Metal-Organic Framework-Supported Mono Bipyridyl-Iron Hydroxyl Catalyst for Selective Benzene Hydroxylation into Phenol.

Direct hydroxylation of benzene to phenol is more appealing in the industry for the economic and environmentally friendly phenol synthesis than the conventional cumene process. We have developed a UiO-metal-organic framework (MOF)-supported mono bipyridyl-Iron(II) hydroxyl catalyst [bpy-UiO-Fe(OH)] for the selective benzene hydroxylation into phenol using HO as the oxidant. The heterogeneous bpy-UiO-Fe(OH) catalyst showed high activity and remarkable phenol selectivity of 99%, giving the phenol mass-specific activity up to 1261 mmolg h at 60 °C.

Gray Matter Atrophy in a 6-OHDA-induced Model of Parkinson's Disease.

Introduction: Magnetic resonance imaging (MRI) based brain morphometric changes in unilateral 6-hydroxydopamine (6-OHDA) induced Parkinson's disease (PD) model can be elucidated using voxel-based morphometry (VBM), study of alterations in gray matter volume and Machine Learning (ML) based analyses.

Methods: We investigated gray matter atrophy in 6-OHDA induced PD model as compared to sham control using statistical and ML based analysis. VBM and atlas-based volumetric analysis was carried out at regional level.

Integration of graph network with kernel SVM and logistic regression for identification of biomarkers in SCA12 and its diagnosis.

Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work proposes a novel four-phase machine learning-based diagnostic framework to find spinocerebellar ataxia type 12 disease-specific atrophic-brain regions and distinguish spinocerebellar ataxia type 12 from healthy using a real structural magnetic resonance imaging dataset.

Isoreticular Metal-Organic Frameworks Confined Mononuclear Ru-Hydrides Enable Highly Efficient Shape-Selective Hydrogenolysis of Polyolefins.

Upcycling nonbiodegradable plastics such as polyolefins is paramount due to their ever-increasing demand and landfills after usage. Catalytic hydrogenolysis is highly appealing to convert polyolefins into targeted value-added products under mild reaction conditions compared with other methods, such as high-temperature incineration and pyrolysis. We have developed three isoreticular zirconium UiO-metal-organic frameworks (UiO-MOFs) node-supported ruthenium dihydrides (UiO-RuH), which are efficient heterogeneous catalysts for hydrogenolysis of polyethylene at 200 °C, affording liquid hydrocarbons with a narrow distribution and excellent selectivity via shape-selective catalysis.

Selective Reduction of Nitro Compounds by Organosilanes Catalyzed by a Zirconium Metal-Organic Framework Supported Salicylaldimine-Cobalt(II) Complex.

Reducing nitro compounds to amines is a fundamental reaction in producing valuable chemicals in industry. Herein, the synthesis and characterization of a zirconium metal-organic framework-supported salicylaldimine-cobalt(II) chloride (salim-UiO-CoCl) and its application in catalytic reduction of nitro compounds are reported. Salim-UiO-Co displayed excellent catalytic activity in chemoselective reduction of aromatic and aliphatic nitro compounds to the corresponding amines in the presence of phenylsilane as a reducing agent under mild reaction conditions.

ADHD diagnosis using structural brain MRI and personal characteristic data with machine learning framework.

An essential yet challenging task is an automatic diagnosis of attention-deficit/hyperactivity disorder (ADHD) without manual intervention. The present study emphasises utilizing structural MRI and personal characteristic (PC) data for developing an automated diagnostic system for ADHD classification. Here, an age-balanced dataset of 316 ADHD and 316 Typically Developing Children (TDC) was prepared from the publicly available dataset.

Identification of brain regions associated with working memory deficit in schizophrenia.

Schizophrenia, a severe psychological disorder, shows symptoms such as hallucinations and delusions. In addition, patients with schizophrenia often exhibit a deficit in working memory which adversely impacts the attentiveness and the behavioral characteristics of a person. Although several clinical efforts have already been made to study working memory deficit in schizophrenia, in this paper, we investigate the applicability of a machine learning approach for identification of the brain regions that get affected by schizophrenia leading to the dysfunction of the working memory.

A novel fuzzy rough selection of non-linearly extracted features for schizophrenia diagnosis using fMRI.

Background And Objectives: Schizophrenia is a severe brain disorder primarily diagnosed through externally observed behavioural symptoms due to the dearth of established clinical tests. Functional magnetic resonance imaging (fMRI) can capture the distortions caused by schizophrenia in the brain activation. Hence, it can be useful for developing a decision model that performs computer-aided diagnosis of schizophrenia.

A combination of dual-tree discrete wavelet transform and minimum redundancy maximum relevance method for diagnosis of Alzheimer's disease.

In this paper, we propose a three-phased method for diagnosis of Alzheimer's disease using the structural magnetic resonance imaging (MRI). In first phase, gray matter tissue probability map is obtained from every brain MRI volume. Further, five regions of interest (ROIs) are extracted as per prior knowledge.