Approach for Named Entity Recognition and Case Identification Implemented by ZuKyo-JA Sub-team at the NTCIR-16 Real-MedNLP Task

In this NTCIR-16 Real-MedNLP shared task paper, we present the methods of the ZuKyo-JA subteam for solving the Japanese part of Subtask1 and Subtask3 (Subtask1-CR-JA, Subtask1-RR- JA, Subtask3-RR-JA). Our solution is based on a sliding- window approach using a Japanese BERT pre-trained masked- language model., which was used as a common architecture for addressing the specific subtasks. We additionally present a method that makes extensive use of medical knowledge for the same case identification subtask3-RR-JA

Leveraging Token-Based Concept Information and Data Augmentation in Few-Resource NER: ZuKyo-EN at the NTCIR-16 Real-MedNLP task

In this paper, we discuss our contribution to the NII Testbeds and Community for Information Access Research (NTCIR) - 16 Real- MedNLP shared task. Our team (ZuKyo) participated in the English subtask: Few-resource Named Entity Recognition. The main challenge in this low-resource task was a low number of training documents annotated with a high number of tags and attributes. For our submissions, we used different general and domain-specific transfer learning approaches in combination with multiple data augmentation methods. In addition, we experimented with models enriched with biomedical concepts encoded as token-based input feature

Risk prediction models for dementia constructed by supervised principal component analysis using miRNA expression data

Alzheimer’s disease (AD) is the most common subtype of dementia, followed by Vascular Dementia (VaD), and Dementia with Lewy Bodies (DLB). Recently, microRNAs (miRNAs) have received a lot of attention as the novel biomarkers for dementia. Here, using serum miRNA expression of 1,601 Japanese individuals, we investigated potential miRNA biomarkers and constructed risk prediction models, based on a supervised principal component analysis (PCA) logistic regression method, according to the subtype of dementia. The final risk prediction model achieved a high accuracy of 0.873 on a validation cohort in AD, when using 78 miRNAs: Accuracy = 0.836 with 86 miRNAs in VaD; Accuracy = 0.825 with 110 miRNAs in DLB. To our knowledge, this is the first report applying miRNA-based risk prediction models to a dementia prospective cohort. Our study demonstrates our models to be effective in prospective disease risk prediction, and with further improvement may contribute to practical clinical use in dementia

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection

Atomic spectrometry update: Review of advances in the analysis of metals, chemicals and materials

There has been a large increase in the number of papers published that are relevant to this review over this review period. The growth in popularity of LIBS is rapid, with applications being published for most sample types. This is undoubtedly because of its capability to analyse in situ on a production line (hence saving time and money) and its minimally destructive nature meaning that both forensic and cultural heritage samples may be analysed. It also has a standoff analysis capability meaning that hazardous materials, e.g. explosives or nuclear materials, may be analysed from a safe distance. The use of mathematical algorithms in conjunction with LIBS to enable improved accuracy has proved a popular area of research. This is especially true for ferrous and non-ferrous samples. Similarly, chemometric techniques have been used with LIBS to aid in the sorting of polymers and other materials. An increase in the number of papers in the subject area of alternative fuels was noted. This was at the expense of papers describing methods for the analysis of crude oils. For nanomaterials, previous years have seen a huge number of single particle and field flow fractionation characterisations. Although several such papers are still being published, the focus seems to be switching to applications of the nanoparticles and the mechanistic aspects of how they retain or bind with other analytes. This is the latest review covering the topic of advances in the analysis of metals, chemicals and materials. It follows on from last year's review1-6 and is part of the Atomic Spectrometry Updates series

Influence of hydrolysis degradation of silane coupling agents on mechanical performance of CAD/CAM resin composites: In silico multi-scale analysis

The aim of this study was to build an in silico computer-aided design and computer-aided manufacturing (CAD/CAM) resin-composite-block (RCB) model with different silane coupling ratios and to evaluate the physical and mechanical properties of the models, including the elastic modulus, Poisson’s ratio, compressive strength, and maximum principal strain. Nanoscale CAD/CAM RCB models were designed by using CAD software that consisted of twelve spherical silica nanofiller particles and a resin matrix. Seven nanoscale models with different silane coupling ratios were prepared with the same filler volume contents. Homogenization analysis was conducted by using voxel-base finite-element analysis software to predict the elastic moduli and Poisson’s ratio of the macro CAD/CAM RCB. Localization analysis was used to analyze the maximum principal strain distribution in the hydrolysis layer. In silico multi-scale analysis demonstrated that the compressive strength of the CAD/CAM RCB was reduced with a decrease in the silane coupling ratios of the fillers.Lee C., Kashima K., Ichikawa A., et al. Influence of hydrolysis degradation of silane coupling agents on mechanical performance of CAD/CAM resin composites: In silico multi-scale analysis. Dental Materials Journal 39, 803 (2020); https://doi.org/10.4012/dmj.2019-223