Automated Triple Pulse Testbed (ATPT) 1.0 – Large-Signal Hardware-in-the-loop Characterization Platform for Power Magnetics

Designing and characterizing magnetic components such as filter inductors are increasingly important for achieving optimized power converters. Due to the complicated material excitation response mechanisms, in-situ characterization is proven to be more reliable than utilizing the core loss data acquired from the traditional empirical model. Current research also indicates that core loss characterizing based on material is shown to be less accurate than measurement at the component level. Moreover, there are few core loss measurement methods that could perform a large-signal characterization that meets the industrial requirement without a complicated set-up or sophisticated procedure. To overcome these challenges, an automated magnetic characterizing platform based on a refined discontinuous test procedure called the Triple Pulse Test (TPT) is proposed. The testbed is used to characterize magnetic components with large-signal rectangular excitations and dc-bias current in a low-cost manner with reduced requirements of hardware. An automated version of this method, referred as the Automated Triple Pulse Testbed (ATPT), alongside the design considerations at both hardware and software levels are presented in the paper. The specific limitations of the large-signal testing condition such as the analysis of saturated current and the influence of demagnetizing effect are introduced. Measurement results generated from ATPT are verified against the open-source MagNet database

Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques

Hypertrophic scar (HS) is a chronic inflammatory skin disease characterized by excessive deposition of extracellular matrix, but the exact mechanisms related to its formation remain unclear, making it difficult to treat. This study aimed to investigate the potential role of cuproptosis in the information of HS. To this end, we used single-cell sequencing and bulk transcriptome data, and screened for cuproptosis-related genes (CRGs) using differential gene analysis and machine learning algorithms (random forest and support vector machine). Through this process, we identified a group of genes, including ATP7A, ULK1, and MTF1, as novel therapeutic targets for HS. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to confirm the mRNA expression of ATP7A, ULK1, and MTF1 in both HS and normal skin (NS) tissues. We also constructed a diagnostic model for HS and analyzed the immune infiltration characteristics. Additionally, we used the expression profiles of CRGs to perform subgroup analysis of HS. We focused mainly on fibroblasts in the transcriptional profile at single-cell resolution. By calculating the cuproptosis activity of each fibroblast, we found that cuproptosis activity of normal skin fibroblasts increased, providing further insights into the pathogenesis of HS. We also analyzed the cell communication network and transcription factor regulatory network activity, and found the existence of a fibroblast-centered communication regulation network in HS, where cuproptosis activity in fibroblasts affects intercellular communication. Using transcription factor regulatory activity network analysis, we obtained highly active transcription factors, and correlation analysis with CRGs suggested that CRGs may serve as potential target genes for transcription factors. Overall, our study provides new insights into the pathophysiological mechanisms of HS, which may inspire new ideas for the diagnosis and treatment

Revealing the roles of glycosphingolipid metabolism pathway in the development of keloid: a conjoint analysis of single-cell and machine learning

Keloid is a pathological scar formed by abnormal wound healing, characterized by the persistence of local inflammation and excessive collagen deposition, where the intensity of inflammation is positively correlated with the size of the scar formation. The pathophysiological mechanisms underlying keloid formation are unclear, and keloid remains a therapeutic challenge in clinical practice. This study is the first to investigate the role of glycosphingolipid (GSL) metabolism pathway in the development of keloid. Single cell sequencing and microarray data were applied to systematically analyze and screen the glycosphingolipid metabolism related genes using differential gene analysis and machine learning algorithms (random forest and support vector machine), and a set of genes, including ARSA,GBA2,SUMF2,GLTP,GALC and HEXB, were finally identified, for which keloid diagnostic model was constructed and immune infiltration profiles were analyzed, demonstrating that this set of genes could serve as a new therapeutic target for keloid. Further unsupervised clustering was performed by using expression profiles of glycosphingolipid metabolism genes to discover keloid subgroups, immune cells, inflammatory factor differences and the main pathways of enrichment between different subgroups were calculated. The single-cell resolution transcriptome landscape concentrated on fibroblasts. By calculating the activity of the GSL metabolism pathway for each fibroblast, we investigated the activity changes of GSL metabolism pathway in fibroblasts using pseudotime trajectory analysis and found that the increased activity of the GSL metabolism pathway was associated with fibroblast differentiation. Subsequent analysis of the cellular communication network revealed the existence of a fibroblast-centered communication regulatory network in keloids and that the activity of the GSL metabolism pathway in fibroblasts has an impact on cellular communication. This contributes to the further understanding of the pathogenesis of keloids. Overall, we provide new insights into the pathophysiological mechanisms of keloids, and our results may provide new ideas for the diagnosis and treatment of keloids

Detecting Neutrinos from Supernova Bursts in PandaX-4T

Neutrinos from core-collapse supernovae are essential for the understanding of neutrino physics and stellar evolution. The dual-phase xenon dark matter detectors can provide a way to track explosions of galactic supernovae by detecting neutrinos through coherent elastic neutrino-nucleus scatterings. In this study, a variation of progenitor masses as well as explosion models are assumed to predict the neutrino fluxes and spectra, which result in the number of expected neutrino events ranging from 6.6 to 13.7 at a distance of 10 kpc over a 10-second duration with negligible backgrounds at PandaX-4T. Two specialized triggering alarms for monitoring supernova burst neutrinos are built. The efficiency of detecting supernova explosions at various distances in the Milky Way is estimated. These alarms will be implemented in the real-time supernova monitoring system at PandaX-4T in the near future, providing the astronomical communities with supernova early warnings.Comment: 9 pages,6 figure

Strategies for Activating Historic District: Micro-reform Implement in Qiulin District, Harbin

This paper explores urban strategies that address existing problems in Qiulin District, which is famous as a historic district with plenty of cultural memories in Harbin, China. In the paper, it firstly introduces the definition of historic district and made a brief review of academic literatures about space activation. It then takes Qiulin District in Harbin as a case to further discuss the current dilemma. In terms of land use, Qiulin District is primarily commercial and residential that increases the difficulty of activating this area. In accordance with demonstrated issues: 1) heritage buildings with limited value capture, 2) diverse business types challenged with urgent upgrading, 3) multiple high-quality resources utilized in weak synergy, it proposes three main strategies to solve problems. With introducing the concept of micro-reform, the strategies: 1) activate heritage buildings to recapture historic value, 2) extend outdoor space and upgrade commercial structure to enhance characteristic streets, 3) design for fun life to build inclusive network, compressively explore a number of possibilities to reflect the current situation and achieve visionary planning. Finally, this paper draws discussion about the significance of space activation for historic districts towards environmental sustainability and human beings

Genome-wide identification and expression analysis of the CBF transcription factor family in Lolium perenne under abiotic stress

C-repeat binding factor (CBF) subfamily genes encoding transcriptional activators are members of the AP2/ERF superfamily. CBFs play important roles in plant tolerance to abiotic stress. In this study, we identified and analyzed the structure, phylogeny, conserved motifs, and expression profiles of 12 CBFs of the grass species Lolium perenne cultured under abiotic stress. The identified LpCBFs were grouped into three phylogenetic clades according to their protein structures and motif organizations. LpCBF expression was differentially induced by cold, heat, water deficit, salinity, and abscisic acid, among which cold treatment induced LpCBF gene expression significantly. Furthermore, association network analysis indicated that different proteins, including certain stress-related proteins, potentially interact with LpCBFs. Altogether, these findings will enhance our understanding of LpCBFs protein structure and function in the regulation of L. perenne stress responses. Our results will provide valuable information for further functional research of LpCBF proteins in L. perenne stress resistance

Insulin-like Growth Factor-2 (IGF-2) in Fibrosis

The insulin family consists of insulin, insulin-like growth factor 1 (IGF-1), insulin-like growth factor 2 (IGF-2), their receptors (IR, IGF-1R and IGF-2R), and their binding proteins. All three ligands are involved in cell proliferation, apoptosis, protein synthesis and metabolism due to their homologous sequences and structural similarities. Insulin-like growth factor 2, a member of the insulin family, plays an important role in embryonic development, metabolic disorders, and tumorigenesis by combining with three receptors with different degrees of affinity. The main pathological feature of various fibrotic diseases is the excessive deposition of extracellular matrix (ECM) after tissue and organ damage, which eventually results in organic dysfunction because scar formation replaces tissue parenchyma. As a mitogenic factor, IGF-2 is overexpressed in many fibrotic diseases. It can promote the proliferation of fibroblasts significantly, as well as the production of ECM in a time- and dose-dependent manner. This review aims to describe the expression changes and fibrosis-promoting effects of IGF-2 in the skin, oral cavity, heart, lung, liver, and kidney fibrotic tissues

MagLearn – Data-driven Machine Learning Framework with Transfer and Few-shot Training for Modeling Magnetic Core Loss

In response to the MagNet Challenge 2023, this paper describes the solution developed by the University of Bristol team, awarded the 3rd Place Outstanding Performance among 24 competing teams worldwide. The core loss of magnetic components has been a challenge for engineers to model due to the lack of full physics models. Classic Steinmetz-Equation-based approaches show significant limitations under power electronics excitations. Data-driven approaches have emerged in recent years as a new solution to this problem as an active research area. Based on the datasets supplied by PowerLab Princeton, this work employs a machine learning framework to predict the core loss of magnetic components from a range of flux density waveforms, e.g. sinusoidal, rectangular, trapezoidal, as the input. The proposed approach builds on an LSTM neural network to extract features from the input B waveforms and predict the power loss value. Designed for the small and imbalanced datasets supplied in the competition, a machine learning pipeline is proposed in this work featuring transfer learning and few-shot training, which is realized through data augmentation and alignment. As a modification to decouple the output from the phase shift of the input waveform, a random shift/flip algorithm is applied in both pre- and post-processing blocks. The performance of the proposed approach is validated through the experimentally measured testing sets, demonstrating a high prediction accuracy

Machine learning-based signature of necrosis-associated lncRNAs for prognostic and immunotherapy response prediction in cutaneous melanoma and tumor immune landscape characterization

BackgroundCutaneous melanoma (CM) is one of the malignant tumors with a relative high lethality. Necroptosis is a novel programmed cell death that participates in anti-tumor immunity and tumor prognosis. Necroptosis has been found to play an important role in tumors like CM. However, the necroptosis-associated lncRNAs’ potential prognostic value in CM has not been identified.MethodsThe RNA sequencing data collected from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Project (GTEx) was utilized to identify differentially expressed genes in CM. By using the univariate Cox regression analysis and machine learning LASSO algorithm, a prognostic risk model had been built depending on 5 necroptosis-associated lncRNAs and was verified by internal validation. The performance of this prognostic model was assessed by the receiver operating characteristic curves. A nomogram was constructed and verified by calibration. Furthermore, we also performed sub-group K-M analysis to explore the 5 lncRNAs’ expression in different clinical stages. Function enrichment had been analyzed by GSEA and ssGSEA. In addition, qRT-PCR was performed to verify the five lncRNAs’ expression level in CM cell line (A2058 and A375) and normal keratinocyte cell line (HaCaT).ResultsWe constructed a prognostic model based on five necroptosis-associated lncRNAs (AC245041.1, LINC00665, AC018553.1, LINC01871, and AC107464.3) and divided patients into high-risk group and low-risk group depending on risk scores. A predictive nomogram had been built to be a prognostic indicator to clinical factors. Functional enrichment analysis showed that immune functions had more relationship and immune checkpoints were more activated in low-risk group than that in high-risk group. Thus, the low-risk group would have a more sensitive response to immunotherapy.ConclusionThis risk score signature could be used to divide CM patients into low- and high-risk groups, and facilitate treatment strategy decision making that immunotherapy is more suitable for those in low-risk group, providing a new sight for CM prognostic evaluation