Environmentally Responsible Bioengineering for Spore Surface Expression of <em>Helicobacter pylori </em>Antigen

The development of genetic technologies and bioengineering are creating an increasing number of genetically engineered microorganisms with new traits for diverse industrial applications such as vaccines, drugs and pollutant degraders. However, the destiny of genetically engineered bacterial spores released into the environment as long-life organisms has remained a big environmental challenge. In this study, an environmentally responsible and sustainable gene technology solution based on the concept of thymine starvation is successfully applied for cloning and expression of a Helicobacter pylori antigen on Bacillus subtilis spore surface. As an example, a recombinant Bacillus subtilis strain A1.13 has been created from a gene fusion of the corresponding N-terminal fragment of spore coat protein CotB in B. subtilis and the entire urease subunit A (UreA) in H. pylori and the fusion showed a high stability of spore surface expression. The outcomes can open the door for developing highly safe spore vectored vaccines against this kind of pathogen and contributing to reduced potential risks of genetically engineered microorganisms released in the environment

A modified dual-population approach for solving multi-objective problems

Maintaining the balance between convergence and diversity plays a vital role in multi-objective evolutionary algorithms (MOEAs). However, most MOEAs cannot reach a satisfying balance, especially when solving problems having complicated pareto optimal sets. In this paper, we present a modified cooperative co-evolution approach for achieving better convergence and diversity simultaneously (namely DPP2). In DPP2, while populations are trying to achieve both criteria, the priority being set for these criteria will be different. One population focuses on achieving better convergence (by using pareto-based ranking scheme), while the other is for ensuring the population diversity (by using the decomposition-based method). After that, we use a cooperation mechanism to integrate the two populations and create a new combined population with hopes of having both characteristics (i.e. converged and diverse). Performance of DPP2 is examined on the well-known benchmarks of multiobjective optimization problems (MOPs) using the hypervolume (HV), the generational distance (GD), the inverted generational distance (IGD) metrics. In comparison with the original version DPP algorithm, experimental results indicated that DPP2 can significantly outperform DPP on the benchmark problems with stable results

A competitive co-evolutionary approach for the multi-objective evolutionary algorithms

In multi-objective evolutionary algorithms (MOEAs), convergence and diversity are two basic issues and keeping a balance between them plays a vital role. There are several studies that have attempted to address this problem, but this is still an open challenge. It is thus the purpose of this research to develop a dual-population competitive co-evolutionary approach to improving the balance between convergence and diversity. We utilize two populations to solve separate tasks. The first population uses Pareto-based ranking scheme to achieve better convergence, and the second one tries to guarantee population diversity via the use of a decomposition-based method. Next, by operating a competitive mechanism to combine the two populations, we create a new one with a view to having both characteristics (i.e. convergence and diversity). The proposed method’s performance is measured by the renowned benchmarks of multi-objective optimization problems (MOPs) using the hypervolume (HV) and the inverted generational distance (IGD) metrics. Experimental results show that the proposed method outperforms cutting-edge coevolutionary algorithms with a robust performance

A hybrid constructed wetland for organic-material and nutrient removal from sewage: Process performance and multi-kinetic models

© 2018 Elsevier Ltd A pilot-scale hybrid constructed wetland with vertical flow and horizontal flow in series was constructed and used to investigate organic material and nutrient removal rate constants for wastewater treatment and establish a practical predictive model for use. For this purpose, the performance of multiple parameters was statistically evaluated during the process and predictive models were suggested. The measurement of the kinetic rate constant was based on the use of the first-order derivation and Monod kinetic derivation (Monod) paired with a plug flow reactor (PFR) and a continuously stirred tank reactor (CSTR). Both the Lindeman, Merenda, and Gold (LMG) analysis and Bayesian model averaging (BMA) method were employed for identifying the relative importance of variables and their optimal multiple regression (MR). The results showed that the first-order–PFR (M2) model did not fit the data (P > 0.05, and R2 0.5). The pollutant removal rates in the case of M1 were 0.19 m/d (CODCr) and those for M3 were 25.2 g/m2∙d for CODCr and 2.63 g/m2∙d for NH4-N. By applying a multi-variable linear regression method, the optimal empirical models were established for predicting the final effluent concentration of five days' biochemical oxygen demand (BOD5) and NH4-N. In general, the hydraulic loading rate was considered an important variable having a high value of relative importance, which appeared in all the optimal predictive models

Approaches for Efficiently Detecting Frontier Cells in Robotics Exploration.

Many robot exploration algorithms that are used to explore office, home, or outdoor environments, rely on the concept of frontier cells. Frontier cells define the border between known and unknown space. Frontier-based exploration is the process of repeatedly detecting frontiers and moving towards them, until there are no more frontiers and therefore no more unknown regions. The faster frontier cells can be detected, the more efficient exploration becomes. This paper proposes several algorithms for detecting frontiers. The first is called Naïve Active Area (NaïveAA) frontier detection and achieves frontier detection in constant time by only evaluating the cells in the active area defined by scans taken. The second algorithm is called Expanding-Wavefront Frontier Detection (EWFD) and uses frontiers from the previous timestep as a starting point for searching for frontiers in newly discovered space. The third approach is called Frontier-Tracing Frontier Detection (FTFD) and also uses the frontiers from the previous timestep as well as the endpoints of the scan, to determine the frontiers at the current timestep. Algorithms are compared to state-of-the-art algorithms such as Naïve, WFD, and WFD-INC. NaïveAA is shown to operate in constant time and therefore is suitable as a basic benchmark for frontier detection algorithms. EWFD and FTFD are found to be significantly faster than other algorithms

Genomic and vaccine preclinical studies reveal a novel mouse-adapted Helicobacter pylori model for the hpEastAsia genotype in Southeast Asia

\ua9 2024 Crown Copyright.Introduction. Helicobacter pylori infection is a major global health concern, linked to the development of various gastrointestinal diseases, including gastric cancer. To study the pathogenesis of H. pylori and develop effective intervention strategies, appropriate animal pathogen models that closely mimic human infection are essential. Gap statement. This study focuses on the understudied hpEastAsia genotype in Southeast Asia, a region marked by a high H. pylori infection rate. No mouse-adapted model strains has been reported previously. Moreover, it recognizes the urgent requirement for vaccines in developing countries, where overuse of antimicrobials is fuelling the emergence of resistance. Aim. This study aims to establish a novel mouse-adapted H. pylori model specific to the hpEastAsia genotype prevalent in Southeast Asia, focusing on comparative genomic and histopathological analysis of pathogens coupled with vaccine preclinical studies. Methodology. We collected and sequenced the whole genome of clinical strains of H. pylori from infected patients in Vietnam and performed comparative genomic analyses of H. pylori strains in Southeast Asia. In parallel, we conducted preclinical studies to assess the pathogenicity of the mouse-adapted H. pylori strain and the protective effect of a new spore-vectored vaccine candidate on male Mlac:ICR mice and the host immune response in a female C57BL/6 mouse model. Results. Genome sequencing and comparison revealed unique and common genetic signatures, antimicrobial resistance genes and virulence factors in strains HP22 and HP34; and supported clarithromycin-resistant HP34 as a representation of the hpEastAsia genotype in Vietnam and Southeast Asia. HP34-infected mice exhibited gastric inflammation, epithelial erosion and dysplastic changes that closely resembled the pathology observed in human H. pylori infection. Furthermore, comprehensive immunological characterization demonstrated a robust host immune response, including both mucosal and systemic immune responses. Oral vaccination with candidate vaccine formulations elicited a significant reduction in bacterial colonization in the model. Conclusion. Our findings demonstrate the successful development of a novel mouse-adapted H. pylori model for the hpEastAsia genotype in Vietnam and Southeast Asia. Our research highlights the distinctive genotype and pathogenicity of clinical H. pylori strains in the region, laying the foundation for targeted interventions to address this global health burden

Deep Nested Clustering Auto-Encoder for Anomaly-Based Network Intrusion Detection

Anomaly-based intrusion detection system(AIDS) plays an increasingly important role in detecting complex,multi-stage network attacks, especially zero-day attacks. Although there have been improvements both in practical applications and the research environment, there are still many unresolved accuracy-related concerns. The two fundamental limitations that contribute to these concerns are: i) the succinct, concise, latent representation learning of the normal network data, and ii) the optimization volume of normal regions in latent space. Recent studies have suggested many ways to learn the latent representation of normal network data in a semi-supervised manner to construct AIDS. However, these approaches are still affected by the above limitations,mainly due to the inability to process high data dimensionality or ineffectively explore the underlying architecture of the data. In this paper, we propose a novel Deep Nested Clustering Auto Encoder (DNCAE ) model to thoroughly overcome the aforementioned difficulties and improve the performance o fnetwork attack detection. The proposed model consists of two nested Deep Auto-Encoders(DAE) to learn the informative and tighter data representation space. In addition, the DNCAE model integrates the clustering technique into the latent layer of the outer DAE to learn the optimal arrangement of datapoints in the latent space. This harmonious combination allows us to effectively deal with the limitations outlined. The performance of the proposed model is evaluated using standard datasets including NSL-KDD,UNSW-NB15, and six scenarios of CIC-IDS2017(Tuesday, Wednesday, Thursday-Morning, Friday-Morning, Friday-Afternoon Port Scan,Friday-Afternoon DDoS).The experimental results strongly confirm that the proposed model clearly out performs th baselines and the existing methods for network anomaly detection. IndexTerms—Latent Representation, DeepAuto-Encoder, Clustering, AnomalyDetection, Intrusion Detection Syste

Marine Scientific Research in the South China Sea

The research project aims to identify options for multilateral marine science research (MSR) mechanisms in South China Sea that could be piloted and discussed with ASEAN partners. The project will enable the UK to expand engagement with ASEAN as a partner of choice for expertise on maritime issues

3D Characterisation of Dry Powder Inhaler Formulations: Developing X-ray Micro Computed Tomography Approaches.

Carrier-based dry powder inhaler (DPI) formulations need to be accurately characterised for their particle size distributions, surface roughnesses, fines contents and flow properties. Understanding the micro-structure of the powder formulation is crucial, yet current characterisation methods give incomplete information. Commonly used techniques like laser diffraction (LD) and optical microscopy (OM) are limited due to the assumption of sphericity and can give variable results depending on particle orientation and dispersion. The aim of this work was to develop new powder analytical techniques using X-ray computed tomography (XCT) that could be employed for non-destructive metrology of inhaled formulations. α-lactose monohydrate powders with different characteristics have been analysed, and their size and shape (sphericity/aspect ratio) distributions compared with results from LD and OM. The three techniques were shown to produce comparable size distributions, while the different shape distributions from XCT and OM highlight the difference between 2D and 3D imaging. The effect of micro-structure on flowability was also analysed through 3D measurements of void volume and tap density. This study has demonstrated for the first time that XCT provides an invaluable, non-destructive and analytical approach to obtain number- and volume-based particle size distributions of DPI formulations in 3D space, and for unique 3D characterisation of powder micro-structure