Prediction and Diagnosis for Unsteady Electromagnetic Vibroacoustic of IPMSMs for Electric Vehicles Considering Rotor Step Skewing and Current Harmonics

Purpose: This study provides a detailed investigation on the prediction and diagnosis of unsteady electromagnetic vibroacoustic performance of IPMSMs for electric vehicles under typical unsteady operating conditions with consideration of rotor step skewing and current harmonics. Methods: Firstly, the control model considering the influence of PWM carrier modulation and rotor step skewing is established. Based on this, the currents of the IPMSM under unsteady operating conditions (driving condition and feedback braking condition) are obtained. Accordingly, the currents calculated through the control model are used as the excitation source of electromagnetic finite element. Then, the electromagnetic vibroacoustic performance under unsteady operating conditions is calculated through electromagnetic force subsection mapping and acoustic transfer vector (ATV) method. Moreover, the conditions where resonance vibroacoustic occurs are diagnosed. Finally, the results of prediction and diagnosis are fully verified by experiments of multiple physical fields. Results and Conclusions: The amplitude errors between prediction results and test results are less than 3.2%. The influence of current harmonics on electromagnetic vibroacoustic can be predicted. The frequency range and speed range of predicted peak vibroacoustic are consistent with the experimental results. The rotor step skewing can be used to weaken the vibroacoustic amplitude of IPMSMs under typical unsteady conditions in the full speed range. This study provides guidance for prediction and diagnosis for electromagnetic vibroacoustic performance of IPMSMs under typical unsteady operating conditions.</p

TeraHz tuning of whispering gallery modes in a PDMS, stand-alone, stretchable microsphere

We report on tuning the optical whispering gallery modes in a poly dimethyl siloxane-based (PDMS) microsphere resonator by more than a THz. The PDMS microsphere system consists of a solid spherical resonator directly formed with double stems on either side. The stems act like tie-rods for simple mechanical stretching of the microresonator over tens of microns, resulting in tuning of the whispering gallery modes by one free spectral range. Further investigations demonstrate that the whispering gallery mode shift has a higher sensitivity (0.13 nm/{\mu}N) to an applied force when the resonator is in its maximally stretched state compared to its relaxed state.Comment: 3 pages, 4 figures, submitted to Optics Letter

Factors associated with crisis pregnancies in Ireland: Findings from three nationally representative sexual health surveys

Background: Findings on the demographic and sexual health characteristics associated with the experience of a crisis pregnancy is important to inform the public health policy of a country, including Ireland. Findings from other jurisdictions have suggested that certain demographic groups are at risk for unintended pregnancies and the disparity between the groups have been growing in recent years. Ireland is a country which experienced much economic and societal change in the first decade of the 21st century, changes which are likely to have affected demographic variables pertaining to sexual health. The current study had two aims: to investigate changes in the socioeconomic characteristics associated with crisis pregnancies over a seven year period [2003 to 2010], and to investigate the recent [2010] socioeconomic risk factors associated with crisis pregnancies in Ireland. Methods: The study compared the results from 18-45 year old women using data from three broadly similar nationally representative Irish sexual health surveys carried out in 2003, 2004-2006 and 2010. Chi square analysis compared of the socioeconomic characteristics across the seven year period and found that a higher proportion of women with two or more children and women for whom religion was not important reported a crisis pregnancy in 2010 compared with earlier years. A logistic regression then investigated the sexual health history and socioeconomic factors associated with the experience of a recent crisis pregnancy using the most recent 2010 data. Results: Receipt of sex education and contraception use at first sex significantly predicted the experiencing of a recent crisis pregnancy. Younger women and those with a lower level of education were more likely to report having experienced a recent crisis pregnancy. Conclusion: Similar demographic groups are at risk for experiencing a crisis pregnancy in Ireland compared with international research, yet the disparities between demographic groups who have experienced a crisis pregnancy appear to be decreasing rather than increasing over a seven year period. Recommendations are made with regard to the provision of continued sex education throughout the lifespan, particularly for those women who are at an increased risk of experiencing a crisis pregnancy

NVIDIA FLARE: Federated Learning from Simulation to Real-World

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world applications. The SDK includes solutions for state-of-the-art FL algorithms and federated machine learning approaches, which facilitate building workflows for distributed learning across enterprises and enable platform developers to create a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package. It allows researchers to apply their data science workflows in any training libraries (PyTorch, TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper introduces the key design principles of NVFlare and illustrates some use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms. Code is available at https://github.com/NVIDIA/NVFlare.Comment: Accepted at the International Workshop on Federated Learning, NeurIPS 2022, New Orleans, USA (https://federated-learning.org/fl-neurips-2022); Revised version v2: added Key Components list, system metrics for homomorphic encryption experiment; Extended v3 for journal submissio

Gene expression profiling of tumour epithelial and stromal compartments during breast cancer progression

The progression of ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) marks a critical step in the evolution of breast cancer. There is some evidence to suggest that dynamic interactions between the neoplastic cells and the tumour microenvironment play an important role. Using the whole-genome cDNA-mediated annealing, selection, extension and ligation assay (WG-DASL, Illumina), we performed gene expression profiling on 87 formalin-fixed paraffin-embedded (FFPE) samples from 17 patients consisting of matched IDC, DCIS and three types of stroma: IDC-S ( 10 mm from IDC or DCIS). Differential gene expression analysis was validated by quantitative real time-PCR, immunohistochemistry and immunofluorescence. The expression of several genes was down-regulated in stroma from cancer patients relative to normal stroma from reduction mammoplasties. In contrast, neoplastic epithelium underwent more gene expression changes during progression, including down regulation of SFRP1. In particular, we observed that molecules related to extracellular matrix (ECM) remodelling (e.g. COL11A1, COL5A2 and MMP13) were differentially expressed between DCIS and IDC. COL11A1 was overexpressed in IDC relative to DCIS and was expressed by both the epithelial and stromal compartments but was enriched in invading neoplastic epithelial cells. The contributions of both the epithelial and stromal compartments to the clinically important scenario of progression from DCIS to IDC. Gene expression profiles, we identified differential expression of genes related to ECM remodelling, and specifically the elevated expression of genes such as COL11A1, COL5A2 and MMP13 in epithelial cells of IDC. We propose that these expression changes could be involved in facilitating the transition from in situ disease to invasive cancer and may thus mark a critical point in disease development

Diversity of Protein and mRNA Forms of Mammalian Methionine Sulfoxide Reductase B1 Due to Intronization and Protein Processing

Background: Methionine sulfoxide reductases (Msrs) are repair enzymes that protect proteins from oxidative stress by catalyzing stereospecific reduction of oxidized methionine residues. MsrB1 is a selenocysteine-containing cytosolic/nuclear Msr with high expression in liver and kidney. Principal Findings: Here, we identified differences in MsrB1 gene structure among mammals. Human MsrB1 gene consists of four, whereas the corresponding mouse gene of five exons, due to occurrence of an additional intron that flanks the stop signal and covers a large part of the 3′-UTR. This intron evolved in a subset of rodents through intronization of exonic sequences, whereas the human gene structure represents the ancestral form. In mice, both splice forms were detected in liver, kidney, brain and heart with the five-exon form being the major form. We found that both mRNA forms were translated and supported efficient selenocysteine insertion into MsrB1. In addition, MsrB1 occurs in two protein forms that migrate as 14 and 5 kDa proteins. We found that each mRNA splice form generated both protein forms. The abundance of the 5 kDa form was not influenced by protease inhibitors, replacement of selenocysteine in the active site or mutation of amino acids in the cleavage site. However, mutation of cysteines that coordinate a structural zinc decreased the levels of 5 and 14 kDa forms, suggesting importance of protein structure for biosynthesis and/stability of these forms. Conclusions: This study characterized unexpected diversity of protein and mRNA forms of mammalian selenoprotein MsrB1

Regulation of human CD4+ T cell differentiation

Naive CD4+ T cells differentiate into specific effector subsets—Th1, Th2, Th17, and T follicular helper (Tfh)—that provide immunity against pathogen infection. The signaling pathways involved in generating these effector cells are partially known. However, the effects of mutations underlying human primary immunodeficiencies on these processes, and how they compromise specific immune responses, remain unresolved. By studying individuals with mutations in key signaling pathways, we identified nonredundant pathways regulating human CD4+ T cell differentiation in vitro. IL12Rβ1/TYK2 and IFN-γR/STAT1 function in a feed-forward loop to induce Th1 cells, whereas IL-21/IL-21R/STAT3 signaling is required for Th17, Tfh, and IL-10–secreting cells. IL12Rβ1/TYK2 and NEMO are also required for Th17 induction. Strikingly, gain-of-function STAT1 mutations recapitulated the impact of dominant-negative STAT3 mutations on Tfh and Th17 cells, revealing a putative inhibitory effect of hypermorphic STAT1 over STAT3. These findings provide mechanistic insight into the requirements for human T cell effector function, and explain clinical manifestations of these immunodeficient conditions. Furthermore, they identify molecules that could be targeted to modulate CD4+ T cell effector function in the settings of infection, vaccination, or immune dysregulation

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i