Comment on 'Generalized Heisenberg algebra coherent states for power-law potentials'

We argue that the statistical features of generalized coherent states for power-law potentials based on Heisenberg algebra, presented in a recent paper by Berrada et al (Phys. Lett. A, 375, 298 (2011)) are incorrect.Comment: 9 pages, 1 figur

Monocyte subsets in atrial fibrillation with preserved left ventricular function

Introduction: Atrial fibrillation is the commonest arrhythmia in the cardiovascular system. It can have debilitating effects, including life changing thromboembolic stroke. Monocytes and their subsets have proven to be both beneficial and detrimental in heart failure and coronary artery disease. Less is known regarding their role in atrial fibrillation. The aims of this thesis were to study the following parameters in patients with atrial fibrillation and preserved left ventricular function: 1) monocyte subset numbers, 2) monocyte subset expression of surface receptors for inflammation, 3) crosstalk between monocytes and platelets in the formation of monocyte-platelet aggregates (MPAs), 4) markers of cardiac fibrosis, 5) utility of Spironolactone to aid in improved exercise capacity in this cohort of patients. Methods: Monocyte subsets were analysed by flow cytometry on venous blood samples in 250 patients with permanent atrial fibrillation and preserved left ventricular function who were double blindly allocated to spironolactone or placebo and followed up in clinic at specified time intervals. The subsets were analysed at 12 and 24 months from the treatment allocation. Plasma levels of cardiac markers of fibrosis were analysed by ELISA at baseline, 12 months and 24 months. The patients underwent cardiopulmonary exercise testing, 6-minute walk test and quality of life assessment at set time intervals during the 2-year study period to assess their relationship to monocyte subsets. Results: CD16 expression on Mon3 subset was associated with a higher Peak VO2 and CD42 expression on MPA associated with Mon3 with a reduced exercise capacity (p value of 0.001 and 0.026 respectively). Quality of life and hospitalisations were unaffected by monocyte subsets. Spironolactone did not impact on primary and secondary outcomes of the study and monocyte characteristics. The markers of cardiac fibrosis did not explain the mechanistic role by which Mon3 influences exercise capacity in this patient population. Conclusion: Differences in monocyte cell surface marker expression influence exercise capacity and functional status in patients with atrial fibrillation and preserved left ventricular function. Spironolactone, however, does not affect such study parameters when compared to placebo. Further studies are required to decipher the mechanisms by which monocyte cell surface markers influence exercise capacity

Spectrum of tamoxifen associated endometrial pathology in breast cancer patients

The objective of the study was to determine the incidence and type of endometrial abnormalities in long-term users of tamoxifen with breast cancer. All patients with a diagnosis of Oestrogen Receptor positive breast cancer on Tamoxifen therapy who had also undergone endometrial biopsy for abnormal bleeding or other symptoms were included. Among the 37 cases that had long-term follow up available, 21(57%) had evidence of endometrial pathology. There were seven cases of simple hyperplasia and thirteen of endometrial polyp. Only one case of endometrial carcinoma was seen. These findings support the association between prolonged tamoxifen therapy and endometrial pathology of possible neoplastic potential. Endometrial pathology is dependent on duration of exposure to Tamoxifen, therefore, close follow up of such patients is recommended

Anti Chlamydial Antibodies in Women with Ectopic Pregnancy

Background: To compare the frequency of chlamydia trachomatis infection in women with ectopic and with normal pregnancies.Methods: In this case-control study diagnosed patients of ectopic pregnancy(EP)were included . The control group comprised of early normal intra uterine pregnancies (1st trimester). A total number of 88, comprising 44 cases and 44 controls were included in this study. Sera from patients was drawn at the time of operation or within the subsequent 24 hours. Anti-chlamydial IgG was performed by ELISA.Results: Sampled cases population (n=44) had mean age distribution 26.48 years while among controls, mean age was 25.32 years. Presenting symptoms of cases showed pelvic pain (54.5%), bleeding (27.3%), vomiting (11.4%) and burning micturition (6.8%). During contraceptive practices , out of 88 patients, 5 cases and 20 controls gave history of safe sex practices. Out of 63 patients, who did not give history of any contraceptive practice, Anti-Chlamydia IgG was detected in 11 cases and 5 controls. Regarding Anti-Chlamydia IgG distribution among cases and controls, IgG was detected in 11(25%) cases and in 5(11.3%) controls.Conclusion: Frequency of anti-chlamydial IgG antibodies was much higher in women with ectopic pregnancy (25%) as compared to healthy controls(11.3%)

The Nexus between Economic Indicators and Economic Growth in Brazil

The objective of the paper is twofold. First is to examine the relationship between economic indicators and economic growth of Brazil economy, second is to look the impact of foreign direct investment on Gross domestic product of Brazil economy. The time series data from 1986-2014 was used of the remittance, foreign direct investment, domestic savings and capital formation to know the impact on Gross domestic product of Brazil. Results have been analyzed by using advanced econometric tools like- unit root test (both ADF and PP), OLS methods and Granger causality test. The results confirmed that, both capital formation and Remittance have positive relationship with GDP, whereas FDI and savings show insignificant influence on GDP of Brazil. In order to minimize the gap between domestic saving and investment and to bring the technology and managerial know-how, remittance could play important role on the way of economic development of Brazil. Similarly the Capital formation is playing an important role in the economic development due to positive impact on GDP. Therefore, government should take pragmatic policy, develop infrastructure, stabilized the political environment, law and order situation. Keywords: Gross Domestic Product, Unit Root test, Granger Causality, Brazi

MS-ADS: multistage spectrogram image-based anomaly detection system for IoT security.

The innovative computing idea of Internet-of-Things (IoT) architecture has gained tremendous popularity over the last decade, resulting in an exponential increase in the connected devices and the data processed in the IoT networks. Since IoT devices collect a massive amount of sensitive information exchanged over the traditional internet, security has become a prime concern due to the more frequent generation of network anomalies. A network-based anomaly detection system can provide the much-needed efficient security solution to the IoT network by detecting anomalies at the network entry points through constant traffic monitoring. Despite enormous efforts by researchers, these detection systems still suffer from lower detection accuracy in detecting anomalies and generate a high false alarm rate and false-negative rate in classifying network traffic. To this end, this paper proposes an efficient Multistage Spectrogram image-based network Anomaly Detection System (MS-ADS) using a deep convolution neural network that utilizes a short-time Fourier Transform to transform flow features into spectrogram images. The results demonstrate that the proposed method achieves high detection accuracy of 99.98% with a reduction in the false alarm rate to 0.006% in classifying network traffic. Also, the proposed scheme improves predicting the anomaly instances by 0.75% to 4.82%, comparing the benchmark methodologies to exhibit its efficiency for the IoT network. To minimize the computational and training cost for the model re-training phase, the proposed solution demonstrates that only 40500 network flows from the dataset suffice to achieve a detection accuracy of 99.5%

Empirical Evaluation of Pre-Trained Deep Learning Networks for Pneumonia Detection

Pneumonia is a significant global health issue, characterized by a substantial mortality risk, impacting a vast number of individuals on a global scale. The quick and precise identification of pneumonia is crucial for the optimal treatment and management of this condition. This research work aims to answer the pressing need for precise diagnostic methods by using two advanced deep learning models, namely VGG19 and ResNet50, for the purpose of pneumonia detection in chest X-ray pictures. Furthermore, the present area of research is on the use of deep learning methodologies in the domain of medical image analysis, namely in the identification of pneumonia cases via the examination of chest X-ray images. The study challenge pertains to the pressing need for accurate and automated pneumonia diagnosis to assist healthcare professionals in making timely and educated judgements. The VGG19 and ResNet50 models were trained and assessed using the comprehensive RSNA Pneumonia dataset. In order to enhance their performance in the classification of chest X-ray pictures as either normal or pneumonia-affected, the models underwent rigorous training and meticulous fine-tuning. Based on the results obtained from our investigation, it was seen that the VGG19 model exhibited a notable accuracy rate of 93\%, surpassing the ResNet50 model's accuracy of 84\%. Furthermore, it is worth noting that both models demonstrated a notable level of precision, recall, and f1-scores in the identification of normal and pneumonia patients. This indicates their potential for accurately classifying such instances. Furthermore, our research findings indicate that deep learning models, namely VGG19, have a high level of efficacy in reliably detecting pneumonia via the analysis of chest X-ray pictures. These models has the capacity to function as helpful tools for expediting and ensuring the precise identification of pneumonia by healthcare practitioners

A Spectrogram Image-Based Network Anomaly Detection System Using Deep Convolutional Neural Network

The dynamics of computer networks have changed rapidly over the past few years due to a tremendous increase in the volume of the connected devices and the corresponding applications. This growth in the network’s size and our dependence on it for all aspects of our life have therefore resulted in the generation of many attacks on the network by malicious parties that are either novel or the mutations of the older attacks. These attacks pose many challenges for network security personnel to protect the computer and network nodes and corresponding data from possible intrusions. A network intrusion detection system (NIDS) can act as one of the efficient security solutions by constantly monitoring the network traffic to secure the entry points of a network. Despite enormous efforts by researchers, NIDS still suffers from a high false alarm rate (FAR) in detecting novel attacks. In this paper, we propose a novel NIDS framework based on a deep convolution neural network that utilizes network spectrogram images generated using the short-time Fourier transform. To test the efficiency of our proposed solution, we evaluated it using the CIC-IDS2017 dataset. The experimental results have shown about 2.5% - 4% improvement in accurately detecting intrusions compared to other deep learning (DL) algorithms while at the same time reducing the FAR by 4.3%-6.7% considering binary classification scenario. We also observed its efficiency for a 7-class classification scenario by achieving almost 98.75% accuracy with 0.56% - 3.72% improvement compared to other DL methodologies

Microbial biotransformation of beclomethasone dipropionate by Aspergillus niger

Na pesquisa presente o fármaco esteróide antiasmático dipropionato de beclometasona foi submetido à biotransformação microbiana pelo Aspergillus niger. O dipropionato de beclometasona foi transformado, pela primeira vez, em metabólitos variados por biotransformação microbiana. Novos metabólitos do fármaco produzidos podem agir como novas moléculas potenciais e podem substituir os fármacos antigos em questão de segurança, eficácia e mínima resistência. Eles foram purificados por cromatografia em camada delgada preparativa e as suas estruturas foram elucidadas usando técnicas espectroscópicas modernas, como 13C NMR, 1H NMR; HMQC; HMQC; COSY, NOESY e espectrometria de massas, por exemplo, EI-MS. Purificaram-se quatro metabólitos, denominados (i) 17-monopropionato de beclometasona; (ii) 21-monopropionato de beclometasona: (iii) beclometasona e (iv) 21-propionato de 9beta,11beta-epoxi-17,21-diidroxi-16beta-metilpregna-1,4-dieno-3,20-diona.In the present research, the steroidal anti-asthmatic drug beclomethasone dipropionate was subjected to microbial biotransformation by Aspergillus niger. Beclomethasone dipropionate was transformed into various metabolites first time from microbial transformation. New drug metabolites produced can act as new potential drug molecules and can replace the old drugs in terms of safety, efficacy, and least resistance. They were purified by preparative thin layer chromatography technique, and their structures were elucidated using modern spectroscopic techniques, such as 13C NMR, 1H NMR, HMQC, HMQC, COSY, and NOESY, and mass spectrometry, such as EI-MS. Four metabolites were purified: (i) beclomethasone 17-monopropionate, (ii) beclomethasone 21-monopropionate, (iii) beclomethasone, and (iv) 9beta,11beta-epoxy-17,21-dihydroxy-16beta-methylpregna-1,4-diene-3,20-dione 21-propionate