Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems

Background For non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem. Methods The multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space. Results The proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels. Conclusion The formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.
Prevalence of Poor Sleep Quality and Associated Factors in Individuals with Rheumatoid Arthritis: A Cross-Sectional Study

Background and Objectives: Poor sleep quality has been frequently observed in individuals with rheumatoid arthritis. In the present study, we analyzed the presence of poor sleep quality in a sample of Mexican individuals with rheumatoid arthritis; then, we compared sociodemographic and clinical characteristics among patients to determine risk factors for poor sleep quality. Materials and Methods: In this cross-sectional study, we included 102 individuals with rheumatoid arthritis from a hospital in Mexico. We evaluated disease activity (DAS28), quality of sleep using the Pittsburgh Sleep Quality Index, and the presence of depression and anxiety with the Hospital Anxiety and Depression Scale. We performed a Chi-square test and a t-test. Then, we performed a logistic regressions model of the associated features in a univariable analysis. Results: Poor sleep quality was observed in 41.75% of the individuals with rheumatoid arthritis. Being married was a proactive factor (OR 0.04, 95% CI 0.1–0.9, p = 0.04), whereas having one’s hips affected or presenting with anxiety and depression was associated with poor sleep quality (OR 4.6, 95% CI 1.2–17.69, p = 0.02). After a multivariate analysis, having anxiety (OR 5.0, 95% CI 1.4–17.7, p < 0.01) and depression (OR 9.2, 95% CI 1.0–8.1, p < 0.01) remained associated with a higher risk of having poor sleep quality. Other clinical characteristics among patients were not significantly different. Conclusions: Our results showed that individuals with rheumatoid arthritis who also presented with depression or anxiety had a higher risk of suffering from poor sleep quality. However, more studies with larger samples are necessary to replicate these results in the Mexican population.
A Case Study from the Past: “The RGC-5 vs. the 661W Cell Line: Similarities, Differences and Contradictions—Are They Really the Same?”

In the pursuit of identifying the underlying pathways of ocular diseases, the use of cell lines such as (retinal ganglion cell-5) RGC-5 and 661W became a valuable tool, including pathologies like retinal degeneration and glaucoma. In 2001, the establishment of the RGC-5 cell line marked a significant breakthrough in glaucoma research. Over time, however, concerns arose about the true nature of RGC-5 cells, with conflicting findings in the literature regarding their identity as retinal ganglion cells or photoreceptor-like cells. This study aimed to address the controversy surrounding the RGC-5 cell line’s origin and properties by comparing it with the 661W cell line, a known cone photoreceptor model. Both cell lines were differentiated according to two prior published redifferentiation protocols under the same conditions using 500 nM of trichostatin A (TSA) and investigated for their morphological and neuronal marker properties. The results demonstrated that both cell lines are murine, and they exhibited distinct morphological and neuronal marker properties. Notably, the RGC-5 cells showed higher expression of the neuronal marker β-III tubulin and increased Thy-1-mRNA compared with the 661W cells, providing evidence of their different properties. The findings emphasize the importance of verifying the authenticity of cell lines used in ocular research and highlight the risks of contamination and altered cell properties.
Pangenotypic triple versus double therapy in HCV-infected patients after prior failure of direct-acting antivirals

Aim of the study Despite the excellent effectiveness of direct-acting antivirals (DAA) in the treatment of hepatitis C virus (HCV) infection, still a few percent of patients fail therapy. The study aimed to determine the effectiveness of triple vs double rescue treatment in such a population. Material and methods The study included all consecutive DAA-experienced patients retreated with pangenotypic options from the EpiTer-2 database, a retrospective national multicenter real-world project evaluating antiviral treatment in HCV-infected patients in 2015-2023. Results The studied population consisted of 269 patients, of whom 208 were treated with the double (P2) and 61 with the triple (P3) pangenotypic option. No statistically significant differences were found between these subpopulations, except a significantly more frequent history of liver transplantation in the P3 group (6.6% vs. 0.5%, p = 0.01). In the P2 group, two-thirds of patients were treated with velpatasvir/sofosbuvir, while in the P3 group the majority of patients received a combination of velpatasvir/sofosbuvir/voxilaprevir. Virological response at the end of therapy was comparable in both analyzed subpopulations, but the sustained virologic response (SVR) rate was significantly higher in triple retherapy, 98.3% vs. 88.7%, p = 0.02, calculated after exclusion of patients lost to follow-up. Lower SVR was achieved in genotype 3-infected men with cirrhosis, 88.9% and 80% in P3 and P2, respectively. Conclusions A comparison of double and triple pangenotypic retherapy in patients after failure of DAA therapy showed a higher sustained virological response in the triple option with a comparable response at the end of therapy. The factors reducing the chances of cure were cirrhosis, genotype 3 infection and male gender.
Hyperspectral imaging combined with CNN for maize variety identification

Introduction As the third largest food crop in the world, maize has wide varieties with similar appearances, which makes identification difficult. To solve the problem of identification of hybrid maize varieties, a method based on hyperspectral image technology combined with a convolutional neural network (CNN) is proposed to identify maize varieties. Methods In this study, 735 maize seeds from seven half-parent hybrid maize varieties were regarded as the research object. The maize seed images in the range of 900 ~ 1700nm were obtained by hyperspectral image acquisition system. The region of interest (ROI) of the embryo surface was selected, and the spectral reflectance of maize seeds was extracted. After Savitzky-Golay (SG) Smoothing pretreatment, Maximum Normalization (MN) pretreatment was performed. The 56 feature wavelengths were selected by Competitive Adaptive Reweighting Algorithm (CARS) and Successive Projection Algorithm (SPA). And the 56 wavelengths were mapped to high-dimensional space by high-dimensional feature mapping and then reconstructed into three-dimensional image features. A five-layer convolution neural network was used to identify three-dimensional image features, and nine (SG+MN)-(CARS+SPA)-CNN maize variety identification models were established by changing the input feature dimension and the depth factor size of the model layer. Results and Discussion The results show that the maize variety classification model works best, when the input feature dimension is 768 and the layer depth factor d is 1.0. At this point, the model accuracy of the test set is 96.65% and the detection frame rate is1000 Fps/s in GPU environment, which can realize the rapid and effective non-destructive detection of maize varieties. This study provides a new idea for the rapid and accurate identification of maize seeds and seeds of other crops.
Patient Experience of Digitalized Follow-up of Antidepressant Treatment in Psychiatric Outpatient Care: Qualitative Analysis

Background Nonadherence to pharmaceutical antidepressant treatment is common among patients with depression. Digitalized follow-up (ie, self-monitoring systems through mobile apps) has been suggested as an effective adjunct to conventional antidepressant treatment to increase medical adherence, improve symptoms of depression, and reduce health care resource use. Objective The aim of this study was to determine patients’ experience of digitalized follow-up using a mobile app as an adjunct to treatment concurrent with a new prescription, a change of antidepressant, or a dose increase. Methods This was a qualitative, descriptive study. Patients at 2 psychiatric outpatient clinics were recruited at the time of changing antidepressant medication. After using a mobile app (either a commercial app or a public app) for 4-6 weeks with daily registrations of active data, such as medical intake and questions concerning general mental health status, individual semistructured interviews were conducted. Recorded data were transcribed and then analyzed using content analysis. Results In total, 13 patients completed the study. The mean age was 35 (range 20-67) years, 8 (61.5%) were female, and all reported high digital literacy. Overall, the emerging themes indicated that the patients found the digital app to be a valuable adjunct to antidepressant treatment but with potential for improvement. Both user adherence and medical adherence were positively affected by a daily reminder and the app’s ease of use. User adherence was negatively affected by the severity of depression. The positive experience of visually presented data as graphs was a key finding, which was beneficial for self-awareness, the patient-physician relationship, and user adherence. Finally, the patients had mixed reactions to the app’s content and requested tailored content. Conclusions The patients identified several factors addressing both medical adherence and user adherence to a digital app when using it for digitalized follow-up concurrent with the critical time related to changes in antidepressant medication. The findings highlight the need for rigorous evidence-based empirical studies to generate sustainable research results.
Chest X-ray Foreign Objects Detection Using Artificial Intelligence

Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.
Feature fusion-based fiber-optic distributed acoustic sensing signal identification method

Fiber-optic distributed acoustic sensing (DAS) systems based on phase-sensitive optical time-domain reflection technology have been widely used for perimeter security and oil and gas pipeline safety monitoring. To address the problem of low recognition accuracy of high-sampling-rate long-sequence signal data (length greater than or equal to 1000 points) collected by the DAS system, we propose a CDIL-CBAM-BiLSTM network model based on feature fusion. The model uses a modified circular dilated convolutional neural network to extract detailed temporal structure information from each signal node, and combines it with bidirectional long short-term memory network using feature fusion to dig deeper into the data. Meanwhile, a convolutional block attention module was introduced to improve the model performance. The experimental results based on 5040 training samples and 2160 test samples show that the proposed model can achieve an average recognition accuracy of more than 99 % for six real disturbance events under perimeter security scenarios, and the recognition time was less than 2 ms. In addition, our method achieved the highest recognition accuracy compared with other methods used in the experiments and can be extended to other areas, such as pipeline safety monitoring and industrial inspection measurements.
Soil and plant factors affecting the estimation of water extraction by crops

This paper briefly reviews some soil and plant factors affecting the estimates of water use by crops. Evapotranspiration is frequently obtained as the residual term in the soil water balance equation so it is important to know the other terms accurately. Direct evaporation from the soil surface is often a substantial component of evapotranspiration (estimates range from 36-75%) and recent advances in micro-lysimetry have provided long-term estimates of this quantity. New developments with time domain reflectometry (TDR) offer prospects of making these measurements in a less time- consuming manner. Estimation of drainage can be made by inspection of changes of water content with time in selected soil layers, but calculation is often difficult because of spatial variability in soil hydraulic properties. The problems of layered and stony soils are discussed and the use of geostatistical approaches and scaling are described. Root systems of arable crops grow deeper during the growing season to secure new supplies of water. The rate of downward root growth of rainfed crops indicates that, for most, crop growth will be limited by the supply of water accessible to the roots.
A Novel Lipid Metabolism and Endoplasmic Reticulum Stress-Related Risk Model for Predicting Immune Infiltration and Prognosis in Colorectal Cancer

Lipid metabolism and endoplasmic reticulum stress exhibit crosstalk in various cancer types, which are closely associated with the progression of colorectal cancer (CRC). This study constructs a prognostic signature based on lipid metabolism and endoplasmic reticulum stress-related genes (LERGs) for CRC patients, aiming to predict the prognosis and immune response. RNA sequencing and clinical data from the TCGA and GEO databases were analyzed to identify differentially expressed LERGs with prognostic relevance using univariate Cox regression. Subsequently, a risk model was developed using the LASSO regression. CRC patients were stratified into low-risk and high-risk groups based on risk scores, with the high-risk cohort demonstrating a poorer clinical prognosis in multiple databases. The risk model showed robust correlations with clinical features, gene mutations, and treatment sensitivity. Significant differences in immune cell infiltration and the expression of immune-related factors were also detected between risk groups, and elevated scores of cytokines and failure factors were detected in single-cell RNA sequencing analysis. This research indicates that lipid metabolism and endoplasmic reticulum stress in CRC are correlated with tumor progression, an immunosuppressive landscape, and alterations of drug sensitivity. The developed risk model can serve as a powerful prognostic tool, offering critical insights for refining clinical management and optimizing treatment in CRC patients.
Fibronectin enhances attachment of human adipose-derived mesenchymal stem cells into polytetrafluoroethylene patch during surgical closure of the atrial and ventricular septal defect

Background : Polytetrafluoroethylene (PTFE) patch is commonly used during surgical closure for atrial septal defect (ASD) and ventricular septal defect (VSD). However, this patch has several limitations such as its inability to grow or remodel, especially in children and young adults. To tackle these limitations, we have tried to use fibronectin and human adipose-derived mesenchymal stem cells (hAMSCs) in the PTFE patch. Objective : To understand the impact of fibronectin to enhance hAMSCs cell-to-cell adherence and cell-to-patch surface attachment into PTFE patches used in the surgical closure of ASD or VSD. Materials and Methods : The hAMSCs were plated and fixated with 15 mL methanol and cluster of differentiation (CD) 90+, CD105+, and CD45 - antibodies were labeled with fluorescein isothiocyanate, rinsed with phosphate-buffered saline, and analyzed under a fluorescence microscope. Fibronectin solution (0.1%) was used to soak patch scaffolds for approximately 2-h duration and then dried for 20 min in the treatment group. The samples were examined with a scanning electron microscope (SEM). Results : SEM examination showed incomplete attachment of the cells even after 10 days in the control group at 1.14 ± 1.13. In contrast, the treatment group showed more cells attached to the patch surface at 31.25 ± 13.28 (P ≤ 0.0001). The observation at 5 days was 17.67 ± 20.21, at 7 days was 12.11 ± 10.94, and at 10 days was 18.83 ± 23.25. There was no significant statistical difference in mean cell per view among each treatment group (P = 0.802). Conclusion : Our work demonstrates that fibronectin has a positive impact on hAMSC attachment seeded onto the PTFE patch. These properties, in combination with their developmental plasticity, have generated tremendous interest in regenerative medicine.
USING A VIRTUAL TWIN OF A BUILDING TO ENSURE SECURITY IN EDUCATIONAL INSTITUTIONS

This research paper delves into the exploration of computer vision technology and digital twins as a means to enhance security measures in educational institutions. The study primarily focuses on the creation of a virtual replica of the first floor of a school and the integration of a person detection algorithm with the existing surveillance cameras. By leverag- ing the capabilities of the digital twin and real-time monitoring, comprehensive surveillance of the premises becomes feasible, resulting in simplified security operations. The paper sheds light on the significant potential of training neural networks for specific security tasks, such as the identification of weapons or the detection of anomalies in human behavior. These trained neural networks can be seamlessly integrated into the digital twin, thus ensuring public safety within the educational environment. The findings of this study provide substantial evidence for the effectiveness of computer vision technology and digital twins in bolstering security measures. The ability to create a virtual representation of the school’s first floor enables com- prehensive monitoring and surveillance, aiding in the prevention and prompt response to se- curity incidents. The integration of person detection algorithms further enhances the system’s capabilities by automatically identifying and tracking individuals within the premises. Addi- tionally, the deployment of neural networks for specialized security tasks adds an extra layer of protection, enabling the identification of potential threats and the detection of abnormal behavior patterns. By employing computer vision technology and digital twins, educational institutions can establish an advanced security infrastructure that optimizes monitoring, en- hances situational awareness, and ensures a safer environment for students, staff, and visitors. The research presented in this paper highlights the tremendous potential and practical impli- cations of these technologies in the realm of educational security.
The effect of hypomagnetic field on survival and mitochondrial functionality of active Paramacrobiotus experimentalis females and males of different age

Even for tardigrades, often called the toughest animals on Earth, a hypomagnetic field (HMF) is an extreme environment. However, studies on the effect of HMF on tardigrades and other invertebrates are scarce. Mitochondria play an important role in an organism’s response to extreme conditions. The effect of HMF on the mitochondrial inner membrane potential (Δψ), a well-known marker of mitochondria functionality, shows that mitochondria are very sensitive to HMF. To measure the HMF effect on Paramacrobiotus experimentalis, we calculated the tardigrade survival rate and Δψ level after HMF treatments of different durations. We also estimated the relationship between the age and sex of the tardigrade and the HMF effect. We observed age- and sex-related differences in Δψ and found that Δψ changes after HMF treatment were dependent on its duration as well as the animal’s age and sex. Furthermore, active P. experimentalis individuals displayed a high survival rate after HMF treatment. The data may contribute to the understanding of tardigrade aging and their resistance to extreme conditions including HMF, which in turn may be useful for future space explorations.