http://repository.ias.ac.in/39608/1/49_pub.pdf

A review of imperative technologies for wastewater treatment I: oxidation technologies at ambient conditions

A review of imperative technologies for wastewater treatment II: hybrid methods

The [2 + 2] photocycloaddition is undisputedly the most important and most frequently used photochemical reaction. In this review, it is attempted to cover all recent aspects of [2 + 2] photocycloaddition chemistry with an emphasis on synthetically relevant, regio-, and stereoselective reactions. The review aims to comprehensively discuss relevant work, which was done in the field in the last 20 years (i.e., from 1995 to 2015). Organization of the data follows a subdivision according to mechanism and substrate classes. Cu(I) and PET (photoinduced electron transfer) catalysis are treated separately in sections 2 and 4, whereas the vast majority of photocycloaddition reactions which occur by direct excitation or sensitization are divided within section 3 into individual subsections according to the photochemically excited olefin.

http://pubs.acs.org/doi/pdf/10.1021/acs.chemrev.5b00723

Recent Advances in the Synthesis of Cyclobutanes by Olefin [2+2] Photocycloaddition Reactions

A review of applications of cavitation in biochemical engineering/biotechnology

CLINICAL NEUROSURGERY Edited by Barnes Wordall. Vol. 18. (Pp. 557; illustrated; £8 25.) Churchill Livingstone: Edinburgh. 1971. CLINICAL NEUROSURGERY Edited by G. T. Tindall. Vol. 19. (Pp. 598; illustrated; £12.) Churchill Livingstone: Edinburgh. 1972. The Congress of Neurosurgeons began in 1951 on the initiative of a group of younger neurosurgeons. In the last 20 years its membership has grown from 69 to over 1,000, but it has retained its original intentions and virility by a constitution which ensured that the office bearers and organizers were always young men. Residents in training are encouraged to join, financial concessions make it possible for them to attend meetings, and these are organized as an educational exercise, by inviting established authorities to give lectures on selected topics, chosen to provide a balanced programme. As a result Clinical Neurosurgery is a valuable volume which all neurosurgeons look forward to each year; it is in quite a different class from the usual conference tome, full of brief and unconnected papers of widely varying quality. As the title suggests Clinical Neurosurgery includes excellent clinical reviews but the two recent volumes include also a section of seminars on fundamental research-in volume 18 on coma and sleep, in volume 19 on basic mechanisms of memory. It is also the custom to invite a senior neurosurgical citizen as guest of honour and his two or three papers afford an opportunity for historical and philosophical reflections as well as an experienced perspective on clinical and experimental work. Add to this the refreshing presidential address, from one of the (angry?) young men of neurosurgery and it will be clear that these volumes really do include something of interest for every neurosurgeon, whatever his own interests or prejudices. It is a relief to be able so warmly to recommend these books, when the question posed by so many other books is whether anyone would really want to read them. The most recent volume has a more consistent theme than former ones, and that is 'head injury'. It includes papers on mechanisms as revealed by animal experiment and by a pathologist who visited the scene of the accident before examining the brains of head injury fatalities. There are chapters on engineering and socio-psychological aspects of accident prevention, as well as down to earth clinical accounts of metabolic disorders, testing for acoustic vestibular 36 damage, and aspects of prognosis. This is a significant contribution to the literature on head injury.

https://jnnp.bmj.com/content/jnnp/37/3/361.3.full.pdf

The working brain (an introduction to neuropsychology)

In this paper a Rough Set Theory based approach is used to mine concise rules from a huge dataset of patients treated for Breast Adenocarcinoma, one of the prominent type of Adenocarcinoma among women. This paper works on a sample dataset containing data of six different classes, organized as objects and attributes. The dataset contains lot of uninformative attributes which has to be filtered out. Various algorithms are applied based on Rough Set Theory to first eliminate noise and redundancy from the dataset and then generate rules .Different set of rules are generated for different reducts obtained by the application of different algorithms. These rules are studied thoroughly to understand the behavior of the patients’ genes diagnosed with Breast Adenocarcinoma. Most important rules are singled out, using metric values, to get a good knowledge about the reasons behind the decision classes.

Rules mining for the diagnosis of breast adenocarcinoma using rough set theory

Bioinformatics data can be used for the ultimate prediction of diseases in different organisms. The microarray technology is a special form of 2D representation of genomic data characterized by an enormous number of genes across a handful of samples. The actual analysis of this data involves extraction or selection of the relevant genes from this vast amount of irrelevant and redundant data. These genes can be further used to predict classes of unknown samples. In this work, we have implemented two popular deep learning segmentation architectures, namely, SegNet and U-Net. These techniques have been applied to the microarray dataset of colon cancer (typically containing tumour and normal tissue samples) to extract the culprit/responsible gene. The performance of the reduced set formed from these genes has been compared across different classifiers using different existing methods of feature selection. It is found that both deep learning based approaches outperform the other methods. Lastly, the biological significance of the genes has also been verified using ontological tools, and the results are significant.

Identification of Differentially Expressed Genes Using Deep Learning in Bioinformatics

Cardiac Diseases I.

The development of a machine learning (ML) based prediction model for Parkinson's disease (PD) using Ayurvedic literature is very rare in practice. In modern era of digitization and artificial intelligence this lack leads us for developing a significant and useful predictive model for PD in light of Ayurveda. As in contemporary Ayurvedic literature, PD has not been fully classified according to all of its motor and non-motor symptoms, with the exception of Kampavata or tremor, one of the cardinal motor symptoms of PD, we have taken the help of MDS-UPDRS-II and MDS-NMSQ scaling system as an initial input for this purpose. Based on the available literature, we determined our tridosha score using these scales, which become our main inputs to the ML algorithm, along with other general health attributes such as age, sex, and BMI. We applied various ML algorithms and ranked our best ML model based on their performance. For training and testing purposes, we used the Fox Insight dataset with n = 80,916 records including PD and control. Finally, we found that Kernel-SVM, SVM, Logistics Regression (LR), and XGBoost are our four most accurate algorithms, with an accuracy more than 92.5% with no dimensionality reduction applied. Here we chose the LR model as our best ML model, depending on the lower false positive rate of 0.045 with an accuracy of 92.6%. The designed LR model is statistically significant with χ2(6) = 70703.137, p =0.000. The LR coefficient was also calculated for probability analysis and future implementation of digital Ayurveda-based PD prediction applications.

https://www.ijam.co.in/index.php/ijam/article/download/3228/1037

Goutam Saha

Papers

Rules mining for the diagnosis of breast adenocarcinoma using rough set theory

Identification of Differentially Expressed Genes Using Deep Learning in Bioinformatics

Cardiac Diseases I.

Development of a machine learning-based Parkinson's disease prediction system through Ayurvedic dosha analysis

Synthetic aperture radar image segmentation using supervised artificial neural network