Alteration in Fluidity of Cell Plasma Membrane in Huntington Disease Revealed by Spectral Phasor Analysis.

Huntington disease (HD) is a late-onset genetic neurodegenerative disorder caused by expansion of cytosine-adenine-guanine (CAG) trinucleotide in the exon 1 of the gene encoding the polyglutamine (polyQ). It has been shown that protein degradation and lipid metabolism is altered in HD. In many neurodegenerative disorders, impaired lipid homeostasis is one of the early events in the disease onset. Yet, little is known about how mutant huntingtin may affect phospholipids membrane fluidity. Here, we investigated how membrane fluidity in the living cells (differentiated PC12 and HEK293 cell lines) are affected using a hyperspectral imaging of widely used probes, LAURDAN. Using phasor approach, we characterized the fluorescence of LAURDAN that is sensitive to the polarity of the immediate environment. LAURDAN is affected by the physical order of phospholipids (lipid order) and reports the membrane fluidity. We also validated our results using a different fluorescent membrane probe, Nile Red (NR). The plasma membrane in the cells expressing expanded polyQ shows a shift toward increased membrane fluidity revealed by both LAURDAN and NR spectral phasors. This finding brings a new perspective in the understanding of the early stages of HD that can be used as a target for drug screening

Beyond Convergence: Identifiability of Machine Learning and Deep Learning Models

Machine learning (ML) and deep learning models are extensively used for parameter optimization and regression problems. However, not all inverse problems in ML are ``identifiable,'' indicating that model parameters may not be uniquely determined from the available data and the data model's input-output relationship. In this study, we investigate the notion of model parameter identifiability through a case study focused on parameter estimation from motion sensor data. Utilizing a bipedal-spring mass human walk dynamics model, we generate synthetic data representing diverse gait patterns and conditions. Employing a deep neural network, we attempt to estimate subject-wise parameters, including mass, stiffness, and equilibrium leg length. The results show that while certain parameters can be identified from the observation data, others remain unidentifiable, highlighting that unidentifiability is an intrinsic limitation of the experimental setup, necessitating a change in data collection and experimental scenarios. Beyond this specific case study, the concept of identifiability has broader implications in ML and deep learning. Addressing unidentifiability requires proven identifiable models (with theoretical support), multimodal data fusion techniques, and advancements in model-based machine learning. Understanding and resolving unidentifiability challenges will lead to more reliable and accurate applications across diverse domains, transcending mere model convergence and enhancing the reliability of machine learning models

Essays on Health and Labor Market Practices in the U.S.

This dissertation investigates the link between different aspects of labor market and individuals’ health. The first chapter analyzes the relationship between the use of four different substances and nonstandard work schedules. Using the NLSY97 and applying standard panel techniques as well as survival analyses, I find that contrary to most previous evidence, nonstandard work schedule is not necessarily associated with an increase in substance use, and in the case of drinking and binge drinking such correlation is actually negative. Evidence also suggests that drug prone individuals tend to work more at nonstandard schedules. Results are robust to the specification at the intensive margin and accounting for long-term exposure to work at nonstandard schedules. The second chapter investigates the effect of alcohol use on job search behavior of young individuals. Using the age of respondents from the NLSY97 both in the year and month formats and applying regression discontinuity design by utilizing the surge in alcohol consumption at age 21, I find that young adults tend to increase their drinking and binge drinking once they are allowed to legally access alcohol. However, I find that the surge in alcohol use at age 21 does not seem to immediately or directly affect the job search behavior of young individuals while they are employed or unemployed. I also find that it does not seem to affect their lack of desire for work. The third chapter investigates the effects of workers’ age, gender, and race relative to those of their supervisors on several measures of the employees’ mental wellbeing. Evidence suggests that men show positive mental health signs when they have supervisors of same gender and race. They also seem to like supervisors who are almost the same age. On the contrary, women’s mental health seems to be negatively affected when they have female supervisors. When the gender match effect is combined with race, it is magnified. Women also report negative mental health signs when all these demographic characteristic matches are happening at the same time. Additional tests suggest that reverse causality does not seem to be a major issue here

The phasor-FLIM fingerprints reveal shifts from OXPHOS to enhanced glycolysis in Huntington Disease.

Huntington disease (HD) is an autosomal neurodegenerative disorder caused by the expansion of Polyglutamine (polyQ) in exon 1 of the Huntingtin protein. Glutamine repeats below 36 are considered normal while repeats above 40 lead to HD. Impairment in energy metabolism is a common trend in Huntington pathogenesis; however, this effect is not fully understood. Here, we used the phasor approach and Fluorescence Lifetime Imaging Microscopy (FLIM) to measure changes between free and bound fractions of NADH as a indirect measure of metabolic alteration in living cells. Using Phasor-FLIM, pixel maps of metabolic alteration in HEK293 cell lines and in transgenic Drosophila expressing expanded and unexpanded polyQ HTT exon1 in the eye disc were developed. We found a significant shift towards increased free NADH, indicating an increased glycolytic state for cells and tissues expressing the expanded polyQ compared to unexpanded control. In the nucleus, a further lifetime shift occurs towards higher free NADH suggesting a possible synergism between metabolic dysfunction and transcriptional regulation. Our results indicate that metabolic dysfunction in HD shifts to increased glycolysis leading to oxidative stress and cell death. This powerful label free method can be used to screen native HD tissue samples and for potential drug screening

Reversibility of Age-related Oxidized Free NADH Redox States in Alzheimer's Disease Neurons by Imposed External Cys/CySS Redox Shifts.

Redox systems including extracellular cysteine/cystine (Cys/CySS), intracellular glutathione/oxidized glutathione (GSH/GSSG) and nicotinamide adenine dinucleotide reduced/oxidized forms (NADH/NAD+) are critical for maintaining redox homeostasis. Aging as a major risk factor for Alzheimer's disease (AD) is associated with oxidative shifts, decreases in anti-oxidant protection and dysfunction of mitochondria. Here, we examined the flexibility of mitochondrial-specific free NADH in live neurons from non-transgenic (NTg) or triple transgenic AD-like mice (3xTg-AD) of different ages under an imposed extracellular Cys/CySS oxidative or reductive condition. We used phasor fluorescence lifetime imaging microscopy (FLIM) to distinguish free and bound NADH in mitochondria, nuclei and cytoplasm. Under an external oxidative stress, a lower capacity for maintaining mitochondrial free NADH levels was found in old compared to young neurons and a further decline with genetic load. Remarkably, an imposed Cys/CySS reductive state rejuvenated the mitochondrial free NADH levels of old NTg neurons by 71% and old 3xTg-AD neurons by 89% to levels corresponding to the young neurons. Using FLIM as a non-invasive approach, we were able to measure the reversibility of aging subcellular free NADH levels in live neurons. Our results suggest a potential reductive treatment to reverse the loss of free NADH in old and Alzheimer's neurons

The emergence of Indo-Anglian poetry: an overview

The history of English language in India begins with the age of colonisation. During this time, India is annexed to the territory of British Empire and Persian language as the official language of the country is replaced by English. This paper is an attempt to overview the development of versified form of Indian writing in English from its early stages up to the present time. In this regard, the six major periods of development of poetry writing by Indian people in English are brought to the fore. The paper depicts the trend of maturity of this unique style of writing, which is frequently referred to as Indo-Anglian Poetry.Keywords: “English,” “Indian Writing,” “Poetry

Processing Polysomnographic Signals, using Independent Component Analysis

International audienceIn this paper several applications of the Independent Component Analysis (ICA) algorithm, for the analysis of biomedical signal recordings have been investigated. One of these applications is the removal of EEG artifacts such as the EOG. It is shown that ICA may serve as a powerful tool, which could help the analysis of biomedical recordings, and give better insights about the underlying sources of some disorders. Another application of the proposed method is the detection of sleep disorders in patients suffering from sleep apnea. The ultimate goal of this approach is to develop an automatic noninvasive data acquisition system, for clinical applications

Spatio-Temporal Crop Aggregation for Video Representation Learning

We propose Spatio-temporal Crop Aggregation for video representation LEarning (SCALE), a novel method that enjoys high scalability at both training and inference time. Our model builds long-range video features by learning from sets of video clip-level features extracted with a pre-trained backbone. To train the model, we propose a self-supervised objective consisting of masked clip feature prediction. We apply sparsity to both the input, by extracting a random set of video clips, and to the loss function, by only reconstructing the sparse inputs. Moreover, we use dimensionality reduction by working in the latent space of a pre-trained backbone applied to single video clips. These techniques make our method not only extremely efficient to train but also highly effective in transfer learning. We demonstrate that our video representation yields state-of-the-art performance with linear, non-linear, and KNN probing on common action classification and video understanding datasets