLC tissue samples, which involves a simple trichloroacetic acid precipitation step. Comparisons of protein recovery between frozen versus OCT-embedded tissue showed excellent consistency with more than 9200 proteins identified. Using an isobaric labeling strategy, we quantified more than 5400 proteins in tumor versus normal OCT-embedded core needle biopsy samples. Gene ontology analysis indicated that a number of proliferative as well as squamous cell carcinoma (SqCC) marker proteins were overexpressed in the tumor, consistent with the patient’s pathology based diagnosis of “poorly differentiated SqCC”. Among the most downregulated proteins in the tumor sample, we noted a number of proteins with potential immunomodulatory functions. Finally, interrogation of the aberrantly expressed proteins using a candidate approach and cross-referencing with publicly available databases led to the identification of potential druggable targets in DNA replication and DNA damage repair pathways. We conclude that our approach allows LC-MS/MS proteomic analyses on OCT-embedded lung cancer specimens, opening the way to bring powerful proteomics into the clinic. .

62 RADIOLOGY AND NUCLEAR MEDICINE↗

UV-POSIT: Web-Based Tools for Rapid and Facile Structural Interpretation of Ultraviolet Photodissociation (UVPD) Mass Spectra

UV-POSIT (Ultraviolet Photodissociation Online Structure Interrogation Tools) is a suite of web-based tools designed to facilitate the rapid interpretation of data from native mass spectrometry experiments making use of 193 nm ultraviolet photodissociation (UVPD). The suite includes four separate utilities which assist in the calculation of fragment ion abundances as a function of backbone cleavage sites and sequence position; the localization of charge sites in intact proteins; the calculation of hydrogen elimination propensity for a-type fragment ions; and mass-offset searching of UVPD spectra to identify unknown modifications and assess false positive fragment identifications. UV-POSIT is implemented as a Python/Flask web application hosted at http://uv-posit.cm.utexas.edu . UV-POSIT is available under the MIT license, and the source code is available at https://github.com/jarosenb/UV-POSIT . .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

C-O Bond Dissociation and Induced Chemical Ionization Using High Energy (CO{sub 2}){sub n}{sup +} Gas Cluster Ion Beam

A gas cluster ion beam (GCIB) source, consisting of CO{sub 2} clusters and operating with kinetic energies of up to 60 keV, has been developed for the high resolution and high sensitivity imaging of intact biomolecules. The CO{sub 2} molecule is an excellent molecule to employ in a GCIB source due to its relative stability and improved focusing capabilities, especially when compared to the conventionally employed Ar cluster source. Here we report on experiments aimed to examine the behavior of CO{sub 2} clusters as they impact a surface under a variety of conditions. Clusters of (CO{sub 2}){sub n}{sup +} (n = 2000~10,000) with varying sizes and kinetic energies were employed to interrogate both an organic and inorganic surface. The results show that C-O bond dissociation did not occur when the energy per molecule is less than 5 eV/n, but that oxygen adducts were seen in increasing intensity as the energy is above 5 eV/n, particularly, drastic enhancement up to 100 times of oxygen adducts was observed on Au surface. For Irganox 1010, an organic surface, oxygen containing adducts were observed with moderate signal enhancement. Molecular dynamics computer simulations were employed to test the hypothesis that the C-O bond is broken at high values of eV/n. These calculations show that C-O bond dissociation occurs at eV/n values less than the C-O bond energy (8.3 eV) by interaction with surface topological features. In general, the experiments suggest that the projectiles containing oxygen can enhance the ionization efficiency of surface molecules via chemically induced processes, and that CO{sub 2} can be an effective cluster ion source for SIMS experiments. .

74 ATOMIC AND MOLECULAR PHYSICS↗

MR Imaging of Patients with Cardiac Implantable Electronic Devices (CIEDs): Implementing a Program and Optimizing CMR

Purpose of review: How to implement a program for MRI in patients with conditional and non-conditional (“legacy”) CIED. How to optimize safety and image quality in CMR? What the radiologists need to know: device programming, pre- and post-MRI CIED parameters. Recent findings: New Center for Medicare and Medicaid Services (CMS) guidelines have opened MRI to many patients with nonconditional devices. Newer pulse sequences for cardiac MRI (CMR) in device patients, including modified cine, late gadolinium enhancement and T1 mapping sequences, have resulted in improved image quality in device patients. We present several cases of CMR in device patients, including imaging of leadless pacemakers subcutaneous ICDs. Imaging pitfalls/artifacts in CMR interpretation are also presented. Summary: An organized institutional workflow with an established protocol involving proper patient screening, device programming, scan monitoring, and pre- and post-ICD interrogation minimizes risk in patients with conditional and non-conditional CIEDs. Although rare, significant changes in lead performance, power-on/reset, inappropriate pacing or ICD discharges and battery voltage depletion should be documented. High-quality scans with accurate scar characterization in CIED patients require the use of wideband sequences. CMS has recently acknowledged MRI as a reasonable test necessary for the diagnosis and treatment of beneficiaries with implanted CIEDs, improving the financial prospects and encouraging future developments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

A Graph Theory-Based Automated Twin Recognition Technique for Electron Backscatter Diffraction Analysis

Abstract The present article introduces a new software, Microstructure Evaluation Tool for Interface Statistics (METIS), that performs high-throughput microstructure statistical analysis from electron backscatter diffraction maps. Emphasis is placed on the detection of twin domains in hexagonal close-packed metals. The numerical framework on which METIS is built leverages graph theory, group structures, and associated numerical algorithms to automatically detect twins and unravel both their intrinsic characteristics features and those pertaining to their interactions. The proposed graphical interface allows for the detection and correction of unlikely twin/parent associations rendering the approach applicable to highly deformed microstructures. Twin statistics and microstructural data are classified and saved in a relational database that can be interrogated via either GUI or SQL requests to reveal a wide spectrum of features of the microstructure. Illustration of the approach is performed in the case of zirconium.

36 MATERIALS SCIENCE↗

Anomaly Detection in Materials Digital Twins with Multiscale ICME for Additive Manufacturing

Detecting anomaly in fatigue and fracture experimental materials science is an interesting yet challenging topic. The reasons are threefold. First, the anomalous microstructure feature that gives rise to structural failure is small, sometimes in the order of 10 -7 of the interrogated volume. This, in turn, results in a highly imbalanced classification problem in machine learning (ML). Second, the consequence is high, in the sense that the test specimen is destructed in such case. Third, the convolution between microstructure stochasticity and the small probability of void nucleation, growth, and coalescence makes failure and fracture a hard-to-predict and challenging problem in materials science due to its irreproducibility, even experimentally. In this paper, we developed a materials digital twin and applied anomaly detection methods to detect voids and anomaly in additive manufacturing (AM). The materials digital twin is driven by two integrated computational materials engineering (ICME) models, which are kinetic Monte Carlo (kMC) and crystal plasticity finite element method (CPFEM). In conclusion, we demonstrated that by using anomaly detection, it is possible to detect voids and other defects in materials digital twin, which paves way for future research in integrating materials digital twin with its physical counterpart.

ICME↗

Hypothesis-Agnostic Network-Based Analysis of Real-World Data Suggests Ondansetron is Associated with Lower COVID-19 Any Cause Mortality

Background: The COVID-19 pandemic generated a massive amount of clinical data, which potentially hold yet undiscovered answers related to COVID-19 morbidity, mortality, long-term effects, and therapeutic solutions.Objectives: The objectives of this study were (1) to identify novel predictors of COVID-19 any cause mortality by employing artificial intelligence analytics on real-world data through a hypothesis-agnostic approach and (2) to determine if these effects are maintained after adjusting for potential confounders and to what degree they are moderated by other variables.Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within the Interrogative Biology® platform was used for Bayesian network learning and hypothesis generation to analyze 16,277 PCR+ patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated Bayesian networks that enabled unbiased identification of significant predictors of any cause mortality for specific COVID-19 patient populations. These findings were further analyzed by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping.Results: We found that in the COVID-19 PCR+ patient cohort, early use of the antiemetic agent ondansetron was associated with decreased any cause mortality 30 days post-PCR+ testing in mechanically ventilated patients.Conclusions: The results demonstrate how a real-world COVID-19-focused data analysis using artificial intelligence can generate unexpected yet valid insights that could possibly support clinical decision making and minimize the future loss of lives and resources.

60 APPLIED LIFE SCIENCES↗

Multivariate statistics applications in scanning transmission electron microscopy X-ray spectrum imaging

A modern scanning transmission electron microscope (STEM) fitted with an energy dispersive X-ray spectroscopy (EDS) system can quickly and easily produce spectrum image (SI) datasets containing so much information (hundreds to thousands of megabytes) that they cannot be comprehensively interrogated by a human analyst. Therefore, advanced mathematical techniques are needed to glean materials science and engineering insight into the processing-structure-properties relationship of the examined material from the SI data. This review will discuss recent advances in the application of multivariate statistical analysis (MVSA) methods to STEM-EDS SI experiments. In particular, the fundamental mathematics of principal component analysis (PCA) and related methods are reviewed, and advanced methods such as multivariate curve resolution (MCR) are discussed. The applications of PCA and MCR-based techniques to solve difficult materials science problems, such as the analysis of a particle fully embedded in a matrix phase are discussed, as well as confounding effects such as rank deficiency that can confuse the results of MVSA computations. Possible future advances and areas in need of study are also mentioned.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗