Data-Driven Optimal Sensor Placement for High-Dimensional System Using Annealing Machine

We propose a novel method for solving optimal sensor placement problem for high-dimensional system using an annealing machine. The sensor points are calculated as a maximum clique problem of the graph, the edge weight of which is determined by the proper orthogonal decomposition (POD) mode obtained from data based on the fact that a high-dimensional system usually has a low-dimensional representation. Since the maximum clique problem is equivalent to the independent set problem of the complement graph, the independent set problem is solved using Fujitsu Digital Annealer. As a demonstration of the proposed method, the pressure distribution induced by the K\'arm\'an vortex street behind a square cylinder is reconstructed based on the pressure data at the calculated sensor points. The pressure distribution is measured by pressure-sensitive paint (PSP) technique, which is an optical flow diagnose method. The root mean square errors (RMSEs) between the pressure measured by pressure transducer and the reconstructed pressures (calculated from the proposed method and an existing greedy method) at the same place are compared. As the result, the similar RMSE is achieved by the proposed method using approximately 1/5 number of sensor points obtained by the existing method. This method is of great importance as a novel approach for optimal sensor placement problem and a new engineering application of an annealing machine

Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technology

Time-series clustering serves as a powerful data mining technique for time-series data in the absence of prior knowledge about clusters. A large amount of time-series data with large size has been acquired and used in various research fields. Hence, clustering method with low computational cost is required. Given that a quantum-inspired computing technology, such as a simulated annealing machine, surpasses conventional computers in terms of fast and accurately solving combinatorial optimization problems, it holds promise for accomplishing clustering tasks that are challenging to achieve using existing methods. This study proposes a novel time-series clustering method that leverages an annealing machine. The proposed method facilitates an even classification of time-series data into clusters close to each other while maintaining robustness against outliers. Moreover, its applicability extends to time-series images. We compared the proposed method with a standard existing method for clustering an online distributed dataset. In the existing method, the distances between each data are calculated based on the Euclidean distance metric, and the clustering is performed using the k-means++ method. We found that both methods yielded comparable results. Furthermore, the proposed method was applied to a flow measurement image dataset containing noticeable noise with a signal-to-noise ratio of approximately 1. Despite a small signal variation of approximately 2%, the proposed method effectively classified the data without any overlap among the clusters. In contrast, the clustering results by the standard existing method and the conditional image sampling (CIS) method, a specialized technique for flow measurement data, displayed overlapping clusters. Consequently, the proposed method provides better results than the other two methods, demonstrating its potential as a superior clustering method.Comment: 13 pages, 4 figure

Controlling genetic heterogeneity in gene-edited hematopoietic stem cells by single-cell expansion

Gene editing using engineered nucleases frequently produces unintended genetic lesions in hematopoietic stem cells (HSCs). Gene-edited HSC cultures thus contain heterogeneous populations, the majority of which either do not carry the desired edit or harbor unwanted mutations. In consequence, transplanting edited HSCs carries the risks of suboptimal efficiency and of unwanted mutations in the graft. Here, we present an approach for expanding gene-edited HSCs at clonal density, allowing for genetic profiling of individual clones before transplantation. We achieved this by developing a defined, polymer-based expansion system and identifying long-term expanding clones within the CD201 +CD150 +CD48 -c-Kit +Sca-1 +Lin - population of precultured HSCs. Using the Prkdc scid immunodeficiency model, we demonstrate that we can expand and profile edited HSC clones to check for desired and unintended modifications, including large deletions. Transplantation of Prkdc-corrected HSCs rescued the immunodeficient phenotype. Our ex vivo manipulation platform establishes a paradigm to control genetic heterogeneity in HSC gene editing and therapy

The Current Role of Stereotactic Body Radiation Therapy (SBRT) in Hepatocellular Carcinoma (HCC)

The role of stereotactic body radiotherapy (SBRT), which can deliver high radiation doses to focal tumors, has greatly increased in not only early-stage hepatocellular carcinoma (HCC), but also in portal vein or inferior vena cava thrombi, thus expanding this therapy to pre-transplantation and the treatment of oligometastases from HCC in combination with immune checkpoint inhibitors (ICI). In early-stage HCC, many promising prospective results of SBRT have been reported, although SBRT is not usually indicated as a first treatment potion in localized HCC according to several guidelines. In the treatment of portal vein or inferior vena cava tumor thrombi, several reports using various dose-fraction schedules have shown relatively good response rates with low toxicities and improved survival due to the rapid advancements in systemic therapy. Although SBRT is regarded as a substitute therapy when conventional bridging therapies to transplantation, such as transarterial chemoembolization (TACE) and radiofrequency ablation (RFA), are not applicable or fail in controlling tumors, SBRT may offer advantages in patients with borderline liver function who may not tolerate TACE or RFA, according to several reports. For oligometastases, the combination of SBRT with ICI could potentially induce an abscopal effect in patients with HCC, which is expected to provide the rationale for SBRT in the treatment of oligometastatic disease in the near future

Stem Cell Antigen 1-Positive Mesenchymal Cells Are the Origin of Follicular Cells during Thyroid Regeneration

Many tissues are thought to contain adult stem/progenitor cells that are responsible for repair of the tissue where they reside upon damage and/or carcinogenesis, conditions when cellular homeostasis becomes uncontrolled. While the presence of stem/progenitor cells of the thyroid has been suggested, how these cells contribute to thyroid regeneration remains unclear. Here we show the origin of thyroid follicular cells and the process of their maturation to become follicular cells during regeneration. By using β-galactosidase (β-gal) reporter mice in conjunction with partial thyroidectomy as a model for thyroid regeneration, and bromodeoxyuridine (BrdU) long label-retaining cell analysis, we demonstrated that stem cell antigen 1 (Sca1) and BrdU-positive, but β-gal and NKX2-1 negative cells were found in the non-follicular mesenchymal area 7 days after partial thyroidectomy. They temporarily co-expressed cytokeratin 14, and were observed in part of follicles by day 35 post-partial thyroidectomy. Sca1, BrdU, β-gal, and NKX2-1-positive cells were found 120 days post-partial thyroidectomy. These results suggested that Sca1 and BrdU positive cells may participate in the formation of new thyroid follicles after partial thyroidectomy. The process of thyroid follicular cell regeneration was recapitulated in ex vivo thyroid slice collagen gel culture studies. These studies will facilitate research on thyroid stem/progenitor cells and their roles in thyroid diseases, particularly thyroid carcinomas