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  1. Background:

    It is a major clinical challenge to ensure the long-term function of transplanted kidneys. Specifically, the injury associated with cold storage of kidneys compromises the long-term function of the grafts after transplantation. Therefore, the molecular mechanisms underlying cold-storage–related kidney injury are attractive therapeutic targets to prevent injury and improve long-term graft function. Previously, we found that constitutive proteasome function was compromised in rat kidneys after cold storage followed by transplantation. Here, we evaluated the role of the immunoproteasome (iproteasome), a proteasome variant, during cold storage (CS) followed by transplantation.


    Established in vivo rat kidney transplant model with or without CS containing vehicle or iproteasome inhibitor (ONX 0914) was used in this study. Theiproteasome function was performed using rat kidney homogenates and fluorescent-based peptide substrate specific to β5i subunit. Western blotting and quantitative RT-PCR were used to assess the subunit expression/level of theiproteasome (β5i) subunit.


    We demonstrated a decrease in the abundance of the β5i subunit of theiproteasome in kidneys during CS, but β5i levels increased in kidneys after CS and transplant. Despite the increase in β5i levels and its peptidase activity within kidneys, inhibiting β5i during CS did not improve graft function after transplantation.


    These results suggest that the pharmacological inhibition of immunoproteasome function during CS does not improve graft function or outcome. In light of these findings, future studies targeting immunoproteasomes during both CS and transplantation may define the role of immunoproteasomes on short- and long-term kidney transplant outcomes.

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    Free, publicly-accessible full text available February 2, 2025
  2. Abstract

    This article analyzes various color quantization methods using multiple image quality assessment indices. Experiments were conducted with ten color quantization methods and eight image quality indices on a dataset containing 100 RGB color images. The set of color quantization methods selected for this study includes well-known methods used by many researchers as a baseline against which to compare new methods. On the other hand, the image quality assessment indices selected are the following: mean squared error, mean absolute error, peak signal-to-noise ratio, structural similarity index, multi-scale structural similarity index, visual information fidelity index, universal image quality index, and spectral angle mapper index. The selected indices not only include the most popular indices in the color quantization literature but also more recent ones that have not yet been adopted in the aforementioned literature. The analysis of the results indicates that the conventional assessment indices used in the color quantization literature generate different results from those obtained by newer indices that take into account the visual characteristics of the images. Therefore, when comparing color quantization methods, it is recommended not to use a single index based solely on pixelwise comparisons, as is the case with most studies to date, but rather to use several indices that consider the various characteristics of the human visual system.

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  3. Abstract

    Machine learning approaches have been used for the automatic detection of Parkinson’s disease with voice recordings being the most used data type due to the simple and non-invasive nature of acquiring such data. Although voice recordings captured via telephone or mobile devices allow much easier and wider access for data collection, current conflicting performance results limit their clinical applicability. This study has two novel contributions. First, we show the reliability of personal telephone-collected voice recordings of the sustained vowel /a/ in natural settings by collecting samples from 50 people with specialist-diagnosed Parkinson’s disease and 50 healthy controls and applying machine learning classification with voice features related to phonation. Second, we utilize a novel application of a pre-trained convolutional neural network (Inception V3) with transfer learning to analyze the spectrograms of the sustained vowel from these samples. This approach considers speech intensity estimates across time and frequency scales rather than collapsing measurements across time. We show the superiority of our deep learning model for the task of classifying people with Parkinson’s disease as distinct from healthy controls.

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  4. Abstract

    Nanostructured dielectric overlayers can be used to increase light absorption in nanometer-thin films used for various optoelectronic applications. Here, the self-assembly of a close-packed monolayer of polystyrene nanospheres is used to template a core–shell polystyrene-TiO2light-concentrating monolithic structure. This is enabled by the growth of TiO2at temperatures below the polystyrene glass-transition temperature via atomic layer deposition. The result is a monolithic, tailorable nanostructured overlayer fabricated by simple chemical methods. The design of this monolith can be tailored to generate significant absorption increases in thin film light absorbers. Finite-difference, time domain simulations are used to explore the design polystyrene-TiO2core–shell monoliths that maximize light absorption in a 40 nm GaAs-on-Si substrate as a model for a photoconductive antenna THz emitter. An optimized core–shell monolith structure generated a greater than 60-fold increase of light absorption at a single wavelength in the GaAs layer of the simulated model device.

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    Free, publicly-accessible full text available June 12, 2024
  5. Abstract

    Power intensification and miniaturization of electronics and energy systems are causing a critical challenge for thermal management. Single-phase heat transfer mechanisms including natural and forced convection of air and liquids cannot meet the ever-increasing demands. Two-phase heat transfer modes, such as evaporation, pool boiling, flow boiling, have much higher cooling capacities but are limited by a variety of practical instabilities, e.g., the critical heat flux (CHF), aka departure from nucleate boiling (DNB) in the nuclear industry, flow maldistribution, flow reversal, among others. These instabilities are often triggered suddenly during normal operation, and if not identified and mitigated in time, will lead to overheating issues and detrimental device failures. For example, when CHF is triggered during pool boiling, the device temperature can ramp up in the order of 150 °C/min. It is thus critical to implement real-time detection and mitigation algorithms for two-phase cooling. In the present work, we have developed an accurate and reliable technology for fault detection of high-performance two-phase cooling systems by coupling acoustic emission (AE) with multimodal fusion using deep learning. We have leveraged the contact AE sensor attached to the heater and hydrophones immersed in the working fluid to enable non-invasive fault detection.

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  6. Abstract

    Over the past several years, a multitude of methods to measure the fairness of a machine learning model have been proposed. However, despite the growing number of publications and implementations, there is still a critical lack of literature that explains the interplay of fair machine learning with the social sciences of philosophy, sociology, and law. We hope to remedy this issue by accumulating and expounding upon the thoughts and discussions of fair machine learning produced by both social and formal (i.e., machine learning and statistics) sciences in this field guide. Specifically, in addition to giving the mathematical and algorithmic backgrounds of several popular statistics-based fair machine learning metrics used in fair machine learning, we explain the underlying philosophical and legal thoughts that support them. Furthermore, we explore several criticisms of the current approaches to fair machine learning from sociological, philosophical, and legal viewpoints. It is our hope that this field guide helps machine learning practitioners identify and remediate cases where algorithms violate human rights and values.

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  7. Abstract Background

    Histone post-translational modifications (PTMs) play an important role in our system by regulating the structure of chromatin and therefore contribute to the regulation of gene and protein expression. Irregularities in histone PTMs can lead to a variety of different diseases including various forms of cancer. Histone modifications are analyzed using high resolution mass spectrometry, which generate large amounts of data that requires sophisticated bioinformatics tools for analysis and visualization. PTMViz is designed for downstream differential abundance analysis and visualization of both protein and/or histone modifications.


    PTMViz provides users with data tables and visualization plots of significantly differentiated proteins and histone PTMs between two sample groups. All the data is packaged into interactive data tables and graphs using the Shiny platform to help the user explore the results in a fast and efficient manner to assess if changes in the system are due to protein abundance changes or epigenetic changes. In the example data provided, we identified several proteins differentially regulated in the dopaminergic pathway between mice treated with methamphetamine compared to a saline control. We also identified histone post-translational modifications including histone H3K9me, H3K27me3, H4K16ac, and that were regulated due to drug exposure.


    Histone modifications play an integral role in the regulation of gene expression. PTMViz provides an interactive platform for analyzing proteins and histone post-translational modifications from mass spectrometry data in order to quickly identify differentially expressed proteins and PTMs.

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  8. Abstract

    The rapid growth and scaling of electronics are causing more severe thermal management challenges. For example, the high-performance computing processors are driving the data center power density to unprecedented levels, approaching the limit of conventional air cooling. In electric vehicles (EVs) and hybrid EVs, the power conversion electronics are integrated into a compact space, leading to ultra-high heat fluxes to dissipate. Among the available thermal management mechanisms, two-phase cooling that involves the phase-change process of the working fluid can maintain electronic devices at safe operating temperatures by taking advantage of the high latent heat of the fluid. Particularly, pool boiling plays a critical role in the two-phase immersion cooling of servers and other IT hardware, integrated cooling for three-dimensional electronic packaging, cooling of the core, and used fuel in nuclear reactors. Two-phase coolers are limited by instabilities such as the critical heat flux (CHF). At the critical heat flux, the temperature increases. It is important to be able to identify the CHF in order to prevent overheating. We aim to develop and compare boiling image classification models to distinguish between 2 boiling regimes. We will leverage principal component analysis (PCA) and K-means clustering to investigate the key differences between bubbles during nucleate boiling (pre-CHF) and transition boiling (post-CHF). We will also compare the results of the unsupervised learning model against popular supervised learning models that have been used for boiling regime classification in existing studies, such as convolutional neural networks, multiplayer perceptrons, and transformers. We successfully created 4 supervised and 1 unsupervised learning models to distinguish between the two types of boiling images.

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  9. We review and define the current state of the art as relating to discrete event simulation in healthcare-related systems. A review of published literature over the past five years (2017–2021) was conducted, building upon previously published work. PubMed and EBSCOhost were searched for journal articles on discrete event simulation in healthcare resulting in identification of 933 unique articles. Of these about half were excluded at the title/abstract level and 154 at the full text level, leaving 311 papers to analyze. These were categorized, then analyzed by category and collectively to identify publication volume over time, disease focus, activity levels by country, software systems used, and sizes of healthcare unit under study. A total of 1196 articles were initially identified. This list was narrowed down to 311 for systematic review. Following the schema from prior systematic reviews, the articles fell into four broad categories: health care systems operations (HCSO), disease progression modeling (DPM), screening modeling (SM), and health behavior modeling (HBM). We found that discrete event simulation in healthcare has continued to increase year-over-year, as well as expand into diverse areas of the healthcare system. In addition, this study adds extra bibliometric dimensions to gain more insight into the details and nuances of how and where simulation is being used in healthcare. 
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  10. Abstract

    Age‐associated mitochondrial dysfunction and oxidative damage are primary causes for multiple health problems including sarcopenia and cardiovascular disease (CVD). Though the role of Nrf2, a transcription factor that regulates cytoprotective gene expression, in myopathy remains poorly defined, it has shown beneficial properties in both sarcopenia and CVD. Sulforaphane (SFN), a natural compound Nrf2‐related activator of cytoprotective genes, provides protection in several disease states including CVD and is in various stages of clinical trials, from cancer prevention to reducing insulin resistance. This study aimed to determine whether SFN may prevent age‐related loss of function in the heart and skeletal muscle. Cohorts of 2‐month‐old and 21‐ to 22‐month‐old mice were administered regular rodent diet or diet supplemented with SFN for 12 weeks. At the completion of the study, skeletal muscle and heart function, mitochondrial function, and Nrf2 activity were measured. Our studies revealed a significant drop in Nrf2 activity and mitochondrial functions, together with a loss of skeletal muscle and cardiac function in the old control mice compared to the younger age group. In the old mice, SFN restored Nrf2 activity, mitochondrial function, cardiac function, exercise capacity, glucose tolerance, and activation/differentiation of skeletal muscle satellite cells. Our results suggest that the age‐associated decline in Nrf2 signaling activity and the associated mitochondrial dysfunction might be implicated in the development of age‐related disease processes. Therefore, the restoration of Nrf2 activity and endogenous cytoprotective mechanisms by SFN may be a safe and effective strategy to protect against muscle and heart dysfunction due to aging.

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