機能性ナノ粒子の生体分子検出・分離への応用

学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 前田 瑞夫, 東京大学教授 伊藤 耕三, 東京大学教授 佐々木 裕次, 東京大学教授 竹谷 純一, 東京大学教授 船津 高志University of Tokyo(東京大学

Relationships Between D-Dimer Levels and Stroke Risk as Well as Adverse Clinical Outcomes After Acute Ischemic Stroke or Transient Ischemic Attack: A Systematic Review and Meta-Analysis

Objective: Abnormal elevation of D-dimer levels is an important indicator of disseminated intravascular clotting. Therefore, we hypothesized that high D-dimer levels were associated with the risk of stroke and adverse clinical outcomes of patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA). Methods: The present meta-analysis aimed to systematically analyze the associations between D-dimer and the risk of stroke as well as the clinical outcomes of patients with post-stroke or TIA. Meanwhile, dose–response analyses were conducted when there were sufficient data available. Three electronic databases including Pubmed, the Embase database, and the Cochrane Library were searched by two investigators independently. All the pooled results were expressed as risk ratios (RRs). Results: Finally, 22 prospective cohort studies were included into this meta-analysis. The results suggested that high D-dimer levels were associated with increased risks of total stroke (RR 1.4, 95%CI 1.20–1.63), hemorrhagic stroke (RR 1.25, 95%CI 0.69–2.25), and ischemic Stroke (RR 1.55, 95%CI 1.22–1.98), and the dose-dependent relationship was not found upon dose–response analyses. Besides, the high D-dimer levels on admission were correlated with increased risks of all-cause mortality [RR 1.77, 95% confidence interval (CI) 1.26–2.49], 5-day recurrence (RR 2.28, 95%CI 1.32–3.95), and poor functional outcomes (RR 2.01, 95%CI 1.71–2.36) in patients with AIS or TIA. Conclusions: On the whole, high D-dimer levels may be associated with the risks of total stroke and ischemic stroke, but not with hemorrhagic stroke. However, dose–response analyses do not reveal distinct evidence for a dose-dependent association of D-dimer levels with the risk of stroke. Besides, high D-dimer levels on admission may predict adverse clinical outcomes, including all-cause mortality, 5-day recurrence, and 90-day poor functional outcomes, of patients with AIS or TIA. More studies are warranted to quantify the effect of D-dimer levels on the risk of stroke or TIA, so as to verify and substantiate this conclusion in the future

4,4′-Bis(benzimidazol-1-yl)biphen­yl

The mol­ecule of the title compound, C26H18N4, resides on a crystallographic inversion centre with a dihedral angle of 44.94 (5)° between the benzimidazole ring system and the benzene ring. The primary hydrogen bond is C—H⋯N and inversion-related pairs of these generate a chain of rings along the c-axis direction; π⋯π stacking involving the benzimidazole groups with inter­planar separations of ca 3.4 Å complete the inter­actions

Robust Decoding of Rich Dynamical Visual Scenes With Retinal Spikes

Sensory information transmitted to the brain activates neurons to create a series of coping behaviors. Understanding the mechanisms of neural computation and reverse engineering the brain to build intelligent machines requires establishing a robust relationship between stimuli and neural responses. Neural decoding aims to reconstruct the original stimuli that trigger neural responses. With the recent upsurge of artificial intelligence, neural decoding provides an insightful perspective for designing novel algorithms of brain-machine interface. For humans, vision is the dominant contributor to the interaction between the external environment and the brain. In this study, utilizing the retinal neural spike data collected over multi trials with visual stimuli of two movies with different levels of scene complexity, we used a neural network decoder to quantify the decoded visual stimuli with six different metrics for image quality assessment establishing comprehensive inspection of decoding. With the detailed and systematical study of the effect and single and multiple trials of data, different noise in spikes, and blurred images, our results provide an in-depth investigation of decoding dynamical visual scenes using retinal spikes. These results provide insights into the neural coding of visual scenes and services as a guideline for designing next-generation decoding algorithms of neuroprosthesis and other devices of brain-machine interface.</p

Red Teaming Deep Neural Networks with Feature Synthesis Tools

Interpretable AI tools are often motivated by the goal of understanding model behavior in out-of-distribution (OOD) contexts. Despite the attention this area of study receives, there are comparatively few cases where these tools have identified previously unknown bugs in models. We argue that this is due, in part, to a common feature of many interpretability methods: they analyze model behavior by using a particular dataset. This only allows for the study of the model in the context of features that the user can sample in advance. To address this, a growing body of research involves interpreting models using \emph{feature synthesis} methods that do not depend on a dataset. In this paper, we benchmark the usefulness of interpretability tools on debugging tasks. Our key insight is that we can implant human-interpretable trojans into models and then evaluate these tools based on whether they can help humans discover them. This is analogous to finding OOD bugs, except the ground truth is known, allowing us to know when an interpretation is correct. We make four contributions. (1) We propose trojan discovery as an evaluation task for interpretability tools and introduce a benchmark with 12 trojans of 3 different types. (2) We demonstrate the difficulty of this benchmark with a preliminary evaluation of 16 state-of-the-art feature attribution/saliency tools. Even under ideal conditions, given direct access to data with the trojan trigger, these methods still often fail to identify bugs. (3) We evaluate 7 feature-synthesis methods on our benchmark. (4) We introduce and evaluate 2 new variants of the best-performing method from the previous evaluation. A website for this paper and its code is at https://benchmarking-interpretability.csail.mit.edu/Comment: In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Identification of CD8+ cytotoxic T lymphocyte epitopes from porcine reproductive and respiratory syndrome virus matrix protein in BALB/c mice

Twenty-seven nanopeptides derived from the matrix (M) protein of porcine reproductive and respiratory syndrome virus (PRRSV) were screened for their ability to elicit a recall interferon-γ (IFN-γ) response from the splenocytes of BALB/c mice following DNA vaccination and a booster vaccination with recombinant vaccinia virus rWR-PRRSV-M. We identified two peptides (amino acid residues K93FITSRCRL and F57GYMTFVHF) as CD8+ cytotoxic T lymphocyte (CTL) epitopes. These peptides elicited significant numbers of IFN-γ secreting cells, compared with other M nonapeptides and one irrelevant nonapeptide. Bioinformatics analysis showed that the former is an H-2Kd-restricted CTL epitope, and the latter is an H-2Dd-restricted CTL epitope. Multiple amino acid sequence alignment among different PRRSV M sequences submitted to GenBank indicated that these two CTL epitopes are strongly conserved, and they should therefore be considered for further research on the mechanisms of cellular immune responses to PRRSV

MLNet: a multi-level multimodal named entity recognition architecture

In the field of human–computer interaction, accurate identification of talking objects can help robots to accomplish subsequent tasks such as decision-making or recommendation; therefore, object determination is of great interest as a pre-requisite task. Whether it is named entity recognition (NER) in natural language processing (NLP) work or object detection (OD) task in the computer vision (CV) field, the essence is to achieve object recognition. Currently, multimodal approaches are widely used in basic image recognition and natural language processing tasks. This multimodal architecture can perform entity recognition tasks more accurately, but when faced with short texts and images containing more noise, we find that there is still room for optimization in the image-text-based multimodal named entity recognition (MNER) architecture. In this study, we propose a new multi-level multimodal named entity recognition architecture, which is a network capable of extracting useful visual information for boosting semantic understanding and subsequently improving entity identification efficacy. Specifically, we first performed image and text encoding separately and then built a symmetric neural network architecture based on Transformer for multimodal feature fusion. We utilized a gating mechanism to filter visual information that is significantly related to the textual content, in order to enhance text understanding and achieve semantic disambiguation. Furthermore, we incorporated character-level vector encoding to reduce text noise. Finally, we employed Conditional Random Fields for label classification task. Experiments on the Twitter dataset show that our model works to increase the accuracy of the MNER task

Research progress on the impact of mineral surface roughness on particle-bubble interaction

Mineral surface roughness is an important factor affecting flotation efficiency. Surface roughness can affect the hydrophobicity of minerals, the adsorption of reagents, and the rupture of liquid films between particles and bubbles, resulting in a significant impact on the interaction process between particles and bubbles. However, there is currently a lack of systematic review work on the influence of roughness on particle-bubble interaction process. Therefore, the authors firstly reviewed the surface roughening modification techniques and roughness testing methods. Secondly, the influence of roughness on the particle-bubble interaction process was systematically discussed from four aspects: flotation kinetics, contact angle, formation time of triple-phase contact line, and interaction force between particles and bubbles. The concept of roughness scale was proposed for its importance in the research of particle-bubble interactions. Thirdly, based on the coupling mechanism between roughness scale and mineral surface hydrophobicity in the particle-bubble interaction process, the importance of mineral surface wetting state in the interaction process between rough surfaces and bubbles was emphasized. The reasons for the inconsistent research conclusions on the impact of roughness on contact angle and flotation performance were also analyzed and discussed. Finally, the conclusions were drawn through critical analysis and literature review, and the prospects for future research directions were outlined. This paper contributes to a better understanding of the influence of mineral surface roughness on the flotation process, and can provide a theoretical support for regulating mineral surface roughness to create favorable flotation conditions and improve the flotation efficiency and selectivity

Characteristics and treatment strategies of aggressive angiomyxoma in women: A retrospective review of 87 cases

ObjectiveAggressive angiomyxoma (AAM) is a rare kind of soft tissue tumor. The purpose of this study is to summarize the clinical manifestations and treatment strategy of AAM in women.MethodWe searched the case reports on AAM in EMBASE, Web of Science and PubMed, China biomedical database, Wanfang database, VIP database, and China National Knowledge Internet from the start of database construction to November 2022 without any language restrictions in place. Then, the obtained case data were extracted, summarized, and analyzed.ResultA total of 74 articles were retrieved involving 87 cases. The age ranges of onset were 2–67 years. The median age at onset was 34 years. The size of the tumor varied greatly among individuals, and about 65.5% of them were asymptomatic. MRI, ultrasound, and needle biopsy were used for diagnosis. Surgery was the primary mode of treatment, but it was prone to relapse. Gonadotropin-releasing hormone agonist (GnRH-a) might be used to reduce the tumor size before the operation and prevent recurrence after the operation. For patients who are unwilling to receive surgical treatment, GnRH-a alone could be attempted.ConclusionDoctors should consider the possibility of AAM in women with genital tumors. For surgery, it must be ensured that the negative surgical margin is recommended and achieved for preventing recurrence, but we should not ignore the impact of the excessive pursuit for a negative margin on the patient’s reproductive function protection and postoperative recovery. Long-term follow-up is necessary regardless of whether patients receive medical treatment or surgical treatment