An efficient lightweight network for image denoising using progressive residual and convolutional attention feature fusion.

While deep learning has become the go-to method for image denoising due to its impressive noise removal capabilities, excessive network depth often plagues existing approaches, leading to significant computational burdens. To address this critical bottleneck, we propose a novel lightweight progressive residual and attention mechanism fusion network that effectively alleviates these limitations. This architecture tackles both Gaussian and real-world image noise with exceptional efficacy. Initiated through dense blocks (DB) tasked with discerning the noise distribution, this approach substantially reduces network parameters while comprehensively extracting local image features. The network then adopts a progressive strategy, whereby shallow convolutional features are incrementally integrated with deeper features, establishing a residual fusion framework adept at extracting encompassing global features relevant to noise characteristics. The process concludes by integrating the output feature maps from each DB and the robust edge features from the convolutional attention feature fusion module (CAFFM). These combined elements are then directed to the reconstruction layer, ultimately producing the final denoised image. Empirical analyses conducted in environments characterized by Gaussian white noise and natural noise, spanning noise levels 15-50, indicate a marked enhancement in performance. This assertion is quantitatively corroborated by increased average values in metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index for Color images (FSIMc), outperforming the outcomes of more than 20 existing methods across six varied datasets. Collectively, the network delineated in this research exhibits exceptional adeptness in image denoising. Simultaneously, it adeptly preserves essential image features such as edges and textures, thereby signifying a notable progression in the domain of image processing. The proposed model finds applicability in a range of image-centric domains, encompassing image processing, computer vision, video analysis, and pattern recognition.

Comparative Analysis of Female Leadership Styles in Public and Private Sector Universities: A Study in Pakistan.

This study aimed to compare the styles of leadership practices of female leaders in public and private Universities in Pakistan. In this study, both quantitative and qualitative data were collected using a mixed-method approach. An adapted and developed questionnaire was used for quantitative data collection, whereas qualitative data were collected through a semi-structured interview schedule. A sample of 200 female leaders was selected for quantitative data collection, while 10 females from the said sample were selected for qualitative data collection through a simple random method. Quantitative data were analyzed using SPSS, whereas qualitative data were analyzed using thematic analysis. Based on the statistical results, this study discovered statistically significant differences in female transformational and transactional leadership styles and significant differences in job performance based on the university sector. This study discovered statistically insignificant differences based on the different positions of female leaders regarding transformational and transactional leadership, and job performance. Moreover, qualitative data revealed that female leaders clearly understood both leadership styles and how to improve job performance by practicing them. The originality of this study concerns the identification of the differences between leadership styles (transformational and transactional) practiced by female leaders of public and private Universities in Pakistan and explains female leaders' perceptions of the role of leadership styles in their job performance.

Generation of Chemical Space of Compounds for Prostate Cancer Treatment: Biological Activity Prediction, Clustering, and Visualization of Chemical Space.

Designing molecules for pharmaceutical purposes has been a significant focus for several decades. The pursuit of novel drugs is an arduous and financially demanding undertaking. Nevertheless, the integration of computer-assisted frameworks presents a swift avenue for designing and screening drug-like compounds. Within the context of this research, we introduce a comprehensive approach for the design and screening of compounds tailored to the treatment of prostate cancer. To forecast the biological activity of these compounds, we employed machine learning (ML) models. Additionally, an automated process involving the deconstruction and reconstruction of molecular building blocks leads to the generation of novel compounds. Subsequently, the ML models were utilized to predict the biological activity of the designed compounds, and the t-SNE method was employed to visualize the chemical space covered by the novel compounds. A meticulous selection process identified the most promising compounds, and their potential for synthesis was assessed, offering valuable guidance to experimental chemists in their investigative endeavors. Furthermore, fingerprint and heatmap analysis were conducted to evaluate the chemical similarity among the selected compounds. This multifaceted approach, encompassing predictive modeling, compound generation, visualization, and similarity assessment, underscores our commitment to refining the process of identifying potential candidates for further exploration in prostate cancer treatment.

Insight into potent TLR2 inhibitors for the treatment of disease caused by Mycoplasma pneumoniae based on machine learning approaches.

Mycoplasma pneumoniae (MP) is one of the most common pathogens that causes acute respiratory tract infections. Children experiencing MP infection often suffer severe complications, lung injury, and even death. Previous studies have demonstrated that Toll-like receptor 2 (TLR2) is a potential therapeutic target for treating the MP-induced inflammatory response. However, the screening of natural compounds has received more attention for the treatment of bacterial infections to reduce the likelihood of bacterial resistance. Herein, we screened compounds by combining molecular docking and machine learning approaches to find potential lead compounds for treating MP infection. First, all compounds were docked with the TLR2 receptor protein to screen for potential candidates. To predict drug bioactivity, a machine learning model (random forest) was trained for TLR2 inhibitors to obtain the predictive model. The model achieved significant squared correlation coefficient (R2) values for the training set (0.85) and validation set (0.84) of compounds. The developed machine learning model was then used to predict the pIC50 values of the top 50 candidates from the Traditional Chinese compounds and Discovery Diversity sets of compounds. As a result, these compounds are capable of inhibiting the inflammatory response induced by MP. However, prior to bringing these compounds to market, it is necessary to verify these results with additional biological testing, including preclinical and clinical studies. Moreover, the present study provides a theoretical basis for the use of natural compounds as potential candidates to treat pneumonia caused by MP.

Prediction of Wellness Condition for Community-Dwelling Elderly via ECG Signals Data-Based Feature Construction and Modeling.

The accelerated growth of elderly populations in many countries and regions worldwide is creating a major burden to the healthcare system. Intelligent approaches for continuous health monitoring have the potential to promote the transition to more proactive and affordable healthcare. Electrocardiograms (ECGs), collected from portable devices, with noninvasive and cost-effective merits, have been widely used to monitor various health conditions. However, the dynamic and heterogeneous pattern of ECG signals makes relevant feature construction and predictive model development a challenging task. In this study, we aim to develop an integrated approach for one-day-forward wellness prediction in the community-dwelling elderly using single-lead short ECG signal data via multiple-features construction and predictive model implementation. Vital signs data from the elderly were collected via station-based equipment on a daily basis. After data preprocessing, a set of features were constructed from ECG signals based on the integration of various models, including time and frequency domain analysis, a wavelet transform-based model, ensemble empirical mode decomposition (EEMD), and the refined composite multiscale sample entropy (RCMSE) model. Then, a machine learning based predictive model was established to map the l-day lagged features to wellness condition. The results showed that the approach developed in this study achieved the best performance for wellness prediction in the community-dwelling elderly. In practice, the proposed approach could be useful in the timely identification of elderly people who might have health risks, and could facilitating decision-making to take appropriate interventions.

An overview of pharmacological activities of baicalin and its aglycone baicalein: New insights into molecular mechanisms and signaling pathways.

The flavonoids, baicalin, and its aglycone baicalein possess multi-fold therapeutic properties and are mainly found in the roots of Oroxylum indicum (L.) Kurz and Scutellaria baicalensis Georgi. These flavonoids have been reported to possess various pharmacological properties, including antibacterial, antiviral, anticancer, anticonvulsant, anti-oxidant, hepatoprotective, and neuroprotective effects. The pharmacological properties of baicalin and baicalein are due to their abilities to scavenge reactive oxygen species (ROS) and interaction with various signaling molecules associated with apoptosis, inflammation, autophagy, cell cycle, mitochondrial dynamics, and cytoprotection. In this review, we summarized the molecular mechanisms underlying the chemopreventive and chemotherapeutic applications of baicalin and baicalein in the treatment of cancer and inflammatory diseases. In addition, the preventive effects of baicalin and baicalein on mitochondrial dynamics and functions were highlighted with a particular emphasis on their anti-oxidative and cytoprotective properties. The current review highlights could be useful for future prospective studies to further improve the pharmacological applications of baicalein and baicalin. These studies should define the threshold for optimal drug exposure, dose optimization and focus on therapeutic drug monitoring, objective disease markers, and baicalin/baicalein drug levels.

A novel compact broadband and radiation efficient antenna design for medical IoT healthcare system.

This paper investigates and develops a novel compact broadband and radiation efficient antenna design for the medical internet of things (M-IoT) healthcare system. The proposed antenna comprises of an umbrella-shaped metallic ground plane (UsMGP) and an improved radiator. A hybrid approach is employed to obtain the optimal results of antenna. The proposed solution is primarily based on the utilization of etching slots and a loaded stub on the ground plane and rectangular patch. The antenna consists of a simple rectangular patch, a 50 Ƹ microstrip feed line, and a portion of the ground plane printed on a relatively inexpensive flame retardant material (FR4) thick substrate with an overall compact dimension of 22 × 28 × 1.5 mm3. The proposed antenna offers compact, broadband and radiation efficient features. The antenna is carefully designed by employing the approximate calculation formulae extracted from the transmission line model. Besides, the parameters study of important variables involved in the antenna design and its influence on impedance matching performance are analyzed. The antenna shows high performance, including impedance bandwidth of 7.76 GHz with a range of 3.65Ƀ11.41 GHz results in 103% wider relative bandwidth at 10 dB return loss, 82% optimal radiation efficiency in the operating band, reasonable gain performance, stable monopole-shaped radiation patterns and strong current distribution across the antenna lattice. The suggested antenna is manufactured, and simulation experiments evaluate its performance. The findings indicate that the antenna is well suited for medical IoT healthcare systems applications.

A framework for efficient brain tumor classification using MRI images.

A brain tumor is an abnormal growth of brain cells inside the head, which reduces the patient's survival chance if it is not diagnosed at an earlier stage. Brain tumors vary in size, different in type, irregular in shapes and require distinct therapies for different patients. Manual diagnosis of brain tumors is less efficient, prone to error and time-consuming. Besides, it is a strenuous task, which counts on radiologist experience and proficiency. Therefore, a modern and efficient automated computer-assisted diagnosis (CAD) system is required which may appropriately address the aforementioned problems at high accuracy is presently in need. Aiming to enhance performance and minimise human efforts, in this manuscript, the first brain MRI image is pre-processed to improve its visual quality and increase sample images to avoid over-fitting in the network. Second, the tumor proposals or locations are obtained based on the agglomerative clustering-based method. Third, image proposals and enhanced input image are transferred to backbone architecture for features extraction. Fourth, high-quality image proposals or locations are obtained based on a refinement network, and others are discarded. Next, these refined proposals are aligned to the same size, and finally, transferred to the head network to achieve the desired classification task. The proposed method is a potent tumor grading tool assessed on a publicly available brain tumor dataset. Extensive experiment results show that the proposed method outperformed the existing approaches evaluated on the same dataset and achieved an optimal performance with an overall classification accuracy of 98.04%. Besides, the model yielded the accuracy of 98.17, 98.66, 99.24%, sensitivity (recall) of 96.89, 97.82, 99.24%, and specificity of 98.55, 99.38, 99.25% for Meningioma, Glioma, and Pituitary classes, respectively.