Materials and structures

Information on materials and structures for use in space is given in viewgraph form. Information is given on the Materials and Structures Division of NASA's Office of Aeronautics and Space Technology. The Division's space research and development budget is given. Further information is given on space materials and structures, space environmental effects, radiation effects, high temperature materials research, metal matrix composites, SiC fiber reinforced titanium alloys, structural dynamics, and control of flexible structures

Small-crack effects in high-strength aluminum alloys

The National Aeronautics and Space Administration and the Chinese Aeronautical Establishment participated in a Fatigue and Fracture Mechanics Cooperative Program. The program objectives were to identify and characterize crack initiation and growth of small cracks (10 microns to 2 mm long) in commonly used US and PRC aluminum alloys, to improve fracture mechanics analyses of surface- and corner-crack configurations, and to develop improved life-prediction methods. Fatigue and small-crack tests were performed on single-edgenotch tension (SENT) specimens and large-crack tests were conducted on center-crack tension specimens for constant-amplitude (stress ratios of -1, 0, and 0.5) and Mini-TWIST spectrum loading. The plastic replica method was used to monitor the initiation and growth of small fatigue cracks at the semicircular notch. Crack growth results from each laboratory on 7075-T6 bare and LC9cs clad aluminum alloys agreed well and showed that fatigue life was mostly crack propagation from a material defect (inclusion particles or void) or from the cladding layer. Finite-element and weight-function methods were used to determine stress intensity factors for surface and corner cracks in the SENT specimens. Equations were then developed and used in a crack growth and crack-closure model to correlate small- and large-crack data and to make life predictions for various load histories. The cooperative program produced useful experimental data and efficient analysis methods for improving life predictions. The results should ultimately improve aircraft structural reliability and safety

Deficits in mitochondrial spare respiratory capacity contribute to the neuropsychological changes of alzheimer’s disease

Alzheimer’s disease (AD) is diagnosed using neuropsychological testing, supported by amyloid and tau biomarkers and neuroimaging abnormalities. The cause of neuropsychological changes is not clear since they do not correlate with biomarkers. This study investigated if changes in cellular metabolism in AD correlate with neuropsychological changes. Fibroblasts were taken from 10 AD patients and 10 controls. Metabolic assessment included measuring total cellular ATP, extracellular lactate, mitochondrial membrane potential (MMP), mitochondrial respiration and glycolytic function. All participants were assessed with neuropsychological testing and brain structural MRI. AD patients had significantly lower scores in delayed and immediate recall, semantic memory, phonemic fluency and Mini Mental State Examination (MMSE). AD patients also had significantly smaller left hippocampal, left parietal, right parietal and anterior medial prefrontal cortical grey matter volumes. Fibroblast MMP, mitochondrial spare respiratory capacity (MSRC), glycolytic reserve, and extracellular lactate were found to be lower in AD patients. MSRC/MMP correlated significantly with semantic memory, immediate and delayed episodic recall. Correlations between MSRC and delayed episodic recall remained significant after controlling for age, education and brain reserve. Grey matter volumes did not correlate with MRSC/MMP. AD fibroblast metabolic assessment may represent an emergent disease biomarker of AD

Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments

Background: Understanding whether the cognitive profile of a patient indicates mild cognitive impairment (MCI) or performance levels within normality is often a clinical challenge. The use of resting-state functional magnetic resonance imaging (RS-fMRI) and machine learning may represent valid aids in clinical settings for the identification of MCI patients. Methods: Machine-learning models were computed to test the classificatory accuracy of cognitive, volumetric [structural magnetic resonance imaging (sMRI)] and blood oxygen level dependent-connectivity (extracted from RS-fMRI) features, in single-modality and mixed classifiers. Results: The best and most significant classifier was the RS-fMRI+Cognitive mixed classifier (94% accuracy), whereas the worst performing was the sMRI classifier (∼80%). The mixed global (sMRI+RS-fMRI+Cognitive) had a slightly lower accuracy (∼90%), although not statistically different from the mixed RS-fMRI+Cognitive classifier. The most important cognitive features were indices of declarative memory and semantic processing. The crucial volumetric feature was the hippocampus. The RS-fMRI features selected by the algorithms were heavily based on the connectivity of mediotemporal, left temporal, and other neocortical regions. Conclusion: Feature selection was profoundly driven by statistical independence. Some features showed no between-group differences, or showed a trend in either direction. This indicates that clinically relevant brain alterations typical of MCI might be subtle and not inferable from group analysis

A dementia classification framework using frequency and time-frequency features based on EEG signals.

Alzheimer's Disease (AD) accounts for 60-70% of all dementia cases, and clinical diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify disease progression or alleviate symptoms are being developed, to assess their efficacy, novel robust biomarkers of brain function are urgently required. This study aims to explore a routine to gain such biomarkers using the quantitative analysis of Electroencephalography (QEEG). This paper proposes a supervised classification framework which uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-Nearest Neighbour (KNN) classification, quantitative evaluation and topographic visualisation. Considering the human brain either as a stationary or a dynamical system, both frequency-based and time-frequency-based features were tested in 40 participants. Results: a) The proposed method can achieve up to 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared to alternative machine learning approaches; b) The features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; c) In the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD. The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and localisation of significant QEEG features. These important findings and the proposed classification framework could be used for the development of a biomarker for the diagnosis and monitoring of disease progression in AD

International survey of COVID-19 management strategies

Background: While individual countries have gained considerable knowledge and experience in coronavirus disease of 2019 (COVID-19) management, an international, comparative perspective is lacking, particularly regarding the measures taken by different countries to tackle the pandemic. This paper elicits the views of health system staff, tapping into their personal expertise on how the pandemic was initially handled. Methods: From May to July 2020, we conducted a cross-sectional, online, purpose-designed survey comprising 70 items. Email lists of contacts provided by the International Society for Quality in Health Care, the Italian Network for Safety in Health Care and the Australian Institute of Health Innovation were used to access healthcare professionals and managers across the world. We snowballed the survey to individuals and groups connected to these organizations. Key outcome measures were attitudes and information about institutional approaches taken; media communication; how acute hospitals were re-organized; primary health organization; personal protective equipment; and staffing and training. Results: A total of 1131 survey participants from 97 countries across the World Health Organization (WHO) regions responded to the survey. Responses were from all six WHO regions; 57.9% were female and the majority had 10 or more years of experience in healthcare; almost half (46.5%) were physicians; and all other major clinical professional groups participated. As the pandemic progressed, most countries established an emergency task force, developed communication channels to citizens, organized health services to cope and put in place appropriate measures (e.g. pathways for COVID-19 patients, and testing, screening and tracing procedures). Some countries did this better than others. We found several significant differences between the WHO regions in how they are tackling the pandemic. For instance, while overall most respondents (71.4%) believed that there was an effective plan prior to the outbreak, this was only the case for 31.9% of respondents from the Pan American Health Organization compared with 90.7% of respondents from the South-East Asia Region (SEARO). Issues with swab testing (e.g. delay in communicating the swab outcome) were less frequently reported by respondents from SEARO and the Western Pacific Region compared with other regions. Conclusion: The world has progressed in its knowledge and sophistication in tackling the pandemic after early and often substantial obstacles were encountered. Most WHO regions have or are in the process of responding well, although some countries have not yet instituted widespread measures known to support mitigation, for example, effective swab testing and social control measures

Investigating Dementia via a multicompartmental poroelastic model of parenchymal tissue

In this paper, a workflow within the VPH-DARE@IT Clinical Research Platform is presented. This is used to model the biomechanical behaviour of perfused brain tissue. This workflow features a 3D multicompartmental poroelastic framework, patient-specific brain anatomy representations and continuous waveforms of internal carotid and vertebral arteries, which are used as a means of personalizing the boundary conditions that feed the arterial compartment of the in-house poroelastic solver. Results are shown comparing CSF/ISF clearance and accumulation in two males of similar age, both are non-smokers, however one is more active and is diagnosed with MCI and experiences less sleep

Subject-specific multiporoelastic model for exploring the risk factors associated with the early stages of Alzheimer's disease

There is emerging evidence suggesting that Alzheimer’s disease is a vascular disorder, caused by impaired cerebral perfusion, which may be promoted by cardiovascular risk factors that are strongly influenced by lifestyle. In order to develop an understanding of the exact nature of such a hypothesis, a biomechanical understanding of the influence of lifestyle factors is pursued. An extended poroelastic model of perfused parenchymal tissue coupled with separate workflows concerning subject-specific meshes, permeability tensor maps and cerebral blood flow (CBF) variability is utilised. The subject-specific datasets used in the modelling of this paper were collected as part of prospective data collection. Two cases were simulated involving male, non-smokers (control and MCI case) during two states of activity (high and low). Results showed a marginally reduced clearance of CSF/ISF, elevated parenchymal tissue displacement and CSF/ISF accumulation and drainage in the MCI case. Peak perfusion remained at 8 mm/s between the two cases