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Get PDF奈良先端科学技術大学院大学修士(工学)master thesi
Improving Speech Translation Accuracy and Time Efficiency With Fine-Tuned wav2vec 2.0-Based Speech Segmentation
No full textSpeech translation (ST) automatically converts utterances in a source language into text in another language. Splitting continuous speech into shorter segments, known as speech segmentation, plays an important role in ST. Recent segmentation methods trained to mimic the segmentation of ST corpora have surpassed traditional approaches. Tsiamas et al. [1] proposed a segmentation frame classifier (SFC) based on a pre-trained speech encoder called wav2vec 2.0. Their method, named SHAS, retains 95$201398% of the BLEU score for ST corpus segmentation. However, the segments generated by SHAS are very different from ST corpus segmentation and tend to be longer with multiple combined utterances. This is due to SHAS's reliance on length heuristics, i.e., it splits speech into segments of easily translatable length without fully considering the potential for ST improvement by splitting them into even shorter segments. Longer segments often degrade translation quality and ST's time efficiency. In this study, we extended SHAS to improve ST translation accuracy and efficiency by splitting speech into shorter segments that correspond to sentences. We introduced a simple segmentation avlgorithm using the moving average of SFC predictions without relying on length heuristics and explored wav2vec 2.0 fine-tuning for improved speech segmentation prediction. Our experimental results reveal that our speech segmentation method significantly improved the quality and the time efficiency of speech translation compared to SHAS.journal articl
Aberrant Large-Scale Network Interactions Across Psychiatric Disorders Revealed by Large-Sample Multi-Site Resting-State Functional Magnetic Resonance Imaging Datasets
No full textBackground and Hypothesis
Dynamics of the distributed sets of functionally synchronized brain regions, known as large-scale networks, are essential for the emotional state and cognitive processes. However, few studies were performed to elucidate the aberrant dynamics across the large-scale networks across multiple psychiatric disorders. In this paper, we aimed to investigate dynamic aspects of the aberrancy of the causal connections among the large-scale networks of the multiple psychiatric disorders.
Study Design
We applied dynamic causal modeling (DCM) to the large-sample multi-site dataset with 739 participants from 4 imaging sites including 4 different groups, healthy controls, schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD), to compare the causal relationships among the large-scale networks, including visual network, somatomotor network (SMN), dorsal attention network (DAN), salience network (SAN), limbic network (LIN), frontoparietal network, and default mode network.
Study Results
DCM showed that the decreased self-inhibitory connection of LIN was the common aberrant connection pattern across psychiatry disorders. Furthermore, increased causal connections from LIN to multiple networks, aberrant self-inhibitory connections of DAN and SMN, and increased self-inhibitory connection of SAN were disorder-specific patterns for SCZ, MDD, and BD, respectively.
Conclusions
DCM revealed that LIN was the core abnormal network common to psychiatric disorders. Furthermore, DCM showed disorder-specific abnormal patterns of causal connections across the 7 networks. Our findings suggested that aberrant dynamics among the large-scale networks could be a key biomarker for these transdiagnostic psychiatric disorders.journal articl
Dephosphorylation of neural wiring protein shootin1 by PP1 phosphatase regulates netrin-1-induced axon guidance
No full textAxon pathfinding is an essential step in neuronal network formation. Shootin1a is a clutch-linker molecule that is mechanically involved in axon outgrowth and guidance. It was previously shown that concentration gradients of axon guidance molecule netrin-1 in the extracellular environment elicit asymmetrically localized Pak1 kinase2013adhesion coupling within growth cones, thereby generating directional forces for turning the growth cone toward the netrin-1 source. However, how the spatial differences in netrin-1 concentration are transduced into the asymmetrically localized signaling within growth cones remains unclear. Moreover, the protein phosphatases that dephosphorylate shootin1a remain unidentified. Here, we report that protein phosphatase-1 (PP1) dephosphorylates shootin1a in growth cones. We found that PP1 overexpression abolished the netrin-1-induced asymmetric localization of phosphorylated shootin1a as well as axon turning. In addition, we show PP1 inhibition reversed the asymmetrically localized shootin1a phosphorylation within growth cones under netrin-1 gradient, thereby changing the netrin-1-induced growth cone turning from attraction to repulsion. These data indicate that PP1-mediated shootin1a dephosphorylation plays a key role in organizing asymmetrically localized phosphorylated shootin1a within growth cones, which regulates netrin-1-induced axon guidance.journal articl
Small GTPase Cdc42, WASP, and scaffold proteins for higher-order assembly of the F-BAR domain protein
No full textThe higher-order assembly of Bin-amphiphysin-Rvs (BAR) domain proteins, including the FCH-BAR (F-BAR) domain proteins, into lattice on the membrane is essential for the formation of subcellular structures. However, the regulation of their ordered assembly has not been elucidated. Here, we show that the higher ordered assembly of growth-arrested specific 7 (GAS7), an F-BAR domain protein, is regulated by the multivalent scaffold proteins of Wiskott-Aldrich syndrome protein (WASP)/neural WASP, that commonly binds to the BAR domain superfamily proteins, together with WISH, Nck, the activated small guanosine triphosphatase Cdc42, and a membrane-anchored phagocytic receptor. The assembly kinetics by fluorescence resonance energy transfer monitoring indicated that the GAS7 assembly on liposomes started within seconds and was further increased by the presence of these proteins. The regulated GAS7 assembly was abolished by Wiskott-Aldrich syndrome mutations both in vitro and in cellular phagocytosis. Therefore, Cdc42 and the scaffold proteins that commonly bind to the BAR domain superfamily proteins promoted GAS7 assembly.journal articl
Disruptions in the Cystic Fibrosis Community’s Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments
No full textBackground:
The COVID-19 pandemic disrupted the needs and concerns of the cystic fibrosis community. Patients with cystic fibrosis were particularly vulnerable during the pandemic due to overlapping symptoms in addition to the challenges patients with rare diseases face, such as the need for constant medical aid and limited information regarding their disease or treatments. Even before the pandemic, patients vocalized these concerns on social media platforms like Reddit and formed communities and networks to share insight and information. This data can be used as a quick and efficient source of information about the experiences and concerns of patients with cystic fibrosis in contrast to traditional survey- or clinical-based methods.
Objective:
This study applies topic modeling and time series analysis to identify the disruption caused by the COVID-19 pandemic and its impact on the cystic fibrosis community’s experiences and concerns. This study illustrates the utility of social media data in gaining insight into the experiences and concerns of patients with rare diseases.
Methods:
We collected comments from the subreddit r/CysticFibrosis to represent the experiences and concerns of the cystic fibrosis community. The comments were preprocessed before being used to train the BERTopic model to assign each comment to a topic. The number of comments and active users for each data set was aggregated monthly per topic and then fitted with an autoregressive integrated moving average (ARIMA) model to study the trends in activity. To verify the disruption in trends during the COVID-19 pandemic, we assigned a dummy variable in the model where a value of “1” was assigned to months in 2020 and “0” otherwise and tested for its statistical significance.
Results:
A total of 120,738 comments from 5827 users were collected from March 24, 2011, until August 31, 2022. We found 22 topics representing the cystic fibrosis community’s experiences and concerns. Our time series analysis showed that for 9 topics, the COVID-19 pandemic was a statistically significant event that disrupted the trends in user activity. Of the 9 topics, only 1 showed significantly increased activity during this period, while the other 8 showed decreased activity. This mixture of increased and decreased activity for these topics indicates a shift in attention or focus on discussion topics during this period.
Conclusions:
There was a disruption in the experiences and concerns the cystic fibrosis community faced during the COVID-19 pandemic. By studying social media data, we were able to quickly and efficiently study the impact on the lived experiences and daily struggles of patients with cystic fibrosis. This study shows how social media data can be used as an alternative source of information to gain insight into the needs of patients with rare diseases and how external factors disrupt them.journal articl
Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach
No full textBackground:
Medication noncompliance is a critical issue because of the increased number of drugs sold on the web. Web-based drug distribution is difficult to control, causing problems such as drug noncompliance and abuse. The existing medication compliance surveys lack completeness because it is impossible to cover patients who do not go to the hospital or provide accurate information to their doctors, so a social media$2013based approach is being explored to collect information about drug use. Social media data, which includes information on drug usage by users, can be used to detect drug abuse and medication compliance in patients.
Objective:
This study aimed to assess how the structural similarity of drugs affects the efficiency of machine learning models for text classification of drug noncompliance.
Methods:
This study analyzed 22,022 tweets about 20 different drugs. The tweets were labeled as either noncompliant use or mention, noncompliant sales, general use, or general mention. The study compares 2 methods for training machine learning models for text classification: single-sub-corpus transfer learning, in which a model is trained on tweets about a single drug and then tested on tweets about other drugs, and multi-sub-corpus incremental learning, in which models are trained on tweets about drugs in order of their structural similarity. The performance of a machine learning model trained on a single subcorpus (a data set of tweets about a specific category of drugs) was compared to the performance of a model trained on multiple subcorpora (data sets of tweets about multiple categories of drugs).
Results:
The results showed that the performance of the model trained on a single subcorpus varied depending on the specific drug used for training. The Tanimoto similarity (a measure of the structural similarity between compounds) was weakly correlated with the classification results. The model trained by transfer learning a corpus of drugs with close structural similarity performed better than the model trained by randomly adding a subcorpus when the number of subcorpora was small.
Conclusions:
The results suggest that structural similarity improves the classification performance of messages about unknown drugs if the drugs in the training corpus are few. On the other hand, this indicates that there is little need to consider the influence of the Tanimoto structural similarity if a sufficient variety of drugs are ensured.journal articl
Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity
No full textActivity cliffs (AC) are formed by pairs of structural analogues that are active against the same target but have a large difference in potency. While much of our knowledge about ACs has originated from the analysis and comparison of compounds and activity data, several studies have reported AC predictions over the past decade. Different from typical compound classification tasks, AC predictions must be carried out at the level of compound pairs representing ACs or nonACs. Most AC predictions reported so far have focused on individual methods or comparisons of two or three approaches and only investigated a few compound activity classes (from 2 to 10). Although promising prediction accuracy has been reported in most cases, different system set-ups, AC definitions, methods, and calculation conditions were used, precluding direct comparisons of these studies. Therefore, we have carried out a large-scale AC prediction campaign across 100 activity classes comparing machine learning methods of greatly varying complexity, ranging from pair-based nearest neighbor classifiers and decision tree or kernel methods to deep neural networks. The results of our systematic predictions revealed the level of accuracy that can be expected for AC predictions across many different compound classes. In addition, prediction accuracy did not scale with methodological complexity but was significantly influenced by memorization of compounds shared by different ACs or nonACs. In many instances, limited training data were sufficient for building accurate models using different methods and there was no detectable advantage of deep learning over simpler approaches for AC prediction. On a global scale, support vector machine models performed best, by only small margins compared to others including simple nearest neighbor classifiers.journal articl
データ クドウ カガク ガ ミチビク ケンキュウ カイハツ ノ パラダイム ヘンカク
No full textvideo/mp4計測されたデータをうまく利用して、新しい分子や材料をコンピュータで設計し、それをロボットに合成させる。さらには製品として品質を管理しながら安定生産を実現する。こうした一連の流れがデータの高度利用の中で実現され、化学研究のパラダイムが変わりつつあります。この講座では、その概要を紹介します。講演場所: 生駒市生涯学習施設 たけまるホール（大ホール）講演者所属: 本学データ駆動型サイエンス創造センター講演日: 2023年10月7日vide