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Rapid and accurate inundation modelling in large floodplains is critical for emergency response and environmental management. This paper describes the development and implementation of a floodplain inundation model that can be used for rapid assessment of inundation in very large floodplains. The model uses high resolution DEM (such as LiDAR DEM) to derive floodplain storages and connectivity between them at different river stages. We tested the performance of the model across several large floodplains in southeast Australia for estimating floodplain inundation extent, volume, and water depth for a few recent flood events. The results are in good agreement with those obtained from high resolution satellite imageries and MIKE 21 two-dimensional hydrodynamic model. The model performed particularly well in the reaches that have confined channels with above 85 % agreement with the flood maps derived from Landsat TM imagery in cell-to-cell comparison. While the model did not performance as well in the flat and complex floodplains, the overall level of agreement of the modelled inundation maps with the satellite flood maps was still satisfactory (60–75 %). The key advantage of this model is demonstrated by its capability to simulate inundation in large floodplains (over 2000 km 2 ) at a very high resolution of 5-m with more than 81 million cells at a reasonably low computational cost. The model is suitable for practical floodplain inundation simulation and scenario modelling under current and future climate conditions.

ABSTRACT Introduction: The practice of removing root canal fillings with solvent materials is frequently required to help an irrigation solution enter the tubules. The current research was aimed at assessing the antifungal properties specifically the candida albicans of the various solvent materials used for the gutta-percha (GP) material. Materials and Methods: Current research was aimed at as a lab method using the disk diffusion technique where the zone of inhibition (ZOI) was calculated. The materials that were analyzed were: orange oil, xylene, turpentine oil, chloroform, and eucalyptus oil. Candida albicans was the test organism employed in the investigation. The agar plates were covered with approximately 500 μL of the suspension. The sterile and empty disks were impregnated with 10 μL of pure GP solvents. These plates were incubated for one day at room temperature. The ZOI's mean diameters were calculated for all five materials and quantified each solvent's fungicidal activity. For intergroup comparison, ANOVA was utilized. P values < 0.05 were deemed substantial. Results: The maximum inhibition exhibited by the Eucalyptus Oil it was 19.01 ± 1.02 mm. This was followed by Xylene. The other three solvents Chloroform, Orange Oil, and Turpentine Oil exhibited a similar ZOI. When all the solvents were compared there was a significant variance of P < 0.001. However, there were significant variances for the Eucalyptus Oil and the Xylene to all the other solvents P < 0.001. Conclusion: This investigation showed that, in comparison to other solvents, the use of eucalyptus oil considerably reduced the levels of Candida Albicans.

Ketogenic diet (KD) is a high-fat, adequate-protein, and low-carbohydrate diet that leads to nutritional ketosis, long known for antiepileptic effects and has been used therapeutically to treat refractory epilepsy. This review attempts to summarize the evidence and clinical application of KD in diabetes, obesity, and other endocrine disorders. KD is usually animal protein based. An empiric vegetarian Indian variant of KD has been provided keeping in mind the Indian food habits. KD has beneficial effects on cardiac ischemic preconditioning, improves oxygenation in patients with respiratory failure, improves glycemic control in diabetics, is associated with significant weight loss, and has a beneficial impact on polycystic ovarian syndrome. Multivitamin supplementations are recommended with KD. Recently, ketones are being proposed as super-metabolic fuel; and KD is currently regarded as apt dietary therapy for \"diabesity.\"

Aim: The effects of Difenoconazole fungicide were assessed on Pethia conchonius (Hamilton, 1882) by examining their behavioural, histopathological and brain acetylcholinesterase activity. Methodology: Acute toxicity test was carried out following the OECD Guideline (2019) to estimate the 96 hr-LC50 of Difenoconazole (1.886 mg l-1). Three concentrations of Difenoconazole, (0.037, 0.188 and 0.377 mg l-1) designated as SLC I, II and III were used to expose the fish for 96 hr. Behaviour was monitored regularly. Brain tissue was collected at 24 hr intervals for histopathological study and biochemical assay of acetylcholinesterase. Temperature, pH, total alkalinity, total hardness, and dissolved oxygen of test water, were also analyzed following the standard method. Results: Exposed fish exhibited significantly reduced acetylcholinesterase activity and mild to severe behavioural changes including sluggish movement, loss of equilibrium, bottom-crowding and excessive mucus secretion in a dose and time-dependent response. Necrosis, vacuolation and layer detachment were also noted in the optic tectum of brain. Interpretation: Inhibition of acetylcholinesterase activity, severe neural damage and behavioural modulations strongly highlighted Difenoconazole's neurotoxic potential. Key words: Biomarker, Difenoconazole, Neurotoxicity, Pethia conchonius, Triazole

Gingivo-buccal oral squamous cell carcinoma (OSCC-GB), an anatomical and clinical subtype of head and neck squamous cell carcinoma (HNSCC), is prevalent in regions where tobacco-chewing is common. Exome sequencing ( n =50) and recurrence testing ( n =60) reveals that some significantly and frequently altered genes are specific to OSCC-GB ( USP9X , MLL4 , ARID2 , UNC13C and TRPM3 ), while some others are shared with HNSCC (for example, TP53 , FAT1 , CASP8 , HRAS and NOTCH1 ). We also find new genes with recurrent amplifications (for example, DROSHA , YAP1 ) or homozygous deletions (for example, DDX3X ) in OSCC-GB. We find a high proportion of C>G transversions among tobacco users with high numbers of mutations. Many pathways that are enriched for genomic alterations are specific to OSCC-GB. Our work reveals molecular subtypes with distinctive mutational profiles such as patients predominantly harbouring mutations in CASP8 with or without mutations in FAT1. Mean duration of disease-free survival is significantly elevated in some molecular subgroups. These findings open new avenues for biological characterization and exploration of therapies. Gingivo-buccal oral squamous cell carcinoma (OSCC-GB) is the leading cancer among males in India. Here, the authors carry out exome sequencing and recurrence testing in patients with OSCC-GB and highlight genes and biological pathways associated with the disease.

Artificial tears are the mainstay of dry eye disease management, but also have a role in corneal abrasion and wound healing, pain and inflammation management, conjunctivitis, keratitis, contact lens rewetting and removal, and foreign body removal. A systematic review of randomized controlled trials (PROSPERO registration CRD42022369619) comparing the efficacy of artificial tears in patients with dry eye to inform prescribing choices using Web of Science, PubMed and Medline databases identified 64 relevant articles. There is good evidence that artificial tears improve symptoms of dry eye disease within a month of regular use, applied about four times a day, but signs generally take several months to improve. Not all patients with dry eye disease benefit from artificial tears, so if there is no benefit over a month, alternative management should be considered. Combination formulations are more effective than single active ingredient artificial tears. Artificial tears containing polyethylene glycol are more effective than those containing carboxymethylcellulose/carmellose sodium and hydroxypropyl methylcellulose. Those classified as having evaporative dry eye disease, benefit from artificial tears with liposomes, especially of higher concentration. The data available is limited by the definition of dry eye disease applied in published studies being variable, as well as the disease severity examined and compliance with artificial tears being rarely quantified.

Background Despite the known effectiveness of long-lasting insecticidal nets (LLINs) in providing protection against malaria, high level of ownership and use are very difficult to achieve and maintain. Nearly 40,000 LLINs were distributed in 2014 as an intervention tool against malaria transmission in 80 villages of Keshkal sub-district in Chhattisgarh, India. This study assessed LLIN coverage, access, utilization pattern, and key determinants for the net use 1 year after mass distribution. Methods In 2015, a cross-sectional household survey was carried out in 80 study clusters (whole village or part of village). From each cluster, 40 households were randomly selected and interviewed using a structured questionnaire adapted from the malaria indicator survey of Roll Back Malaria guidelines. Information on demographic characteristics, LLIN ownership, and its use on the night before the survey, and physical condition of LLINs were recorded. Results 2970 households were interviewed with a total of 15,003 individuals present in the households during the night before the survey. Nearly 98% of households had at least one LLIN and 59.4% of the surveyed population reportedly used an LLIN the previous night. LLIN use varied from 41 to 94% between the study clusters. Nearly 89% of the LLINs were found in good physical condition (without holes). However, proportion of household with at least one LLIN per two persons was only 39%. Conclusion Universal coverage of LLINs was inadequate in the study clusters making it difficult for all household members to use an LLIN. LLIN use varied between clusters and was highest in children under 5 years of age. Health education campaigns and creating awareness about the benefit of sleeping under the LLINs in providing protection against malaria is required not only to high risk groups of pregnant women and children below 5 years of age but all the members of the family to have an epidemiological impact of this intervention at the community level. Relatively high net use despite poor access to LLINs indicates an overall desire to use nets when they are available. The main barrier to increased use of nets is the low coverage at household level.

DLR’s Earth Sensing Imaging Spectrometer (DESIS) is mounted on the International Space Station (ISS). DESIS records data in the spectral range from 400 to 1000 nm with a spectral and spatial resolution of 2.55 nm and 30 m respectively. The high spectral resolution enables in detecting a target object distinctly in remotely sensed imagery which has many useful applications in different fields of surveillance and monitoring. In present work two different case studies have been carried out that use DESIS data for target detection. In the first case study brick kilns are detected in DESIS data using Adaptive Coherence Estimator (ACE) algorithm. In the second case study Photovoltaic (PV) panels are considered as target object and linear spectral unmixing is employed to distinctly detect them in the image. From experimental results it is observed that the first target which were sparsely located in the image is detected very precisely with F1 score value of 0.97. The accuracy of the output of PV panel detection is observed to be more than 98%. Both the case studies show the potential of DESIS data in target detection which is a very important application of hyperspectral remote sensing.

Propagation of arbitrary amplitude solitary waves is investigated in an unmagnetized quantum pair-ion plasma through the usage of Sagdeev pseudopotential approach in the framework of quantum hydrodynamics model. Bohm potential is elucidated to have significant impact on the structure of solitary wave. We would like to demonstrate that the regions of stability for the solitary waves of this quantum plasma system are well determined by studying the phase portrait. Analytical calculations are employed to simplify the basic equations, which are then studied numerically. The numerical analysis of Sagdeev potential for small value of quantum diffraction parameter(H) shows that for such plasma, there exists only compressive solitons. The effect of different plasma parameters on the solitonic structure are traced .

Background Investigation of brain cancer can detect the abnormal growth of tissue in the brain using computed tomography (CT) scans and magnetic resonance (MR) images of patients. The proposed method classifies brain cancer on shape-based feature extraction as either benign or malignant. The authors used input variables such as shape distance (SD) and shape similarity measure (SSM) in fuzzy tools, and used fuzzy rules to evaluate the risk status as an output variable. We presented a classifier neural network system (NNS), namely Levenberg-Marquardt (LM), which is a feed-forward back-propagation learning algorithm used to train the NN for the status of brain cancer, if any, and which achieved satisfactory performance with 100% accuracy. Methods The proposed methodology is divided into three phases. First, we find the region of interest (ROI) in the brain to detect the tumors using CT and MR images. Second, we extract the shape-based features, like SD and SSM, and grade the brain tumors as benign or malignant with the concept of SD function and SSM as shape-based parameters. Third, we classify the brain cancers using neuro-fuzzy tools. In this experiment, we used a 16-sample database with SSM ([mu]) values and classified the benignancy or malignancy of the brain tumor lesions using the neuro-fuzzy system (NFS). Results We have developed a fuzzy expert system (FES) and NFS for early detection of brain cancer from CT and MR images. In this experiment, shape-based features, such as SD and SSM, were extracted from the ROI of brain tumor lesions. These shape-based features were considered as input variables and, using fuzzy rules, we were able to evaluate brain cancer risk values for each case. We used an NNS with LM, a feed-forward back-propagation learning algorithm, as a classifier for the diagnosis of brain cancer and achieved satisfactory performance with 100% accuracy. The proposed network was trained with MR image datasets of 16 cases. The 16 cases were fed to the ANN with 2 input neurons, one hidden layer of 10 neurons and 2 output neurons. Of the 16-sample database, 10 datasets for training, 3 datasets for validation, and 3 datasets for testing were used in the ANN classification system. From the SSM (µ) confusion matrix, the number of output datasets of true positive, false positive, true negative and false negative was 6, 0, 10, and 0, respectively. The sensitivity, specificity and accuracy were each equal to 100%. Conclusion The method of diagnosing brain cancer presented in this study is a successful model to assist doctors in the screening and treatment of brain cancer patients. The presented FES successfully identified the presence of brain cancer in CT and MR images using the extracted shape-based features and the use of NFS for the identification of brain cancer in the early stages. From the analysis and diagnosis of the disease, the doctors can decide the stage of cancer and take the necessary steps for more accurate treatment. Here, we have presented an investigation and comparison study of the shape-based feature extraction method with the use of NFS for classifying brain tumors as showing normal or abnormal patterns. The results have proved that the shape-based features with the use of NFS can achieve a satisfactory performance with 100% accuracy. We intend to extend this methodology for the early detection of cancer in other regions such as the prostate region and human cervix.