Relationship of some risk factors and symptoms in patients with acute coronary syndrome

Acute coronary syndrome (ACS) is one of the major causes of death in the worldwide. Clinical manifestations are different. So it's necessary to have knowledge about the types of symptoms experienced by patients with ACS. This study was performed to assay the Relationship of some risk factors and symptoms in patients with acute coronary syndrome. This cross-sectional study, were studied 294 patients with acute coronary syndrome at least 24 hours after admission had survived. Data was collected by a questionnaire that included demographic data form and check list of some symptoms and history of risk factors. There was a significant relationship between STEMI with vomiting (OR=1.94) and anxiety (OR=1.83) and UA with vomiting (OR=0.42). Between sex with weakness (OR=2.29) and anxiety (OR=1.82), diabetes with dyspenea (OR=1.8), weakness (OR=1.02) and tinnitus (OR=2.06) and hyperlipidemia with weakness (OR=2.35) and tinnitus (OR=2.49) was available significant difference. The findings of this study indicate that the appearance of symptoms of acute coronary syndrome were different as for ECG changes and risk factors, and more focused on those symptoms that they are common with any other diseases. Since, many of the symptoms of acute coronary syndrome can be potentially dangerous and life threatening, accurate diagnosis and timely action is crucial for the patients

Effects of window position on natural cross ventilation in vernacular architecture of mazandaran (case study: SARI)

Among the most effective architectures is vernacular architecture of Mazandaran whose incorporation in nature is so delicate that appears to be an essential part of it. Paying more attention to vernacular architecture and promoting it can be helpful in reducing energy consumption. Increasing use of fossil fuels in heating and cooling systems of buildings that come with excessive costs can result from inattention to vernacular architecture principles. However taking them into consideration can be an effective solution for reducing energy consumption. This research aimed to study the effects of window position on natural cross ventilation in Vernacular architecture of Mazandaran applying Descriptive - analytical approach. For this reason, a number of vernacular buildings, located in Sari, were studied regarding numbers and sizes of bilateral opening and its function in ventilation. Variables in this research were tested using SPSS and Regression correlation coefficient; additionally, all 3 formulas suggested in the results were evaluated to achieve an optimal model. In this study, for a desirable ventilator, for every percent added to the room area, the windows showed a 0.87 percent increase in size, and for every additional story, the optimal ventilation grew 30 percent. On the other hand, in high-rise apartments, the protrusion contribution in ventilation system was highlighted. This research study aims to clarify the principles of proper ventilation in vernacular architecture which have long been forgotten. Keywords: natural ventilation; window; vernacular architecture; temperate and wet climate

Multi-Queue Request Scheduling for Profit Maximization in IaaS Clouds

[EN] In cloud computing, service providers rent heterogeneous servers from cloud providers, i.e., Infrastructure as a Service (IaaS), to meet requests of consumers. The heterogeneity of servers and impatience of consumers pose great challenges to service providers for profit maximization. In this article, we transform this problem into a multi-queue model where the optimal expected response time of each queue is theoretically analyzed. A multi-queue request scheduling algorithm framework is proposed to maximize the total profit of service providers, which consists of three components: request stream splitting, requests allocation, and server assignment. A request stream splitting algorithm is designed to split the arriving requests to minimize the response time in the multi-queue system. An allocation algorithm, which adopts a one-step improvement strategy, is developed to further optimize the response time of the requests. Furthermore, an algorithm is developed to determine the appropriate number of required servers of each queue. After statistically calibrating parameters and algorithm components over a comprehensive set of random instances, the proposed algorithms are compared with the state-of-the-art over both simulated and real-world instances. The results indicate that the proposed multi-queue request scheduling algorithm outperforms the other algorithms with acceptable computational time.This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1400800, in part by the National Natural Science Foundation of China under Grants 61872077 and 61832004, and in part by the Collaborative InnovationCenter of Wireless Communications Technology. The work of Quan Z. Sheng was supported in part by Australian Research Council Future Fellowship under Grant FT140101247 and in part by Discovery Project under Grant DP180102378. The work of Ruben Ruiz was supported in part by the Spanish Ministry of Science, Innovation, and Universities through the project OPTEP-Port Terminal Operations Optimization under Grant RTI2018-094940-B-I00 financed with FEDER fundsWang, S.; Li, X.; Sheng, QZ.; Ruiz García, R.; Zhang, J.; Beheshti, A. (2021). Multi-Queue Request Scheduling for Profit Maximization in IaaS Clouds. IEEE Transactions on Parallel and Distributed Systems. 32(11):2838-2851. https://doi.org/10.1109/TPDS.2021.3075254S28382851321

A Comprehensive Survey on Graph Summarization with Graph Neural Networks

As large-scale graphs become more widespread, more and more computational challenges with extracting, processing, and interpreting large graph data are being exposed. It is therefore natural to search for ways to summarize these expansive graphs while preserving their key characteristics. In the past, most graph summarization techniques sought to capture the most important part of a graph statistically. However, today, the high dimensionality and complexity of modern graph data are making deep learning techniques more popular. Hence, this paper presents a comprehensive survey of progress in deep learning summarization techniques that rely on graph neural networks (GNNs). Our investigation includes a review of the current state-of-the-art approaches, including recurrent GNNs, convolutional GNNs, graph autoencoders, and graph attention networks. A new burgeoning line of research is also discussed where graph reinforcement learning is being used to evaluate and improve the quality of graph summaries. Additionally, the survey provides details of benchmark datasets, evaluation metrics, and open-source tools that are often employed in experimentation settings, along with a discussion on the practical uses of graph summarization in different fields. Finally, the survey concludes with a number of open research challenges to motivate further study in this area.Comment: 20 pages, 4 figures, 3 tables, Journal of IEEE Transactions on Artificial Intelligenc

ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligence

Generative Pre-trained Transformer (GPT) is a state-of-the-art machine learning model capable of generating human-like text through natural language processing (NLP). GPT is trained on massive amounts of text data and uses deep learning techniques to learn patterns and relationships within the data, enabling it to generate coherent and contextually appropriate text. This position paper proposes using GPT technology to generate new process models when/if needed. We introduce ProcessGPT as a new technology that has the potential to enhance decision-making in data-centric and knowledge-intensive processes. ProcessGPT can be designed by training a generative pre-trained transformer model on a large dataset of business process data. This model can then be fine-tuned on specific process domains and trained to generate process flows and make decisions based on context and user input. The model can be integrated with NLP and machine learning techniques to provide insights and recommendations for process improvement. Furthermore, the model can automate repetitive tasks and improve process efficiency while enabling knowledge workers to communicate analysis findings, supporting evidence, and make decisions. ProcessGPT can revolutionize business process management (BPM) by offering a powerful tool for process augmentation, automation and improvement. Finally, we demonstrate how ProcessGPT can be a powerful tool for augmenting data engineers in maintaining data ecosystem processes within large bank organizations. Our scenario highlights the potential of this approach to improve efficiency, reduce costs, and enhance the quality of business operations through the automation of data-centric and knowledge-intensive processes. These results underscore the promise of ProcessGPT as a transformative technology for organizations looking to improve their process workflows.Comment: Accepted in: 2023 IEEE International Conference on Web Services (ICWS); Corresponding author: Prof. Amin Beheshti ([email protected]

CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter Performance

We propose a novel direction to improve the denoising quality of filtering-based denoising algorithms in real time by predicting the best filter parameter value using a Convolutional Neural Network (CNN). We take the use case of BM3D, the state-of-the-art filtering-based denoising algorithm, to demonstrate and validate our approach. We propose and train a simple, shallow CNN to predict in real time, the optimum filter parameter value, given the input noisy image. Each training example consists of a noisy input image (training data) and the filter parameter value that produces the best output (training label). Both qualitative and quantitative results using the widely used PSNR and SSIM metrics on the popular BSD68 dataset show that the CNN-guided BM3D outperforms the original, unguided BM3D across different noise levels. Thus, our proposed method is a CNN-based improvement on the original BM3D which uses a fixed, default parameter value for all images.Comment: 2019 International Conference on Image Analysis and Recognitio

Angiotensin-converting enzyme genotype and late respiratory complications of mustard gas exposure

<p>Abstract</p> <p>Background</p> <p>Exposure to mustard gas frequently results in long-term respiratory complications. However the factors which drive the development and progression of these complications remain unclear. The Renin Angiotensin System (RAS) has been implicated in lung inflammatory and fibrotic responses. Genetic variation within the gene coding for the Angiotensin Converting Enzyme (ACE), specifically the Insertion/Deletion polymorphism (I/D), is associated with variable levels of ACE and with the severity of several acute and chronic respiratory diseases. We hypothesized that the ACE genotype might influence the severity of late respiratory complications of mustard gas exposure.</p> <p>Methods</p> <p>208 Kurdish patients who had suffered high exposure to mustard gas, as defined by cutaneous lesions at initial assessment, in Sardasht, Iran on June 29 1987, underwent clinical examination, spirometric evaluation and ACE Insertion/Deletion genotyping in September 2005.</p> <p>Results</p> <p>ACE genotype was determined in 207 subjects. As a continuous variable, FEV₁% predicted tended to be higher in association with the D allele 68.03 ± 20.5%, 69.4 ± 21.4% and 74.8 ± 20.1% for II, ID and DD genotypes respectively. Median FEV₁% predicted was 73 and this was taken as a cut off between groups defined as having better or worse lung function. The ACE DD genotype was overrepresented in the better spirometry group (Chi²4.9 p = 0.03). Increasing age at the time of exposure was associated with reduced FEV₁%predicted (p = 0.001), whereas gender was not (p = 0.43).</p> <p>Conclusion</p> <p>The ACE D allele is associated with higher FEV₁% predicted when assessed 18 years after high exposure to mustard gas.</p

Antimicrobial lubricant formulations containing poly(hydroxybenzene)-trimethoprim conjugates synthesized by tyrosinase

Poly(hydroxybenzene)-trimethoprim conjugates were prepared using methylparaben as substrate of the oxida- tive enzyme tyrosinase. MALDI-TOF MS analysis showed that the enzymatic oxidation of methylparaben alone leads to the poly(hydroxybenzene) formation. In the presence of tri- methoprim, the methylparaben tyrosinase oxidation leads poly(hydroxybenzene)-trimethoprim conjugates. All of these compounds were incorporated into lubricant hydroxyethyl cellulose/glycerol mixtures. Poly(hydroxybenzene)-trimetho- prim conjugates were the most effective phenolic structures against the bacterial growth reducing by 96 and 97 % of Escherichia coli and Staphylococcus epidermidis suspen- sions, respectively (after 24 h). A novel enzymatic strategy to produce antimicrobial poly(hydroxybenzene)-antibiotic conjugates is proposed here for a wide range of applications on the biomedical field.The authors Idalina Gonçalves and Cláudia Botelho would like to acknowledge the NOVO project (FP7-HEALTH- 2011.2.3.1- 5) for funding. Loïc Hilliou acknowledges the financial support by FCT – Foundation for Science and Technology, Portugal (501100001871), through Grant PEst-C/CTM/LA0025/2013 - Strategic Project - LA 25 - 2013–2014, and by Programa Operacional Regional do Norte (ON.2) through the project BMatepro – Optimizing Materials and Processes^, with reference NORTE-07-0124-FEDER-000037 FEDER COMPETE

Epithelial to Mesenchymal Transition of a Primary Prostate Cell Line with Switches of Cell Adhesion Modules but without Malignant Transformation

Background: Epithelial to mesenchymal transition (EMT) has been connected with cancer progression in vivo and the generation of more aggressive cancer cell lines in vitro. EMT has been induced in prostate cancer cell lines, but has previously not been shown in primary prostate cells. The role of EMT in malignant transformation has not been clarified. Methodology/Principal Findings: In a transformation experiment when selecting for cells with loss of contact inhibition, the immortalized prostate primary epithelial cell line, EP156T, was observed to undergo EMT accompanied by loss of contact inhibition after about 12 weeks in continuous culture. The changed new cells were named EPT1. EMT of EPT1 was characterized by striking morphological changes and increased invasion and migration compared with the original EP156T cells. Gene expression profiling showed extensively decreased epithelial markers and increased mesenchymal markers in EPT1 cells, as well as pronounced switches of gene expression modules involved in cell adhesion and attachment. Transformation assays showed that EPT1 cells were sensitive to serum or growth factor withdrawal. Most importantly, EPT1 cells were not able to grow in an anchorage-independent way in soft agar, which is considered a critical feature of malignant transformation. Conclusions/Significance: This work for the first time established an EMT model from primary prostate cells. The results show that EMT can be activated as a coordinated gene expression program in association with early steps of transformation. The model allows a clearer identification of the molecular mechanisms of EMT and its potential role in malignant transformation

Immunosenescence and lymphomagenesis

One of the most important determinants of aging-related changes is a complex biological process emerged recently and called \u201cimmunosenescence\u201d. Immunosenescence refers to the inability of an aging immune system to produce an appropriate and effective response to challenge. This immune dysregulation may manifest as increased susceptibility to infection, cancer, autoimmune disease, and vaccine failure. At present, the relationship between immunosenescence and lymphoma in elderly patients is not defined in a satisfactory way. This review presents a brief overview of the interplay between aging, cancer and lymphoma, and the key topic of immunosenescence is addressed in the context of two main lymphoma groups, namely Non Hodgkin Lymphoma (NHL) and Hodgkin Lymphoma (HL). Epstein Barr Virus (EBV) plays a central role in the onset of neoplastic lymphoproliferation associated with immunological changes in aging, although the pathophysiology varies vastly among different disease entities. The interaction between immune dysfunction, immunosenescence and Epstein Barr Virus (EBV) infection appears to differ between NHL and HL, as well as between NHL subtypes