Development of hot drawing process for nitinol tube

In recent years, Nitinol, near-equiatomic nickel-titanium alloys, have found growing applications in medical technology and joining technology, due to their special characteristics such as shape memory, superplasticity and biocompatibility. The production of Nitinol tube cost-effectively remains a technical challenge. In this paper, we describe a hot drawing process for Nitinol tube production. A Nitinol tube blank and a metal core are assembled together. The assembly is hot drawn for several passes to a final diameter. The metal core is then plastically stretched to reduce its diameter and removed from the tube. Hot drawing process has been applied to Ni50.7Ti and Ni47Ti44Nb9 alloys. Nitinol tubes of 13.6 mm outer diameter and 1 mm wall thickness have been successfully produced from a tube blank of 20 mm outer diameter and 3.5 mm thickness

High rate, fast timing Glass RPC for the high η\eta CMS muon detectors

The HL-LHC phase is designed to increase by an order of magnitude the amount of data to be collected by the LHC experiments. To achieve this goal in a reasonable time scale the instantaneous luminosity would also increase by an order of magnitude up to $6 \cdot 10^{34}$ cm$^{-2}$s$^{-1}$. The region of the forward muon spectrometer ($|\eta| > 1.6$) is not equipped with RPC stations. The increase of the expected particles rate up to 2 kHz/cm$^2$ ( including a safety factor 3 ) motivates the installation of RPC chambers to guarantee redundancy with the CSC chambers already present. The actual RPC technology of CMS cannot sustain the expected background level. A new generation Glass-RPC (GRPC) using low resistivity glass (LR) is proposed to equip at least the two most far away of the four high eta muon stations of CMS. The design of small size prototypes and the studies of their performances under high rate particles flux is presented.Comment: 5 pages, 5 figures, proceeding for the conference VCI 201

FERONIA interacts with ABI2-type phosphatases to facilitate signaling cross-talk between abscisic acid and RALF peptide in Arabidopsis

[EN] Receptor-like kinase FERONIA (FER) plays a crucial role in plant response to small molecule hormones [e.g., auxin and abscisic acid (ABA)] and peptide signals [e.g., rapid alkalinization factor (RALF)]. It remains unknown how FER integrates these different signaling events in the control of cell growth and stress responses. Under stress conditions, increased levels of ABA will inhibit cell elongation in the roots. In our previous work, we have shown that FER, through activation of the guanine nucleotide exchange factor 1 (GEF1)/4/10-Rho of Plant 11 (ROP11) pathway, enhances the activity of the phosphatase ABA Insensitive 2 (ABI2), a negative regulator of ABA signaling, thereby inhibiting ABA response. In this study, we found that both RALF and ABA activated FER by increasing the phosphorylation level of FER. The FER loss-of-function mutant displayed strong hypersensitivity to both ABA and abiotic stresses such as salt and cold conditions, indicating that FER plays a key role in ABA and stress responses. We further showed that ABI2 directly interacted with and dephosphorylated FER, leading to inhibition of FER activity. Several other ABI2-like phosphatases also function in this pathway, and ABA-dependent FER activation required PYRABACTIN RESISTANCE (PYR)/PYR1-LIKE (PYL)/REGULATORY COMPONENTS OF ABA RECEPTORS (RCAR)-A-type protein phosphatase type 2C (PP2CA) modules. Furthermore, suppression of RALF1 gene expression, similar to disruption of the FER gene, rendered plants hypersensitive to ABA. These results formulated a mechanism for ABA activation of FER and for cross-talk between ABA and peptide hormone RALF in the control of plant growth and responses to stress signals.We thank Dr. Alice Cheung, Dr. Daniel Moura, Grossniklaus Ueli, Dr. Jigang Li, and Dr. Nieng Yan for providing plant, ABI1 antibody, or plasmid materials, and Dr. Legong Li for assistance in laser confocal microscopy. This work was supported by grants from National Natural Science Foundation of China (NSFC-31400232, 31571444), the State Key Laboratory of Molecular Developmental Biology (2015-MDB-KF-12), the Fundamental Research Funds for the Central Universities of China, and a grant from the National Science Foundation.Chen, J.; Yu, F.; Liu, Y.; Du, C.; Li, X.; Zhu, S.; Wang, X.... (2016). FERONIA interacts with ABI2-type phosphatases to facilitate signaling cross-talk between abscisic acid and RALF peptide in Arabidopsis. Proceedings of the National Academy of Sciences. 113(37):E5519-E5527. https://doi.org/10.1073/pnas.1608449113SE5519E55271133

Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis

BackgroundArtificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging.MethodsStudies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis.ResultsThe systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78–0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73–0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77–0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66–0.82).ConclusionThe models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging.Systematic Review Registrationhttps://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167

Development of front-end electronic modules for gamma ray and charged particle detectors

Le travail de cette thèse s’inscrit dans le cadre de deux projets de développement : CLaRyS (caméras gamma prompts) et CMS muon (détecteur iRPC (improved Resistive Plate Chambers )). Il porte d’une parte, sur la conception et la mise en oeuvre d’une électronique frontale de mesure de temps par TDC (Time-to-Digital Converter) sur FPGA, et d’autre part, sur l’intégration de TDC dans des systèmes d’application. Plusieurs TDCs se sont développés en s’appuyant sur la méthode Nutt (combinaison de mesure du temps grossier et du temps fin avec interpolation), pour optimiser la plage dynamique de mesure et la résolution temporelle. Nous avons conçu, pour le projet CLaRyS, un TDC multi-phase avec l’enregistrement déclenché par le signal à mesurer ; et pour le projet iRPC, trois versions de TDC TDL (Tapped Delay Line). Le TDC multi-phase utilise un minimum de ressource de FPGA, tandis que les trois versions du TDC-TDL sont respectivement pour les besoins spécifiques : la mesure de largeur d’impulsion du signal (TOT : Time-Over-Threshold), l’immunité au bruit et une grande précision de mesure temporelle (< 3.6 ps en RMS (Root-Mean-Square)). D’autre part, une carte frontale « hodoscope » pour une caméra gamma a été développée dans le cadre du projet CLaRyS. Elle incorpore un FPGA StratixII GX intégrant le TDC multi-phase sur 3 voies identiques. Ce développement inclut des microprogrammes sur FPGA (« firmware ») contrôlant le TDC et la communication des données. D’autres firmwares avec des fonctions similaires ont été mis en oeuvre sur les FPGAs de deux autres cartes frontales : carte « absorbeur » de la caméra gamma et carte FEBV1 du projet iRPC. Ces réalisations « hardware » et « firmware » ont été testées et validées en conditions expérimentales avec faisceaux d’ions, rayonnements cosmiques et sources radioactives.The work of this thesis is part of two development projects: CLaRyS (prompt gamma cameras) and CMS muon (iRPC (improved Resistive Plate Chambers) detector). It relates on the one hand, to the design and implementation of a front-end electronic time measurement by TDC (Time-to-Digital Converter) on FPGA, and on the other hand, to the integration of TDC in application systems. Several TDCs have been developed based on the Nutt method (combination of coarse time measurement and fine time measurement with interpolation), to optimize the dynamic range of measurement and the temporal resolution. We designed, for the CLaRyS project, a multiphase TDC with recording triggered by the signal to be measured; and for the iRPC project, three versions of TDL (Tapped Delay Line) -TDC. The multiphase TDC uses minimal FPGA resources, while the three versions of the TDL-TDC are respectively for specific needs: signal pulse width measurement (TOT: Time-Over-Threshold), noise immunity and high precision time measurement (<3.6 ps in RMS (Root-Mean-Square)). On the other hand, a "hodoscope" front-end board for a gamma camera has been developed as part of the CLaRyS project. It incorporates a StratixII GX FPGA integrating the multiphase TDC on 3 identical channels. This development includes firmware on FPGA controlling the TDC and data communication. Other firmwares with similar functions have been implemented on the FPGAs of two other front-end boards: "absorber" board of the gamma camera and FEBV1 board of the iRPC project. These "hardware" and "firmware" achievements have been tested and validated in experimental conditions with ion beams, cosmic rays and radioactive sources

Research on maize canopy center recognition based on nonsignificant color difference segmentation.

Weed control is a substantial challenge in field management. A better weed control method at an earlier growth stage is important for increasing yields. As a promising weed control technique, intelligent weeding based on machine vision can avoid the harm of chemical weeding. For machine vision, it is critical to extract and segment crops from their background. However, there is still no optimal solution for object tracking with occlusion under a similar color background. In this study, it was found that the gray distribution of a maize canopy follows the gradient law. Therefore, the recognition method based on the HLS-SVM (HLS color space and Support Vector Machine) and on the grayscale gradient was developed. First, the HLS color space was used to detect the maize canopy. Second, the SVM method was used to segment the central region of the maize canopy. Finally, the maize canopy center was identified according to the gradient law. The results showed that the average segmentation time was 0.49 s, the average segmentation quality was 87.25%, and the standard deviation of the segmentation was 3.57%. The average recognition rate of the center position was 93.33%. This study provided a machine vision method for intelligent weeding agricultural equipment as well as a theoretical reference for further agricultural machine vision research