Role of miRNAs in Human T Cell Leukemia Virus Type 1 Induced T Cell Leukemia: A Literature Review and Bioinformatics Approach

Human T cell leukemia virus type 1 (HTLV-1) was identified as the first pathogenic human retrovirus and is estimated to infect 5 to 10 million individuals worldwide. Unlike other retroviruses, there is no effective therapy to prevent the onset of the most alarming diseases caused by HTLV-1, and the more severe cases manifest as the malignant phenotype of adult T cell leukemia (ATL). MicroRNA (miRNA) dysfunction is a common feature of leukemogenesis, and it is no different in ATL cases. Therefore, we sought to analyze studies that reported deregulated miRNA expression in HTLV-1 infected cells and patients’ samples to understand how this deregulation could induce malignancy. Through in silico analysis, we identified 12 miRNAs that stood out in the prediction of targets, and we performed functional annotation of the genes linked to these 12 miRNAs that appeared to have a major biological interaction. A total of 90 genes were enriched in 14 KEGG pathways with significant values, including TP53, WNT, MAPK, TGF-β, and Ras signaling pathways. These miRNAs and gene interactions are discussed in further detail for elucidation of how they may act as probable drivers for ATL onset, and while our data provide solid starting points for comprehension of miRNAs’ roles in HTLV-1 infection, continuous effort in oncologic research is still needed to improve our understanding of HTLV-1 induced leukemia.     

Automatic symbolic derivation of order conditions for two-stage complex Rosenbrock schemes

Rosenbrock methods are known to be good in numerical modeling of stiff problems. In this work, a new symbolic algorithm is proposed for automating the derivation of order conditions for two-stage Rosenbrock schemes with complex coefficients. Software implementation of the proposed algorithm is discussed in detail.     

Automatic verification of reduction techniques in Higher Order Logic

In this paper we propose an automatic methodology to verify the soundness of model checking reduction techniques. The idea is to use the consistency of the specifications to verify if the reduced model is faithful to the original one. The user provides the reduction technique, the specification and the system under verification. Then, using Higher Order Logic he verifies automatically if the reduction technique is soundly applied. The method is completely defined in an MDG–HOL special integration platform that combines an automatic high level model checking tool Multiway Decision Graphs (MDGs) within the HOL theorem prover. We provide two case studies, the first one is the reduction using SAT–MDG of an Island Tunnel Controller and the second one is the MDG–HOL assume-guarantee reduction of the Look-Aside Interface. The obtained results of our approach offer a considerable gain in terms of the correctness of heuristics and reduction techniques as applied to commercial model checking, however a small penalty is paid in terms of CPU time and memory usage.     

The Role of Antibodies in the Treatment of SARS-CoV-2 Virus Infection,and Evaluating Their Contribution to Antibody-Dependent Enhancement of Infection

Antibodies play a crucial role in the immune response, in fighting off pathogens as well as helping create strong immunological memory. Antibody-dependent enhancement (ADE) occurs when non-neutralising antibodies recognise and bind to a pathogen, but are unable to prevent infection, and is widely known and is reported as occurring in infection caused by several viruses. This narrative review explores the ADE phenomenon, its occurrence in viral infections and evaluates its role in infection by SARS-CoV-2 virus, which causes coronavirus disease 2019 (COVID-19). As of yet, there is no clear evidence of ADE in SARS-CoV-2, though this area is still subject to further study.     

Enhanced frontier-based exploration for indoor environment with multiple robots

In this paper, the exploration and map-building of unknown environment by a team of mobile robots is intensively investigated. A new exploration technique is proposed to increase the exploration efficiency. In particular, the new technique has two main objectives: firstly, it aims at reducing the exploration time and the traveled distance by reducing the overlap which takes place when a certain area in the environment is explored by more than one robot. To achieve this, a new procedure to assign the next target location for each individual robot is proposed. And secondly, it aims at reducing computations complexity required by target selection and path planning tasks. More importantly, the proposed technique obviates the need for environment segmentation complex procedures which is adopted in some previous important research works. The new technique is intensively tested with different environments. The results showed the effectiveness of the proposed technique.     

6D μ cooling using a solenoid-dipole ring cooler for a muon collider

Six dimensional cooling of large emittance μ⁺ and μ⁻ beams is required in order to obtain the desired luminosity for a muon collider. We propose to use a ring cooler that employs both dipoles and solenoids with the additional requirement that the arcs of the ring be achromatic. We describe the lattice and the beam dynamics of the proposed ring, and demonstrate that the lattice gives substantial cooling in all 6 phase space dimensions.     

Processor control in computerized data-acquisition systems

A method is proposed for detecting single and double processor errors during arithmetic and logicaloperations with reduced time consumption.     

Artificial neural network application of modeling failure rate for Boeing 737 tires

This paper presents an application of artificial neural network (ANN) technique for conducting the reliability analysis of Boeing 737 tires. For this purpose, an ANN model utilizing the feed‐forward back‐propagation algorithm as a learning rule is developed. The inputs to the neural network are the flight operational time and the number of landings as independent variables and the output is the failure rate of the tires. Two years of data are used for failure rate prediction model and validation. Model validation, which reflects the suitability of the model for future predictions, is performed by comparing the predictions of the model with that of Weibull regression model. The results show that the failure rate predicted by the ANN is closer in agreement with the actual data than the failure rate predicted by the Weibull model. The present work also identifies some of the common tire failures and presents representative results based on the established model for the most frequently occurring tire failure. Copyright © 2010 John Wiley & Sons, Ltd.     

Tuning fuzzy PD and PI controllers using reinforcement learning

In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi–Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen’s implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.     

Real-time quality authentication of honey using atmospheric pressure chemical ionisation mass spectrometry (APCI-MS)

The aim of this study was to use gas chromatography-mass spectrometry (GC-MS) and APCI-MS techniques to detect adulteration in honey. The key volatile compounds in the headspace of the adulterated honey were marked by GC-MS and their representative fragment ions were utilised in scanning honey samples using the real-time APCI-MS system. The PLS models validated using independent data sets resulted in coefficient of the determination () of 0.97 and 0.96 and root mean square error in prediction (RMSEP) of 2.62 and 2.45 for the GC-MS and APCI-MS data sets respectively. The most efficient volatiles from GC-MS analysis and their corresponding fragment ions m/z from APCI-MS data analysis were then identified and used to develop new PLS models to predict the level of adulteration. The best PLS model gave of 0.95 and RMEP of 2.60% in the independent validation set indicating that the model was very accurate in predicting the level of adulteration.