Mathematical model of immune response to hepatitis B

A new detailed mathematical model for dynamics of immune response to hepatitis B is proposed, which takes into account contributions from innate and adaptive immune responses, as well as cytokines. Stability analysis of different steady states is performed to identify parameter regions where the model exhibits clearance of infection, maintenance of a chronic infection, or periodic oscillations. Effects of nucleoside analogues and interferon treatments are analysed, and the critical drug efficiency is determined

Effects of Viral and cytokine delays on dynamics of autoimmunity

A major contribution to the onset and development of autoimmune disease is known to come from infections. An important practical problem is identifying the precise mechanism by which the breakdown of immune tolerance as a result of immune response to infection leads to autoimmunity. In this paper, we develop a mathematical model of immune response to a viral infection, which includes T cells with different activation thresholds, regulatory T cells (Tregs), and~a cytokine mediating immune dynamics. Particular emphasis is made on the role of time delays associated with the processes of infection and mounting the immune response. Stability analysis of various steady states of the model allows us to identify parameter regions associated with different types of immune behaviour, such as, normal clearance of infection, chronic infection, and autoimmune dynamics. Numerical simulations are used to illustrate different dynamical regimes, and to identify basins of attraction of different dynamical states. An important result of the analysis is that not only the parameters of the system, but also the initial level of infection and the initial state of the immune system determine the progress and outcome of the dynamics

Development of Artificial Intelligence Approach to Nowcasting and Forecasting Oyster Norovirus Outbreaks along the U.S. Gulf Coast

Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide. This study presents an Artificial Intelligence (AI)-based approach to identifying the primary cause of oyster norovirus outbreaks, nowcasting and forecasting the growing risk of oyster norovirus outbreaks in coastal waters. AI models were developed using Artificial Neural Networks (ANNs) and Genetic Programming (GP) methods and time series of epidemiological and environmental data. Input variable selection techniques, including Random Forests (RF) and Forwards Binary Logistic Regression (FBLR), were used to identify the significant model input variables among six independent environmental predictors including water temperature, solar radiation, gage height, salinity, wind, and rainfall and various combinations of the variables with different time lags. In terms of nowcasting, a risk-based GP model was developed to nowcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast, showing the true positive and negative rates of 78.53% and 88.82%, respectively. In terms of forecasting, an ANN model, called ANN-2Day, was presented. The forecasting model was capable of reproducing all historical oyster norovirus outbreaks with the true positive and negative rates of 100.00% and 99.84%, respectively. The sensitivity analysis results of the ANN-2Day model further indicated that oyster norovirus outbreaks were generally linked to the extreme combination of antecedent environmental conditions characterized by low water temperature, low solar radiation, low gage height, low salinity, strong wind, and heavy precipitation. In addition to the GP and ANN-2Day models, a remote sensing–based model was constructed using MODIS Aqua level 2 products. The remote sensing-based model enabled oyster management authorities to expand the prediction of norovirus outbreak risks from areas where monitoring data were accessible to other oyster harvest areas where monitoring stations are not available. In conclusion, the developed AI models enables public health agencies and oyster harvesters to better plan for management interventions and thus makes it possible to achieve a paradigm shift of their daily management and operation from primarily reacting to epidemic incidents of norovirus infection after they have occurred to eliminating (or at least reducing) the risk of costly incidents

Hybrid modeling and prediction of oyster norovirus outbreaks

This paper presents a hybrid model for predicting oyster norovirus outbreaks by combining the Artificial Neural Networks (ANNs) and Principal Component Analysis (PCA) methods and using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote-sensing data. Specifically, 10 years (2007-2016) of cloud-free MODIS Aqua data for water leaving reflectance and environmental data were extracted from the center of each oyster harvest area. Then, the PCA was utilized to compress the size of the MODIS Aqua data. An ANN model was trained using the first 4 years of the data from 2007 to 2010 and validated using the additional 6 years of independent datasets collected from 2011 to 2016. Results indicated that the hybrid PCA-ANN model was capable of reproducing the 10 years of historical oyster norovirus outbreaks along the Northern Gulf of Mexico coast with a sensitivity of 72.7% and specificity of 99.9%, respectively, demonstrating the efficacy of the hybrid model

Radix-3 NTT-Based Polynomial Multiplication for Lattice-Based Cryptography

The lattice-based cryptography is considered a strong candidate amongst many other proposed quantum-safe schemes for the currently deployed asymmetric cryptosystems that do not seem to stay secure when quantum computers come into play. Lattice-based algorithms possess a time-consuming operation of polynomial multiplication. As it is relatively the highest time-consuming operation in lattice-based cryptosystems, one can obtain fast polynomial multiplication by using number theoretic transform (NTT). In this paper, we focus on and develop a radix-3 NTT polynomial multiplication and compute its computational complexity. In addition, utilizing the ring structure, we propose two parameter sets of CRYSTALS-KYBER, one of the four round-three finalists in the NIST Post-Quantum Competition

Performance enhancement of thin‐film composite membranes in water desalination process by wood sawdust

In this study, polysulfone/wood sawdust (PSf/WSD) mixed matrix membrane (MMM) was prepared as a novel substrate layer of thin‐film composite (TFC) membrane in water desalination. The main aim was to evaluate how different amounts of WSD (0‐5 wt%) and PSf concentrations (12‐16 wt%) in the porous substrate affect the properties of the final TFC membranes in the separation of organic and inorganic compounds. Morphological and wettability studies demonstrated that the addition of small amount of WSD (less than or equal to 1 wt%) in the casting solution resulted in more porous but similar hydrophobic substrates, while high loading (greater than or equal to 2 wt%) of WSD not only changed the substrate wettability and morphology but also increased and decreased the swelling and mechanical properties of substrate layer. Therefore, PA layer formed thereon displayed extensively varying film morphology, interfacial properties, and separation performance. Based on approximately stable permeate flux (ASPF) and apparent salt rejection efficiency (ASRE), the best TFC membrane was prepared over the substrate with 12 to 14 wt% of PSf and around 0.5 to 1 wt% of WSD. Although notable improvements in permeate flux were obtained by adding a small amount of sawdust, the results clearly indicate that the salt rejection mechanism of TFC membrane was different from the glycerin rejection mechanism. Furthermore, durability results of TFC membranes showed that in continuous operation for 30 days, TFC‐14/0.5 and TFC‐14/01 have the maximum plateau levels of stable permeate flux and salt rejection among the all TFC membranes.Post-print / Final draf