Glacial Lake Outburst Flood (GLOF) Hazard Mitigation at Himalayan Region, Nepal

Glacier retreat is a strong indicator of climate change and global warming. The anthropogenic changes in the Earth's atmosphere are mostly to blame for the climate extremes and their consequences in the last few decades. The Himalayan region is no exclusion to the trend. As glaciers begin to retreat, the glacial lake starts to fill or form behind the natural moraine or ice dam in the glaciers. The sudden release of the water, known as the Glacial Lake Outburst Flood (GLOF), can release a large amount of water and sediment. There have been various destructive GLOFs recorded in Nepal since the 1960s. It is vital to understand the GLOF dynamics, geomorphology and historical events to mitigate the GLOF hazards in the region. An advanced approach based on remote sensing data and empirical evidence is more suitable to tackle these issues. This research investigated 11 among 30 past events recorded in the HKH region (Nepal) to establish the causes and triggering factors that led to the catastrophic failure, which helped establish the vulnerability assessment of these glacial lakes. This eventually led to creating a GLOF vulnerability assessment framework that is unique and useful to the communities. This research concluded that 40% of the GLOF events was due to the moraine dam failure. In the retrospective approach, 5 out of 11 glacial lakes scored a very high total vulnerability score (TVR), which suffered catastrophic events in the past. The TVR of the currently existing 21 potential dangerous glacial lakes (PDGL) in Nepal was also conducted using the proposed assessment framework that concluded the 7 very high, 4 high, 5 medium, and the rest are low. Hence, this assessment tool's reliability is very high. This research also concluded that there should integrated approach to climate change adaptation and hazard mitigations in the region

Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet

Catheter segmentation in 3D ultrasound is important for computer-assisted cardiac intervention. However, a large amount of labeled images are required to train a successful deep convolutional neural network (CNN) to segment the catheter, which is expensive and time-consuming. In this paper, we propose a novel catheter segmentation approach, which requests fewer annotations than the supervised learning method, but nevertheless achieves better performance. Our scheme considers a deep Q learning as the pre-localization step, which avoids voxel-level annotation and which can efficiently localize the target catheter. With the detected catheter, patch-based Dual-UNet is applied to segment the catheter in 3D volumetric data. To train the Dual-UNet with limited labeled images and leverage information of unlabeled images, we propose a novel semi-supervised scheme, which exploits unlabeled images based on hybrid constraints from predictions. Experiments show the proposed scheme achieves a higher performance than state-of-the-art semi-supervised methods, while it demonstrates that our method is able to learn from large-scale unlabeled images.Comment: Accepted by MICCAI 202

Review on catalytic cleavage of C-C inter-unit linkages in lignin model compounds: Towards lignin depolymerisation

Lignin depolymerisation has received considerable attention recently due to the pressing need to find sustainable alternatives to fossil fuel feedstock to produce chemicals and fuels. Two types of interunit linkages (C–C and C–O linkages) link several aromatic units in the structure of lignin. Between these two inter-unit linkages, the bond energies of C–C linkages are higher than that of C–O linkages, making them harder to break. However, for an efficient lignin depolymerisation, both types of inter-unit linkages have to be broken. This is more relevant because of the fact that many delignification processes tend to result in the formation of additional C–C inter-unit bonds. Here we review the strategies reported for the cleavage of C–C inter-unit linkages in lignin model compounds and lignin. Although a number of articles are available on the cleavage of C–O inter-unit linkages, reports on the selective cleavage of C–C inter-unit linkages are relatively less. Oxidative cleavage, hydrogenolysis, two-step redox-neutral process, microwave assisted cleavage, biocatalytic and photocatalytic methods have been reported for the breaking of C–C inter-unit linkages in lignin. Here we review all these methods in detail, focused only on the breaking of C–C linkages. The objective of this review is to motivate researchers to design new strategies to break this strong C–C inter-unit bonds to valorise lignins, technical lignins in particular

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guessing. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as patient-specific biomarker trends. The submission system remains open via the website https://tadpole.grand-challenge.org, while code for submissions is being collated by TADPOLE SHARE: https://tadpole-share.github.io/. Our work suggests that current prediction algorithms are accurate for biomarkers related to clinical diagnosis and ventricle volume, opening up the possibility of cohort refinement in clinical trials for Alzheimer's disease

Oxidation of Alcohols and Activated Alkanes with Lewis Acid-Activated TEMPO

The reactivity of MCl3(η(1)-TEMPO) (M = Fe, 1; Al, 2; TEMPO = 2,2,6,6-tetramethylpiperidine-N-oxyl) with a variety of alcohols, including 3,4-dimethoxybenzyl alcohol, 1-phenyl-2-phenoxyethanol, and 1,2-diphenyl-2-methoxyethanol, was investigated using NMR spectroscopy and mass spectrometry. Complex 1 was effective in cleanly converting these substrates to the corresponding aldehyde or ketone. Complex 2 was also able to oxidize these substrates; however, in a few instances the products of overoxidation were also observed. Oxidation of activated alkanes, such as xanthene, by 1 or 2 suggests that the reactions proceed via an initial 1-electron concerted proton-electron transfer (CPET) event. Finally, reaction of TEMPO with FeBr3 in Et2O results in the formation of a mixture of FeBr3(η(1)-TEMPOH) (23) and [FeBr2(η(1)-TEMPOH)]2(μ-O) (24), via oxidation of the solvent, Et2O

Particle Morphology Dependent Superhydrophobicity In Treated Diatomaceous Earth/Polystyrene Coatings

Superhydrophobic surfaces have been prepared from three different types of diatomaceous earth (DE) particles treated with 3-(heptafluoroisopropoxy)propyltrimethoxysilane (HFIP-TMS) and low molecular mass polystyrene. The untreated particles, consisting of CelTix DE (disk shape), DiaFil DE (rod shape) and EcoFlat DE (irregular), were studied using particle size analysis, bulk density, pore volume and surface area analysis (via Brunauer–Emmett–Teller, BET, methods). The treated particles were characterized with thermogravimetric analysis (TGA), contact angles, scanning electron microscopy, profilometry, and FTIR spectroscopy. The minimum amount of silane coupling agent on the DE surfaces required to obtain superhydrophobicity of the particles was determined and found to be dependent on the particle morphology. In the coatings made from different particles with 2.4 wt% HFIP-TMS, the minimum amounts of treated particles (loadings) for superhydrophobicity was determined with the less dense CelTix DE requiring about 30 wt%, DiaFil DE requiring about 40 wt%, and EcoFlat DE each requiring about 60 wt% loading of treated particles

Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation

We propose a novel semi-supervised image segmentation method that simultaneously optimizes a supervised segmentation and an unsupervised reconstruction objectives. The reconstruction objective uses an attention mechanism that separates the reconstruction of image areas corresponding to different classes. The proposed approach was evaluated on two applications: brain tumor and white matter hyperintensities segmentation. Our method, trained on unlabeled and a small number of labeled images, outperformed supervised CNNs trained with the same number of images and CNNs pre-trained on unlabeled data. In ablation experiments, we observed that the proposed attention mechanism substantially improves segmentation performance. We explore two multi-task training strategies: joint training and alternating training. Alternating training requires fewer hyperparameters and achieves a better, more stable performance than joint training. Finally, we analyze the features learned by different methods and find that the attention mechanism helps to learn more discriminative features in the deeper layers of encoders.Comment: Accepted at MICCAI 201