Deep learning framework based on Spectral and Spatial properties for Land-Cover Classification using Landsat Hyperspectral Images

Hyperspectral Imaging is used to monitor the earth on basis of spectral continuous data ranges starting from visible to short wave infrared region of the electromagnetic spectrum. It enables the detailed identification and classification of minerals and land cover on basis of with improved spectral and spatial resolutions provides the opportunity to obtain accurate land-cover classification. Several challenges have been generated due to Hughes phenomenon (curse of dimensionality) and Quantification of land cover in urban area. In order to alleviate those problems, novel framework named as Deep learning framework on spectral and spatial properties on Landsat image has been proposed which composed of several techniques. Initially hyperspectral (HS) data exploitation model on identification of pure spectral signatures (endmembers) and their corresponding fractional abundances in each pixel of the HS data cube has been proposed. Feature reduction strategy based on principle component analysis has been employed to generate reduced dimensionality of the features on retaining the most useful information. The reduced features have been taken for the spectral analysis and spatial analysis using Multiobjective Discrete Spectral and Spatial optimized representation model through encompassing the sparse and low-rank structure on the spectral signature of pixels. Identification and mapping of the land cover classification categorized as agriculture area and bare land has been identified using spectral indices (end members). The spectral indices calculation provides the type of land cover on the pixel purity index and it classifies based on the spectral and spatial value using N finder algorithm. N finder Algorithm is a change vector analysis. Further Ensemble based method has been proposed in addition to generate diverse classification results and the discrete high correlation classifier method which can enhance the accuracy and diversity of a single classifier simultaneously. Finally an efficient agriculture land cover spectral evolution mapping has been proposed using Multivariate principle component analysis. It is considered as change detection method explores efficiently the context of images, which leads to a good tradeoff between wider receptive field and the use of Context towards mapping Agriculture Land cover spectral evolution. It computes the spectral correlation between two images on spectral similarity. It predicts the accurately on temporal changes of the earth surfaces. Experimental analysis has been carried out using Landsat-8 dataset to evaluating the performance of the proposed representative framework using available spectral indices against the state of art approaches. Proposed framework achieves accuracy of 99% on reflectance value against the different wavelength which superior with other existing classification approaches

Estimation of relative bilateral renal function of potential voluntary kidney donors using various computerized tomography methods

OBJECTIVE: To investigate four computed tomographic (CT) based methods of split renal function and to compare results Tc 99 DTPA scintigraphy. 1. Semi-automated volume method, 2. Attenuation capacity method, 3. Modified ellipsoid method, 4. Parenchymal area method. METHODS: Sixty two potential renal donors with both CT and nuclear renography were prospectively evaluated for estimated split function using 4 CT methods to determine accuracy. For the CT methods, correlation, ease in image post-processing, and the ability of CT-derived methods to determine the dominant kidney before renal transplantation were evaluated using a nuclear renography reference standard. RESULTS: Three of the 4 CT methods (split renal volume, modified ellipsoid method and attenuation capacity) showed similar strong Pearson’s correlation (r = 0.74 to 0.79). Bland-Altman analysis revealed similar performance in differences (SDs G3.0%) between these CT measures and reference standard. The parenchymal area method showed the least correlation (r= 0.54). Each CT-based method showed excellent agreement ( 98.4%) with renography regarding the determination of dominant kidney. CONCLUSION: Excellent correlation with nuclear split renal function supports the use of CT alone for the imaging assessment for many potential renal donors, including the decision of which kidney to harvest. Among the CT-based methods, the modified ellipsoid method can be performed rapidly with high accuracy and reproducibility

Enhancing Electric Vehicle Efficiency with a Novel SIMO DC-DC Converter: Integrating Multiple Speed Transmissions and Regenerative Braking

An innovative single-input multi-output (SIMO) Direct Current (DC)-DC switched capacitor (SC) converter charged and discharged the flying capacitors by utilising one separate independent voltage source (Vbat or Vin) to control the EV's speed. The switching pulse interval remained constant, allowing for the generation of different voltage ratios. To regulate the velocities of EV, this study developed a number of speed transmissions. Utilising the SIMO DC-DC converter, the chosen and controlled battery voltage was produced. For motoring, or forwarding operation, four transmissions were used, while the other three transmissions were used for regenerative braking. A total of seven transmissions were generated by the proposed converter. Regenerative braking involved feeding the recovered voltage back into the battery, while the motor operated using energy from the fuel cell, photovoltaic cells, and the battery. Electronic add- ons like LED lights, the electric vehicle sound system, and charging ports for mobile devices and laptops all relied on the SIMO DC-DC converter. For various gear ratios, the suggested SIMO DC-DC converter used the energy restored in regenerative braking to recharge the battery. To further validate the proposed system, modelling, simulation, and analysis are employed. The 12 V fixed-voltage input and the 12 to 53 V output voltages are its intended uses. To ensure the system was reliable, it was simulated by the PSIM tool. Validation of the proposed converter provides strong proof of concept for regenerative braking and braking procedures

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.Cancer Research UK, Grant/Award Number: FC001003; Changzhou Science and Technology Bureau, Grant/Award Number: CE20200503; Department of Energy and Climate Change, Grant/Award Numbers: DE-AR001213, DE-SC0020400, DE-SC0021303; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Institut national de recherche en informatique et en automatique (INRIA), Grant/Award Number: Cordi-S; Lietuvos Mokslo Taryba, Grant/Award Numbers: S-MIP-17-60, S-MIP-21-35; Medical Research Council, Grant/Award Number: FC001003; Japan Society for the Promotion of Science KAKENHI, Grant/Award Number: JP19J00950; Ministerio de Ciencia e Innovación, Grant/Award Number: PID2019-110167RB-I00; Narodowe Centrum Nauki, Grant/Award Numbers: UMO-2017/25/B/ST4/01026, UMO-2017/26/M/ST4/00044, UMO-2017/27/B/ST4/00926; National Institute of General Medical Sciences, Grant/Award Numbers: R21GM127952, R35GM118078, RM1135136, T32GM132024; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM078221, R01GM093123, R01GM109980, R01GM133840, R01GN123055, R01HL142301, R35GM124952, R35GM136409; National Natural Science Foundation of China, Grant/Award Number: 81603152; National Science Foundation, Grant/Award Numbers: AF1645512, CCF1943008, CMMI1825941, DBI1759277, DBI1759934, DBI1917263, DBI20036350, IIS1763246, MCB1925643; NWO, Grant/Award Number: TOP-PUNT 718.015.001; Wellcome Trust, Grant/Award Number: FC00100

Sequence and Structure Signatures of Cancer Mutation Hotspots in Protein Kinases

Protein kinases are the most common protein domains implicated in cancer, where somatically acquired mutations are known to be functionally linked to a variety of cancers. Resequencing studies of protein kinase coding regions have emphasized the importance of sequence and structure determinants of cancer-causing kinase mutations in understanding of the mutation-dependent activation process. We have developed an integrated bioinformatics resource, which consolidated and mapped all currently available information on genetic modifications in protein kinase genes with sequence, structure and functional data. The integration of diverse data types provided a convenient framework for kinome-wide study of sequence-based and structure-based signatures of cancer mutations. The database-driven analysis has revealed a differential enrichment of SNPs categories in functional regions of the kinase domain, demonstrating that a significant number of cancer mutations could fall at structurally equivalent positions (mutational hotspots) within the catalytic core. We have also found that structurally conserved mutational hotspots can be shared by multiple kinase genes and are often enriched by cancer driver mutations with high oncogenic activity. Structural modeling and energetic analysis of the mutational hotspots have suggested a common molecular mechanism of kinase activation by cancer mutations, and have allowed to reconcile the experimental data. According to a proposed mechanism, structural effect of kinase mutations with a high oncogenic potential may manifest in a significant destabilization of the autoinhibited kinase form, which is likely to drive tumorigenesis at some level. Structure-based functional annotation and prediction of cancer mutation effects in protein kinases can facilitate an understanding of the mutation-dependent activation process and inform experimental studies exploring molecular pathology of tumorigenesis

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Dense connected convolution neural network for land cover classification

Hyperspectral Imaging is employed to monitor the earth regions on basis of spectral continuous data ranges initializing from visible wave infrared region to short wave infrared region of the electromagnetic spectrum. It authorizes the detailed recognition and classification of land cover on account of spectral feature space. Hyperspectral images seemed to be presented by employing traditional unsupervised and supervised classifier with regards to classification. Various problems seemed to cause Hughes phenomenon as it represents the curse of dimensionality issues. In spite of mitigating those challenges, a deep ensemble classification model seemed to be proposed in this work. It process the data features using various convolution layers of the network along modelling the activation function as a simple structure for classification of the hyperspectral data based on the spectral values using Softmax layer and error function to minimize the losses. Dense Connected Convolution Neural Network projected in this work as it has high potential to effectively classify the spectral features with learnt weights from one individual convolution layer to convolution layers. The main idea of Dense Convolution Neural Network is to produce discriminative classification results and to enhance the accuracy and diversity of a classifier simultaneously.&nbsp

Free Radical Scavenging and Antioxidant effects of Tolfenamic Acid in L-NAME-Induced Hypertensive Rats

Objectives: The aim of this study was to determine the antioxidant and free radical scavenging properties of tolfenamic acid (TA) against N-Nitro-L-arginine methyl ester hydrochloride (L-NAME) induced hypertension. Materials and Methods: The rats were divided into five groups at random: Group I (control rats), Group II (control TA), Group III (L-NAME), Group IV (L-NAME+TA), and Group V (L-NAME+Enalapril). For four weeks, rats were given L-NAME (40mg/kg body weight) dissolved in drinking water to induce hypertension. Intraperitoneal injections of TA (50mg/kg body weight) and enalapril (0.7 mg/kg body weight) were given to rats. Results: The results showed that the In vitro free radical scavenging effect of TA on DPPH and ABTS was concentration dependent. In vivo studies with L-NAME resulted in increases in blood pressure, lipid hydroperoxides (LOOH), and thiobarbituric acid reactive substances (TBARS). In addition, the level of the non-enzymatic antioxidant reduced glutathione (GSH) and other enzyme antioxidant activities in the heart and aorta, including superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (Gpx), are reduced. The level of nitric oxide metabolism in the erythrocyte aorta of L-NAME rats was increased. Conclusions: These findings imply that tolfenamic acid acts as an antihypertensive and antioxidant agent in L-NAME-induced hypertension