The NASA/industry Design Analysis Methods for Vibrations (DAMVIBS) program: McDonnell-Douglas Helicopter Company achievements

This paper presents a summary of some of the work performed by McDonnell Douglas Helicopter Company under NASA Langley-sponsored rotorcraft structural dynamics program known as DAMVIBS (Design Analysis Methods for VIBrationS). A set of guidelines which is applicable to dynamic modeling, analysis, testing, and correlation of both helicopter airframes and a large variety of structural finite element models is presented. Utilization of these guidelines and the key features of their applications to vibration modeling of helicopter airframes are discussed. Correlation studies with the test data, together with the development and applications of a set of efficient finite element model checkout procedures, are demonstrated on a large helicopter airframe finite element model. Finally, the lessons learned and the benefits resulting from this program are summarized

Novel approaches using human induced pluripotent stem cells and microRNAs in the development of relevant human hepatocyte models for drug-induced liver injury

Drug-induced liver injury (DILI) remains a prominent cause of patient morbidity and mortality, partly due to the lack of relevant in vitro hepatic models for accurate screening for drug-induced hepatotoxicity at the early stages of drug development, and also the lack of sophisticated in vitro model systems to mechanistically understand the pathways that are perturbed following drug exposure. This thesis describes our endeavour to develop more relevant in vitro human hepatocyte models via novel investigative approaches using insights gained from the rapidly advancing research areas of human induced pluripotent stem cells and microRNAs (miRs). An emerging hepatic model is hepatocyte-like cells (HLCs) generated from human induced pluripotent stem cells (hiPSCs), though the functional phenotype of HLCs in general remains limited in comparison with the gold standard in vitro model of human primary hepatocytes (hPHs). As studies have shown that hiPSCs retain transient epigenetic memories of the donor cells despite cellular reprogramming with a resultant skewed propensity to differentiate towards the cell-type of origin, we evaluated the contribution of epigenetic memory towards hepatic differentiation by comparing HLCs generated from hPH- and non-hPH-derived hiPSC lines derived from a single donor. Our findings suggested that they were functionally similar, although comparison using hiPSC lines derived from other donors is still required to be conclusive. Although hPHs remain the gold standard in vitro model for DILI, they are commonly harvested from liver tissue of poor quality and rapidly lose their in vivo phenotype during extended in vitro culture, limiting its utility to acute toxicity studies only. Using an unbiased miR expression profiling approach, we identified a set of differentially-expressed miRs in dedifferentiating hPHs which are associated with many of the previously delineated perturbed pathways and biological functions. However, validation experiments are now required to confirm our findings from the bioinformatics analyses. Another approach taken to develop relevant and functional hepatic models includes efforts to better emulate the in vivo liver tissue environment by using complex hepatic models co-cultured with non-parenchymal cells. However, for the application of these models in the study of drug-induced toxicity, a hepatocyte-specific marker of hepatocyte perturbation is needed to discriminate non-specific cellular toxicity contributed by non-hepatocyte cell types present within the model. We demonstrated that the detection of miR-122 in cell culture media can be applied as a hepatocyte-enriched marker of toxicity in heterogeneous cultures of hepatic cells. In summary, this thesis describes our contribution towards the continuing efforts to develop new and improve on existing hepatic models for DILI by evaluating the contribution of epigenetic memory towards the functional phenotype of HLCs, delineating the changing miR profile of dedifferentiating hPHs, and introduced the concept of using miR-122 as a cell-type specific marker of hepatocyte perturbation with a potential to bridge in vitro and in vivo findings

Neutron diffraction reveals sequence-specific membrane insertion of pre-fibrillar islet amyloid polypeptide and inhibition by rifampicin

AbstractHuman islet amyloid polypeptide (hIAPP) forms amyloid deposits in non-insulin-dependent diabetes mellitus (NIDDM). Pre-fibrillar hIAPP oligomers (in contrast to monomeric IAPP or mature fibrils) increase membrane permeability, suggesting an important role in the disease. In the first structural study of membrane-associated hIAPP, lamellar neutron diffraction shows that oligomeric hIAPP inserts into phospholipid bilayers, and extends across the membrane. Rifampicin, which inhibits hIAPP-induced membrane permeabilisation in functional studies, prevents membrane insertion. In contrast, rat IAPP (84% identical to hIAPP, but non-amyloidogenic) does not insert into bilayers. Our findings are consistent with the hypothesis that membrane-active pre-fibrillar hIAPP oligomers insert into beta cell membranes in NIDDM

Nonlinear latent representations of high-dimensional task-fMRI data: Unveiling cognitive and behavioral insights in heterogeneous spatial maps

Finding an interpretable and compact representation of complex neuroimaging data is extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. However, hand-crafted representations, as well as linear transformations, may inadequately capture the considerable variability across individuals. Here, we implemented a data-driven approach using a three-dimensional autoencoder on two large-scale datasets. This approach provides a latent representation of high-dimensional task-fMRI data which can account for demographic characteristics whilst also being readily interpretable both in the latent space learned by the autoencoder and in the original voxel space. This was achieved by addressing a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics (‘latent indices’) and establish a multivariate mapping to non-imaging measures. Our model, trained with multi-task fMRI data from the Human Connectome Project (HCP) and UK biobank task-fMRI data, demonstrated high performance in age and sex predictions and successfully captured complex behavioral characteristics while preserving individual variability through a latent representation. Our model also performed competitively with respect to various baseline models including several variants of principal components analysis, independent components analysis and classical regions of interest, both in terms of reconstruction accuracy and strength of association with behavioral variables

Predictors of care home and hospital admissions and their costs for older people with Alzheimer’s disease: findings from a large London case register

Objectives. To examine links between clinical and other characteristics of people with Alzheimer’s disease living in the community, likelihood of care home or hospital admission, and associated costs. Design. Observational data extracted from clinical records using natural language processing and Hospital Episode Statistics. Statistical analyses examined effects of cognition, physical health, mental health, sociodemographic factors and living circumstances on risk of admission to care home or hospital over 6 months and associated costs, adjusting for repeated observations. Setting. Catchment area for South London and Maudsley National Health Service Foundation Trust, provider for 1.2 million people in Southeast London. Participants. Every individual with diagnosis of Alzheimer’s disease seen and treated by mental health services in the catchment area, with at least one rating of cognition, not resident in care home at time of assessment (n=3075). Interventions. Usual treatment. Main outcome measures. Risk of admission to, and days spent in three settings during 6-month period following routine clinical assessment: care home, mental health inpatient care, and general hospital inpatient care. Results. Predictors of probability of care home or hospital admission and/or associated costs over 6 months include cognition, functional problems, agitation, depression, physical illness, previous hospitalisations, age, gender, ethnicity, living alone, and having a partner. Patterns of association differed considerably by destination. Conclusions. Most people with dementia prefer to remain in their own homes, and funding bodies see this as cheaper than institutionalisation. Better treatment in the community that reduces health and social care needs of Alzheimer’s patients would reduce admission rates. Living alone, poor living circumstances and functional problems all raise admission rates, and so major cuts in social care budgets increase the risk of high-cost admissions which older people do not want. Routinely collected data can be used to reveal local patterns of admission and costs

Alzheimer's disease heterogeneity revealed by neuroanatomical normative modeling

INTRODUCTION: Overlooking the heterogeneity in Alzheimer's disease (AD) may lead to diagnostic delays and failures. Neuroanatomical normative modeling captures individual brain variation and may inform our understanding of individual differences in AD-related atrophy. METHODS: We applied neuroanatomical normative modeling to magnetic resonance imaging from a real-world clinical cohort with confirmed AD (n = 86). Regional cortical thickness was compared to a healthy reference cohort (n = 33,072) and the number of outlying regions was summed (total outlier count) and mapped at individual- and group-levels. RESULTS: The superior temporal sulcus contained the highest proportion of outliers (60%). Elsewhere, overlap between patient atrophy patterns was low. Mean total outlier count was higher in patients who were non-amnestic, at more advanced disease stages, and without depressive symptoms. Amyloid burden was negatively associated with outlier count. DISCUSSION: Brain atrophy in AD is highly heterogeneous and neuroanatomical normative modeling can be used to explore anatomo-clinical correlations in individual patients

Two-week isocaloric time-restricted feeding decreases liver inflammation without significant weight loss in obese mice with non-alcoholic fatty liver disease

Prolonged, isocaloric, time-restricted feeding (TRF) protocols can promote weight loss, improve metabolic dysregulation, and mitigate non-alcoholic fatty liver disease (NAFLD). In addition, 3-day, severe caloric restriction can improve liver metabolism and glucose homeostasis prior to significant weight loss. Thus, we hypothesized that short-term, isocaloric TRF would improve NAFLD and characteristics of metabolic syndrome in diet-induced obese male mice. After 26 weeks of ad libitum access to western diet, mice either continued feeding ad libitum or were provided with access to the same quantity of western diet for 8 h daily, over the course of two weeks. Remarkably, this short-term TRF protocol modestly decreased liver tissue inflammation in the absence of changes in body weight or epidydimal fat mass. There were no changes in hepatic lipid accumulation or other characteristics of NAFLD. We observed no changes in liver lipid metabolism-related gene expression, despite increased plasma free fatty acids and decreased plasma triglycerides in the TRF group. However, liver Grp78 and Txnip expression were decreased with TRF suggesting hepatic endoplasmic reticulum (ER) stress and activation of inflammatory pathways may have been diminished. We conclude that two-week, isocaloric TRF can potentially decrease liver inflammation, without significant weight loss or reductions in hepatic steatosis, in obese mice with NAFLD

Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study:a machine learning approach

Schizophrenia and related disorders have heterogeneous outcomes. Individualized prediction of long-term outcomes may be helpful in improving treatment decisions. Utilizing extensive baseline data of 523 patients with a psychotic disorder and variable illness duration, we predicted symptomatic and global outcomes at 3-year and 6-year follow-ups. We classified outcomes as (1) symptomatic: in remission or not in remission, and (2) global outcome, using the Global Assessment of Functioning (GAF) scale, divided into good (GAF &gt;= 65) and poor (GAF &lt; 65). Aiming for a robust and interpretable prediction model, we employed a linear support vector machine and recursive feature elimination within a nested cross-validation design to obtain a lean set of predictors. Generalization to out-of-study samples was estimated using leave-one-site-out cross-validation. Prediction accuracies were above chance and ranged from 62.2% to 64.7% (symptomatic outcome), and 63.5-67.6% (global outcome). Leave-one-site-out cross-validation demonstrated the robustness of our models, with a minor drop in predictive accuracies of 2.3% on average. Important predictors included GAF scores, psychotic symptoms, quality of life, antipsychotics use, psychosocial needs, and depressive symptoms. These robust, albeit modestly accurate, long-term prognostic predictions based on lean predictor sets indicate the potential of machine learning models complementing clinical judgment and decision-making. Future model development may benefit from studies scoping patient's and clinicians' needs in prognostication.</p

Sertraline and mirtazapine versus placebo in subgroups of depression in dementia: findings from the HTA-SADD randomized controlled trial

Objective Studies have shown that antidepressants are no better than placebo in treating depression in dementia. The authors examined antidepressant efficacy in subgroups of depression in dementia with different depressive symptom profiles. Methods This study focuses on exploratory secondary analyses on the randomized, parallel-group, double-blind, placebo-controlled Health Technology Assessment Study of the Use of Antidepressants for Depression in Dementia (HTA-SADD) trial. The setting included old-age psychiatry services in nine centers in England. The participants included 326 patients meeting National Institute of Neurological and Communicative Disorders and Stroke/Alzheimer's Disease and Related Disorders Association probable/possible Alzheimer disease criteria, and Cornell Scale for Depression in Dementia (CSDD) scores of 8 or more. Intervention was placebo (n = 111), sertraline (n = 107), or mirtazapine (n = 108). Latent class analyses (LCA) on baseline CSDD items clustered participants into symptom-based subgroups. Mixed-model analysis evaluated CSDD improvement at 13 and 39 weeks by randomization in each subgroup. Results LCA yielded 4 subgroups: severe (n = 34), psychological (n = 86), affective (n = 129), and somatic (n = 77). Mirtazapine, but not sertraline, outperformed placebo in the psychological subgroup at week 13 (adjusted estimate: –2.77 [standard error (SE) 1.16; 95% confidence interval: –5.09 to –0.46]), which remained, but lost statistical significance at week 39 (adjusted estimate: –2.97 [SE 1.59; 95% confidence interval: –6.15 to 0.20]). Neither sertraline nor mirtazapine outperformed placebo in the other subgroups. Conclusion Because of the exploratory nature of the analyses and the small sample sizes for subgroup analysis there is the need for caution in interpreting these data. Replication of the potential effects of mirtazapine in the subgroup of those with depression in dementia with “psychological” symptoms would be valuable. These data should not change clinical practice, but future trials should consider stratifying types of depression in dementia in secondary analyses