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Deep learning is a very powerful analytic tool to recognize the patterns in data to make appropriate predictions. It has tremendous potential in data analyses, particularly for cell biology domain, caused by the growing scale and inherent complexity of biological data. The core purpose of this research work is to design, implement, and calibrate an efficient deep convolutional neural network (DCNN) architecture in the context of binary-class classification problem. This diversified network is developed to precisely identify human induced pluripotent stem cell-derived endothelial cells (hiPSC-derived EC) based on photomicrograph. The proposed architecture is cerebrally developed with numerous convolutional modules, multiple kernel sizes, various pooling layers, activation functions and strides, nevertheless fewer trainable parameters to strengthen the network and enhance its performance. The proposed feature fusion framework is compared with the classifier fusion approach in terms of Matthews's correlation coefficient (MCC), training time, inference time, number of layers, number of parameters, graphics processing unit (GPU) memory utilization, and floating-point operations (FLOPS). Specifically, it achieves 94.6% sensitivity, 94.5% specificity, and 94.7% precision. Induced pluripotent stem cell (iPS) dataset is also introduced in this research work that has 16278 images which are labelled by three independent and experienced human experts of cell biology domain to facilitate future research. Experimental results show that the proposed framework offers an innovative and attainable algorithm for accelerating and systematizing the classification task along with saving time and effort. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are known as life-threatening illnessescaused by severe abnormalities in the cardiovascular system. They are a leading cause of mortality and morbidity worldwide.Nanotechnology integrated substantialinnovations in cardiovascular diagnostic and therapeutic at the nanoscale. This in-depth analysis explores cutting-edge methods for diagnosing CVDs, including nanotechnological interventions and crucial components for identifying risk factors, developing treatment plans, and monitoring patients' progress with chronic CVDs.Intensive research has gone into making nano-carriers that can image and treat patients. To improve the efficiency of treating CVDs, the presentreview sheds light on a decision-tree-based solution by investigating recent and innovative approaches in CVD diagnosis by utilizing nanoparticles (NPs). Treatment choices for chronic diseases like CVD, whose etiology might take decades to manifest, are very condition-specific and disease-stage-based. Moreover, thisreview alsobenchmarks the changing landscape of employing NPs for targeted and better drug administration while examining the limitations of various NPs in CVD diagnosis, including cost, space, time, and complexity. To better understand and treatment of cardiovascular diseases, the conversation moves on to the nano-cardiovascular possibilities for medical research.We also focus on recent developments in nanoparticle applications, the ways they might be helpful, and the medical fields where they may find future use. Finally, this reviewadds to the continuing conversation on improved diagnosis and treatment approaches for cardiovascular disorders by discussing the obstacles and highlighting the revolutionary effects of nanotechnology. [ABSTRACT FROM AUTHOR]

Abstract: The growth of demand for electrical energy in the rapidly expanding towns, cities and industries exceeds the growth of the power being made available. Therefore, energy conservation is becoming an increasingly important issue in Saudi buildings. The objective of this study was to investigate the impact of alternative energy conservation measures on energy requirements in office buildings in hot–humid climates. The study was conducted on a five-story office building located in Dammam, Saudi Arabia, which has been in use since 1998. Different types of HVAC systems were selected and different feasible and practical operational energy conservation measures (ECMs) were evaluated using the energy simulation software of Visual DOE 4.0. Previous studies conducted in this area were reviewed. Data was collected through review of design drawings, building audit and the analysis of 4 years of utility bills. All the collected data was analyzed and the utility bills data was used to calibrate the base case of the existing building using Visual DOE energy simulation software. Conclusions and recommendations were developed for conserving energy using various appropriate ECMs in office buildings in hot and humid climates. [Copyright &y& Elsevier]

• Recent studies show the potential of deep learning for binary classification of skin lesions. • This research proposes an advanced deep convolution neural network model for multi-class classification of skin lesions. • The proposed model is designed with several layers, and multiple filter sizes, but fewer filters and parameters. • It is evaluated by the experimental results on the ISIC-19 database in terms of sensitivity, specificity, and other metrics. • It outperforms state-of-the-art algorithms, exhibiting 0.991 AUROC. As an analytic tool in medicine, deep learning has gained great attention and opened new ways for disease diagnosis. Recent studies validate the effectiveness of deep learning algorithms for binary classification of skin lesions (i.e., melanomas and nevi classes) with dermoscopic images. Nonetheless, those binary classification methods cannot be applied to the general clinical situation of skin cancer screening in which multi-class classification must be taken into account. The main objective of this research is to develop, implement, and calibrate an advanced deep learning model in the context of automated multi-class classification of skin lesions. The proposed Deep Convolutional Neural Network (DCNN) model is carefully designed with several layers, and multiple filter sizes, but fewer filters and parameters to improve efficacy and performance. Dermoscopic images are acquired from the International Skin Imaging Collaboration databases (ISIC-17, ISIC-18, and ISIC-19) for experiments. The experimental results of the proposed DCNN approach are presented in terms of precision, sensitivity, specificity, and other metrics. Specifically, it attains 94 % precision, 93 % sensitivity, and 91 % specificity in ISIC-17. It is demonstrated by the experimental results that this proposed DCNN approach outperforms state-of-the-art algorithms, exhibiting 0.964 area under the receiver operating characteristics (AUROC) in ISIC-17 for the classification of skin lesions and can be used to assist dermatologists in classifying skin lesions. As a result, this proposed approach provides a novel and feasible way for automating and expediting the skin lesion classification task as well as saving effort, time, and human life. [ABSTRACT FROM AUTHOR]

Objective: We sought to determine the attributes of successful and unsuccessful fellowship applicants of the American Board of Obstetrics and Gynecology Inc (ABOG)-approved fellowship programs and to identify salient differences between subspecialty applicants. Study Design: Anonymous questionnaires were completed by obstetrics and gynecology fellowship applicants using a web-based survey after match day of 2012. Fellowship applicant practices were evaluated and included importance of prematch preparations, interview process, networking practices, and postmatch reflections. Results: A total of 327 fellowship applicants applying to programs accredited by the ABOG were surveyed, and 200 completed the survey (61% response rate). A comparison between prematch educational preparations pursued by applicants showed that matched applicants were more likely to come from allopathic medical schools (94%), attain membership in Alpha Omega Alpha and/or Phi Beta Kappa (27%), and receive a letter of recommendation from a nationally known subspecialist (77%) than unmatched applicants (P = .03, .005, and .007, respectively). Applicants to reproductive endocrinology and infertility were more likely than female pelvic medicine and reconstructive surgery to be members of academic honor societies (P = .008). Research publication was common among matched subspecialist applicants, with over half publishing 1-3 peer-reviewed manuscripts prior to matching. Applicants to gynecologic oncology did more visiting electives than any other specialty applicants (P < .001). Conclusion: Successful obstetrics and gynecology fellowship applicants have superior prematch preparations, strong letters of recommendation from leaders in their field of interest, and multiple research publications. These data will guide applicants to a critical self-analysis before deciding to apply. [Copyright &y& Elsevier]

Gallium nitride (GaN) is a promising semiconductor for the fabrication of a wide range of electronic and optoelectronic devices. For GaN grown on widely used sapphire substrate, bundles of threading dislocations (TDs) are induced by thermal and lattice mismatch. These TDs intersect the surface of GaN and degrade the internal quantum efficiency (IQE) of GaN-based devices. To address this issue, two innovative techniques are proposed for effective filtration of TDs. First, a novel design of serpentine channel pattern sapphire substrate (SCPSS) is applied for effective filtration of TDs at the interface. Transmission electron microscopy (TEM) indicated that SCPSS plays an effective role in the filtration of TDs; however, cathodoluminescence (CL) revealed that many TDs still intersect the surface of GaN. To further optimize the growth of GaN, the temperature was ramped down to the intermediate level and an interlayer (IL) of InGaN was introduced near the window of SCPSS. CL analysis indicated that additional use of InGaN-IL further reduced the TDs. Raman microscopy showed that incorporation of IL lead to drastic reduction in compressive stress. Atomic force microscopy (AFM) confirmed the achievement of excellent surface topography with InGaN-IL. Moreover, it is observed that growth of GaN by SCPSS is better than conventional growth because of improved surface morphology and low TDs. The reduction of TDs by SCPSS and InGaN-IL is an effective approach for the growth of high-quality GaN towards efficient devices. [Display omitted] - Reduction of TDs in GaN grown by novel design of pattern sapphire substrate. - Role of interlayer of InGaN in the filtration of threading dislocations. - Blocking and bending of threading dislocations by PSS and interlayer of InGaN. - Effects of interlayer of InGaN on the crystal quality of GaN. - High quality growth of GaN grown on SCPSS and role of InGaN interlayer. [ABSTRACT FROM AUTHOR]

Artemisia sphaerocephala Krasch (ASK) is an important member of Compositae (Asteraceae) family. Its seeds have been widely used as traditional medicine and to improve the quality of food. Water soluble and water insoluble polysaccharides are found in the seeds of this plant. Research has been conducted on the extraction of polysaccharides, their modification and determination of their structure. To date different techniques for extraction purposes have been applied which are reviewed here. Antioxidant, antidiabetic, anti-obesogenic, antitumor, and immunomodulatory activities have been explored using in vivo and in vitro methods. Moreover, these polysaccharides have been used as packaging material and as a sensing component for monitoring the freshness of packaged food. Some experimental results have shown that the quality of foods is also improved by using them as a food additive. We have also indicated some of the potential areas that are needed to be explored. [ABSTRACT FROM AUTHOR]