Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations

Characterizing the phase space distribution of particle beams in accelerators is a central part of accelerator understanding and performance optimization. However, conventional reconstruction-based techniques either use simplifying assumptions or require specialized diagnostics to infer high-dimensional ($>$ 2D) beam properties. In this Letter, we introduce a general-purpose algorithm that combines neural networks with differentiable particle tracking to efficiently reconstruct high-dimensional phase space distributions without using specialized beam diagnostics or beam manipulations. We demonstrate that our algorithm accurately reconstructs detailed 4D phase space distributions with corresponding confidence intervals in both simulation and experiment using a single focusing quadrupole and diagnostic screen. This technique allows for the measurement of multiple correlated phase spaces simultaneously, which will enable simplified 6D phase space distribution reconstructions in the future

Advancements of remote data acquisition and processing in unmanned vehicle technologies for water quality monitoring: An extensive review

Regular water quality monitoring is becoming desirable due to the increase in water pollution caused by both climate change and the generation of industrial chemicals. Unmanned vehicles have emerged as key technologies for remote data acquisition, providing fast and accurate methods for water quality monitoring. However, current research on unmanned vehicles has not systematically examined their features and limitations, which are crucial for identifying future research directions and applications of unmanned vehicle technologies. Therefore, this study extensively reviews the advancements in remote data acquisition and processing using unmanned vehicle technologies for water quality monitoring to provide valuable insights for future research. First, the types of unmanned vehicles and their application ranges for water quality monitoring are summarized. Among the unmanned vehicle technologies, unmanned aerial vehicles are considered primary platforms for water quality monitoring due to their wide data acquisition range and their ability to accommodate diverse sensors and samplers. Also, the types of samplers and sensors mounted on the unmanned vehicles are analyzed based on their characteristics. It is concluded that spectral sensors offer the most cost-effective approach for acquiring real-time water quality data. Furthermore, algorithms that convert image data into water quality data are examined, focusing on data preprocessing, analysis, and validation. The findings reveal a close relationship between the analysis of spectral characteristics of each water quality parameter and the wavelength ranges of red and red-edge. Lastly, future research directions for unmanned vehicle technologies are further suggested based on the summarized technological limitations

Demonstration of Autonomous Emittance Characterization at the Argonne Wakefield Accelerator

Transverse beam emittance plays a key role in the performance of high-brightness accelerators. Characterizing beam emittance is often carried out using a quadrupole scan, which fits beam matrix elements to experimental measurements using first-order beam dynamics. Despite its simplicity at face value, this procedure is difficult to automate due to practical limitations. Key issues that must be addressed include maintaining beam size measurement validity by keeping beams within the radius of diagnostic screens, ensuring that measurement fitting produces physically valid results, and accurately characterizing emittance uncertainty. We describe a demonstration of the Bayesian exploration technique towards solving this problem at the Argonne Wakefield Accelerator, enabling a turn-key, autonomous quadrupole scan tool that can be used to quickly measure beam emittances at various locations in accelerators with limited operator input

MET amplification, protein expression, and mutations in pulmonary adenocarcinoma

Objectives: MET amplification, protein expression, and splice mutations at exon 14 are known to cause dysregulation of the MET/HGF pathway. Our study aimed to confirm the relationship among MET amplification, protein expression, and mutations in pulmonary adenocarcinoma. Materials and methods: MET protein expression by immunohistochemistry (IHC) and MET amplification by fluorescence in situ hybridization (FISH) were evaluated in 316 surgically resected lung adenocarcinomas. Patients were divided into 4 groups (IHC-negative/FISH-negative, IHC-negative/FISH-positive, IHC-positive/FISH-negative, and IHC-positive/FISH-positive), and 15-20 tumors in each group were randomly selected for mutation analyses to find splice mutations at exon 14. Results: An IHC score of 0-3 was found in 168 (53.2%), 71 (22.5%), 59 (18.7%), and 18 (5.7%) tumors, respectively. The mean gene copy number (GCN) was 3.56; MET FISH positivity was detected in 123 (38.9%) samples, and 26 (8.2%) of them were gene amplifications. MET amplification were significantly associated with the IHC score (P< 0.001, chi(2) test). Splice mutations were identified in only 2 (2.9%) of 70 cases. One had a MET IHC score of 2 and negative FISH without amplification; The other had a MET IHC score of 0 and positive FISH without amplification. MET IHC or FISH results were not prognostic indicators of overall survival in multivariate analysis. Conclusion: There is a significant relationship between MET amplification and protein expression, and selection of tumors with amplification using IHC was effective. However, because of its rarity, a selection strategy for mutated tumors is implausible using IHC or FISH. (c) 2015 Elsevier Ireland Ltd. All rights reserved.

Nutritional status in the era of target therapy: poor nutrition is a prognostic factor in non-small cell lung cancer with activating epidermal growth factor receptor mutations

Background/Aims: Pretreatment nutritional status is an important prognostic factor in patients treated with conventional cytotoxic chemotherapy. In the era of target therapies, its value is overlooked and has not been investigated. The aim of our study is to evaluate the value of nutritional status in targeted therapy. Methods: A total of 2012 patients with non-small cell lung cancer (NSCLC) were reviewed and 630 patients with activating epidermal growth factor receptor (EGFR) mutation treated with EGFR tyrosine kinase inhibitor (TKI) were enrolled for the final analysis. Anemia, body mass index (BMI), and prognostic nutritional index (PNI) were considered as nutritional factors. Hazard ratio (HR), progression-free survival (PFS) and overall survival (OS) for each group were calculated by Cox proportional analysis. In addition, scores were applied for each category and the sum of scores was used for survival analysis. Results: In univariable analysis, anemia (HR, 1.29; p = 0.015), BMI lower than 18.5 (HR, 1.98; p = 0.002), and PNI lower than 45 (HR, 1.57; p < 0.001) were poor prognostic factors for PFS. Among them, BMI and PNI were independent in multi-variable analysis. All of these were also significant prognostic values for OS. The higher the sum of scores, the poorer PFS and OS were observed. Conclusions: Pretreatment nutritional status is a prognostic marker in NSCLC patients treated with EGFR TKI. Hence, baseline nutritional status should be more carefully evaluated and adequate nutrition should be supplied to these patients.

Prospective Evaluation of Changes in Tumor Size and Tumor Metabolism in Patients with Advanced Gastric Cancer Undergoing Chemotherapy: Association and Clinical Implication

A change in tumor size is a well-validated and commonly used value for evaluating response to chemotherapy in cancer. Metabolic changes induced by chemotherapy are related to prognosis in several tumor types. However, the clinical implication of metabolic changes in patients with advanced gastric cancer (AGC) undergoing chemotherapy remains unclear. We aimed to evaluate response of tumor size and metabolism in AGC during chemotherapy and to reveal the relationship between them in view of their impact on patient survival. Methods: We prospectively enrolled patients with AGC before the initiation of first-line palliative chemotherapy. Using baseline and follow-up contrast-enhanced CT and F-18-FDG PET, we assessed the tumor diameter, SUVmax, and total lesion glycolysis in each lesion and their changes during chemotherapy at the same time. We included all lesions with the maximal longest diameters over 1 cm on CT, and each lesion was evaluated by matched F-18-FDG PET. We analyzed the association between changes in tumor metabolism and tumor size and performed outcome analysis on overall survival (OS) and progression-free survival (PFS). Results: Seventy-four patients were enrolled, and the number of all lesions included in this study was 620. Compared with adeno-carcinomas, poorly cohesive carcinomas demonstrated lower SUVmax irrespective of tumor size (P < 0.001). Human epidermal growth factor receptor 2 (HER2)-positive tumors showed higher SUVmax than HER2-negative tumors (P = 0.002). The changes in SUVmax due to chemotherapy had a linear correlation with the changes in tumor size of each lesion, and a 30% tumor size reduction was associated with a 50% SUVmax reduction (P < 0.001). Total lesion glycolysis changes also correlated with tumor size changes (P < 0.001). Better OS and PFS were obtained in patients with both tumor size and SUVmax reduction than in patients with either size or SUVmax reduction only (OS, P = 0.003; PFS, P = 0.038). Conclusion: Changes in tumor metabolism induced by chemotherapy correlated with changes in tumor size in AGC. Considering both changes in metabolism and size could help predict a more accurate prognosis for AGC patients undergoing chemotherapy

Pretreatment albumin-to-globulin ratio as a predictive marker for tyrosine kinase inhibitor in non-small cell lung cancer

BACKGROUND: A low albumin-to-globulin ratio (AGR) has been known as a prognostic factor for cancer-related mortality. However, no study has elucidated its usefulness as a predictive factor in the era of targeted therapy, and so, we evaluated this in the present study. METHODS: We retrospectively analyzed 2012 non-small cell lung cancer (NSCLC) patients treated with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). Among these patients, 645 patients who had EGFR mutation and suitable pretreatment laboratory values were included. AGR was calculated 2 months before treatment and 4 months after treatment in each patient. The optimal cutoff value of AGR, and progression free survival (PFS) were also determined. RESULTS: The optimal cutoff value of AGR was 1.17, which yielded a highest HR of 1.89 (P = 1.17. Pretreatment AGR showed an independent predictive value (adjusted HR 1.80, P < 0.001) when age, performance status, and pre-TKI systemic treatment was adjusted for. CONCLUSIONS: We suggest that patients with NSCLC with EGFR mutations who have AGR values lower than 1.17 at the beginning of EGFR TKI treatment should be considered to have a high risk of early EGFR TKI failure.