Peptide conjugate hydrogelators

Molecular gelators are currently receiving a great deal of attention. These are small molecules which, under the appropriate conditions, assemble in solution to, in the majority of cases, give long fibrillar structures which entangle to form a three-dimensional network. This immobilises the solvent, resulting in a gel. Such gelators have potential application in a number of important areas from drug delivery to tissue engineering. Recently, the use of peptide-conjugates has become prevalent with oligopeptides (from as short as two amino acids in length) conjugated to a polymer, alkyl chain or aromatic group such as naphthalene or fluorenylmethoxycarbonyl (Fmoc) being shown to be effective molecular gelators. The field of gelation is extremely large; here we focus our attention on the use of these peptide-conjugates as molecular hydrogelators

B- and T-cell epitopes of tropomyosin, the major shrimp allergen

Seafood allergy is often encountered on ingestion of crustaceans such as shrimp, lobster, crab, and crayfish (1). On eating cooked shrimp, sensitive individuals experience a wide spectrum of reactions ranging from abdominal discomfort to anaphylaxis. The presence of cross-reacting heat-stable allergens in crustacean food was first recognized by Hoffman et al. (2) and Lehrer et al. (3). Subsequently, the major allergen was isolated and characterized from the shrimp species Paneaus indicus (Pen i 1) (4) and I? aztecm (Pen a 1) (5). Pen i 1 (originally designated Sa-TI) and Pen a 1, with mol. mass of 34 and 36 kDa, respectively, contain 301 and 312 amino-acid residues with a predominance of gluta- mate/glutamine and asparatate/asparagine

Pulmonary Embolism following Radiofrequency Ablation for Varicose Vein Treated with Thrombolytic Therapy: A Case Report and Review of Literature

Radiofrequency ablation (RFA) for varicose vein may have life-threatening complications such as deep vein thrombosis and pulmonary embolism (PE). Here, we reported a case report of PE following RFA, which required thrombolysis to save the patient

Quadruple-ridged flared horn feed with internal RFI band rejection filter

This paper presents a new technique to reject the radio frequency interference (RFI) from the nearby radar at 9.4 GHz, which causes about 20% blockage of the sky coverage in the Very Long Baseline Inter ferometry (VLBI) telescope at the Goddard Geophysical Astronomical Observatory (GGAO), in Greenbelt, MD. An internal notch filter is proposed by inserting and optimizing two-quarter wavelength slots within the wide band Quad-Ridge Flared Horn (QRFH) feed to achieve RFI band rejection at 9.4 GHz. The simulated result shows about 95 % rejection at 9.4 GHz. The estimated attenuation due to the band rejection slots is of the order of .01 dB at frequencies distant from the rejection frequency. This technique will open the door for designing wideband feeds with RFI band rejection characteristics for different RFI sources

Multi-class segmentation skin diseases using improved tuna swarm-based U-EfficientNet

Abstract Early location of melanoma, a dangerous shape of skin cancer, is basic for patients. Indeed, for master dermatologists, separating between threatening and generous melanoma could be a troublesome errand. Surgical extraction taken after early determination of melanoma is at its way to dispense with the malady that will result in passing. Extraction of generous injuries, on the other hand, will result in expanded dismalness and superfluous wellbeing care costs. Given the complexity and likeness of skin injuries, it can be troublesome to create an accurate determination. The proposed EfficientNet and UNet are combined and arrange to extend division exactness. Also, to decrease data misfortune amid the learning stage, adjusted fish swarm advancement (IMSO) is utilized to fine-tune the U-EfficientNet’s movable parameters. In this paper, a ViT-based design able to classify melanoma versus noncancerous injuries is displayed. On the HAM1000 and ISIC-2018 datasets, the proposed ViT demonstrated accomplished the normal precision of 99.78% and 10.43% FNR with computation time of 134.4632s of ISIC-2018 datasets. The proposed ViT show accomplished the normal exactness of 99.16% and 9.38% FNR in with computation time of 133.4782s of HAM1000 dataset

A study on the inhibition of oxidative stress, inflammation and apoptosis by Terminalia arjuna against acetaminophen-induced hepatotoxicity in wistar albino rats

The liver in the process of detoxifying chemicals is prone to injury due to overuse of therapeutic drugs like Acetaminophen and the liver cell death is largely inflammatory mediated. The present study aims to find out the effect of Terminalia arjuna (TA) bark against acetaminophen (APAP) induced liver cell death by testing the antioxidant levels, oxidative stress, and inflammation and apoptosis markers. In the present study, five groups (6 animals in each group) were considered for the experimental animal study. Control group, Acetaminophen (APAP) group, N-Acetylcysteine (NAC) group, Terminalia arjuna (TA) 250 mg/kg and Terminalia arjuna (TA) 500 mg/kg group. The antioxidant Glutathione (GSH), Lipid peroxidation (MDA), Interleukin 1β (IL-1β) levels, caspase-9 levels, and Protein kinase B (P-AKT) gene expression levels were assessed. The results indicated that pre-treated animals with Terminalia arjuna high dose bark had shown increased Glutathione (GSH) levels, thinned out malondialdehyde (MDA) levels, Inhibited IL-1β level, Caspase-9 levels and elevated gene expression level of P-AKT to regulate the cell signaling pathway. Hence, the study indicates that a high dose of TA 500 mg/kg ameliorated acetaminophen-induced hepatotoxicity

A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features

Due to recent advances in the Vehicular Internet of Things (VIoT), a large volume of traffic trajectory data has been generated. The trajectory data is highly unstructured and pre-processing it is a very cumbersome task, due to the complexity of the traffic data. However, the accuracy of traffic flow learning models depends on the quantity and quality of preprocessed data. Hence, there is a significant gap between the size and quality of benchmarked traffic datasets and the respective learning models. Additionally, generating a custom traffic dataset with required feature points in a constrained environment is very difficult. This research aims to harness the power of the deep learning hybrid model with datasets that have fewer feature points. Therefore, a hybrid deep learning model that extracts the optimal feature points from the existing dataset using a stacked autoencoder is presented. Handcrafted feature points are fed into the hybrid deep neural network to predict the travel path and travel time between two geographic points. The chengdu1 and chengdu2 standard reference datasets are used to realize our hypothesis of the evolution of a hybrid deep neural network with minimal feature points. The hybrid model includes the graph neural networks (GNN) and the residual networks (ResNet) preceded by the stacked autoencoder (SAE). This hybrid model simultaneously learns the temporal and spatial characteristics of the traffic data. Temporal feature points are optimally reduced using Stacked Autoencoder to improve the accuracy of the deep neural network. The proposed GNN + Resnet model performance was compared to models in the literature using root mean square error (RMSE) loss, mean absolute error (MAE) and mean absolute percentile error (MAPE). The proposed model was found to perform better by improving the travel time prediction loss on chengdu1 and chengdu2 datasets. An in-depth comprehension of the proposed GNN + Resnet model for predicting travel time during peak and off-peak periods is also presented. The model’s RMSE loss was improved up to 22.59% for peak hours traffic data and up to 11.05% for off-peak hours traffic data in the chengdu1 dataset

A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features

Due to recent advances in the Vehicular Internet of Things (VIoT), a large volume of traffic trajectory data has been generated. The trajectory data is highly unstructured and pre-processing it is a very cumbersome task, due to the complexity of the traffic data. However, the accuracy of traffic flow learning models depends on the quantity and quality of preprocessed data. Hence, there is a significant gap between the size and quality of benchmarked traffic datasets and the respective learning models. Additionally, generating a custom traffic dataset with required feature points in a constrained environment is very difficult. This research aims to harness the power of the deep learning hybrid model with datasets that have fewer feature points. Therefore, a hybrid deep learning model that extracts the optimal feature points from the existing dataset using a stacked autoencoder is presented. Handcrafted feature points are fed into the hybrid deep neural network to predict the travel path and travel time between two geographic points. The chengdu1 and chengdu2 standard reference datasets are used to realize our hypothesis of the evolution of a hybrid deep neural network with minimal feature points. The hybrid model includes the graph neural networks (GNN) and the residual networks (ResNet) preceded by the stacked autoencoder (SAE). This hybrid model simultaneously learns the temporal and spatial characteristics of the traffic data. Temporal feature points are optimally reduced using Stacked Autoencoder to improve the accuracy of the deep neural network. The proposed GNN + Resnet model performance was compared to models in the literature using root mean square error (RMSE) loss, mean absolute error (MAE) and mean absolute percentile error (MAPE). The proposed model was found to perform better by improving the travel time prediction loss on chengdu1 and chengdu2 datasets. An in-depth comprehension of the proposed GNN + Resnet model for predicting travel time during peak and off-peak periods is also presented. The model’s RMSE loss was improved up to 22.59% for peak hours traffic data and up to 11.05% for off-peak hours traffic data in the chengdu1 dataset

ELISA for the detection of venoms from four medically important snakes of India

A double antibody sandwich enzyme linked immunosorbent assay (ELISA) was developed to detect Echis carinatus venom in various organs (brain, heart, lungs, liver, spleen and kidneys) as well as tissue at the site of injection of mice, at various time intervals (1, 6, 12, 18, 24 h and 12 h intervals up to 72 h) after death. The assay could detect E. carinatus venom levels up to 2.5 ng/ml of tissue homogenate and the venom was detected up to 72 h after death. A highly sensitive and species-specific avidin-biotin microtitre ELISA was also developed to detect venoms of four medically important Indian snakes (Bungarus caeruleus, Naja naja, E. carinatus and Daboia russelli russelli) in autopsy specimens of human victims of snake bite. The assay could detect venom levels as low as 100 pg/ml of tissue homogenate. Venoms were detected in brain, heart, lungs, liver, spleen, kidneys, tissue at the bite area and postmortem blood. In all 12 human victim cadavers tested the culprit species were identified. As observed in mice, tissue at the site of bite area showed the highest concentration of venom and the brain showed the least. Moderate amounts of venoms were found in liver, spleen, kidneys, heart and lungs. Development of a simple, rapid and species-specific diagnostic kit based on this ELISA technique useful to clinicians is discussed