A free boundary tumor model with time dependent nutritional supply

A non-autonomous free boundary model for tumor growth is studied. The model consists of a nonlinear reaction diffusion equation describing the distribution of vital nutrients in the tumor and a nonlinear integro-differential equation describing the evolution of the tumor size. First the global existence and uniqueness of a transient solution is established under some general conditions. Then with additional regularity assumptions on the consumption and proliferation rates, the existence and uniqueness of steady-state solutions is obtained. Furthermore the convergence of the transient solutions toward the steady-state solution is verified. Finally the long time behavior of the solutions is investigated by transforming the time-dependent domain to a fixed domain.Ministerio de Economía y Competitividad (MINECO). EspañaEuropean Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)Junta de AndalucíaNational Natural Science Foundation of ChinaSimons Foundatio

On the Development and Application of FOG

Gyroscope is a type of angular velocity measuring device, which can precisely determine the orientation of moving objects. It was first employed in navigation and later became an inertial navigation instrument widely used in modern aviation, aerospace, and national defense industries. As a vital representative of gyroscope, the fiber-optic gyroscope (FOG) has advantages in terms of compact structure, high precision, high sensitivity, and high environmental adaptability. FOG has been broadly utilized in many fields, and is also a key component of modern navigation instruments. In this paper, the history, classification, performance indicators, and application requirements of gyroscope are briefly summarized. The development history of FOG based on Sagnac effect is described in detail. The three generations of FOG are interferometric FOG, resonant FOG, and stimulated Brillouin scattering FOG. At the same time, this chapter summarizes the development and research situation of FOG in the United States, Japan, France, and other major developing countries, and compares the application of FOG in various international companies

Fault diagnosis for rotating machinery based on multi-differential empirical mode decomposition

The fault diagnosis of rotating machinery has crucial significance for the safety of modern industry, and the fault feature extraction is the key link of the diagnosis process. As an effective time-frequency method, Empirical Mode Decomposition (EMD) has been widely used in signal processing and feature extraction. However, the mode mixing phenomenon may lead to confusion in the identification of multi frequency signals and restricts the applications of EMD. In this paper, a novel method based on Multi-Differential Empirical Mode Decomposition (MDEMD) was proposed to extract the energy distribution characteristics of fault signals. Firstly, multi-order differential signals were deduced and decomposed by EMD. Then, their energy distribution characteristics were extracted and utilized to construct the feature matrix. Finally, taking the feature matrix as input, the classifiers were applied to diagnosis the existence and severity of rotating machinery faults. Simulative and practical experiments were implemented respectively, and the results demonstrated that the proposed method, i.e. MDEMD, is able to eliminate the mode mixing effectively, and the feature matrix extracted by MDEMD has high separability and universality, furthermore, the fault diagnosis based on MDEMD can be accomplished more effectively and efficiently with satisfactory accuracy

LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening

In computational pathology, multiple instance learning (MIL) is widely used to circumvent the computational impasse in giga-pixel whole slide image (WSI) analysis. It usually consists of two stages: patch-level feature extraction and slide-level aggregation. Recently, pretrained models or self-supervised learning have been used to extract patch features, but they suffer from low effectiveness or inefficiency due to overlooking the task-specific supervision provided by slide labels. Here we propose a weakly-supervised Label-Efficient WSI Screening method, dubbed LESS, for cytological WSI analysis with only slide-level labels, which can be effectively applied to small datasets. First, we suggest using variational positive-unlabeled (VPU) learning to uncover hidden labels of both benign and malignant patches. We provide appropriate supervision by using slide-level labels to improve the learning of patch-level features. Next, we take into account the sparse and random arrangement of cells in cytological WSIs. To address this, we propose a strategy to crop patches at multiple scales and utilize a cross-attention vision transformer (CrossViT) to combine information from different scales for WSI classification. The combination of our two steps achieves task-alignment, improving effectiveness and efficiency. We validate the proposed label-efficient method on a urine cytology WSI dataset encompassing 130 samples (13,000 patches) and FNAC 2019 dataset with 212 samples (21,200 patches). The experiment shows that the proposed LESS reaches 84.79%, 85.43%, 91.79% and 78.30% on a urine cytology WSI dataset, and 96.88%, 96.86%, 98.95%, 97.06% on FNAC 2019 dataset in terms of accuracy, AUC, sensitivity and specificity. It outperforms state-of-the-art MIL methods on pathology WSIs and realizes automatic cytological WSI cancer screening.Comment: This paper was submitted to Medical Image Analysis. It is under revie

A Foundation Model for General Moving Object Segmentation in Medical Images

Medical image segmentation aims to delineate the anatomical or pathological structures of interest, playing a crucial role in clinical diagnosis. A substantial amount of high-quality annotated data is crucial for constructing high-precision deep segmentation models. However, medical annotation is highly cumbersome and time-consuming, especially for medical videos or 3D volumes, due to the huge labeling space and poor inter-frame consistency. Recently, a fundamental task named Moving Object Segmentation (MOS) has made significant advancements in natural images. Its objective is to delineate moving objects from the background within image sequences, requiring only minimal annotations. In this paper, we propose the first foundation model, named iMOS, for MOS in medical images. Extensive experiments on a large multi-modal medical dataset validate the effectiveness of the proposed iMOS. Specifically, with the annotation of only a small number of images in the sequence, iMOS can achieve satisfactory tracking and segmentation performance of moving objects throughout the entire sequence in bi-directions. We hope that the proposed iMOS can help accelerate the annotation speed of experts, and boost the development of medical foundation models.Comment: 5 pages, 7 figures, 3 table

Instructive Feature Enhancement for Dichotomous Medical Image Segmentation

Deep neural networks have been widely applied in dichotomous medical image segmentation (DMIS) of many anatomical structures in several modalities, achieving promising performance. However, existing networks tend to struggle with task-specific, heavy and complex designs to improve accuracy. They made little instructions to which feature channels would be more beneficial for segmentation, and that may be why the performance and universality of these segmentation models are hindered. In this study, we propose an instructive feature enhancement approach, namely IFE, to adaptively select feature channels with rich texture cues and strong discriminability to enhance raw features based on local curvature or global information entropy criteria. Being plug-and-play and applicable for diverse DMIS tasks, IFE encourages the model to focus on texture-rich features which are especially important for the ambiguous and challenging boundary identification, simultaneously achieving simplicity, universality, and certain interpretability. To evaluate the proposed IFE, we constructed the first large-scale DMIS dataset Cosmos55k, which contains 55,023 images from 7 modalities and 26 anatomical structures. Extensive experiments show that IFE can improve the performance of classic segmentation networks across different anatomies and modalities with only slight modifications. Code is available at https://github.com/yezi-66/IFEComment: Accepted by MICCAI 202

Genome-wide identification and characterization of bZIP transcription factors and their expression profile under abiotic stresses in Chinese pear (Pyrus bretschneideri)

Background: In plants, basic leucine zipper transcription factors (TFs) play important roles in multiple biological processes such as anthesis, fruit growth & development and stress responses. However, systematic investigation and characterization of bZIP-TFs remain unclear in Chinese white pear. Chinese white pear is a fruit crop that has important nutritional and medicinal values. Results: In this study, 62 bZIP genes were comprehensively identified from Chinese Pear, and 54 genes were distributed among 17 chromosomes. Frequent whole-genome duplication (WGD) and dispersed duplication (DSD) were the major driving forces underlying the bZIP gene family in Chinese white pear. bZIP-TFs are classified into 13 subfamilies according to the phylogenetic tree. Subsequently, purifying selection plays an important role in the evolution process of PbbZIPs. Synteny analysis of bZIP genes revealed that 196 orthologous gene pairs were identified between Pyrus bretschneideri, Fragaria vesca, Prunus mume, and Prunus persica. Moreover, cis-elements that respond to various stresses and hormones were found on the promoter regions of PbbZIP, which were induced by stimuli. Gene structure (intron/exon) and different compositions of motifs revealed that functional divergence among subfamilies. Expression pattern of PbbZIP genes differential expressed under hormonal treatment abscisic acid, salicylic acid, and methyl jasmonate in pear fruits by real-time qRT-PCR. Conclusions: Collectively, a systematic analysis of gene structure, motif composition, subcellular localization, synteny analysis, and calculation of synonymous (Ks) and non-synonymous (Ka) was performed in Chinese white pear. Sixty-two bZIP-TFs in Chinese pear were identified, and their expression profiles were comprehensively analyzed under ABA, SA, and MeJa hormones, which respond to multiple abiotic stresses and fruit growth and development. PbbZIP gene occurred through Whole-genome duplication and dispersed duplication events. These results provide a basic framework for further elucidating the biological function characterizations under multiple developmental stages and abiotic stress responses.This work was performed at the school of Life Sciences, Anhui agricultural university, Hefei, China and was supported by National Natural Science Foundation of China (No. 31640068) and Natural Science Youth Foundation of Anhui Agricultural University (No. 2019zd01). These funding bodies had no role in the design of the study, collection, analysis, and interpretation of data or in writing the manuscript

Cosmic test of sTGC detector prototype made in China for ATLAS experiment upgrade

Following the Higgs particle discovery, the Large Hadron Collider complex will be upgraded in several phases allowing the luminosity to increase to $7 \times 10^{34}cm^{-2}s^{-1}$. In order to adapt the ATLAS detector to the higher luminosity environment after the upgrade, part of the ATLAS muon end-cap system, the Small Wheel, will be replaced by the New Small Wheel. The New Small Wheel includes two kinds of detectors: small-strip Thin Gap Chambers and Micromegas. Shandong University, part of the ATLAS collaboration, participates in the construction of the ATLAS New Small Wheel by developing, producing and testing the performance of part of the small-strip Thin Gap Chambers. This paper describes the construction and cosmic-ray testing of small-strip Thin Gap Chambers in Shandong University

Brassinosteroids affect wood development and properties of Fraxinus mandshurica

IntroductionXylem development plays a crucial role in wood formation in woody plants. In recent years, there has been growing attention towards the impact of brassinosteroids (BRs) on this xylem development. In the present study, we evaluated the dynamic variation of xylem development in Fraxinus mandshurica (female parent, M8) and a novel interspecific hybrid F. mandshurica × Fraxinus sogdiana (1601) from May to August 2020.MethodsWe obtained RNA-Seq transcriptomes of three tissue types (xylem, phloem, and leaf) to identify the differences in xylem-differentially expressed genes (X-DEGs) and xylem-specifically expressed genes (X-SEGs) in M8 and 1601 variants. We then further evaluated these genes via weighted gene co-expression network analysis (WGCNA) alongside overexpressing FmCPD, a BR biosynthesis enzyme gene, in transient transgenic F. mandshurica.ResultsOur results indicated that the xylem development cycle of 1601 was extended by 2 weeks compared to that of M8. In addition, during the later wood development stages (secondary wall thickening) of 1601, an increased cellulose content (14%) and a reduced lignin content (11%) was observed. Furthermore, vessel length and width increased by 67% and 37%, respectively, in 1601 compared with those of M8. A total of 4589 X-DEGs were identified, including enzymes related to phenylpropane metabolism, galactose metabolism, BR synthesis, and signal transduction pathways. WGCNA identified hub X-SEGs involved in cellulose synthesis and BR signaling in the 1601 wood formation–related module (CESA8, COR1, C3H14, and C3H15); in contrast, genes involved in phenylpropane metabolism were significantly enriched in the M8 wood formation–related module (CCoAOMT and CCR). Moreover, overexpression of FmCPD in transient transgenic F. mandshurica affected the expression of genes associated with lignin and cellulose biosynthesis signal transduction. Finally, BR content was determined to be approximately 20% lower in the M8 xylem than in the 1601 xylem, and the exogenous application of BRs (24-epi brassinolide) significantly increased the number of xylem cell layers and altered the composition of the secondary cell walls in F. mandshurica.DiscussionOur findings suggest that BR biosynthesis and signaling play a critical role in the differing wood development and properties observed between M8 and 1601 F. mandshurica