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- Angewandte Computer‐ und Biowissenschaften (180) (remove)
In bioinformatics one important task is to distinguish between native and mirror protein models based on the structural information. This information can be obtained from the atomic coordinates of the protein backbone. This thesis tackles the problem of distinction of these conformations, looking at the statistics of the dihedral angles’ distribution regarding the protein backbone. This distribution is visualized in Ramachandran plots. By means of an interpretable machine learning classification method – Generalized Matrix Learning Vector Quantization – we are able to distinguish between native and mirror protein models with high accuracy. Further, the classifier model supplies supplementary information on the important distributional regions for distinction, like α-helices and β-strands.
Brassica oleracea like all crucifers plants have a defense mechanism against natural enemies, which are chemical compounds formed form the enzymatic degradation of glucosinolates. In the presence of epithiospecifier proteins (ESP), the hydrolysis of glucosinolates will form epithionitriles or nitriles depending on the glucosinolate structure, This research proved that three predicted sequences (ESP) taken from NCBI database has a role in the enzymatic hydrolysis of glucosinolates in Brassica oleracea.
The emerging Internet of Things (IoT) technology interconnects billions of embedded devices with each other. These embedded devices are internet-enabled, which collect, share, and analyze data without any human interventions. The integration of IoT technology into the human environment, such as industries, agriculture, and health sectors, is expected to improve the way of life and businesses. The emerging technology possesses challenges and numerous
security threats. On these grounds, it is a must to strengthen the security of IoT technology to avoid any compromise, which affects human life. In contrast to implementing traditional cryptosystems on IoT devices, an elliptic curve cryptosystem (ECC) is used to meet the limited resources of the devices. ECC is an elliptic curve-based public-key cryptography which provides equivalent security with shorter key size compared to other cryptosystems such as Rivest–Shamir–Adleman (RSA). The security of an ECC hinges on the hardness to solve the elliptic curve discrete logarithm problem (ECDLP). ECC is faster and easier to implement and also consumes less power and bandwidth. ECC is incorporated in internationally recognized standards for lightweight applications due to the
benefits ECC provides.
In the following study we evaluated capabilities of how a simple autoencoder can be used to trainGeneralized Learning Vector Quantization classifier. Specifically, we proved that the bottlenecks of an autoencoder serve as an "information filter" which tries to best represent the desired output in that particular layer in the statistical sense of mutual information.
Autoencoder model was trained for purely unsupervised task and leveraged the advantages by learning feature representations. As a result, the model got the significant value of the accuracy. Implementation and tuning of the model was carried out using Tensor Flow [1].
An extra study has been dedicated to improve traditional GLVQ algorithm taken from sklearn-lvg [2] using the bottleneck from an autoencoder.
The study has revealed potential of bottlenecks of an autoencoder as pre-processing tool in improving the accuracy of GLVQ. Specifically, the model was capable to identify 75% improvements of accuracy in GLVQ comparing to original one, which has about 62%. Consequently, the research exposed the need for further improvement of the model in the present problem case.
A Protein is a large molecule that consists of a vast number of atoms; one can only imagine the complexity of such a molecule. Protein is a series of amino acids that bind to each other to form specific sequences known as peptide chains. Proteins fold into three-dimensional conformations (or so-called protein’s native structure) to perform their functions. However, not every protein folds into a correct structure as a result of mutations occurring in their amino acid sequences. Consequently, this mutation causes many protein misfolding diseases. Protein folding is a severe problem in the biological field. Predicting changes in protein stability free energy in relation to the amino acid mutation (ΔΔG) aids to better comprehend the driving forces underlying how proteins fold to their native structures. Therefore, measuring the difference in Gibbs free energy provides more insight as to how protein folding occurs. Consequently, this knowledge might prove beneficial in designing new drugs to treat protein misfolding related diseases. The protein-energy profile aids in understanding the sequential, structural, and functional relationship, by assigning an energy profile to a protein structure. Additionally, measuring the changes in the protein-energy profile consequent to the mutation (ΔΔE) by using an approach derived from statistical physics will lead us to comprehend the protein structure thoroughly. In this work, we attempt to prove that ΔΔE values will be approximate to ΔΔG values, which can lead the future studies to consider that the energy profile is a good predictor of protein binding affinity as Gibbs free energy to solve the protein folding problem.
Probabilistic Micropayments
(2022)
Probabilistic micropayments are important cryptography research topics in electronic commerce. The Probabilistic micropayments have the potential to be researched in order to obtain efficient algorithms with low transaction costs and high speeding computer power. To delve into the topic, it is vital to scrutinize the cryptographic preliminaries such as hash functions and digital signatures. This thesis investigates the important probabilistic methods based on a centralized or decentralized network. Firstly, centralized networks such as lottery-based tickets, Payword, coin-flipping, and MR2 are described, and an approach based on blind signatures is also discussed. Then, decentralized network methods such as MICROPAY3, a transferable scheme on the blockchain network, along with an efficient model for cryptocurrencies, are explained. Then we compare the different probabilistic micropayment methods by improving their drawback with a new technique. To set the results from the theoretical analysis of different methods into some context, we analyze the attacks that reduce the security and, therefore, the system’s efficiency. Particularly, we discuss various methods for detecting double-spending and eclipse attacks occurrence
Digital data is rising day by day and so is the need for intelligent, automated data processing in daily life. In addition to this, in machine learning, a secure and accurate way to classify data is important. This holds utmost importance in certain fields, e.g. in medical data analysis. Moreover, in order to avoid severe consequences, the accuracy and reliability of the classification are equally important. So if the classification is not reliable, instead of accepting the wrongly classified data point, it is better to reject such a data point. This can be done with the help of some strategies by using them on top of a trained model or including them directly in the objective function of the desired training model. We discuss such strategies and analyze the results on data sets in this thesis.
The application described in this thesis has been created, built and designed to help nurses or any medical personnel all around the world in being able to access a real-time database to store patient records like Patient Name, Patient ID, Patient Age and Date of Birth, and the Symptoms that the patient is experiencing. A real-time database is a live database where all changes made to it are reflected across all devices accessing it. This application will be beneficial especially in countries where access to a computer or medical equipment is not always possible. A phone is always ready use and at the reach of the hand, users of this application will always be able to access the data at any given time and place. We will be able to add a new patient or search for existing patients. In addition, this application allows us to take RAW medical images that can be used to identify anomalies in the blood sample. RAW images are important for this application because they’re uncompressed, which means, they do not lose any quality or details. The users of this application are the medical personnel that will be taking care of the patients. These users will have to create a profile on the database in order to use the application, since their data, like user ID, will be used in order to control the behaviour of the data retrieved and stored. We will also discuss the current and future features of this application, as well as, the benefits of this application when it comes to the medical personnel, as well as patients. Finally, we will also go
over the implementation of such application from a hardware perspective, as well as a software one.
Die ubiquitäre, anthropogene Ausbreitung von pharmazeutischen Substanzen in der Umwelt, stellt ein weitreichendes Problem für alle Lebewesen dar. Hormone, die auf unterschiedlichsten Wegen in den aquatischen Lebensraumgelangen, sind unter anderem verantwortlich für das Fischsterben. Da sich die Anwesenheit dieser Stoffe allgemein und besonders bei Hormonen in der Umwelt nur auf geringe Konzentrationen begrenzt und sie dabei dennoch ein großes Risiko darstellen, sind geeignete Methoden zum Nachweis und zur Entfernung dieser Stoffe aus dem aquatischen System notwendig. Diezbezüglich sollen bereits etablierte biosensorische Detektionsmethoden unter Einsatz von Aptameren untersucht werden, um niedermolekulare Substanzen zu detektieren. Das Zielmolekül stellt dabei das Hormon 17ß-Estradiol, dass auf der Beobachtungsliste der EU Wasserrahmenrichtlinie steht und zu welchem bereits spezifische Aptamere veröffentlich sind, dar.