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 /

A rotation and translation invariant method for 3D organ image classification using deep convolutional neural networks

Three-dimensional (3D) medical image classification is useful in applications such as disease diagnosis and content-based medical image retrieval. It is a challenging task due to several reasons. First, image intensity values are vastly different depending on the image modality. Second, intensity values within the same image modality may vary depending on the imaging machine and artifacts may also be introduced in the imaging process. Third, processing 3D data requires high computational power. In recent years, significant research has been conducted in the field of 3D medical image classification. However, most of these make assumptions about patient orientation and imaging direction to simplify the problem and/or work with the full 3D images. As such, they perform poorly when these assumptions are not met. In this paper, we propose a method of classification for 3D organ images that is rotation and translation invariant. To this end, we extract a representative two-dimensional (2D) slice along the plane of best symmetry from the 3D image. We then use this slice to represent the 3D image and use a 20-layer deep convolutional neural network (DCNN) to perform the classification task. We show experimentally, using multi-modal data, that our method is comparable to existing methods when the assumptions of patient orientation and viewing direction are met. Notably, it shows similarly high accuracy even when these assumptions are violated, where other methods fail. We also explore how this method can be used with other DCNN models as well as conventional classification approaches

A Comparative Analysis of Programming Language Preferences Among Computer Science and Non-Computer Science Students

In the face of the growing importance of programming skills across various fields, understanding student preferences for programming languages becomes crucial. This study delves into this very topic, examining which languages resonate most with computer science majors and students from non-computer science backgrounds. We don’t just identify the popular choices; we also explore the underlying reasons behind these preferences through surveys. The analysis reveals a fascinating interplay between factors like a student’s learning experience, their career aspirations, and even their interests, all of which influence their preference for specific programming languages. This newfound knowledge empowers us to refine programming education for a diverse student body, ensuring they’re well-equipped for the demands of the digital world. Our findings hold value for curriculum designers, educators, and industry professionals alike. By understanding the evolving demands and preferences of students, these stakeholders can craft more relevant and engaging programming education experiences. Ultimately, this fosters interdisciplinary collaboration in the digital age, a key element for success in today’s interconnected world. This research not only contributes to the growing body of knowledge on programming language preferences but also offers practical insights for the betterment of programming instruction and the promotion of collaboration across disciplines within the digital landscape.&nbsp

Formulation and Quality Optimization of Effervescent Tablet of Glipizide: An Approach to Comfort Anti-Diabetic Medication

The present study is targeted to formulate and prepare effervescent tablets of Glipizide to provide more elegancy, comfortability, and improved pharmacokinetics in diabetic treatment than the conventional dosage. Three formulations (F1, F2, and F3) of the effervescent tablet of Glipizide (5mg) were formulated with different amounts and ratios of excipients. By wet granulation technique, 60 tablets for every formulation were prepared with a weight of 700mg per tablet. Then, the features of both granules and tablets were evaluated by published methods. The angle of repose, Hausner ratio, Carr's index, Loss on drying (LOD), and Moisture Content (MC) used to measure granules property successfully proved right follow ability and compressibility. In contrast, physical and drug content related investigation failed to determine the perfectness of all three formulations. Friability on the formulations was around 0.70%, indicating the expected USP limit of friability (0.5 to 1%). The mean disintegration time of the formulations was from 95s to 105s. The tablet potency assay found 95.20% for F1, 88.80% for F2, and 97.40% for F3. The dissolution pattern of the drug followed a linear relationship with time. After one and a half hours, the highest amount of 59.20% cumulative dissolution was determined for F3 that revealed its strategic improvement of the drug solubility. As Glipizide is a poorly water-soluble drug, the effervescent tablet might mitigate disintegration and dissolution-related limitations and, consequently, enhance the drug's bioavailability

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

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

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.

A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images

Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone can provide, integrating such information to be used in segmentation is a challenging task. Numerous methods have been introduced to solve the problem of multi-modal medical image segmentation in recent years. In this paper, we propose a solution for the task of brain tumor segmentation. To this end, we first introduce a method of enhancing an existing magnetic resonance imaging (MRI) dataset by generating synthetic computed tomography (CT) images. Then, we discuss a process of systematic optimization of a convolutional neural network (CNN) architecture that uses this enhanced dataset, in order to customize it for our task. Using publicly available datasets, we show that the proposed method outperforms similar existing methods