One-Pot Deconstruction and Conversion of Lignocellulose Into Reducing Sugars by Pyridinium-Based Ionic Liquid-Metal Salt System.

One-Pot Deconstruction and Conversion of Lignocellulose Into Reducing Sugars by Pyridinium-Based Ionic Liquid-Metal Salt System.

Steadily declining fossil resources and exceeds the energy needs of the most worrying concern today. The only way out is to develop a biomass processing protocol is efficient, safe, and economical that can lead towards biofuels and chemicals.

This research is one of the consequences such as those involving the deconstruction and conversion constituent carbohydrate wheat straw into reducing sugars by reaction of the pot is promoted by Lewis acidic ionic liquids based on pyridinium (PyILs) mixed with metal salts of different (MCL ).

Various parameters such as the type of metal salt, the amount of metal salt loading, time, temperature, biomass particle size, and water content affects the deconstruction of wheat straw has been evaluated and optimized.

One-Pot Deconstruction and Conversion of Lignocellulose Into Reducing Sugars by Pyridinium-Based Ionic Liquid-Metal Salt System.
One-Pot Deconstruction and Conversion of Lignocellulose Into Reducing Sugars by Pyridinium-Based Ionic Liquid-Metal Salt System.

Study of cervical precancerous lesions detection by spectroscopy and support vector machine.

Background and Purpose: Diffuse reflectance spectroscopy (DRS) offers rapid, non-invasive, and inexpensive alternative for the diagnosis of cervical cancer. We aim to develop a method for screening for precancerous lesions by DRS.

Material and methods: parameter characteristics of cervical tissue taken from the spectrum, including the parameters of optical characteristics such as absorption and scattering coefficients, and some slopes and local spectral parameters.

Data were randomly divided into training (60%) and test (40%) set. Of the 210 patients included, 166 healthy, 22 had cervical erosion, and 31 had cervical intraepithelial neoplasia (CIN). Support vector machine (SVM) algorithms used to classify normal and abnormal cervical tissue by 11 parameters.

Results characteristics: The SVM with linear kernel function, applied to the training data, can distinguish the network with lesions from healthy tissue with an accuracy of 1:00. When the classifiers are applied to the test set, and CIN cervical erosion can be distinguished from healthy tissue with an accuracy of 0.95 ([Formula: see text] 0.03) .Conclusions: This study showed that the diagnostic algorithm can be valuable for non diagnosis -invasive cervical cancer. This is a significant step toward the development of a network assessment tool for cervical cancer.