Interaction of Phytophthora cinnamomi with model and native plant species

Phytophthora cinnamomi causes plant disease globally and resistance is rare. Using biochemical, molecular and transcriptomic approaches and analyses, phytohormones were found to regulate disease. The pathogen was also found to produce specific proteins that facilitated host colonisation. The study thus provided a detailed understanding of how some plants survive infection.<br /

Multi-Scale CNN: An Explainable AI-Integrated Unique Deep Learning Framework for Lung-Affected Disease Classification

Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial to save lives, but the availability of testing equipment remains a challenge, often coupled with issues of reliability. Recent research has highlighted the potential of Chest X-Ray (CXR) images in identifying various lung diseases, including COVID-19, fibrosis, pneumonia, and more. In this comprehensive study, four publicly accessible datasets have been combined to create a robust dataset comprising 6650 CXR images, categorized into seven distinct disease groups. To effectively distinguish between normal and six different lung-related diseases (namely, bacterial pneumonia, COVID-19, fibrosis, lung opacity, tuberculosis, and viral pneumonia), a Deep Learning (DL) architecture called a Multi-Scale Convolutional Neural Network (MS-CNN) is introduced. The model is adapted to classify multiple numbers of lung disease classes, which is considered to be a persistent challenge in the field. While prior studies have demonstrated high accuracy in binary and limited-class scenarios, the proposed framework maintains this accuracy across a diverse range of lung conditions. The innovative model harnesses the power of combining predictions from multiple feature maps at different resolution scales, significantly enhancing disease classification accuracy. The approach aims to shorten testing duration compared to the state-of-the-art models, offering a potential solution toward expediting medical interventions for patients with lung-related diseases and integrating explainable AI (XAI) for enhancing prediction capability. The results demonstrated an impressive accuracy of 96.05%, with average values for precision, recall, F1-score, and AUC at 0.97, 0.95, 0.95, and 0.94, respectively, for the seven-class classification. The model exhibited exceptional performance across multi-class classifications, achieving accuracy rates of 100%, 99.65%, 99.21%, 98.67%, and 97.47% for two, three, four, five, and six-class scenarios, respectively. The novel approach not only surpasses many pre-existing state-of-the-art (SOTA) methodologies but also sets a new standard for the diagnosis of lung-affected diseases using multi-class CXR data. Furthermore, the integration of XAI techniques such as SHAP and Grad-CAM enhanced the transparency and interpretability of the model’s predictions. The findings hold immense promise for accelerating and improving the accuracy and confidence of diagnostic decisions in the field of lung disease identification

A vision-based machine learning method for barrier access control using vehicle license plate authentication

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications

Mekanistiska och morfologiska studier av Aβ amyloidbildning genom yt-plasmon resonans

Alzheimer’s disease (AD) is the most common form of dementia and apart from the individual suffering AD also causes a large economic burden for society. AD is associated with progressive neurodegeneration and atrophy of the brain. Extracellular fibrillar assemblies of the amyloid-β peptide (Aβ) in the brain represent a clinical hallmark of AD and these are today considered to be the initial cause of the disease.  The tissue-damaging properties of Aβ assemblies are, however, linked to their structures. Aβ represents a spectrum of peptides between 38-43 residues that can adopt several structures that differ both concerning their morphology and pathological properties. The mechanisms by which Aβ self-assembles, the binding strength of these structures to Aβ monomers, as well as the cross-interaction between different Aβ variants are today not fully understood. Aβ amyloid formation follows a nucleation-dependent mechanism which implies that a kinetically unfavorable nucleus must form before the formation of an amyloid fibril. The elongation of the fibril then proceeds via a template-dependent mechanism where monomeric peptides are incorporated in a highly ordered manner. Using SPR the template-dependent mode of elongation can be selectively monitored. Here, we have used the technique to probe the binding strength of Aβ fibrils and in paper 1 the role of pH and the intrinsic histidines in the Aβ sequence were investigated. The result shows that the histidines do not contribute to the previously observed increase in fibrillar strength at low pH. In paper 2 we analyzed the cross-templation between the in vivo most common variants of Aβ, represented by Aβ1-40 and Aβ1-42. Within this work, we revealed two intrinsic mechanisms preventing Aβ to adopt the structure of the significantly more pathogenic Aβ1-42 variant. In paper 3 we characterized the effect of apolipoprotein E (ApoE) on Aβ amyloid formation. ApoE is today the strongest genetic linker to the development of AD and a well-known binding partner to Aβ fibrils in vivo. Using SPR we can here show that ApoE can prevent Aβ fibril elongation. Although ApoE effectively impairs fibril formation, preventing elongation may result in alternative assemblies with higher cytotoxic properties which hence may explain its pathological effect. In paper 4 we have linked SPR to scanning electron microscopy (SEM). The work presents a novel and generic approach to simultaneously monitor the kinetic properties of amyloid formation, the binding of ligands, and its morphology. We have here specifically probed the binding properties of ApoE to Aβ fibrils, and in combination with immunogold staining technique revealed its binding pattern. Taken together this work pioneers the use of SPR as a powerful technique to elucidate Aβ amyloid formation and the complex enigma of factors causing AD.

Health Impacts of Climate Change And WASH Strategies In Coastal Area of Bangladesh

The coastal regions of Bangladesh are composed of Khulna, Barisal and Chittagong divisions. The coastal areas are frequently affected by Salinity, Cyclones, Sea level Rise, Tidal Surges, Water Logging and River Erosion. A severe tropical cyclone hits in coastal area of Bangladesh, on average, every 3 years. Climate change especially cyclones, flood and Salinity is a major constraint of safe drinking water supply and sanitation facilities. The phenomenon of re-curvature of tropical cyclones in the Bay of Bengal is the single most cause of the disproportional large impact of storm surges on the Bangladesh coast. Cyclone devastates all the drinking water sources and causes destruction to sanitation facilities. In this context, till recent time, many people are compelled to drink such polluted water without any sort of purification and consequently suffer from water borne diseases. This study was found that, above 80% people suffered from diarrhoea but major portion, infant below 5 and above 70 years old. Moreover, more than 60 – 70% people are infected by skin diseases and dysentery. Furthermore, average 30% people are affected by fever and cholera within one year. In addition, field survey on toilet facilities in the study area identified that there were 59.61% simple pit latrines, 24.56% pour flush latrines, remaining 5.8% inhabitants had no toilet facilities and 10.03% others in Kaligonj and Assasuni coastal region. Before the adaptation WASH strategies , only 5.2% of the population used soap , about 7.65% used ash, about 42.19% used soil and remainining percents used only water for hand washing after defecation. After ensuring safe drinking water, health improved sanitary latrine and reduction of water logging through homestead area as well as plinth rise, study was initiated with satisfactory progress on health impact. However, the awareness development campaign on hygiene practices in this study area had vividly increased the use of soap, ash and soil for hand washing after defecation around to be about 3.69% and 7.76% and 14.09% respectively. In this case, Overall, improvement in waterborne diseases like diarrhoea, dysentery, cholera, fever and skin diseases  was found above 40% with facilitating safe drinking water, improved sanitation and hygiene practices.

Seaweed extract-stimulated priming in arabidopsis Thaliana and Solanum lycopersicum

Plant priming is an induced physiological state where plants are protected from biotic and abiotic stresses. Whether seaweed extracts promote priming is largely unknown as is the mechanism by which priming may occur. In this study, we examined the effect of a seaweed extract (SWE) on two distinct stages of plant priming (priming phase and post-challenge primed state) by characterising (i) plant gene expression responses using qRT-PCR and (ii) signal transduction responses by evaluating reactive oxygen species (ROS) production. The SWE is made from the brown algae Ascophyllum nodosum and Durvillaea potatorum. The priming phase was examined using both Arabidopsis thaliana and Solanum lycopersicum. At this stage, the SWE up-regulated key priming-related genes, such as those related to systemic acquired resistance (SAR) and activated the production of ROS. These responses were found to be temporal (lasting 3 days). The post-challenge primed state was examined using A. thaliana challenged with a root pathogen. Similarly, defence response-related genes, such as PR1 and NPR1, were up-regulated and ROS production was activated (lasting 5 days). This study found that SWE induces plant priming-like responses by (i) up-regulating genes associated with plant defence responses and (ii) increasing production of ROS associated with signalling responses.</jats:p

How to unravel the key functions of cryptic oomycete elicitin proteins and their role in plant disease

Pathogens and plants are in a constant battle with one another, the result of which is either the restriction of pathogen growth via constitutive or induced plant defense responses or the pathogen colonization of plant cells and tissues that cause disease. Elicitins are a group of highly conserved proteins produced by certain oomycete species, and their sterol binding ability is recognized as an important feature in sterol&ndash;auxotrophic oomycetes. Elicitins also orchestrate other aspects of the interactions of oomycetes with their plant hosts. The function of elicitins as avirulence or virulence factors is controversial and is dependent on the host species, and despite several decades of research, the function of these proteins remains elusive. We summarize here our current understanding of elicitins as either defense-promoting or defense-suppressing agents and propose that more recent approaches such as the use of &lsquo;omics&rsquo; and gene editing can be used to unravel the role of elicitins in host&ndash;pathogen interactions. A better understanding of the role of elicitins is required and deciphering their role in host&ndash;pathogen interactions will expand the strategies that can be adopted to improve disease resistance and reduce crop losses