Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Transcutaneous Trigeminal Electrical Stimulation and Sound Therapy in Patients with Tinnitus

Purpose Tinnitus is one of the most common health conditions worldwide. Although various methods of treatment have been used, the condition is still difficult to manage or cure. This study aimed to evaluate the therapeutic effects of transcutaneous trigeminal electrical stimulation (TTES) combined with notched sound therapy (NST) on patients with tinnitus. Materials and Methods A clinical trial was conducted prospectively from September 2020 to September 2021 at a single center in South Korea. In total, 14 patients took part in this trial. Periodic visits and tele-monitoring were used to assess treatment compliance and collect data, including electroencephalography (EEG), photoplethysmography (PPG), tinnitus handicap inventory (THI), tinnitus magnitude index, Beck Depression Inventory (BDI), Pittsburgh Sleep Quality Index (PSQI), and 36-item short-form survey (SF-36) results. Results Changes after intervention were analyzed with paired t-test. This study showed that alpha waves in the left hemisphere measured by EEG (p=0.024), autonomic nervous system balance (p=0.007), and stress level (p=0.022) measured by PPG significantly changed after intervention. Also, THI scores especially emotional symptoms (p=0.029) and catastrophic symptoms (p=0.043) decreased after treatment. The SF-36 score, both mental component summary and physical component summary score (each p<0.001), increased significantly, whereas the PSQI score (p<0.001) and BDI score (p<0.001) decreased after TTES and NST. Conclusion Based on the results of our study, we could confirm that TTES combined with NST can significantly improve tinnitus, catastrophic symptoms, and the overall quality of life of patients.
Robotic Single-Micropipette Enucleation Method for Zona-free Oocytes

Somatic cell nuclear transfer is a mammalian cloning method that involves replacing the oocyte nucleus with a somatic cell nucleus. One of the most critical steps in this process is the efficient production of large numbers of enucleated oocytes. While a single-micropipette-based manual enucleation method has been widely used in manual cloning, it is still a manual operation that requires exceptional technical skills and experience. Additionally, it is difficult to accurately control the amount of enucleation and to meet the demand for high-throughput enucleation. Therefore, we propose a robotic enucleation method using a single micropipette for zona-free oocytes, which allows for the detection and control of the amount of enucleation, as well as continuous enucleation on a large scale. A single oocyte enucleation can be completed in less than 13 seconds. This robotic enucleation method provides a solution for enhancing the operational efficiency of robotic somatic cell nuclear transfer.
Electrophysiological properties of layer 2/3 pyramidal neurons in the primary visual cortex of a retinitis pigmentosa mouse model (rd10)

Retinal degeneration is one of the main causes of visual impairment and blindness. One group of retinal degenerative diseases, leading to the loss of photoreceptors, is collectively termed retinitis pigmentosa. In this group of diseases, the remaining retina is largely spared from initial cell death making retinal ganglion cells an interesting target for vision restoration methods. However, it is unknown how downstream brain areas, in particular the visual cortex, are affected by the progression of blindness. Visual deprivation studies have shown dramatic changes in the electrophysiological properties of visual cortex neurons, but changes on a cellular level in retinitis pigmentosa have not been investigated yet. Therefore, we used the rd10 mouse model to perform patch-clamp recordings of pyramidal neurons in layer 2/3 of the primary visual cortex to screen for potential changes in electrophysiological properties resulting from retinal degeneration. Compared to wild-type C57BL/6 mice, we only found an increase in intrinsic excitability around the time point of maximal retinal degeneration. In addition, we saw an increase in the current amplitude of spontaneous putative inhibitory events after a longer progression of retinal degeneration. However, we did not observe a long-lasting shift in excitability after prolonged retinal degeneration. Together, our results provide evidence of an intact visual cortex with promising potential for future therapeutic strategies to restore vision.
Deep learning for COVID‐19 contamination analysis and prediction using ECG images on Raspberry Pi 4

This paper's primary goal is to diagnose COVID‐19 contamination based on the artificial intelligence approach automatically. We used convolutional neural network deep learning algorithm for analyzing the ECG images to detect cardiac abnormalities, consequent of the contamination by the SARS‐CoV‐2 virus, responsible for the COVID‐19 epidemic. We designed, trained, and evaluated the performance of two deep learning models (MobileNetV2 and VGG16) in detecting and distinguishing between two different classes (healthy subjects and COVID‐19 positive cases). Indeed, this virus attacks the human respiratory system, which could affect the heart system. Thus, developing a deep learning model could help for a quick and efficient diagnosis, prediction, and physician decision‐making. The performed deep learning model will be used for predicting abnormal cardiac activities consequent to the contamination by the virus. The overall classification rate achieved by the models was 99.34% and 99.67% for MobileNetV2 and VGG16, respectively. Therefore, this approach can efficiently contribute to the diagnosis of COVID‐19 contamination.
FouriER: Link Prediction by Mixing Tokens with Fourier-enhanced MetaFormer

Knowledge graph link prediction has been researched for many years. With the steady development of data, the demand for missing link prediction in knowledge bases is growing. In this study, we propose FouriER, a model using Fourier transforms integrated into MetaFormer architecture to learn features from embeddings better but more computationally cost-eﬀective than the self-attention mechanism in Transformer models. Furthermore, we transform embeddings to a 2D form and stack them that beneﬁt the model in learning interactions between entities and relations more eﬃciently. As a result, we found that our model outper-formed baseline models on two benchmark datasets in our experiments.
Genes Involved in DNA Damage Cell Pathways and Health of the Oldest-Old (85+)

Some sources report a connection of cellular senescence with chronic pathological conditions; however, the association between particular cellular processes and general health is rarely examined. This study aims to test the relationship of general health with DNA damage pathways that play a crucial role in senescence. The association of ten selected SNPs with subjective and objective general health and functional ability indicators has been tested in 314 oldest-old people from Croatia. Multivariate logistic regression was employed to simultaneously test the impact of variables potentially influencing targeted health and functional ability variables. The best model, explaining 37.1% of the variance, has six independent significant predictors of functional ability scores: rs16847897 in TERC, rs533984 in MRE11A, and rs4977756 in CDKN2B, chronic disease count, Mini-Mental State Examination scores, and age at surveying. In conclusion, the examined ten loci involved in DNA damage repair pathways showed a more significant association with self-rated health and functional ability than with the number of disease or prescribed medicaments. The more frequent, longevity-related homozygote (GG) in rs16847897 was associated with all three aspects of self-assessments—health, mobility, and independence—indicating that this TERC locus might have a true impact on the overall vitality of the oldest-old persons.
Distributed Situation Awareness Consensus Optimization of Unmanned Group Systems

The situation awareness consensus is a critical and challenging problem for an autonomous and collaborative unmanned group system. The current situation awareness consensus model is based on a centralized framework, which is not suitable for large-scale unmanned group systems. In this paper, a distributed situation awareness consensus model is established, which includes the preference expression in the individual perception stage, and the preference adjustment in the system collaboration stage. On such a basis, a novel distributed situation awareness consensus optimization algorithm is proposed to coordinate a heterogeneous unmanned group of individuals with different intelligence levels to achieve a high-quality situation awareness consensus. The condition for an unmanned group system to reach a reasonable situation awareness consensus is given based on the LaSalle invariant principle. The comparative simulations are carried out to show the feasibility, validity, and superiority of our proposed algorithm.
Analysis of the development of energy consumption of electricity and fuels in Slovakia

As the standard of living rises, the energy consumption of electricity and fuels increases not only in Slovakia but across the European Union. Future energy demand will be based on population growth, and it is the key factor of economic and social development of the society, with an emphasis being put on improving the quality of life. This study focused on the development of the final energy consumption (FEC) of individual fuels, electricity and heat, including their shares in the final energy consumption in selected economy sectors in Slovakia. Time-series analysis methods were applied to predict energy consumption for the next three years. Although we have witnessed a decrease in the total energy consumption in individual sectors in Slovakia in the years 2005–2015, energy consumption generally exhibits an increasing trend. In all of the analysed sectors, the development of final consumption of energy from renewable energy sources (RESs) and wastes exhibited a rising trend; nevertheless, the results of our evaluation indicated that Slovakia still has some major shortcomings as to the utilisation of RESs
Oracally efficient spline-backfitted kernel smoothing of additive partial linear measurement error model

Abstract We consider statistical inference for additive partial linear models when the linear covariate is measured with error. A bias-corrected spline-backfitted kernel smoothing method is proposed. Under mild assumptions, the proposed component function and parameter estimator are oracally efficient and fast to compute. The nonparametric function estimator’s pointwise distribution is asymptotically equivalent to an function estimator in partial linear model. Finite-sample performance of the proposed estimators is assessed by simulation experiments. The proposed methods are applied to Boston house data set.
The Correlation between Objective Ligament Laxity and the Clinical Outcome of Mechanically Aligned TKA

Instability is one of the causes of failure in total knee arthroplasty (TKA). The aim of this study was to analyze the correlation between objective ligament laxity and the clinical outcome of mechanically aligned TKA. Fifty-one knees in 47 patients were evaluated at a minimum follow-up of 6 months. The correlation between the angular displacement and functional scores (Knee Society Score and Knee Injury and Osteoarthritis Score) was analyzed. A negative correlation (p-value < 0.05) was observed between medial laxity ≥5° at 0, 30, 60, and 90° of flexion and the outcome measures. Lateral laxity did not correlate with the clinical outcome. At 30° of knee flexion, a total varus and valgus laxity ≥10° was related to poorer outcomes. The same amount of angular displacement did not influence the outcome in the other flexion angles. There was no difference in single-radius vs multi-radius implants in terms of medial and lateral laxity and clinical outcome. A valgus displacement ≥5° measured at 0, 30, 60, and 90 degrees of flexion correlated with an inferior clinical outcome. In contrast, the same amount of displacement measured on the lateral compartment did not influence the clinical outcome after TKA.
The role of clay minerals in the preservation of Precambrian organic‐walled microfossils

Precambrian organic‐walled microfossils (OWMs) are primarily preserved in mudstones and shales that are low in total organic carbon (TOC). Recent work suggests that high TOC may hinder OWM preservation, perhaps because it interferes with chemical interactions involving certain clay minerals that inhibit the decay of microorganisms. To test if clay mineralogy controls OWM preservation, and if TOC moderates the effect of clay minerals, we compared OWM preservational quality (measured by pitting on fossil surfaces and the deterioration of wall margins) to TOC, total clay, and specific clay mineral concentrations in 78 shale samples from 11 lithologic units ranging in age from ca. 1650 to 650 million years ago. We found that the probability of finding well‐preserved microfossils positively correlates with total clay concentrations and confirmed that it negatively correlates with TOC concentrations. However, we found no evidence that TOC influences the effect of clay mineral concentrations on OWM preservation, supporting an independent role of both factors on preservation. Within the total clay fraction, well‐preserved microfossils are more likely to occur in shales with high illite concentrations and low berthierine/chamosite concentrations; however, the magnitude of their effect on preservation is small. Therefore, there is little evidence that bulk clay chemistry is important in OWM preservation. Instead, we propose that OWM preservation is largely regulated by physical properties that isolate organic remains from microbial degradation such as food scarcity (low TOC) and low sediment permeability (high total clay content): low TOC increases the diffusive distances between potential carbon sources and heterotrophic microbes (or their degradative enzymes), while high clay concentrations reduce sediment pore space, thereby limiting the diffusion of oxidants and degradative enzymes to the sites of decay.
Insights into Repeated Renal Injury Using RNA-Seq with Two New RPTEC Cell Lines

Renal proximal tubule epithelial cells (RPTECs) are a primary site for kidney injury. We created two RPTEC lines from CD-1 mice immortalized with hTERT (human telomerase reverse transcriptase) or SV40 LgT antigen (Simian Virus 40 Large T antigen). Our hypothesis was that low-level, repeated exposure to subcytotoxic levels of 0.25–2.5 μM cisplatin (CisPt) or 12.5–100 μM aflatoxin B1 (AFB1) would activate distinctive genes and pathways in these two differently immortalized cell lines. RNA-seq showed only LgT cells responded to AFB1 with 1139 differentially expressed genes (DEGs) at 72 h. The data suggested that AFB1 had direct nephrotoxic properties on the LgT cells. However, both the cell lines responded to 2.5 μM CisPt from 3 to 96 h expressing 2000–5000 total DEGs. For CisPt, the findings indicated a coordinated transcriptional program of injury signals and repair from the expression of immune receptors with cytokine and chemokine secretion for leukocyte recruitment; robust expression of synaptic and substrate adhesion molecules (SAMs) facilitating the expression of neural and hormonal receptors, ion channels/transporters, and trophic factors; and the expression of nephrogenesis transcription factors. Pathway analysis supported the concept of a renal repair transcriptome. In summary, these cell lines provide in vitro models for the improved understanding of repeated renal injury and repair mechanisms. High-throughput screening against toxicant libraries should provide a wider perspective of their capabilities in nephrotoxicity.
Intracranial arterial stenosis and recurrence in stroke patients with different risk stratifications by Essen stroke risk score

ABSTRACT Objectives We sought to investigate whether the prognostic value of intracranial arterial stenosis (ICAS) is consistent across different risk stratifications using the Essen Stroke Risk score (ESRS). Methods We derived data from the Clopidogrel in High-Risk Patients with Acute Nondisabling Cerebrovascular Events trial. Patients without complete baseline brain imaging data were excluded. Participants were categorized into different risk groups based on ESRS (low risk, 0–2, and high risk ≥ 3). The main outcome was stroke recurrence within 3 and 12 months. Hazard ratios (HRs) and 95% confidence intervals (95%CIs) of ICAS, and other factors associated with stroke recurrence within 3 and 12 months were estimated using the Cox regression method. Results During the 3-month follow-up, 54 patients (7.9%) had recurrent stroke in the low-risk group, and 39 patients (9.6%) had recurrent stroke in the high-risk group. ICAS was associated with a higher risk of stroke within 3 months (HR = 2.761; 95%CI = 1.538–4.957; P < 0.001) in the low-risk group, but not in the high-risk group (HR = 1.501; 95%CI = 0.701–3.213; P = 0.296). ICAS was independently associated with higher recurrent risk in the low-risk group (HR = 2.540; 95%CI = 1.472–4.381; P < 0.001), but not in the high-risk group (HR = 1.951; 95%CI = 0.977–3.893; P = 0.058) within 12 months. Conclusion ICAS was an independent predictor of both 3- and 12-month stroke recurrence in low-risk but not high-risk patients with minor ischemic stroke or transient ischemic attack according to ESRS stratification.
Tobacco Leaf Segmentation Based on Improved MASK RCNN Algorithm and SAM Model

High-precision segmentation of tobacco leaves is a prerequisite for analysis of phenotypic information. Challenges such as mutual occlusion and fuzzy edges make leaf segmentation difficult. This paper proposes an improved algorithm based on the Mask Region-based Convolutional Neural Networks (MASK RCNN) model and an instance segmentation method based on the SAM model to address these challenges. First, the MASK RCNN model is enhanced by incorporating a feature fusion layer and a hybrid attention mechanism, which improves the segmentation performance. The improved MASK RCNN model achieves an Avg.MIoU metric of approximately 85.10%, which is an improvement of 11.10% over the original algorithm. It also achieves an Avg.MPA metric of about 84.94%, indicating an improvement of 10.84%. Second, the Segment Anything Model (SAM) model is presented for the first time for tobacco leaf segmentation, providing empirical support for its application in the tobacco field. The SAM model demonstrates accurate segmentation of tobacco leaf images at different growth stages, demonstrating its good generality. In conclusion, the proposed methods effectively address the challenges in tobacco leaf segmentation, resulting in improved accuracy and performance. These techniques provide significant technical support for tobacco leaf phenotype research.
From Lagrangian Products to Toric Domains via the Toda Lattice

In this paper we use the periodic Toda lattice to show that certain Lagrangian product configurations in the classical phase space are symplectically equivalent to toric domains. In particular, we prove that the Lagrangian product of a certain simplex and the Voronoi cell of the root lattice $A_n$ is symplectically equivalent to a Euclidean ball. As a consequence, we deduce that the Lagrangian product of an equilateral triangle and a regular hexagon is symplectomorphic to a Euclidean ball in dimension 4.
Surface Electromyography and Artificial Intelligence for Human Activity Recognition—A Systematic Review on Methods, Emerging Trends Applications, Challenges, and Future Implementation

Human activity recognition (HAR) has become increasingly popular in recent years due to its potential to meet the growing needs of various industries. Electromyography (EMG) is essential in various clinical and biological settings. It is a metric that helps doctors diagnose conditions that affect muscle activation patterns and monitor patients’ progress in rehabilitation, disease diagnosis, motion intention recognition, etc. This review summarizes the various research papers based on HAR with EMG. Over recent years, the integration of Artificial Intelligence (AI) has catalyzed remarkable advancements in the classification of biomedical signals, with a particular focus on EMG data. Firstly, this review meticulously curates a wide array of research papers that have contributed significantly to the evolution of EMG-based activity recognition. By surveying the existing literature, we provide an insightful overview of the key findings and innovations that have propelled this field forward. It explore the various approaches utilized for preprocessing EMG signals, including noise reduction, baseline correction, filtering, and normalization, ensure that the EMG data is suitably prepared for subsequent analysis. In addition, we unravel the multitude of techniques employed to extract meaningful features from raw EMG data, encompassing both time-domain and frequency-domain features. These techniques are fundamental to achieving a comprehensive characterization of muscle activity patterns. Furthermore, we provide an extensive overview of both Machine Learning (ML) and Deep Learning (DL) classification methods, showcasing their respective strengths, limitations, and real-world applications in recognizing diverse human activities from EMG signals. In examining the hardware infrastructure for HAR with EMG, the synergy between hardware and software is underscored as paramount for enabling real-time monitoring. Finally, we also discovered open issues and future research direction that may point to new lines of inquiry for ongoing research toward EMG-based detection.
Diagnóstico de glaucoma em imagens de fundo de olho utilizando estatística espacial

O glaucoma e uma doenca da retina considerada a segunda principal causa de cegueira no mundo, atingindo uma prevalencia aproximada entre 1% e 2% da populacao, segundo dados da Organizacao Mundial de Saude (OMS). E normalmente ocasionado pelo aumento da pressao do liquido no nervo optico, levando a perda progressiva e irreversivel da visao. O diagnostico precoce do glaucoma e, portanto, fundamental para garantir um tratamento rapido e adequado, sendo capaz de minimizar o dano e evitar a perda da visao. Assim, o uso de sistemas computacionais de auxilio a deteccao e diagnostico (Computer Aided Detection - CAD e Computer Aided Diagnosis - CADx) para auxiliar o especialista tem aumentado as chances de diagnosticos corretos. Imagens fotograficas de fundo de olho (retinografias) sao usadas como um valioso recurso em diagnosticos medicos, pois frequentemente apresentam indicacoes sobre doencas oculares como o glaucoma. Neste contexto, este trabalho apresenta uma metodologia baseada em processamento de imagem para diagnosticar o glaucoma a partir da analise de textura da imagem representada usando Compound Local Binary Pattern e estatistica espacial aplicada em imagens medicas de retinografias. O metodo e aplicado sobre a regiao de interesse que representa o disco optico, cuja segmentacao baseia-se na marcacao do especialista contida na base publica RIM-ONE. As amostras sao pre-processadas atraves do metodo das cores oponentes seguida da equalizacao de histograma. O Indice de Moran e a funcao K de Ripley sao utilizadas para descrever a textura da regiao do disco optico. A Maquina de Vetores de Suporte e usada para realizar a classificacao. O metodo apresentou resultados promissores, alcancando como melhor resultado uma acuracia de 95,08%, sensibilidade de 93,4% e especificidade de 96,4%, e demonstra um notavel desempenho da metodologia proposta quando comparada aos metodos disponiveis na literatura para diagnostico de glaucoma.