A typing scheme for neisseria gonorrhoeae

Strathclyde theses - ask staff. Thesis no. : T3809Growth of gonococci in vitro was studied during an attempt to find a typing scheme for epidemiological purposes. Many strains were found to be capable of remaining viable for several weeks in a liquid medium formulated particularly to prolong survival in laboratory cultures. Gonococcal strains were found to survive even longer when they were kept in liquid culture media at 30°C which is known to be their minimal growth temperature. The feasibility of using gonococcal bacteriocins (the gonocins) in typing gonococci was investigated, and was found to be impracticable, because some inhibitory short-chain fatty acids were observed to interfere with, and to dominate, the inhibitory activity of gonocins. These fatty acids were found by gas-liquid chromatographic analysis mainly to be acetic acid and isovaleric acid. When these two acids were added to uninoculated media in concentrations equivalent to what was produced during the metabolic activity of gonococci, some gonococcal strains were inhibited on that media. The Colicins of Shigella sonnei were found to be possible substitutes for gonocins. Ten Colicin Type strains inhibited gonococci selectively. Thus it was possible to divide ninety two strains into groups on the basis of their sensitivity to the colicins. Certain limitations made it difficult to evaluate this typing scheme fully. Yet some indication was given that it could be a useful tool for epidemiological studies. In spite of the high stability of colicinogeny of Shigella sonnei strains, some inhibitory by-products might accumulate in the medium when the Colicin Type strains were incubated for four days. In this way the inhibitory activity of colicins against gonococci might increase. But by gas-liquid chromatographic analysis it was found that the by-products, namely acetic and propionic acids, which were produced after incubating the Colicin Type strains for one day were not adequate to cause misinterpretation of the activity of colicins. These by-products however, might accumulate after prolonged incubation and reach an inhibitory concentration which interfered with the colicin activity. It was demonstrated that Colicin Type strains used in the typing scheme should be incubated for not longer than one day.Growth of gonococci in vitro was studied during an attempt to find a typing scheme for epidemiological purposes. Many strains were found to be capable of remaining viable for several weeks in a liquid medium formulated particularly to prolong survival in laboratory cultures. Gonococcal strains were found to survive even longer when they were kept in liquid culture media at 30°C which is known to be their minimal growth temperature. The feasibility of using gonococcal bacteriocins (the gonocins) in typing gonococci was investigated, and was found to be impracticable, because some inhibitory short-chain fatty acids were observed to interfere with, and to dominate, the inhibitory activity of gonocins. These fatty acids were found by gas-liquid chromatographic analysis mainly to be acetic acid and isovaleric acid. When these two acids were added to uninoculated media in concentrations equivalent to what was produced during the metabolic activity of gonococci, some gonococcal strains were inhibited on that media. The Colicins of Shigella sonnei were found to be possible substitutes for gonocins. Ten Colicin Type strains inhibited gonococci selectively. Thus it was possible to divide ninety two strains into groups on the basis of their sensitivity to the colicins. Certain limitations made it difficult to evaluate this typing scheme fully. Yet some indication was given that it could be a useful tool for epidemiological studies. In spite of the high stability of colicinogeny of Shigella sonnei strains, some inhibitory by-products might accumulate in the medium when the Colicin Type strains were incubated for four days. In this way the inhibitory activity of colicins against gonococci might increase. But by gas-liquid chromatographic analysis it was found that the by-products, namely acetic and propionic acids, which were produced after incubating the Colicin Type strains for one day were not adequate to cause misinterpretation of the activity of colicins. These by-products however, might accumulate after prolonged incubation and reach an inhibitory concentration which interfered with the colicin activity. It was demonstrated that Colicin Type strains used in the typing scheme should be incubated for not longer than one day

Decision-making for the demolition or adaptation of buildings

This paper considers why the decision may be made either to demolish or adapt existing buildings on brownfield sites and compares real-life decisions to those produced by theoretical design-support tools. Five case studies, including three individual buildings and two master plan sites of multiple buildings, were investigated through interviews with stakeholders. Reasons for retention included heritage value, architectural quality and government incentives, while reasons for demolition included maximising land value, lack of architectural significance and poor building condition. The analysis showed that the theoretical tools were useful for their intended purpose of analysing a portfolio of assets but that they could be improved by providing higher weightings for heritage values and extending the tools to assess different end uses and forms of adaptation. By testing the tools on master plan sites, the paper also identifies urban design variables, such as land efficiency, which would need to be incorporated for this purpose.The authors gratefully acknowledge the Engineering and Physical Sciences Research Council (EPSRC) for funding this research through the EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment (EPSRC grant reference number EP/L016095/1)

Security-oriented cloud computing platform for critical infrastructures

The rise of virtualisation and cloud computing is one of the most significant features of computing in the last 10 years. However, despite its popularity, there are still a number of technical barriers that prevent it from becoming the truly ubiquitous service it has the potential to be. Central to this are the issues of data security and the lack of trust that users have in relying on cloud services to provide the foundation of their IT infrastructure. This is a highly complex issue, which covers multiple inter-related factors such as platform integrity, robust service guarantees, data and network security, and many others that have yet to be overcome in a meaningful way. This paper presents a concept for an innovative integrated platform to reinforce the integrity and security of cloud services and we apply this in the context of Critical Infrastructures to identify the core requirements, components and features of this infrastructure

Using deep learning models for learning semantic text similarity of Arabic questions

Question-answering platforms serve millions of users seeking knowledge and solutions for their daily life problems. However, many knowledge seekers are facing the challenge to find the right answer among similar answered questions and writer’s responding to asked questions feel like they need to repeat answers many times for similar questions. This research aims at tackling the problem of learning the semantic text similarity among different asked questions by using deep learning. Three models are implemented to address the aforementioned problem: i) a supervised-machine learning model using XGBoost trained with pre-defined features, ii) an adapted Siamese-based deep learning recurrent architecture trained with pre-defined features, and iii) a Pre-trained deep bidirectional transformer based on BERT model. Proposed models were evaluated using a reference Arabic dataset from the mawdoo3.com company. Evaluation results show that the BERT-based model outperforms the other two models with an F1=92.99%, whereas the Siamese-based model comes in the second place with F1=89.048%, and finally, the XGBoost as a baseline model achieved the lowest result of F1=86.086%

A transfer learning with deep neural network approach for diabetic retinopathy classification

Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural net- work (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently

Magec: An image searching tool for detecting forged images in forensic investigation

© 2016 IEEE. Manipulation of digital images for the purpose of forgery is a rapidly growing phenomenon that poses a challenge for cyber-crime investigators. Distinguishing original images from duplicates and the number of original copies within the same media are some examples of challenges presented by duplicate digital images. In this paper, we present a new image-searching tool called, Magec, to detect duplicate image(s) on digital media, using the original image modification attributes as a signature. First, we describe the tool and the methods used to detect duplicate images, then we evaluate the tool\u27s performance based on the number of folders it searches and the number of files it searches for. Later, we present the analysis of the tool using different operating system attributes. The goal is to find copies of the same object that is hidden; compressed images, or images saved with different attributes and demonstrates which one is the original image and thereby deduce which ones are copies. This research helps in better utilization of small/limited capacity devices, where limited storage capacity may be a problem. The experimental results prove that the presented search tool provides faster and accurate results. Finally, the conducted tests on the Magec tool analyzed, and verified, and the results are presented alongside with challenges identified

Optimizing Project Delivery through Augmented Reality and Agile Methodologies

The construction sector, which has a long history to use visualisation to envisage proposed designs and project delivery, is beginning to see the benefits of augmented reality and agile project management methodologies. This study investigated the benefits of augmented reality and agile project management methodologies. Convergent design method was considered valuable and the most straightforward for this study, as different types of quantitative and qualitative data were required to be collected and analysed. The participants drawn from the construction sector revealed a number of augmented and agile determinants that facilitated the delivery of construction and integration of project teams. The participants suggested that the proposed ARGILE framework increases client understanding of the tasks output, increases client involvement and collaboration with the project team. It was further established that the proposed ARGILE framework enhances project time management, embeds the client and empowers multidisciplinary team, increases collaboration and communication

Synthesis and Structural Determination of 6-O-prop-2-ynyl-1,2:3,4-di-O-Isopropylidene-α-D-Galactose

في هذا العمل، تم تحضير إثير ألكينيل سكر مهم في خطوتين متتاليتين بدءًا من سكر د-جالاكتوز المتاح تجاريًا. إن هذا النوع من المركبات يعد ذات أهمية في تحضير مركبات ذات فعالية حيوية مثل الترايازولات و الآيسوكسازولات. في الخطوة الأولى، تم تفاعل الجالاكتوز (3) مع الأسيتون في وجود كبريتات النحاس اللامائي (II) لإنتاج 2،1: 4،3-ثنائي- O- أيزوبروبليدين- د-ألفا- جالاكتوز (4) بمنتوج جيد. ثم تفاعل المركب الأخير مع زيادة من بروميد البروبارجيل في مذيب ثنائي مثيل فورمأمايد بوجود حبيبات هيدروكسيد الصوديوم لتعطي الجزيء المستهدف 5 في بمنتوج جيد جدًا. إن درجة حرارة هذه الخطوة تعد حاسمة في تحديد منتوج التفاعل. تم تحديد الوضعية الفراغية للمركب 5 بإستخدام تقنية الرنين المغناطيسي النووي وحسابات   نظرية الكثافة الوظيفيةDFT .In this work, an important sugar alkynyl ether has been synthesized in two subsequent steps starting from commercially available D-galactose (3). This kind of compounds is highly significant in the synthesis of biologically active molecules such as 1,2,3-triazole and isoxazoles. In the first step, galactose (3) was reacted with acetone in the presence of anhydrous copper (II) sulfate to produce 1,2:3,4-di-O-isopropylidene-α-D-galactose (4) in good yield. The latter was reacted with excess of 3-bromoprop-1-yne in DMF in the presence of NaOH pellets to afford the target molecule 5 in a very good yield. The temperature of this step is crucial in determining the reaction yield. The exact structure of compound 5 is identified using NMR technique and DFT calculations.

Transfer deep learning approach for detecting coronavirus disease in X-ray images

Currently, the whole world is fighting a very dangerous and infectious disease caused by the novel coronavirus, called COVID-19. The COVID-19 is rapidly spreading around the world due to its high infection rate. Therefore, early discovery of COVID-19 is crucial to better treat the infected person as well as to slow down the spread of this virus. However, the current solution for detecting COVID-19 cases including the PCR test, CT images, epidemiologically history, and clinical symptoms suffer from high false positive. To overcome this problem, we have developed a novel transfer deep learning approach for detecting COVID-19 based on x-ray images. Our approach helps medical staff in determining if a patient is normal, has COVID-19, or other pneumonia. Our approach relies on pre-trained models including Inception-V3, Xception, and MobileNet to perform two tasks: i) binary classification to determine if a person infected with COVID-19 or not and ii) a multi-task classification problem to distinguish normal, COVID-19, and pneumonia cases. Our experimental results on a large dataset show that the F1-score is 100% in the first task and 97.66 in the second task