Expression and regulatory effects on cancer cell behavior of NELL1 and NELL2 in human renal cell carcinoma

We thank Professors Michael Rehli, Yoshiaki Ito, and Kristian Helin for gifting plasmids, Dr. Alasdair MacKenzie (University of Aberdeen) for helpful discussion, and Mr. Takashi Mizukami, Ms. Ryoko Tokuda, and Ms. Sanae Funaoka (Kanazawa University) for technical assistance.Peer reviewedPublisher PD

Zero-Shot Edge Detection with SCESAME: Spectral Clustering-based Ensemble for Segment Anything Model Estimation

This paper proposes a novel zero-shot edge detection with SCESAME, which stands for Spectral Clustering-based Ensemble for Segment Anything Model Estimation, based on the recently proposed Segment Anything Model (SAM). SAM is a foundation model for segmentation tasks, and one of the interesting applications of SAM is Automatic Mask Generation (AMG), which generates zero-shot segmentation masks of an entire image. AMG can be applied to edge detection, but suffers from the problem of overdetecting edges. Edge detection with SCESAME overcomes this problem by three steps: (1) eliminating small generated masks, (2) combining masks by spectral clustering, taking into account mask positions and overlaps, and (3) removing artifacts after edge detection. We performed edge detection experiments on two datasets, BSDS500 and NYUDv2. Although our zero-shot approach is simple, the experimental results on BSDS500 showed almost identical performance to human performance and CNN-based methods from seven years ago. In the NYUDv2 experiments, it performed almost as well as recent CNN-based methods. These results indicate that our method effectively enhances the utility of SAM and can be a new direction in zero-shot edge detection methods.Comment: 11 pages, accepted to WACV 2024 Worksho

An Automatic Self-explanation Sample Answer Generation with Knowledge Components in a Math Quiz

Part of the Lecture Notes in Computer Science book series (LNCS, volume 13356)Little research has addressed how systems can use the learning process of self-explanation to provide scaffolding or feedback. Here, we propose a model automatically generating sample self-explanations with knowledge components required to solve a math quiz. The proposed model contains three steps: vectorization, clustering, and extraction. In an experiment using 1434 self-explanation answers from 25 quizzes, we found 72% of the quizzes generated sample answers with all necessary knowledge components. The similarity between human-created and machine-generated sentences was 0.719, with a significant correlation of R = 0.48 for the best performing generation model by BERTScore. These results suggest that our model can generate sample answers with the necessary key knowledge components and be further improved by using the BERTScore

Unsupervised techniques for generating a standard sample self-explanation answer with knowledge components in a math quiz

Self-explanation is a widely recognized and effective pedagogical method. Previous research has indicated that self-explanation can be used to evaluate students’ comprehension and identify their areas of difficulty on mathematical quizzes. However, most analytical techniques necessitate pre-labeled materials, which limits the potential for large-scale study. Conversely, utilizing collected self-explanations without supervision is challenging because there is little research on this topic. Therefore, this study aims to investigate the feasibility of automatically generating a standardized self-explanation sample answer from unsupervised collected self-explanations. The proposed model involves preprocessing and three machine learning steps: vectorization, clustering, and extraction. Experiments involving 1, 434 self-explanation answers from 25 quizzes indicate that 72% of the quizzes generate sample answers containing all the necessary knowledge components. The similarity between human-generated and machine-generated sentences was significant with moderate positive correlation, r(23) = .48, p < .05.The best-performing generative model also achieved a high BERTScore of 0.715. Regarding the readability of the generated sample answers, the average score of the human-generated sentences was superior to that of the machine-generated ones. These results suggest that the proposed model can generate sample answers that contain critical knowledge components and can be further improved with BERTScore. This study is expected to have numerous applications, including identifying students’ areas of difficulty, scoring self-explanations, presenting students with reference materials for learning, and automatically generating scaffolding templates to train self-explanation skills

Automated labeling of PDF mathematical exercises with word N-grams VSM classification

In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and difficult to scale. It recognizes that automated labeling eases the workload on experts, as seen in previous studies using automatic classification algorithms for research papers and Japanese mathematical exercises. However, these studies didn’t delve into fine-grained labeling. In addition to that, as the use of materials in the system becomes more widespread, paper materials are transformed into PDF formats, which can lead to incomplete extraction. However, there is less emphasis on labeling incomplete mathematical sentences to tackle this problem in the previous research. This study aims to achieve precise automated classification even from incomplete text inputs. To tackle these challenges, we propose a mathematical exercise labeling algorithm that can handle detailed labels, even for incomplete sentences, using word n-grams, compared to the state-of-the-art word embedding method. The results of the experiment show that mono-gram features with Random Forest models achieved the best performance with a macro F-measure of 92.50%, 61.28% for 24-class labeling and 297-class labeling tasks, respectively. The contribution of this research is showing that the proposed method based on traditional simple n-grams has the ability to find context-independent similarities in incomplete sentences and outperforms state-of-the-art word embedding methods in specific tasks like classifying short and incomplete texts

EXAIT: Educational eXplainable Artificial Intelligent Tools for personalized learning

As artificial intelligence systems increasingly make high-stakes recommendations and decisions automatically in many facets of our lives, the use of explainable artificial intelligence to inform stakeholders about the reasons behind such systems has been gaining much attention in a wide range of fields, including education. Also, in the field of education there has been a long history of research into self-explanation, where students explain the process of their answers. This has been recognized as a beneficial intervention to promote metacognitive skills, however, there is also unexplored potential to gain insight into the problems that learners experience due to inadequate prerequisite knowledge and skills that are required, or in the process of their application to the task at hand. While this aspect of self-explanation has been of interest to teachers, there is little research into the use of such information to inform educational AI systems. In this paper, we propose a system in which both students and the AI system explain to each other their reasons behind decisions that were made, such as: self-explanation of student cognition during the answering process, and explanation of recommendations based on internal mechanizes and other abstract representations of model algorithms

Improving bonding strength by non-thermal atmospheric pressure plasma-assisted technology for A5052/PEEK direct joining

The direct bonding of A5052 aluminum (Al) alloy to the engineering polymer poly(ether ether ketone) (PEEK) using an atmospheric pressure plasma-assisted process was demonstrated. The effect of plasma irradiation on the bonding surface of metal resin on the bonding strength following thermal press fitting method was investigated. Specimens bonded by plasma irradiation on the PEEK surface only showed a high tensile shear stress of 15.5 MPa. With increasing plasma irradiation time, the bond strength of the samples bonded to the PEEK surface by plasma irradiation increased. The increase in the bond strength between metals and polymers following direct bonding is caused by the addition of oxygen functional groups on the polymer. In contrast, specimens in which only the Al was exposed to the plasma showed a decrease in bond strength compared with unirradiated samples. This reduction in bond strength is attributed to the forming magnesium oxide, which forms in the early stages of participation due to plasma irradiation.The version of record of this article, first published in International Journal of Advanced Manufacturing Technology, is available online at Publisher’s website: https://doi.org/10.1007/s00170-023-12747-

Influence of pre-treatment with non-thermal atmospheric pressure plasma on bond strength of TP340 titanium-PEEK direct bonding

The version of record of this article, first published in International Journal of Advanced Manufacturing Technology, is available online at Publisher’s website: https://doi.org/10.1007/s00170-024-14160-z.Direct bonding of a TP340 titanium to PEEK by hot pressing via pre-treatment of non-thermal atmospheric pressure plasma jet has been demonstrated. The plasma irradiation effect on the bonding surface on the bond strength after hot pressing was investigated. The tensile shear strength of TP340-PEEK joined by hot pressing after plasma pre-treatment was measured by comparing specimens bonded using conventional hot pressing and those bonded using adhesives. The plasma treatment to the TP340 side resulted in the formation of TiO2, which is chemically fed to oxide formation due to the irradiation of oxygen radicals generated by the plasma, resulting in a bond strength of less than 1 MPa, similar to the bond strength of the untreated specimens. The plasma irradiation effect on the PEEK side on the bond strength of TP340-PEEK bonded samples was also investigated. The bonding strength was increased by plasma irradiation to PEEK. As the plasma irradiation time was increased, the bonding strength gradually increased to 9.2 MPa, which is about 19 times higher than the bonding strength without plasma irradiation. These results suggest that oxygen radicals in the atmospheric pressure RF plasma jet produced oxygen-containing surface functional groups on the PEEK surface, which increased the strength of the TP340-PEEK direct joining

Influence of pre-treatment using non-thermal atmospheric pressure plasma jet on aluminum alloy A1050 to PEEK direct joining with hot-pressing process

The version of record of this article, first published in International Journal of Advanced Manufacturing Technology, is available online at Publisher’s website: https://doi.org/10.1007/s00170-023-12827-7.Aluminum alloy A1050 to polyetheretherketone (PEEK) direct joining with hot-pressing process via pre-treatment using non-thermal atmospheric pressure plasma jet has been performed. The effect of plasma irradiation on the tensile shear strength of A1050-PEEK direct bonded specimens joined by a combination of hot-pressing process and pre-plasma treatment using non-thermal atmospheric pressure plasma jet was investigated. A1050-PEEK bonded samples with plasma-treated PEEK only showed high tensile shear stress of 13.4 MPa. This increase in tensile shear strength is attributed to the addition of oxygen functional groups on the surface of the PEEK by reactive oxygen species produced by the plasma jet

Neuromelanin‐Sensitive Magnetic Resonance Imaging Using DANTE Pulse

BACKGROUND: Neuromelanin-sensitive magnetic resonance imaging techniques have been developed but currently require relatively long scan times. The aim of this study was to assess the ability of black-blood delay alternating with nutation for tailored excitation-prepared T1-weighted variable flip angle turbo spin echo (DANTE T1-SPACE), which provides relatively high resolution with a short scan time, to visualize neuromelanin in the substantia nigra pars compacta (SNpc). METHODS: Participants comprised 49 healthy controls and 25 patients with Parkinson's disease (PD). Contrast ratios of SNpc and hyperintense SNpc areas, which show pixels brighter than thresholds, were assessed between DANTE T1-SPACE and T1-SPACE in healthy controls. To evaluate the diagnostic ability of DANTE T1-SPACE, the contrast ratios and hyperintense areas were compared between healthy and PD groups, and receiver operating characteristic analyses were performed. We also compared areas under the curve (AUCs) between DANTE T1-SPACE and the previously reported gradient echo neuromelanin (GRE-NM) imaging. Each analysis was performed using original images in native space and images transformed into Montreal Neurological Institute space. Values of P < 0.05 were considered significant. RESULTS: DANTE T1-SPACE showed significantly higher contrast ratios and larger hyperintense areas than T1-SPACE. On DANTE T1-SPACE, healthy controls showed significantly higher contrast ratios and larger hyperintense areas than patients with PD. Hyperintense areas in native space analysis achieved the best AUC (0.94). DANTE T1-SPACE showed AUCs as high as those of GRE-NM. CONCLUSIONS: DANTE T1-SPACE successfully visualized neuromelanin of the SNpc and showed potential for evaluating PD. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society