Pentacoordinate Silicon Complexes as Precursors to Silicate Glasses and Ceramics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65650/1/j.1151-2916.1994.tb07242.x.pd

ProAll-D: protein allergen detection using long short term memory - a deep learning approach

Background: An allergic reaction is the immune system\u27s overreacting to a previously encountered, typically benign molecule, frequently a protein. Allergy reactions can result in rashes, itching, mucous membrane swelling, asthma, coughing, and other bizarre symptoms. To anticipate allergies, a wide range of principles and methods have been applied in bioinformatics. The sequence similarity approach\u27s positive predictive value is very low and ineffective for methods based on FAO/WHO criteria, making it difficult to predict possible allergens. Method: This work advocated the use of a deep learning model LSTM (Long Short-Term Memory) to overcome the limitations of traditional approaches and machine learning lower performance models in predicting the allergenicity of dietary proteins. A total of 2,427 allergens and 2,427 non-allergens, from a variety of sources, including the Central Science Laboratory and the NCBI are used. The data was divided 80:20 for training and testing purposes. These techniques have all been implemented in Python. To describe the protein sequences of allergens and non-allergens, five E-descriptors were used. E1 (hydrophilic character of peptides), E2 (length), E3(propensity to form helices), E4(abundance and dispersion), and E5 (propensity of beta strands) are used to make the variable-length protein sequence to uniform length using ACC transformation. A total of eight machine learning techniques have been taken into consideration. Results: The Gaussian Naive Bayes as accuracy of 64.14 %, Radius Neighbour\u27s Classifier with 49.2 %, Bagging Classifier was 85.8 %, ADA Boost was 76.9 %, Linear Discriminant Analysis has 76.13 %, Quadratic Discriminant Analysis was 84.2 %, Extra Tree Classifier was 90%, and LSTM is 91.5 %. Conclusion: As the LSTM, has an AUC value of 91.5 % is regarded best in predicting allergens. A web server called ProAll-D has been created that successfully identifies novel allergens using the LSTM approach. Users can use the link https://doi.org/10.17632/tjmt97xpjf.1 to access the ProAll-D server and data

Development and Optimization of Solid Lipid Nanoparticle for Topical Delivery

The aim of present work was to develop and evaluate solid lipid nanoparticle (SLNs) based gel for topical delivery of anti-inflammatory drug. Material and method Nabumetone loaded SLNs were developed by hot homogenization followed by ultra- sonication technique using compritol 888 ATO as solid lipid and tween 80 as a surfactant. Developed SLNs were evaluated for particle size, entrapment efficiency (EE) and drug release profile. Process and formulation parameters were optimized. Differential scanning calorimetry (DSC) and X-ray diffraction (XRD) studies were carried out on SLNs to mark the change in the drug and lipid modification. The Nabumetone based gels were prepared using carbopol 940 as gelling agent. Results and conclusion: The F14 batch had shown maximum entrapment efficiency up to 94.40 and sustained drug release for more than 7 hours. The particle size of optimized batch (F14) was found to be 16.54. Keywords: Solid lipid nanoparticle, Entrapment efficiency, Colloidal carrier

An AI framework for Change Analysis and Forecast Modelling of Temporal Series of Satellite Images

The study focuses on change analysis and predicting future LULC map of capital city of Karnataka state, India. The chosen study area is more prone to urbanisation and greatly affected by population in recent years. Spatial-temporal data from 1989-2019 are considered. LULC classes comprise of Water bodies, Urban, Forest, Vegetation and Openland. An optimal LULC maps from 1989 to 2019 obtained by deep neural network technique are used to perform change analysis which would mainly give the change LULC map with number and percentage of change pixels. According to the analysis performed major change as environmental affecting factor was noticed between 2009 and 2019 where in urban with the area of 189.3861 sq. km remain unchanged and noticeable transitions from other LULC classes to urban. Later, time series classification was performed using Cellular Automata, Cellular Automata-Neural Networks, techniques to predict the LULC map of 2024. Among these CA-NN outperformed with an average kappa coefficient of 0.83. Also, this was validated with projected LULC map of 2024 provided by USGS

A case report of 29 year old male patient with breast carcinoma: diagnosed on fine needle aspiration cytology

Breast cancer has been considered a female dominated disease. Carcinoma of male breast is a rare disease representing 1% of all breast cancers and less than 1 % of all cancers in men. The mean age at presentation is mainly in sixties. We here present a case of male breast cancer presented at very young age of 29 years, diagnosed on fine needle aspiration which was confirmed later on histopathological examination