Peptide-enhanced mRNA transfection in cultured mouse cardiac fibroblasts and direct reprogramming towards cardiomyocyte-like cells.

The treatment of myocardial infarction is a major challenge in medicine due to the inability of heart tissue to regenerate. Direct reprogramming of endogenous cardiac fibroblasts into functional cardiomyocytes via the delivery of transcription factor mRNAs has the potential to regenerate cardiac tissue and to treat heart failure. Even though mRNA delivery to cardiac fibroblasts has the therapeutic potential, mRNA transfection in cardiac fibroblasts has been challenging. Herein, we develop an efficient mRNA transfection in cultured mouse cardiac fibroblasts via a polyarginine-fused heart-targeting peptide and lipofectamine complex, termed C-Lipo and demonstrate the partial direct reprogramming of cardiac fibroblasts towards cardiomyocyte cells. C-Lipo enabled the mRNA-induced direct cardiac reprogramming due to its efficient transfection with low toxicity, which allowed for multiple transfections of Gata4, Mef2c, and Tbx5 (GMT) mRNAs for a period of 2 weeks. The induced cardiomyocyte-like cells had α-MHC promoter-driven GFP expression and striated cardiac muscle structure from α-actinin immunohistochemistry. GMT mRNA transfection of cultured mouse cardiac fibroblasts via C-Lipo significantly increased expression of the cardiomyocyte marker genes, Actc1, Actn2, Gja1, Hand2, and Tnnt2, after 2 weeks of transfection. Moreover, this study provides the first direct evidence that the stoichiometry of the GMT reprogramming factors influence the expression of cardiomyocyte marker genes. Our results demonstrate that mRNA delivery is a potential approach for cardiomyocyte generation

Occupational Factors Associated with Changes in the Body Mass Index of Korean Male Manual Workers

OBJECTIVES: This study was carried out to analyze and compare the occupational factors that could influence changes in body mass index (BMI) in male manual workers stratified into short-term and long-term work experience groups. METHODS: The subjects were 299 male manual workers (sampled systematically) from 27 workplaces, who had undergone travelling medical examinations at a university hospital between March 28 and May 10, 2013, and had also undergone medical examinations at the same hospital in 2012. Their general and occupational characteristics were investigated through a structured, self-administered questionnaire. The BMI at each point in time was calculated based on the anthropometric results of the medical examinations. Multiple regression analyses were conducted on outcomes of the BMI change and predictors composed of the general and occupational characteristics, with the subjects stratified into groups with 5 years or less (short-term) versus more than 5 years (long-term) of work experience at the present post. RESULTS: In the short-term work experience group, the BMI increases of 3-shift workers and groups reporting disagreement with feeling “insufficient job control” and “lack of reward” at work, two of the subscales of job stress, were significantly higher than those of daytime workers and high-stress groups, respectively. In the long-term work experience group, However, although the BMI increase for 3-shift workers was also significantly higher than that of daytime workers, none of the job stress factors were significantly associated with a BMI increase, whereas the social factors of education and marital status were significant, and some lifestyle factors (such as smoking and regular exercise) were also significant. CONCLUSION: This study showed that, except for 3-shift work, the factors associated with BMI increase could differ depending on the length of job experience. Consequently, different strategies may be needed for workers with short-term versus long-term job experience when designing interventions for preventing their obesity

Ionized gas outflows in infrared-bright dust-obscured galaxies selected with WISE and SDSS

We present the ionized gas properties of infrared (IR)-bright dust-obscured galaxies (DOGs) that show an extreme optical/IR color, $(i - [22])_{\rm AB} > 7.0$, selected with the Sloan Digital Sky Survey (SDSS) and Wide-field Infrared Survey Explorer (WISE). For 36 IR-bright DOGs that show [OIII]$\lambda$5007 emission in the SDSS spectra, we performed a detailed spectral analysis to investigate their ionized gas properties. In particular, we measured the velocity offset (the velocity with respect to the systemic velocity measured from the stellar absorption lines) and the velocity dispersion of the [OIII] line. We found that the derived velocity offset and dispersion of most IR-bright DOGs are larger than those of Seyfert 2 galaxies (Sy2s) at $z < 0.3$, meaning that the IR-bright DOGs show relatively strong outflows compared to Sy2s. This can be explained by the difference of IR luminosity contributed from active galactic nucleus, $L_{\rm IR}$ (AGN), because we found that (i) $L_{\rm IR}$ (AGN) correlates with the velocity offset and dispersion of [OIII] and (ii) our IR-bright DOGs sample has larger $L_{\rm IR}$ (AGN) than Sy2s. Nevertheless, the fact that about 75% IR-bright DOGs have a large ($>$ 300 km s$^{-1}$) velocity dispersion, which is a larger fraction compared to other AGN populations, suggests that IR-bright DOGs are good laboratories to investigate AGN feedback. The velocity offset and dispersion of [OIII] and [NeIII]$\lambda$3869 are larger than those of [OII]$\lambda$3727, which indicates that the highly ionized gas tends to show more stronger outflows.Comment: 19 pages, 16 figures, and 2 tables, accepted for publication in Ap

Capsule network with shortcut routing

This study introduces "shortcut routing," a novel routing mechanism in capsule networks that addresses computational inefficiencies by directly activating global capsules from local capsules, eliminating intermediate layers. An attention-based approach with fuzzy coefficients is also explored for improved efficiency. Experimental results on Mnist, smallnorb, and affNist datasets show comparable classification performance, achieving accuracies of 99.52%, 93.91%, and 89.02% respectively. The proposed fuzzy-based and attention-based routing methods significantly reduce the number of calculations by 1.42 and 2.5 times compared to EM routing, highlighting their computational advantages in capsule networks. These findings contribute to the advancement of efficient and accurate hierarchical pattern representation models.Comment: 8 pages, published at IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E104.A(8

Convolution channel separation and frequency sub-bands aggregation for music genre classification

In music, short-term features such as pitch and tempo constitute long-term semantic features such as melody and narrative. A music genre classification (MGC) system should be able to analyze these features. In this research, we propose a novel framework that can extract and aggregate both short- and long-term features hierarchically. Our framework is based on ECAPA-TDNN, where all the layers that extract short-term features are affected by the layers that extract long-term features because of the back-propagation training. To prevent the distortion of short-term features, we devised the convolution channel separation technique that separates short-term features from long-term feature extraction paths. To extract more diverse features from our framework, we incorporated the frequency sub-bands aggregation method, which divides the input spectrogram along frequency bandwidths and processes each segment. We evaluated our framework using the Melon Playlist dataset which is a large-scale dataset containing 600 times more data than GTZAN which is a widely used dataset in MGC studies. As the result, our framework achieved 70.4% accuracy, which was improved by 16.9% compared to a conventional framework

Integrated Parameter-Efficient Tuning for General-Purpose Audio Models

The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of building task-specific models for target tasks. In the field of audio research, task-agnostic pre-trained models with high transferability and adaptability have achieved state-of-the-art performances through fine-tuning for downstream tasks. Nevertheless, re-training all the parameters of these massive models entails an enormous amount of time and cost, along with a huge carbon footprint. To overcome these limitations, the present study explores and applies efficient transfer learning methods in the audio domain. We also propose an integrated parameter-efficient tuning (IPET) framework by aggregating the embedding prompt (a prompt-based learning approach), and the adapter (an effective transfer learning method). We demonstrate the efficacy of the proposed framework using two backbone pre-trained audio models with different characteristics: the audio spectrogram transformer and wav2vec 2.0. The proposed IPET framework exhibits remarkable performance compared to fine-tuning method with fewer trainable parameters in four downstream tasks: sound event classification, music genre classification, keyword spotting, and speaker verification. Furthermore, the authors identify and analyze the shortcomings of the IPET framework, providing lessons and research directions for parameter efficient tuning in the audio domain.Comment: 5 pages, 3 figures, submit to ICASSP202

One-Step Knowledge Distillation and Fine-Tuning in Using Large Pre-Trained Self-Supervised Learning Models for Speaker Verification

The application of speech self-supervised learning (SSL) models has achieved remarkable performance in speaker verification (SV). However, there is a computational cost hurdle in employing them, which makes development and deployment difficult. Several studies have simply compressed SSL models through knowledge distillation (KD) without considering the target task. Consequently, these methods could not extract SV-tailored features. This paper suggests One-Step Knowledge Distillation and Fine-Tuning (OS-KDFT), which incorporates KD and fine-tuning (FT). We optimize a student model for SV during KD training to avert the distillation of inappropriate information for the SV. OS-KDFT could downsize Wav2Vec 2.0 based ECAPA-TDNN size by approximately 76.2%, and reduce the SSL model's inference time by 79% while presenting an EER of 0.98%. The proposed OS-KDFT is validated across VoxCeleb1 and VoxCeleb2 datasets and W2V2 and HuBERT SSL models. Experiments are available on our GitHub

Dispersion of Vascular Plant in Kumo-do, Korea

AbstractThe vascular plants observed in the area were composed of a total of 228 taxa; 72 families, 172 genus, 201 species, 25 varieties, 1 sub-species and 1 cross species. The only endangered plants found in the area were Milletia japonica (Siebold & Zucc.) A.Gray. The endemic plants growing in the Geumodo except transplanted plants were Lespedeza x maritima Nakai and Carpinus coreana Nakai. which accounted for 0.8% of the vascular plants in Geumodo, 228 taxa. Specialized plants of Geumodo were a total of 41 species; 30 taxa in Grade I, 1 taxon in Grade II, 9 taxa in Grade III and 1 taxon in Grade V. Milletia japonica (Siebold & Zucc.) A.Gray was the only species found in important Grade IV to V. Currently, ferries ply to the island, attracting many tourists. This poses a threat to the rare plants living in the island and presses down the island to develop. Therefore, in the long-term perspective, the conservation plan such as comprehensive research and monitoring on the ecosystem shall be established to protect evergreen broad-leaved forests