Music Familiarity Affects EEG Entrainment When Little Attention Is Paid

To investigate the brain's response to music, many researchers have examined cortical entrainment in relation to periodic tunes, periodic beats, and music. Music familiarity is another factor that affects cortical entrainment, and electroencephalogram (EEG) studies have shown that stronger entrainment occurs while listening to unfamiliar music than while listening to familiar music. In the present study, we hypothesized that not only the level of familiarity but also the level of attention affects the level of entrainment. We simultaneously presented music and a silent movie to participants and we recorded an EEG while participants paid attention to either the music or the movie in order to investigate whether cortical entrainment is related to attention and music familiarity. The average cross-correlation function across channels, trials, and participants exhibited a pronounced positive peak at time lags around 130 ms and a negative peak at time lags around 260 ms. The statistical analysis of the two peaks revealed that the level of attention did not affect the level of entrainment, and, moreover, that in both the auditory-active and visual-active conditions, the entrainment level is stronger when listening to unfamiliar music than when listening to familiar music. This may indicate that the familiarity with music affects cortical activities when attention is not fully devoted to listening to music

EpilepsyLLM: Domain-Specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge

With large training datasets and massive amounts of computing sources, large language models (LLMs) achieve remarkable performance in comprehensive and generative ability. Based on those powerful LLMs, the model fine-tuned with domain-specific datasets posseses more specialized knowledge and thus is more practical like medical LLMs. However, the existing fine-tuned medical LLMs are limited to general medical knowledge with English language. For disease-specific problems, the model's response is inaccurate and sometimes even completely irrelevant, especially when using a language other than English. In this work, we focus on the particular disease of Epilepsy with Japanese language and introduce a customized LLM termed as EpilepsyLLM. Our model is trained from the pre-trained LLM by fine-tuning technique using datasets from the epilepsy domain. The datasets contain knowledge of basic information about disease, common treatment methods and drugs, and important notes in life and work. The experimental results demonstrate that EpilepsyLLM can provide more reliable and specialized medical knowledge responses

Early diagnosis of acute renal allograft rejection: efficacy of macrophage migration inhibition test as an immunological diagnosis

1. Three cases of acute rejection were detected by macrophage migration inhibition tests (MIT) conducted directly on seven patients who had received renal allografts. The macrophage migration inhibitory factor (MIF) activity was positive in all cases 1-2 days before the appearance of acute rejection. 2. After the administration of a high dose of Solu-Medrol (1g/day for 3 days) to suppress the acute rejection, MIF activity recovered to its normal level 3 days later. These findings seem to indicate that MIT yields immunologically useful criteria for the early detection of an acute rejection.</p

Decomposition methods for machine learning with small, incomplete or noisy datasets

In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive useful supervised or unsupervised classification methods. Correct handling of incomplete, noisy or small datasets in machine learning is a fundamental and classic challenge. In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial generation of new data samples (data augmentation). We illustrate the application of these signal decomposition methods in diverse selected practical machine learning examples including: brain computer interface, epileptic intracranial electroencephalogram signals classification, face recognition/verification and water networks data analysis. We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Sole Casals, Jordi. Center for Advanced Intelligence; JapónFil: Marti Puig, Pere. University of Catalonia; EspañaFil: Sun, Zhe. RIKEN; JapónFil: Tanaka,Toshihisa. Tokyo University of Agriculture and Technology; Japó

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue “Machine Learning Methods with Noisy, Incomplete or Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Zhe, Sun. Lab. Adaptive Intelligence - Riken; JapónFil: Tanaka, Toshihisa. Tokyo University of Agriculture and Technology; JapónFil: Marti Puig, Pere. University of Vic; EspañaFil: Solé Casals, Jordi. University of Vic; Españ