13197 research outputs found
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International Society for Extracellular Vesicles workshop. QuantitatEVs: Multiscale analyses, from bulk to single extracellular vesicle
No full textThe “QuantitatEVs: multiscale analyses, from bulk to single vesicle” workshop aimed to discuss quantitative strategies and harmonized wet and computational approaches toward the comprehensive analysis of extracellular vesicles (EVs) from bulk to single vesicle analyses with a special focus on emerging technologies. The workshop covered the key issues in the quantitative analysis of different EV-associated molecular components and EV biophysical features, which are considered the core of EV-associated biomarker discovery and validation for their clinical translation. The in-person-only workshop was held in Trento, Italy, from January 31st to February 2nd, 2023, and continued in Milan on February 3rd with “Next Generation EVs,” a satellite event dedicated to early career researchers (ECR). This report summarizes the main topics and outcomes of the workshop.journal articl
How Maintainable is Proficient Code? A Case Study of Three PyPI Libraries
No full textPython is very popular because it can be used for a wider audience of developers, data scientists, machine learning experts and so on. Like other programming languages, there are beginner to advanced levels of writing Python code. However, like all software, code constantly needs to be maintained as bugs and the need for new features emerge. Although the Zen of Python states that "Simple is better than complex." However, we hypothesize that more elegant and proficient code might be harder for the developer to maintain. To study this relationship between the understanding of code maintainability and code proficiency, we present an exploratory study into the complexity of Python code on three Python libraries. Specifically, we investigate the risk level of proficient code inside a file. As a starting point, we mined and collected the proficiency of code from three PyPI libraries totaling 3,003 files. We identified several instances of high proficient code that was also high risk, with examples being simple list comprehensions, ‘enumerate’ calls, generator expressions, simple dictionary comprehensions, and the ‘super’ function. Our early examples revealed that most code-proficient development presented a low maintainability risk, yet there are some cases where proficient code is also risky to maintenance. We envision that the study should help developers identify scenarios where and when using proficient code might be detrimental to future code maintenance activities.conference pape
Cascade Fluorescence Modulation in Photochromic Microcapsules
No full textCertain derivatives of terarylene are able to undergo a highly efficient oxidative cycloreversion cascade effect, a ring opening reaction with quantum yields above unity, resulting in a colored-to-colorless transition in solution. In the presence of chloroform, high-energy UV and X-rays can trigger this phenomenon, potentially acting as a visual detection system for ionizing radiation. However, chloroform is sensitive to different irradiation wavelengths without distinction, making it difficult to adapt to a reusable device. Chlorobenzene was chosen as an alternative halogenated solvent, as it offers wavelength selectivity between photocyclization and cascade effect cycloreversion. Nile Red was also incorporated into the system with the aim of improving the sensitivity of the visual detection via fluorescence photoswitching. Finally, microencapsulation of both terarylene and Nile Red was targeted to obtain both the cascade effect and photoswitching in a single system. In microcapsules made from a Pickering emulsion, this terarylene$2013Nile Red system showed high fatigue resistance to repeated photocyclization and cycloreversion irradiation, giving access to repeated ON/OFF fluorescence photoswitching. The cascade effect was also successfully demonstrated along with fluorescence recovery, showing the versatility of the two phenomena in different media.journal articl
Web-Biometrics for User Authenticity Verification in Zero Trust Access Control
No full textIn the case of remote work, once a user completes account authentication, they can continue to access confidential data without further verification of user identity. Thus, if a user’s device or authentication information is leaked/stolen by an adversary or shared with a third party, the confidentiality of the data is violated. Previous studies have proposed classical biometrics-based verification of user authenticity. This method could identify whether a user was an adversary or not, but could not verify whether the account operator was a legitimate user. Moreover, since the architecture of ZTAC was not designed for the use of biometrics, it could not effectively control the various access patterns of adversaries in remote work. In this study, we propose a user authenticity verification method based on biometrics, designed for use in ZTAC. By designing and monitoring web biometrics that can verify that the account operator is a legitimate user, we aim to realize a system that provides appropriate access control for accounts after authentication according to the verification results of behavioral/cognitive patterns on the browser. Achieving dynamic access control independent of authentication results enables secure management of confidential data in remote work. In the evaluation experiment, we classified the access patterns of adversaries in remote work and confirmed that the system can appropriately control each of them. Additionally, measuring the response time to access requests showed that the evaluation experiment resulted in delays of no more than approximately 130 milliseconds, confirming that the migration to the proposed method has a very low impact on the system load.journal articl
The Nedd4L ubiquitin ligase is activated by FCHO2-generated membrane curvature
No full textThe C2-WW-HECT domain ubiquitin ligase Nedd4L regulates membrane sorting during endocytosis through the ubiquitination of cargo molecules such as the epithelial sodium channel (ENaC). Nedd4L is catalytically autoinhibited by an intramolecular interaction between its C2 and HECT domains, but the protein’s activation mechanism is poorly understood. Here, we show that Nedd4L activation is linked to membrane shape by FCHO2, a Bin-Amphiphysin-Rsv (BAR) domain protein that regulates endocytosis. FCHO2 was required for the Nedd4L-mediated ubiquitination and endocytosis of ENaC, with Nedd4L co-localizing with FCHO2 at clathrin-coated pits. In cells, Nedd4L was specifically recruited to, and activated by, the FCHO2 BAR domain. Furthermore, we reconstituted FCHO2-induced recruitment and activation of Nedd4L in vitro. Both the recruitment and activation were mediated by membrane curvature rather than protein$2013protein interactions. The Nedd4L C2 domain recognized a specific degree of membrane curvature that was generated by the FCHO2 BAR domain, with this curvature directly activating Nedd4L by relieving its autoinhibition. Thus, we show for the first time a specific function (i.e., recruitment and activation of an enzyme regulating cargo sorting) of membrane curvature by a BAR domain protein.journal articl
Interactive Segmentation With Incremental Watershed Cuts
No full textvideo/mp4We present an incremental method for computing seeded watershed cuts for interactive image segmentation. We propose an algorithm based on the hierarchical image representation called the binary partition tree. We show that this algorithm fits perfectly in an interactive segmentation process by handling user interactions, seed addition or removal, in linear time with respect to the number of affected pixels. Additionally, we design a parallel algorithm for the longest step of the method, namely the connected component labeling of minimum spanning forests. Run time comparisons with several state-of-the-art interactive and non-interactive watershed methods show that the proposed method can handle user interactions much faster than previous methods with a significant speedup ranging from 10 to 60 on both 2D and 3D images, thus improving the user experience on large images.講演日: 2024年7月26日講演場所: エーアイ大講義室, AI Inc. Seminar Hall （L1）vide
Generating Distributable Surrogate Corpus for Medical Multi-label Classification
No full textIn medical and social media domains, annotated corpora are often hard to distribute due to copyrights and privacy issues. To overcome this situation, we propose a new method to generate a surrogate corpus for a downstream task by using a text generation model. We chose a medical multi-label classification task, MedWeb, in which patient-generated short messages express multiple symptoms. We first fine-tuned text generation models with different prompting designs on the original corpus to obtain synthetic versions of that corpus. To assess the viability of the generated corpora for the downstream task, we compared the performance of multi-label classification models trained either on the original or the surrogate corpora. The results and the error analysis showed the difficulty of generating surrogate corpus in multi-label settings, suggesting text generation under complex conditions is not trivial. On the other hand, our experiment demonstrates that the generated corpus with a sentinel-based prompting is comparatively viable in a single-label (multiclass) classification setting.conference pape
Scalable Pythagorean Mean-based Incident Detection in Smart Transportation Systems
No full textModern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. To materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. In this article, we first propose a scalable data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. Second, using cluster-level detection, we propose a folded Gaussian classifier to pinpoint the particular segment in a cluster where the incident happened in an automated manner. We perform extensive experimental validation using mobility data collected from four cities in Tennessee and compare with the state-of-the-art ML methods to prove that our method can detect incidents within each cluster in real-time and outperforms known ML methods.journal articl
Overview of the 9th Social Media Mining for Health Applications (#SMM4H) Shared Tasks at ACL 2024 $2013 Large Language Models and Generalizability for Social Media NLP
No full textFor the past nine years, the Social Media Mining for Health Applications (#SMM4H) shared tasks have promoted community-driven development and evaluation of advanced natural language processing systems to detect, extract, and normalize health-related information in publicly available user-generated content. This year, #SMM4H included seven shared tasks in English, Japanese, German, French, and Spanish from Twitter, Reddit, and health forums. A total of 84 teams from 22 countries registered for #SMM4H, and 45 teams participated in at least one task. This represents a growth of 180% and 160% in registration and participation, respectively, compared to the last iteration. This paper provides an overview of the tasks and participating systems. The data sets remain available upon request, and new systems can be evaluated through the post-evaluation phase on CodaLab.conference pape
Applying Syntax-Prosody Mapping Hypothesis and Boundary-Driven Theory to Neural Sequence-to-Sequence Speech Synthesis
No full textThis study presents a novel approach to Japanese speech synthesis by applying the syntax-prosody mapping hypothesis and the boundary-driven theory, both from linguistics. Focusing on the phonological phenomena of initial lowering and rhythmic boost, our research introduces the Recursive Phonological Model, which significantly outperforms traditional methods in both objective and subjective evaluation experiments. This study proposes new objective evaluation criteria for Japanese speech synthesis. These criteria offer a more rigorous and linguistically grounded methodology for assessing the quality of synthesized speech. The Recursive Phonological Model accurately captures both the presence and absence of initial lowering, a common phenomenon in Japanese speech. This is the first model to successfully reflect such syntactic variations through intonation, demonstrating its advanced ability to handle complex phonological patterns. Additionally, the model demonstrates a unique proficiency in reproducing the rhythmic boost phenomenon, despite rhythmic boost being absent in the training data. This ability underscores the importance of learning phonological boundaries in speech synthesis. Our approach not only yields more natural-sounding speech but also enriches the field by incorporating complex linguistic theories in the computational process. This research thus marks a significant advance in the naturalness and linguistic accuracy of speech synthesis, with broader implications for computational linguistics and artificial intelligence.journal articl