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Breeding annual crops for enhanced root depth and biomass is considered a promising intervention to accrue soil organic carbon (SOC) in croplands, with benefits for climate change mitigation and soil health. In annual crops, genetic technology (seed) is replaced every year as part of a farmer's fixed costs, making breeding solutions to climate change more scalable and affordable than management approaches. However, mechanistic understanding and quantitative estimates of SOC accrual potentials from crops with enhanced root phenotypes are lacking. Maize is the highest acreage and yielding crop in the US, characterized by relatively low root biomass confined to the topsoil, making it a suitable candidate for improvement that could be rapidly scaled. We ran a 2‐year field experiment to quantify the formation and composition (i.e., particulate (POM), coarse and fine mineral–associated organic matter (chaOM and MAOM, respectively) of new SOC to a depth of 90 cm from the decomposition of isotopically labeled maize roots and exudates. Additionally, we used the process‐based MEMS 2 model to simulate the SOC accrual potential of maize root ideotypes enhanced to either shift root production to deeper depths or increase root biomass allocation, assuming no change in overall productivity. In our field experiment, maize root decomposition preferentially formed POM, with doubled efficiency below 50 cm, while root exudates preferentially formed MAOM. Modeling showed that shifting root inputs to deeper layer or increasing allocation to roots resulted in a deterministic increase in SOC, ranging from 0.05 to 0.15 Mg C ha−1 per year, which are at the low end of the range of published SOC per hectare annual accrual estimates from adoption of a variety of crop management practices. Our analysis indicates that for maize, breeding for increasing root inputs as a strategy for SOC accrual has limited impact on a per‐hectare basis, although given that globally maize is produced on hundreds of millions of hectares each year, there is potential for this technology and its effect to scale. For maize–soy system that dominates US acres, changes in the overall cropping system are needed for sizable greenhouse gas reductions and SOC accrual. This study demonstrated a modeling and experimental framework to quantify and forecast SOC changes created by changing crop root inputs. [ABSTRACT FROM AUTHOR]

Complications of acute pancreatitis can be disastrous if appropriate treatment is not initiated. Pancreatic necrosis can occur without the presence of symptoms; however, in some cases, it can be accompanied by organic failure, abscess, pseudocyst, fistulas, and pancreatic exocrine disfunction. The surgical treatment of pancreatic necrosis can be managed with open surgical debridement of necrotic tissue. Hence, complications after surgery can appear even in patients without clinical background; complications mostly appear if the surgical technique is not done properly. We present a case of a 47-year-old woman who appeared with abdominal pain, nausea, vomiting, and oral intake intolerance. Symptoms were present for 1 week; she was admitted to the nearest clinic, and surgical management was offered. The patient went to an open pancreatic necrosectomy; however, she presented purulent exudate from the surgical wound and drains. Was referred to our center; on abdominal contrasted computed tomography, a transmesenteric defect and cutaneous-pancreatic fistula were found. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a severe retinal condition primarily affecting diabetic people. This is mostly due to excessive blood sugar levels, which induce retinal blood vessel damage. Diabetic Retinopathy must be detected early to avoid vision loss. Exudates on the retina are the primary indicator of diabetic retinopathy. Therefore, their detection would be crucial for automated illness monitoring and grading. This research provides novel classification and segmentation methods for accurately classifying DR images. The process involves four stages: preprocessing, DR classification, Exudates segmentation, and performance computation. In the preprocessing stage, the source RGB retinal images are separated into three bands, R-Image, G-Image, and B-Image, and each sub-band image is then subjected to data augmentation. The data-augmented images are classified through the proposed Hybrid Centric Convolutional Neural Networks (HCCNN) classifier to produce the classification results (Grade 0 or Grade 1). The proposed exudate lesions segmentation method is utilized to predict and find the exudates in the DR image. The exudates segmented retinal Image is then used to do performance computations for sensitivity (Se), specificity (Sp), and accuracy (Acc), as well as computational complexity analysis. The standard datasets (SYSU and HRF) and the real-time dataset are subjected to the HCCNN approach. The mean Se, Sp, and Acc obtained by this HCCNN method are 98.65%, 98.76%, and 98.96% for the SYSU and 98.58%, 98.65%, and 98.6% for the HRF dataset, respectively. Our method obtains an average detection ratio of 91.66% in the real-time dataset. The suggested HCCNN approach produces findings that outperform the conventional methods and help doctors identify the DR disease early to prevent further vision loss. [ABSTRACT FROM AUTHOR]

Diabetes results in diabetic retinopathy. which occurs due to a change in the vasculature structure. Diabetic abnormalities such as microaneurysms, haemorrhages, and "exudates" describe the stage of diabetic retinopathy. Early-stage detection gains prime importance to avoid permanent sight loss. Exudates are one of the most noticeable signs of diabetic retinopathy. This work presents the automatic and non-invasive approach to detect exudates using requisitely adjusted morphological operators. The use of Standard deviation of histogram equalized image after optic disc detection and deletion is the work's key contribution. The standard deviation offers the tendency of distributing the favourable exudate pixels towards the mean value. The proposed method is implemented on the "IDRiD (Indian Diabetic Retinopathy Image Dataset)". The detected exudates are compared with provided ground-truths reported accuracy of 99.13%. The outcomes prove its effectiveness of the proposed method and can aid ophthalmologists in diagnosis. [ABSTRACT FROM AUTHOR]

Xylem sap proteomics provides crucial insights into plant defense and root-to-shoot communication. This study highlights the sensitivity and reproducibility of xylem sap proteome analyses, using a single plant per sample to track over 3000 proteins in two model crop plants, Solanum tuberosum and Hordeum vulgare. By analyzing the flg22 response, we identified immune response components not detectable through root or shoot analyses. Notably, we discovered previously unknown elements of the plant immune system, including calcium/calmodulin-dependent kinases and G-type lectin receptor kinases. Despite similarities in the metabolic pathways identified in the xylem sap of both plants, the flg22 response differed significantly: S. tuberosum exhibited 78 differentially abundant proteins, whereas H. vulgare had over 450. However, an evolutionarily conserved overlap in the flg22 response proteins was evident, particularly in the CAZymes and lipid metabolism pathways, where lipid transfer proteins and lipases showed a similar response to flg22. Additionally, many proteins without conserved signal sequences for extracellular targeting were found, such as members of the HSP70 family. Interestingly, the HSP70 response to flg22 was specific to the xylem sap proteome, suggesting a unique regulatory role in the extracellular space similar to that reported in mammalians. [ABSTRACT FROM AUTHOR]

Negative pressure wound therapy is currently one of the most popular treatment approaches that provide a series of benefits to facilitate healing, including increased local blood perfusion with reduced localized oedema and control of wound exudate. The porous foam dressing is a critical element in the application of this therapy and its choice is based on its ability to manage exudate. Industry standards often employ aqueous solutions devoid of proteins to assess dressing performance. However, such standardized tests fail to capture the intricate dynamics of real wounds, oversimplifying the evaluation process. This study aims to evaluate the technical characteristics of two different commercial polyurethane foam dressings during negative pressure wound therapy. We introduce an innovative experimental model designed to evaluate the effects of this therapy on foam dressings in the presence of viscous exudates. Our findings reveal a proportional increase in dressing fibre occupancy as pressure intensifies, leading to a reduction in dressing pore size. The tests underscore the pressure system's diminished efficacy in fluid extraction with increasing fluid viscosity. Our discussion points to the need of establishing standardized guidelines for foam dressing selection based on pore size and the necessity of incorporating real biological exudates into industrial standards. [ABSTRACT FROM AUTHOR]

Macrophytes and cladocerans represent the main antagonistic groups that regulate phytoplankton biomass; however, the mechanism behind this interaction is unclear. In laboratory conditions, we separately evaluated the effects of three submerged macrophytes (Ceratophyllum demersum, Myriophyllum aquaticum, and Stuckenia pectinata), as well as their exudates, and plant-associated microbiota (POM < 25 µm) + exudates on the population growth of Daphnia cf. pulex and Simocephalus cf. mixtus. Living Ceratophyllum, exudates, and POM < 25 µm + exudates exhibited the most robust positive effects on Simocephalus density and the rate of population increase (r). Subsequently, we examined the effects of Ceratophyllum on the filtration and feeding rates of Simocephalus and Daphnia, revealing significant (p < 0.001) promotion of filtration and feeding in Simocephalus but not in Daphnia. To elucidate the specific effects of this macrophyte on Simocephalus demography, we assessed selected life table variables across the same treatments. The treatments involving exudates and living Ceratophyllum resulted in approximately 40% longer survivorship and significantly (p < 0.01) enhanced fecundity. Our findings indicate that exudates from submerged macrophytes positively influence Simocephalus demography by increasing filtration rates, survivorship, and fecundity. This synergy suggests a substantial impact on phytoplankton abundance. [ABSTRACT FROM AUTHOR]

Diabetes is considered to be the foundation of a slew of other health issues and late consequences, according to medical experts. The rise in diabetes-related illnesses has presented a challenge to the healthcare industry. Diabetic neuropathy, diabetic nephropathy, and diabetic retinopathy are just a few complications that can arise from diabetes. Diabetic Retinopathy is characterized by red lesions, bright lesions, and neovascularization. Bright lesions (exudates) are the second clinically visible lesions that appear after red lesions. The present challenge in DR detection is to make early diagnosis of DR disease more accessible by minimizing the cost and personnel requirements while preserving or enhancing DR detection quality. The challenge can be solved by using automated or computer-assisted DR detection in retinal images. The precise location and shape of blood vessels and the optic disc play critical roles in accurately diagnosing and classifying dark and bright lesions for the early detection of DR. The primary aim of the proposed model is to create an automated model for identifying bright lesions for non-proliferative stage diabetic retinopathy screening using deep learning architecture. It presents algorithms for removing the background of the images, eliminating the optic disc (OD), and the segmentation of candidate lesions. The model was trained and evaluated using MESSIDOR and e-ophtha Ex public datasets and obtained maximum accuracy, sensitivity, specificity, and F1-score of 97.54%, 90.34%, 98.24%, 93.28% and 96.32%, 95.73%, 97.12%, 96.74% respectively. [ABSTRACT FROM AUTHOR]

The application of disease-resistant varieties is the most cost-effective method for solving the problem of clubroot. "Shangpin," a disease-resistant variety of Chinese cabbage with broad-spectrum immunity to Plasmodiophora brassicae (P. brassicae), was screened in a previous study. Based on 16S rRNA sequencing technology, we annotated the compositional differences between the rhizosphere, rhizoplane, and endosphere bacterial communities of "Shangpin" and "83-1" under P. brassicae stress. Alpha diversity analysis showed that the abundance of microorganisms in the root system of "83-1" changed more than that of "Shangpin" after P. brassicae infestation, and Beta diversity analysis indicated that Flavobacterium and Sphingomonas may mediate clubroot resistance, while Nitrospira, Nitrosospira, and Pseudomonas may mediate P. brassicae infestation among the bacteria in the Top 10 abundances. Microbial functional analyses showed that the root microorganisms of "83-1" were metabolically weakened after P. brassicae inoculation and were inhibited in competition with pathogenic bacteria. Conversely, the root microorganisms of "Shangpin" maintained the strength of their metabolic capacity, which took a favorable position in competition with the pathogen and inhibited the growth and development of the pathogen, thus showing resistance. Root secretions of "Shangpin" significantly inhibited the incidence and disease index of clubroot, which indicated that under clubroot stress, resistant varieties maintain root microbial diversity and microbial community functions through specific root exudates, enriching the genera Flavobacterium and Sphingomonas, thus showing resistance. The results of this study reveal the resistance mechanism of resistant varieties to clubroot and provide new insights into the prevention and control of clubroot in Chinese cabbage. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy stands out as one of the highly prevalent causes of vision loss in working people worldwide. In computer vision, deep learning based strategies are seen as a viable solution for efficient diabetic retinopathy detection. We present a UNet-based deep learning architecture for diabetic retinopathy segmentation of blood vessels, exudates, and microaneurysms. Traditional methods often consider the features only from the last convolution unit and discard the remaining features, resulting in low-quality feature maps. However, boundary information plays important role in medical image segmentation. To overcome this, we introduce a skip connection mechanism to concatenate all attributes from each layer. Additionally, we utilize an upsampling layer to aggregate the features at the final sigmoid layer. Finally, we apply channel and spatial attention mechanisms to generate the semantic feature map. Therefore, the proposed approach overcomes the issues of existing methods by incorporating dense skip connection along with channel and spatial attention mechanism which helps to retain the substantial information of image. We tested proposed approach on several publicly available datasets such as IDRiD, DIARETDB1, STARE, ChaseDB1, DRIVE, and HRF datasets. The comparative analysis shows that the proposed approach achieves superior performance, with an average accuracy of 98.10%, average sensitivity of 97.60%, and average specificity of 98.2% for segmentation. [ABSTRACT FROM AUTHOR]

Open in new tab Download slide OBJECTIVES The goal of this histological study was to assess the biocompatibility of vascular patches used in the repair of congenital heart defects. METHODS We examined tissue-engineered bovine (n = 7) and equine (n = 7) patches and autologous human pericardium (n = 7), all explanted due to functional issues or follow-up procedures. Techniques like Movat-Verhoeff, von Kossa and immunohistochemical staining were used to analyse tissue composition, detect calcifications and identify immune cells. A semi-quantitative scoring system was implemented to evaluate the biocompatibility aspects, thrombus formation, extent of pannus, inflammation of pannus, cellular response to patch material, patch degradation, calcification and neoadventitial inflammation. RESULTS We observed distinct material degradation patterns among types of patches. Bovine patches showed collagen disintegration and exudate accumulation, whereas equine patches displayed edematous swelling and material dissolution. Biocompatibility scores were lower in terms of cellular response, degradation and overall score for human autologous pericardial patches compared to tissue-engineered types. The extent of pannus formation was not influenced by the type of patch. Bovine patches had notable calcifications causing tissue hardening, and foreign body giant cells were more frequently seen in equine patches. Plasma cells were frequently detected in the neointimal tissue of engineered patches. CONCLUSIONS Our results confirm the superior biocompatibility of human autologous patches and highlight discernible variations in the changes of patch material and the cellular response to patch material between bovine and equine patches. Our approach implements the semi-quantitative scoring of various aspects of biocompatibility, facilitating a comparative quantitative analysis across all types of patches, despite their inherent differences. [ABSTRACT FROM AUTHOR]

Root hairs are considered important for rhizosphere formation, which affects root system functioning. Through interactions with soil microorganisms mediated by root exudation, root hairs may affect the phenotypes and growth of young plants. We tested this hypothesis by integrating results from two experiments: (1) a factorial greenhouse seedling experiment with Zea mays B73‐wt and its root‐hairless mutant, B73‐rth3, grown in live and autoclaved soil, quantifying 15 phenotypic traits, seven growth rates, and soil microbiomes and (2) a semi‐hydroponic system quantifying root exudation of maize genotypes. Possibly as compensation for lacking root hairs, B73‐rth3 seedlings allocated more biomass to roots and grew slower than B73‐wt seedlings in live soil, whereas B73‐wt seedlings grew slowest in autoclaved soil, suggesting root hairs can be costly and their benefits were realized with more complete soil microbial assemblages. There were substantial differences in root exudation between genotypes and in rhizosphere versus non‐rhizosphere microbiomes. The microbial taxa enriched in the presence of root hairs generally enhanced growth compared to taxa enriched in their absence. Our findings suggest the root hairs' adaptive value extends to plant‐microbe interactions mediated by root exudates, affecting plant phenotypes, and ultimately, growth. Summary Statement: An experiment with seedlings of Zea mays B73 and its root‐hairless mutant (Z. mays B73‐rth3) with contrasting soil microbiomes showed that root hairs are beneficial towards plant growth but can be costly. Root hairs' adaptive value was partly mediated by plant‐microbe interactions involving exudates, which feedback to plant phenotypes and growth. [ABSTRACT FROM AUTHOR]

Background: The rhizosheath, a cohesive soil layer firmly adhering to plant roots, plays a vital role in facilitating water and mineral uptake. In pearl millet, rhizosheath formation is genetically controlled and influenced by root exudates. Here, we investigated the impact of root exudates on the microbiota composition, interactions, and assembly processes, and rhizosheath structure in pearl millet using four distinct lines with contrasting soil aggregation abilities. Results: Utilizing 16S rRNA gene and ITS metabarcoding for microbiota profiling, coupled with FTICR-MS metabonomic analysis of metabolite composition in distinct plant compartments and root exudates, we revealed substantial disparities in microbial diversity and interaction networks. The ß-NTI analysis highlighted bacterial rhizosphere turnover driven primarily by deterministic processes, showcasing prevalent homogeneous selection in root tissue (RT) and root-adhering soil (RAS). Conversely, fungal communities were more influenced by stochastic processes. In bulk soil assembly, a combination of deterministic and stochastic mechanisms shapes composition, with deterministic factors exerting a more pronounced role. Metabolic profiles across shoots, RT, and RAS in different pearl millet lines mirrored their soil aggregation levels, emphasizing the impact of inherent plant traits on microbiota composition and unique metabolic profiles in RT and exudates. Notably, exclusive presence of antimicrobial compounds, including DIMBOA and H-DIMBOA, emerged in root exudates and RT of low aggregation lines. Conclusions: This research underscores the pivotal influence of root exudates in shaping the root-associated microbiota composition across pearl millet lines, entwined with their soil aggregation capacities. These findings underscore the interconnectedness of root exudates and microbiota, which jointly shape rhizosheath structure, deepening insights into soil–plant-microbe interactions and ecological processes shaping rhizosphere microbial communities. Deciphering plant–microbe interactions and their contribution to soil aggregation and microbiota dynamics holds promise for the advancement of sustainable agricultural strategies. 46mkfZGLAk7WD-UFCK_cLi Video Abstract [ABSTRACT FROM AUTHOR]

Patients with diabetes usually develop a condition called diabetic retinopathy (DR), resulting from retinal damage. This impairment usually happens when the glucose levels in the blood are elevated, finally causing a blockage in the blood vessels that feed a part of the eye called the retina and finally severing it from the blood supply. Therefore, the eye attempts to produce fresh blood cells. But these cells are either poorly developed or weak. So, it can be leaked out easily. Hence, to lessen the severe effects of this disease, these patients must be diagnosed as soon as possible. Earlier, a number of approaches were put forth to recognise this illness using machine learning algorithms, image processing, and other techniques. The diagnosis process of this disease involves pre-processing of coloured images of the fundus, extraction of clinical features and classification of retinopathy. In this research, fundus photography of the retina is utilised to accelerate the detection of various kinds of retinopathy caused by diabetes based on convolutional neural network (CNN) pre-trained transfer learning algorithm. Inception V3 and Xception are used in this model to determine and categorise diabetic retinopathy, respectively. As a result, people with this disease can lower their risk of exposure to permanent blindness. [ABSTRACT FROM AUTHOR]

Co-planting two or more species on the same piece of land, with overlapping time or not, has been suggested to increase both crop production and long-term sustainability. On soils that are slightly or moderately contaminated with heavy metals, hyperaccumulators have been co-planted with crops to clean the soil and produce safe agricultural products. Despite the increasing number of greenhouse experiments and field trials that investigate the co-planting mechanisms and efficiency, the consistency, stability, and applicability of this technology and its contribution to sustainability remain unclear. From published literature, we collected 118 co-planting combinations involving hyperaccumulators, and compared them with their monoculture controls. Co-planting averagely decreased the shoot arsenic concentration by ∼23.4% and cadmium by ∼13.4%. Co-planting controls the crop contamination as long as the hyperaccumulator and crop species are correctly selected, and the soil heavy metal is within the safe range. Further, a sustainability assessment criterion for the utilization of contaminated agricultural soil was proposed, taking As-contaminated soil as an example. A decision framework and a guideline for co-planting were established to aid in the decision-making. The outlook of co-planting as a sustainable solution and the future development were prospected. [ABSTRACT FROM AUTHOR]

Given the immense importance of medical image segmentation and the challenges associated with manual execution, a diverse range of automated medical image segmentation methods have been developed, primarily focusing on specific modalities of images. This paper introduces an innovative segmentation algorithm that effectively segments exudates, hemorrhages, microaneurysms, and blood vessels within retinal images using an enhanced MesNet (MesU-Net) model. By combining the MES-Net model with the U-Net model, this approach achieves accurate results in a shorter period. Consequently, it holds significant potential for clinical application in computer-aided diagnosis. The IDRID and DRIVE datasets are utilized to assess the efficacy of the proposed model for retinal segmentation. The presented method attains segmentation accuracy rates of 97.6%, 98.1%, 99.2%, and 83.7% for exudates, hemorrhages, microaneurysms, and blood vessels, respectively. This proposed model also holds promise for extension to address other medical image segmentation challenges in the future. [ABSTRACT FROM AUTHOR]

The escalating prevalence of diabetes globally, exacerbated by lifestyle changes postpandemic-- including increased screen time, sedentary behavior, and remote work-- has consequently driven a surge in associated complications, notably, Diabetic Retinopathy (DR). This ocular complication presents a pressing concern due to its potential to precipitate irreversible vision loss. Consequently, the necessity for timely and accurate DR detection is paramount, especially in circumstances where conventional diagnostic approaches are either challenging or financially prohibitive. Capitalizing on the prowess of fuzzy logic in managing uncertainties, this study introduces an innovative application of Extended Fuzzy Logic for the early-stage detection of DR. Rather than focusing solely on overt symptoms, this approach discerns subtle similarities in retinal irregularities between diabetic patients and non-diabetic individuals. To quantify these similarities, the 'f-validity' value was computed based on DR risk factors and associated symptoms, which were subsequently transformed into membership function values. The aggregation of these values was facilitated by the Ordered Weighted Averaging (OWA) operator. The experimental outcomes of this approach align satisfactorily with expert anticipations, boasting an accuracy of 90%, a precision of 92.2%, and a sensitivity of 75%. These results, when juxtaposed against contemporary studies in the field, underscore the promise of this scheme in advancing early diagnostics of DR. The study thus proposes a potential solution that leverages the power of fuzzy logic to address the burgeoning challenge of DR. [ABSTRACT FROM AUTHOR]

Background Pleural effusion may occur in different infections or as a complication of pulmonary disease, malignant disease. Exudative pleural effusion results from local or systemic disease that directly injure the pleural surface. To know intrapleural pathology, correct diagnosis of pleural effusion is essential. For this many parameters have been proposed for segregation of exudates from transudates. Cholinesterase level in pleural effusion of diverse etiologies helps to differentiate between transudates and exudates. Methods The study was conducted in the Department of General Medicine, SCB Medical College & Hospital, Cuttack during the period from June 2019 to September 2020. 100 consecutive patients admitted were included in the study group. Thoracocentesis was done in all patients and samples were sent for biochemical, microbiological and cytological tests. Cholinesterase estimation was done by photometer 5010V5+. Final diagnosis was done by clinical, biochemical, cytological and microbiological results. Results Out of 100 cases, 78 cases were male and 22 cases were females, with male: female ratio 3.4:1. Tubercular effusion was the most common cause followed by congestive cardiac failure. Fever was the most common clinical presentation (62%). The mean total cell count was more in exudate (2911.820+/- 1511.48). The pleural fluid cholinesterase level of 943.5U/L was 96% sensitive and 93% specificity. Conclusion Pleural fluid cholinesterase level of 943.5U/dl gives a more reasonable sensitivity and specificity (96% sensitive and 93% specific) to differentiate from exudates and transudates, while that of 981U/dl is more specific(92.6% sensitive and >99% specific ) and can be taken as taken as gold standard as specificity approaches 100%. The result of present study revealed that pleural fluid cholinesterase is the most accurate parameter for the differentiation of transudates and exudates. [ABSTRACT FROM AUTHOR]

Exudates are a common sign of diabetic retinopathy, which is a disease that affects the blood vessels in the retina. Early detection of exudates is critical to avoiding vision problems through continuous screening and treatment. In traditional clinical practice, the involved lesions are manually detected using photographs of the fundus. However, this task is cumbersome and time‐consuming and requires intense effort due to the small size of the lesion and the low contrast of the images. Thus, computer‐assisted diagnosis of retinal disease based on the detection of red lesions has been actively explored recently. In this paper, we present a comparison of deep convolutional neural network (CNN) architectures and propose a residual CNN with residual skip connections to reduce the parameter for the semantic segmentation of exudates in retinal images. A suitable image augmentation technique is used to improve the performance of network architecture. The proposed network can robustly segment exudates with high accuracy, which makes it suitable for diabetic retinopathy screening. A comparative performance analysis of three benchmark databases: E‐ophtha, DIARETDB1, and Hamilton Ophthalmology Institute's Macular Edema, is presented. The proposed method achieves a precision of 0.95, 0.92, 0.97, accuracy of 0.98, 0.98, 0.98, sensitivity of 0.97, 0.95, 0.95, specificity of 0.99, 0.99, 0.99, and area under the curve of 0.97, 0.94, and 0.96, respectively. Research Highlights: The research focuses on the detection and segmentation of exudates in diabetic retinopathy, a disease affecting the retina.Early detection of exudates is important to avoid vision problems and requires continuous screening and treatment.Currently, manual detection is time‐consuming and requires intense effort.The authors compare qualitative results of the state‐of‐the‐art convolutional neural network (CNN) architectures and propose a computer‐assisted diagnosis approach based on deep learning, using a residual CNN with residual skip connections to reduce parameters.The proposed method is evaluated on three benchmark databases and demonstrates high accuracy and suitability for diabetic retinopathy screening. [ABSTRACT FROM AUTHOR]

Otoscopic and radiographic examinations are effective tools to diagnose the ear affections like otitis externa, chronic hypertropic otitis externa, neoplasia, pathological changes of ear canal, etc. The otoscopic examination of 84 dogs revealed presence of different types of texture and colour of exudates in the ear canal. It revealed that the inner wall of external ear canal suffered with erythema, ulceration, hyperplasia of inner wall, hyperplasia of ceruminous glands, presence of excessive hair and polyp. Radiography revealed stenosis, calcification of ear canal, bilateral involvement of ear and increased opacity of the tympanic bulla. Otoscopy and radiography are thus practical and accessible tools for detecting changes in the ear canal and other related structures. [ABSTRACT FROM AUTHOR]

Sustainable hydrogels are an innovative biodegradable alternative to traditional packaging materials. They offer exceptional water absorption capacity and high biocompatibility, making them ideal food absorbents to reduce plastic waste, extend shelf life and ensure the safety and quality of packaged foods. In this study, hydrogels based on gelatin, microcrystalline cellulose (MCC), and nanocrystalline cellulose (NCC) were developed, characterized, and applied in the packaging of chicken breasts. For this, MCC was isolated from the banana pseudostem and commercial NCC was incorporated into a gelatin solution to produce the hydrogel materials by film casting. The resulting hydrogels were analyzed in terms of morphology, structural properties, water absorption capacity, mechanical strength, and color properties. The results showed that the incorporation of MCC and NCC significantly improved the mechanical integrity of the hydrogels, which prevented premature deformation of the hydrogels when they absorbed moisture. In addition, changes in the color properties of chicken breast samples in contact with the hydrogels were observed, indicating their ability to preserve food quality. Subsequently, the effectiveness of the hydrogels for chicken breast storage at 4 °C for 4 days was validated. The results demonstrated that the hydrogels developed in this study are a sustainable and environmentally friendly alternative to traditional packaging materials that can extend the shelf life of food products while maintaining their physical and microbiological integrity. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy causes blindness in diabetics. Early identification and frequent screening of diabetic retinopathy can slow disease progression and visual loss. Retinal lesions result from diabetic retinopathy. Dark and brilliant retinal lesions predominate. Color, shape, and size distinguish lesions. Exudates are bright, while microaneurysms (MAs) and hemorrhages (HEMs) are dark. This study presents a retinal lesion screening method for diabetic retinopathy. The data is saturated at low and high intensities; picture intensity values are adjusted to enhance contrast. This study presents a unique rhombus multilevel retinal image segmentation method. In the proposed study, preprocessing, segmentation algorithms, morphological operation,median filter and gradient are all designed as parts of an effective automated system. With 40 photos, the recommended method produced accuracy and specificity of 99.9% and 99.5%, respectively. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) remains one of the world's frequent eye illnesses, leading to vision loss among working-aged individuals. Hemorrhages and exudates are examples of signs of DR. However, artificial intelligence (AI), particularly deep learning (DL), is poised to impact nearly every aspect of human life and gradually transform medical practice. Insight into the condition of the retina is becoming more accessible thanks to major advancements in diagnostic technology. AI approaches can be used to assess lots of morphological datasets derived from digital images in a rapid and noninvasive manner. Computer-aided diagnosis tools for automatic detection of DR early-stage signs will ease the pressure on clinicians. In this work, we apply two methods to the color fundus images taken on-site at the Cheikh Zaïd Foundation's Ophthalmic Center in Rabat to detect both exudates and hemorrhages. First, we apply the U-Net method to segment exudates and hemorrhages into red and green colors, respectively. Second, the You Look Only Once Version 5 (YOLOv5) method identifies the presence of hemorrhages and exudates in an image and predicts a probability for each bounding box. The segmentation proposed method obtained a specificity of 85%, a sensitivity of 85%, and a Dice score of 85%. The detection software successfully detected 100% of diabetic retinopathy signs, the expert doctor detected 99% of DR signs, and the resident doctor detected 84%. [ABSTRACT FROM AUTHOR]

Pseudomembranes in the large and small intestines are common in hospitalized patients that are immunosuppressed or on certain oral antibiotics. Pseudomembranous enterocolitis, histologically characterized by volcanic-like eruption of inflammatory cellular exudate from the mucosal surface, is mainly attributed to Clostridium difficile toxins and often presents with symptomatic diarrhea. Rarely, there are case reports of similar pseudomembranous lesions limited to the stomach in the absence of intestinal involvement. In this paper, we present a case of localized pseudomembranous gastritis in a 76-year-old patient with personal history limited to prior gastrointestinal bleed, liver cirrhosis, alcohol dependence, diabetes mellitus, and hypertension who was referred to the emergency department from his primary care physician's office due to low hemoglobin. [ABSTRACT FROM AUTHOR]

Global banana production is affected by Fusarium wilt, a devastating disease caused by the soilborne root‐infecting fungus, Fusarium oxysporum f. sp. cubense (Foc). Fusarium wilt is notoriously difficult to manage because infection arises through complex below‐ground interactions between Foc, the plant, and the soil microbiome in the root–soil interface, defined as the rhizosphere. Interactions in the rhizosphere play a pivotal role in processes associated with pathogen development and plant health. Modulation of these processes through manipulation and management of the banana rhizosphere provides an auspicious prospect for management of Fusarium wilt. Yet, a fundamental understanding of interactions in the banana rhizosphere is still lacking. The objective of this review is to discuss the state‐of‐the‐art of the relatively scant data available on banana below‐ground interactions in relation to Fusarium wilt and, as a result, to highlight key research gaps. Specifically, we seek to understand (a) the biology of Foc and its interaction with banana; (b) the ecology of Foc, including the role of root‐exuded metabolites in rhizosphere interactions; and (c) soil management practices and how they modulate Fusarium wilt. A better understanding of molecular and ecological factors influencing banana below‐ground interactions has implications for the development of targeted interventions in the management of Fusarium wilt through manipulation of the banana rhizosphere. [ABSTRACT FROM AUTHOR]

Plant-microbe interactions are critical for ecosystem functioning and driving rhizosphere processes. To fully understand the communication pathways between plants and rhizosphere microbes, it is crucial to measure the numerous processes that occur in the plant and the rhizosphere. The present review first provides an overview of how plants interact with their surrounding microbial communities, and in turn, are affected by them. Next, different optical biosensing technologies that elucidate the plant-microbe interactions and provide pathogenic detection are summarized. Currently, most of the biosensors used for detecting plant parameters or microbial communities in soil are centered around genetically encoded optical and electrochemical biosensors that are often not suitable for field applications. Such sensors require substantial effort and cost to develop and have their limitations. With a particular focus on the detection of root exudates and phytohormones under biotic and abiotic stress conditions, novel low-cost and in-situ biosensors must become available to plant scientists. [ABSTRACT FROM AUTHOR]

Aspergillus versicolor, a widely distributed fungus, is associated with pollution and carcinogenic hazards. This study aimed to examine the functions of the A. versicolor exudate and laid a scientific foundation for improving our understanding, utilization, and control of A. versicolor. The A. versicolor exudate proteome, ion content, and amino acid components were determined using label‐free quantitation, atomic absorption spectrophotometry, and high‐performance liquid chromatography, respectively. In total, 502 proteins were identified in the A. versicolor exudate. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and cluster of orthologous group analyses were used to annotate the functional classification and pathways of the aligned proteins. Proteins identified in the exudate were mainly enriched in carbohydrate metabolic process, translation, oxidoreductase activity, oxidoreductase activity, hydrolase activity, cell wall‐related processes, catalytic activity, and unknown functions. The exudate comprised Na, K, Ca, Fe, and Mg cations. Among the 17 types of amino acids detected in the exudate, 7 were essential and 10 were nonessential. The exudate may be involved in the vital processes of A. versicolor. Additionally, the exudate may play an important role in the growth, development, reproduction, homeostasis, nutrient supply for regrowth, and virulence of A. versicolor. [ABSTRACT FROM AUTHOR]

Root exudates are important for shaping root-associated microbiomes. However, studies on a wider range of metabolites in exudates are required for a comprehensive understanding about their influence on microbial communities. We identified maize inbred lines that differ in exudate concentrations of 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) and γ-aminobutyric acid (GABA) using a semi-hydroponic system. These lines were grown in the field to determine the changes in microbial diversity and gene expression due to varying concentrations of DIMBOA and GABA in exudates using 16S rRNA amplicon sequencing and metatranscriptomics. Results showed individual and interaction effects of DIMBOA and GABA on the rhizosphere and root endosphere β-diversity, most strongly at the V10 growth stage. The main bacterial families affected by both compounds were Ktedonobacteraceae and Xanthomonadaceae. Higher concentrations of DIMBOA in exudates affected the rhizosphere metatranscriptome, enriching for metabolic pathways associated with plant disease. This study validated the use of natural variation within plant species as a powerful approach for understanding the role of root exudates on microbiome selection. We also showed that a semi-hydroponic system can be used to identify maize genotypes that differ in GABA and DIMBOA exudate concentrations under field conditions. The impact of GABA exudation on root-associated microbiomes is shown for the first time. [ABSTRACT FROM AUTHOR]

Copyright of Mycosystema is the property of Mycosystema Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pleural effusions (PEs) are common in clinical practice and can be due to many different underlying diseases such as cancer, congestive heart failure, or pneumonia. An accurate differential diagnostic categorization is essential, as the treatment and prognosis of PEs largely depend on its cause. In this study, we tested the hypothesis that nitrite and nitrate concentrations in PEs are associated with the inflammation and infection conditions. We therefore measured the nitrite and nitrate levels in 143 PE samples using a sensitive liquid chromatography-tandem mass spectrometry method and investigated their diagnostic potential in differentiating PEs. The results showed that nitrite concentrations and nitrite/nitrate ratios were higher in exudates than in transudates (NO2−: 2.12 vs. 1.49 μM; NO2−/NO3−: 23.3 vs. 14.0). Both the nitrite concentrations and the nitrite/nitrate ratios were positively correlated with the three Light's criteria. Moreover, the receiver operating characteristic curve analysis revealed that the nitrite/nitrate ratio with an area under the curve of 0.71 could be a potential diagnostic biomarker in separating infectious PEs (IPEs) from other types of PEs. Taken together, the nitrite/nitrate ratio not only reflected the statuses of inflammation, but also the nitrate reduction by pathogenic bacteria infection in the pleural cavity. The nitrite/nitrate ratio could be a better biomarker in the differential diagnosis of PEs than the nitrite concentration alone. [ABSTRACT FROM AUTHOR]

Cover crops provide many agroecosystem services, including weed suppression, which is partially exerted through release of allelopathic benzoxazinoid (BX) compounds. This research (1) characterizes changes in concentrations of BX compounds in shoots, roots, and soil at three growth stages (GS) of cereal rye (Secale cereale L.), and (2) their degradation in soil over time following termination. Concentrations of shoot dominant BX compounds, DIBOA-glc and DIBOA were lowest at GS 83 (boot). The root dominant BX compound, HMBOA-glc, concentration was least at GS 54 (elongation). Rhizosphere soil BX concentrations were 1000 times smaller than in root tissues. Dominant compounds in soil were HMBOA-glc and HMBOA. Soil BX compound concentrations were similar near root crowns and between-rows. Soil BX concentrations following cereal rye termination declined exponentially over time in three of four treatments: incorporated shoots (S) and roots (R), no-till S + R (cereal rye rolled flat), and no-till R (shoots removed); no-till S had consistently low concentrations. In treatments showing changes, soil concentrations of HMBOA-glc and HMBOA increased above initial concentrations on the day following cereal rye termination. Concentrations of these two compounds decreased more rapidly than the other compounds. Placement of shoots on the surface of an area where cereal rye had not grown (no-till S) did not increase soil concentrations of BX compounds. The short duration and complex dynamics of BX compounds in soil prior to and following termination illustrate the limited window for enhancing weed suppression directly by cereal rye allelochemicals; valuable information for programs breeding for enhanced weed suppression. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy(DR) is a health condition that affects the retinal blood vessels(BV) and arises in over half of people living with diabetes. Exudates(EX) are significant indications of DR. Early detection and treatment can prevent vision loss in many cases. EX detection is a challenging problem for ophthalmologists due to its different sizes and elevations as retinal fundus images frequently have irregular illumination and are poorly contrasting. Manual detection of EX is a time-consuming process to diagnose a mass number of diabetic patients. In the domain of signal processing, both SIFT (scale-invariant feature transform) and SURF (speed-up robust feature) methods are predominant in scale-invariant location retrieval and have shown a range of advantages. But, when extended to medical images with corresponding weak contrast between reference features and neighboring areas, these methods cannot differentiate significant features. Considering these, in this paper, a novel method is proposed based on modified KAZE features, which is an emerging technique to extract feature points and extreme learning machine autoencoders(ELMAE) for robust and fast localization of the EX in fundus images. The main stages of the proposed method are pre-processing, OD localization, dimensionality reduction using ELMAE, and EX localization. The proposed method is evaluated based on the freely accessible retinal database DIARETDB0, DIARETDB1, e-Ophtha, MESSIDOR, and local retinal database collected from Silchar Medical College and Hospital(SMCH). The sensitivity, specificity, and accuracy obtained by the proposed method are 96.5%, 96.4%, and 97%, respectively, with the processing time of 3.19 seconds per image. The results of this study are satisfactory with state-of-the-art methods. The results indicate that the approach taken can detect EX with less processing time and accurately from the fundus images. [ABSTRACT FROM AUTHOR]

An automated Diabetic Retinopathy (DR) introspection scheme for early detection of retinopathy signs is realized in this paper. Presence of exudates in retina is considered as an early sign of DR. Therefore, the proposed methodology aims at detection of exudates by transforming the acquired fundus image into a high-dimensional feature map labelled as Spatial-Spectral-Statistical (SSS) feature map that uniquely represents the individual image pixels using a novel characterization scheme. At the onset, a slightly novel pre-processing scheme is fused into the mechanism to address the non-uniform illumination issues present in images. Later, separate feature characterizers and descriptors pertaining to the diverse domains are engaged for extraction of the different features from the input fundus image. These distinct features are then blended to yield the feature map representing the given image. Then a supervised classifier categorizes these features and finally aids in deciding the presence or absence of exudates for the given input. Extensive investigation and relative comparisons performed on publicly available dataset namely DIARETDB0, DIARETDB1 and MESSIDOR demonstrate a consistent average classification accuracy of 97.99%, an attribute owed to the unique feature aggregation scheme that also, makes the methodology robust under different imaging problems. [ABSTRACT FROM AUTHOR]

The severity of diabetic retinopathy can lead to blindness if undiagnosed and untreated. The presence of hard exudates is one of the diabetic retinopathy symptoms. Therefore, automatic segmentation of hard exudates can provide an important diagnosis for diabetic retinopathy. Due to the relatively small dimensions of the exudates and the availability of the optic disc that has similar color, the exudate segmentation is a challenge in itself. In this study, we propose a modification of the fully convolutional network model (FCN-8) by combining FCN-8 and shortcuts to improve the performance of FCN-8. Each shortcut consists of a convolutional layer and batch normalization to reduce input degradation. Prior to processing the hard exudates using a modified FCN-8, the optic disc was removed from the retinal image by detecting the area using Faster R-CNN based on the Alexnet architecture. For training and testing, we applied the IDRiD dataset to evaluate the performance of our proposed architecture. Experiments show that our proposed architecture provides accuracy, sensitivity, specificity of 98.18 %, 81.7%, and 98.37 % respectively. Our proposed method gives higher sensitivity compared to Autoencoder, U-Net, FCN-32, FCN-16, and FCN-8. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy is a chronic condition that causes vision loss if not detected early. In the early stage, it can be diagnosed with the aid of exudates which are called lesions. However, it is arduous to detect the exudate lesion due to the availability of blood vessels and other distractions. To tackle these issues, we proposed a novel exudates classification from the fundus image known as hybrid convolutional neural network (CNN)-based binary local search optimizer–based particle swarm optimization algorithm. The proposed method from this paper exploits image augmentation to enlarge the fundus image to the required size without losing any features. The features from the resized fundus images are extracted as a feature vector and fed into the feed-forward CNN as the input. Henceforth, it classifies the exudates from the fundus image. Further, the hyperparameters are optimized to reduce the computational complexities by utilization of binary local search optimizer (BLSO) and particle swarm optimization (PSO). The experimental analysis is conducted on the public ROC and real-time ARA400 datasets and compared with the state-of-art works such as support vector machine classifiers, multi-modal/multi-scale, random forest, and CNN for the performance metrics. The classification accuracy is high for the proposed work, and thus, our proposed outperforms all the other approaches. [ABSTRACT FROM AUTHOR]

Plant exudates are an important food source for many primates. The Critically Endangered Javan slow loris (Nycticebus javanicus) was previously found to prefer Acacia decurrens exudate in an anthropogenically disturbed site, while its feeding habits in secondary natural forest remain unknown. Knowledge of the chemical characteristics of the plant exudates that Javan slow lorises consume is limited, especially with respect to those that they feed on in natural forests. As plant exudates may contain plant secondary metabolites (PSM), which are considered unpalatable in high concentrations, differences in PSM composition may drive feeding preferences. This research aims firstly to confirm exudate consumption by the Javan slow loris in a lowland tropical forest in Central Java, and secondly to identify the chemical characteristics of the exudates consumed. We followed wild slow lorises in Kemuning Forest, Central Java and observed their behaviour. We investigated the gum-producing trees that were utilized by the slow lorises by tapping the exudates and examining their nutritional and PSM contents. We found that exudates are the predominant food source for the Javan slow loris in this lowland forest, and that their nutritional contents are similar to those of exudates consumed by lorises in anthropogenically disturbed areas. Significant differences in polysaccharide and flavonoid contents were found between consumed and unconsumed exudates. Knowledge of the diet of the Javan slow loris is crucial to its conservation, and our findings confirm the importance of exudates in its diet. We also highlight the need to preserve natural slow loris habitat, and to manage the diets of these species in captivity. The results of this study indicate that plant exudates should constitute a significant portion of the diet of captive slow lorises, and that the presence of exudate-producing trees is vital in areas into which slow lorises are to be translocated. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy is one of the leading causes of blindness in today's world. One of the major causes of Diabetic retinopathy is diabetes and also this occurs due to hereditary reasons. DR is classified into proliferative, non-proliferative and diabetic maculopathy. This paper approaches to one of the signs of non-proliferative DR called as exudates (commonly called hard exudates) and several methods which is introduced to detect them in retina. The work includes the algorithms, outcomes, datasets used and other results related with it. The results are compared by tabulating the evaluations and procedures. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a disease of the retina caused by diabetes. The existence of exudates in the retina is the primary visible sign of DR. Early exudate detection can prevent patients from the severe conditions of DR An intelligent framework is proposed that serves two purposes. First, it highlights the features of exudate from fundus images using an image processing approach. Afterwards, the enhanced features are used as input to train Alexnet for the detection of exudates. The proposed framework is comprised on three stages that include pre-processing, image segmentation, and classification. During the pre-processing stage, image quality is enhanced using contrast enhancement, median filtering, and removal of Optic Disc (OD). In the segmentation stage, image segmentation is performed using morphological operations. During the classification phase, these segmented images are then applied as input to Alexnet for fine-tuning of features and classification. A new data set of fundus images is also developed using data from a local hospital. CNN classifier Alexnet is applied on a newly developed local data set of fundus retinal images with 96.6% accuracy and on a benchmark public research data set DIARETDB1 with 98.88% accuracy. The statistical evaluation and comparative analysis of the proposed approach with existing state-of-the-art techniques validate that the proposed framework is robust and outperforms other methods for early detection of exudates in the fundus. [ABSTRACT FROM AUTHOR]

A hydrocellular functional material as a wound dressing is developed and it is found to be superior in its efficacy as compared to some of the comparator controls in diabetic wound healing studies. A study on wound contraction and Histopathological analysis is done in rats. The efficacy of the dressing is comparable to the established wound dressings like Carboxymethyl cellulose alginate dressings and autolytic enzyme based hydrogel. It is found to be superior to Polyhexamethylene biguanide dressing used as reference controls in this study. The reason for good wound healing performance of the dressing can be attributed to a combined property of effective exudates management and broad spectrum antimicrobial effect. The concept of functional hydro cellular material has shown good results due to the excellent balance of exudates pickup and drying it out. This ensures moist wound healing conditions on the wound. Because of its porous nature it allows good air flow and gaseous exchange in the structure. The cationic sites created on the surface of the dressing ensure a good antimicrobial action on the exudates in the dressing. It reduces the infection load on the wound. The nonleaching property of the dressing also helps in preventing the generation of more resistant and mutant strains of the microbes. The developed dressing can be used as a relatively durable long lasting dressing for wound management in diabetic wounds. The need of repetitive wound dressing changes can be brought down with this concept of dressing. It is not only cost effective in terms of its material cost but also is a cost effective solution when entire wound management cost is considered. Such novel wound dressing material can change the quality of life of diabetic wound patients especially in developing world, where access to functional advanced wound care dressings is limited. [ABSTRACT FROM AUTHOR]

Improved management and breeding increased maize (Zea mays L.) yields over the last century, but nutritional efficiency was usually not the focus. This study investigates whether old and recently released flint and dent maize seedlings vary in the phosphorus (P) acquisition and utilization. P use efficiency (PUE) and related traits were measured and compared at two P levels in a calcareous soil. PUE and P acquisition efficiency (PAE) from founder flints to elite flints declined over the last decades. This was associated with smaller root systems, reduced ability to exploit external P, decreased rhizosphere pH and shorter root hairs in low P. Comparing flints with doubled haploid landraces (DH_LR), old and elite dents and hybrids revealed that dents started to acquire exogenous P earlier and had improved PUE. Most DH_LRs had similar PUE as elite flints. When evaluating root traits associated with P efficiency, seed P was also critical, and it is important to stack different root traits to optimize PUE, P utilization efficiency (PUtE) and PAE in breeding programmes. The root hair length, the ability to acidify the rhizosphere and the root diameter in flint and dent pools may be utilized to improve P use in maize germplasm. [ABSTRACT FROM AUTHOR]

Recurrent Apthous Ulcers (RAU) has affected mankind through time immemorial. It is the most commonly prevalent oral mucosal lesion manifesting as painful ulcers involving non – keratinised oral mucosa. This review was done to assess herbal intervention in RAU patients for outcomes of ulcer size and pain intensity. Literature search of published articles in Medline, Scopus, Ovid and Journal of Web upto August 2020 were reviewed for the pre-described outcomes. Revman 5.4 software was used for study analysis. Total 9 articles were finally chosen for qualitative analysis. Meta analytic comparison demonstrated the ulcer reduction (CI = -2.22 to – 0.09; p <0.001) and pain intensity (CI = -4.60 to – 0.08; p <0.001) was reduced in the herbal group as compared to the controls. A definite evidence of herbal intervention was noted in alleviating RAU signs and symptoms. [ABSTRACT FROM AUTHOR]

To improve our understanding of early microbial colonization of pristine minerals and their group-specific C utilization, we exposed minerals (illite/goethite/quartz) amended with artificial root exudates (ARE, glucose, and citric acid) in grassland soils for a period of 24 weeks. FTIR spectra indicated that mineral-associated ARE were used within the first 2 weeks of exposure and were replaced by other carbohydrates derived from living or dead cells as well as soil-borne C sources transported into the mineralosphere after heavy rain events. Fungi and Gram-positive bacteria incorporated ARE-derived C more rapidly than Gram-negative bacteria. Gram-negative bacteria presumably profited indirectly from the ARE by cross-feeding on mineral-associated necromass of fungi and Gram-positive bacteria. The Gram-negative bacterial phyla Verrucomicrobia, Planctomycetes, Gemmatimonadetes, Armatimonadetes, and Chloroflexi showed a positive correlation with Gram-negative PLFA abundances. After 24 weeks of exposure in the grassland soils, abundances of soil microorganisms in the mineralosphere reached only 3.1% of the population density in soil. In conclusion, both bacteria and fungi slowly colonize new surfaces such as pristine minerals, but quickly assimilate artificial root exudates, creating an active microbial community in the mineralosphere. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a disease that occurs in the eye which results in blindness as it passes to proliferative stage. Diabetes can significantly result in symptoms like blurring of vision, kidney failure, nervous damage. Hence it has become necessary to identify retinal damage that occurs in diabetic eye due to raised glucose level in its initial stage itself. Hence automated detection of anamoly has become very essential. The appearance of crimson and yellow lesions is considered as the earliest symptoms of DR which are called as hemorrhages and exudates. If DR is analysed at initial stage, blindness does not occur. The damage in retina can hinder the light that passes through nerves of the eye leading to visual loss. The motivation behind this research is to reduce the number of false positives by accurate detection which is possible using proposed fuzzy system based on ANN. Though several classifiers are available to detect the exudates this paper makes analysis of support vector machine using radial basis kernel function with proposed ANN technique. Also, adaptive neuro fuzzy inference system segmentation is performed after feature extraction technique, which makes classifer to outperform. The evaluation results showed that proposed artificial neural network based on fuzzy approach attained significant results compared to other classifiers. Moreover, the proposed algorithm has significant accuracy of 94% and minimum error rate has been observed. [ABSTRACT FROM AUTHOR]

An eye disease that damages the retina of diabetic patients is known as diabetic retinopathy (DR). The severity of the disease is found by different lesions such as hemorrhages, microaneurysms, exudates etc., these are the early stage symptoms of non-proliferative DR for early analysis of DR. A single framework for instinctive Lesion Detection used for diagnosis of the disease easily by screening is proposed. It consists of four steps: luminosity and contrast enhancement, removal of extracted blood vessels and optic disc (OD), lesion detection and classification based on lesions. Gamma correction and CLAHE for luminosity and contrast enhancement. Principle component analysis for vessel extraction and using convex hull transform for OD detection. After background subtraction, lesions are detected using morphological operations and classification based on count of lesions. The proposed algorithm is analyzed using the publically available datasets and evaluated using the metrics of specificity, sensitivity and accuracy. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy is one of the most common complications of diabetes. Hemorrhages, micro-aneurysms, exudates are one of the earlier signs of diabetic retinopathy. This paper proposes an algorithm of matched filter with morphological operation for the detection of lesions in the fundus retinal image. Contrast limited adaptive histogram equalization method is used for the extraction of vessels. The noise is removed from the images using matched filter. After enhancement, the thresholding is applied for vessel extraction. The threshold of the image is done by the iterative self-organizing data analysis technique algorithm method. After removal of optic disk and the blood vessels from the retinal image, the morphology method is used to easily identify different types of lesions. The diabetic retinopathy images were collected from DIARETDB1; the morphology operation method is analyzed using the metrics of sensitivity, specificity and accuracy. The proposed method's detection accuracy value for the recognition of micro-aneurysms, exudates and hemorrhages was 98.43%, 98.06% and 98.68% compared with the results of the differential evolution algorithm. Detection of lesion such as micro-aneurysms, hemorrhages and exudates was possible. When compared with the differential evolution algorithm, morphological method achieved good accuracy for the detection of diabetic retinopathy. [ABSTRACT FROM AUTHOR]

Copyright of Revista Mexicana de Ingeniería Biomédica is the property of Sociedad Mexicana de Ingenieria Biomedica, A.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Der Ophthalmologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The importance of the research is to detect disorders in retinal images using machine learning techniques. Eyes play an important role in our day to day life and are the most valuable gift we have in retinal eye research, the retinal vessel parameters and accurate AVR measurement is consider as the important issues in image processing techniques. Diseases like glaucoma, exudates, and diabetic retinopathy can be observed in earlier stages by using retinal images. Diabetic retinopathy also known as diabetic eye disease which affects up to 80% of all patients who have diabetes eventually leads to blindness. To diagnosis the various disorder in earlier stage by using the retinal image based on the methodology of image processing and machine learning techniques. The retinal image is used to detect the diabetes in early stages by evaluating all the retinal blood vessels together. The proposed novel algorithm known as multi-resolution curvelet Transform and normalized graph cut segmentation to detect the optic disc and blood vessels in the fundus images efficiently. In earlier stage of this research, the pre-processing of fundus image operation for image filtration and color contrast enhancement, and next is image segmentation for blood vessels achieved by image processing techniques such as thresholding, texture, and morphological operation and finally machine learning classification algorithm are executed using convolutional neural network. This construction results in a multi resolution, local, and directional image expansion using contour segments. Results indicate that neural network is better than the other techniques for vessels classification. [ABSTRACT FROM AUTHOR]

Introduction: Pleural effusion is an accumulation of fluid in the pleural space. It can be transudative or exudative. Mechanisms like alteration in Starling's forces lead to transudative effusions while inflammation and infiltration by infections, malignancy, connective tissue diseases, etc lead to exudative effusions. Tuberculosis, viral, bacterial infections, and malignancy are common causes of exudative effusions whereas congestive heart failure, renal failure, and liver failure, etc are common causes of transudative effusions. Nearly 40% of patients with malignancy have pleural effusion at the time of presentation. Bronchogenic carcinoma, carcinoma of the breast, lymphoma are the leading causes of malignant pleural effusion (MPE) followed by gastrointestinal, genitourinary, and gynecological causes. Pleural fluid Adenosine DeAminase (ADA) has good diagnostic sensitivity and specificity for tuberculosis whereas pleural fluid cytology /biopsy are the main diagnostic modalities for MPE. However pleural fluid cytology is positive in only 48.5% of cases in the first sample but the yield increases with repeated analysis or other more invasive investigations like blind pleural biopsy/thoracoscopy. In cases with negative pleural fluid cytology, a biochemical marker known as Cancer ratio i.e serum LDH and pleural fluid ADA can be useful in predicting malignant causes. A cancer ratio cutoff of more than 20 helps in guiding the physician for further workups like FDG PET or tumor markers in evaluating malignancies. With this background our study aimed at the usefulness of cancer ratio in patients with exudative pleural effusion. Materials and Methods: It's a cross sectional observational study done for a period of 18months.100 adult patients with exudative pleural effusions were recruited into the study. Those who didn't give consent, hemodynamically unstable, whose diagnosis is known were excluded. Serum LDH, pleural fluid ADA were done in all cases and the cancer ratio is validated for diagnosis of malignant effusions. Results: The mean age of patients was 55.48±9.32 years. There were 57 malignant and 43 nonmalignant cases. Bronchogenic carcinoma was the leading cause of MPE and tuberculosis was the commonest cause of non-malignant pleural effusions. Mean serum LDH, Pleural fluid ADA, and cancer ratio in malignant cases and nonmalignant cases was 434.54 and 350.04IU/ml,19.05 and 32.97IU/ml and 25.13, 20.45 respectively. The sensitivity of cancer ratio was 70.17%, specificity was 76.74%, Positive predictive value was 80% and Negative predictive value was 66.6%. Conclusion: Cancer ratio is an easy and valid diagnostic tool in suspecting malignant pleural effusions with good sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Copyright of An-Najah University Journal for Research, A: Natural Sciences is the property of An-Najah National University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)