34 research outputs found
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Electrification and Socio-Economic Empowerment of Women in India
This study examines the effect of quality of electrification on empowerment of women in terms of economic autonomy, agency, mobility, decision-making abilities, and time allocation in fuel collection in India. It moves beyond the consensus of counting electried households as a measure of progress in gender parity, and analyzes how the quality of electrification affects women's intra-household bargaining power, labor supply decision and fuel collection time. We develop a set of indices using principal component analysis from a large cross-section of gender-disaggregated survey. We use two stage least squares instrumental variables regression to assess the causal effect of access and hours of electricity on women's empowerment using geographic instrumental variables along with district and caste fixed effects. The results show that quality of electrication has significant positive effects on all empowerment indices. However, the effect differs at the margin of defficiency, location, living standards and education. The study recommends revisiting the paradigm of access to electrification and women empowerment by focusing on the quality of not only extensive but also intensive electrification to enhance life and economic opportunities for women and their households
Electrification and Welfare for the Marginalized: Evidence from India
Uneven electrication can be a source of welfare disparity. Given the recent progress of electrication in India, we analyze the differences in access and reliability of electricity, and its impact on household welfare for marginalized and dominant social groups by caste and religion. We carry out longitudinal analysis from a national survey, 2005-2012, using OLS, fixed effects, and panel instrumental variable regressions. Our analysis shows that marginalized groups (Hindu Schedule Caste/Schedule Tribe and Muslims) had higher likelihood of electricity access compared to the dominant groups (Hindu forward castes and Other Backward Caste). In terms of electricity reliability, marginalized groups lost less electricity hours in a day as compared to dominant groups. Results showed that electrification enabled marginalized households to increase their consumption, assets and move out of poverty; the effects were more pronounced in rural areas. The findings are robust to alternative ways of measuring consumption, and use of more recent data set, 2015-2018. We posit that electri_cation improved the livelihoods of marginalized groups. However, it did not reduce absolute disparities among social groups
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
Mathematical modeling and forecasting of COVID-19: experience in Santiago de Cuba province
In the province of Santiago de Cuba, Cuba, the COVID-19 epidemic has a limited progression that shows an early small-number peak of infections. Most published mathematical models fit data with high numbers of confirmed cases. In contrast, small numbers of cases make it difficult to predict the course of the epidemic. We present two known models adapted to capture the noisy dynamics of COVID-19 in the Santiago de Cuba province. Parameters of both models were estimated using the approximate-Bayesian-computation framework with dedicated error laws. One parameter of each model was updated on key dates of travel restrictions. Both models approximately predicted the infection peak and the end of the COVID-19 epidemic in Santiago de Cuba. The first model predicted 57 reported cases and 16 unreported cases. Additionally, it estimated six initially exposed persons. The second model forecasted 51 confirmed cases at the end of the epidemic. In conclusion, an opportune epidemiological investigation, along with the low number of initially exposed individuals, might partly explain the favorable evolution of the COVID-19 epidemic in Santiago de Cuba. With the available data, the simplest model predicted the epidemic evolution with greater precision, and the more complex model helped to explain the epidemic phenomenology
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
Automatic segmentation of right ventricle in cardiac cine MR images using a saliency analysis
PURPOSE: Accurate measurement of the right ventricle (RV) volume is important for the assessment of the ventricular function and a biomarker of the progression of any cardiovascular disease. However, the high RV variability makes difficult a proper delineation of the myocardium wall. This paper introduces a new automatic method for segmenting the RV volume from short axis cardiac magnetic resonance (MR) images by a salient analysis of temporal and spatial observations.
METHODS: The RV volume estimation starts by localizing the heart as the region with the most coherent motion during the cardiac cycle. Afterward, the ventricular chambers are identified at the basal level using the isodata algorithm, the right ventricle extracted, and its centroid computed. A series of radial intensity profiles, traced from this centroid, is used to search a salient intensity pattern that models the inner-outer myocardium boundary. This process is iteratively applied toward the apex, using the segmentation of the previous slice as a regularizer. The consecutive 2D segmentations are added together to obtain the final RV endocardium volume that serves to estimate also the epicardium.
RESULTS: Experiments performed with a public dataset, provided by the RV segmentation challenge in cardiac MRI, demonstrated that this method is highly competitive with respect to the state of the art, obtaining a Dice score of 0.87, and a Hausdorff distance of 7.26 mm while a whole volume was segmented in about 3 s.
CONCLUSIONS: The proposed method provides an useful delineation of the RV shape using only the spatial and temporal information of the cine MR images. This methodology may be used by the expert to achieve cardiac indicators of the right ventricle function
The role of acyl-coenzyme A carboxylase complex in lipstatin biosynthesis of Streptomyces toxytricini
Streptomyces toxytricini produces lipstatin, a specific inhibitor of pancreatic lipase, which is derived from two fatty acid moieties with eight and 14 carbon atoms. The pccB gene locus in 10.6 kb fragment of S. toxytricini chromosomal DNA contains three genes for acyl-coenzyme A carboxylase (ACCase) complex accA3, pccB, and pccE that are presumed to be involved in secondary metabolism. The pccB gene encoding a β subunit of ACCase [carboxyltransferase (CT)] was identified upstream of pccE gene for a small protein of ε subunit. The accA3 encoding the α subunit of ACCase [biotin carboxylase (BC)] was also identified downstream of pccB gene. When the pccB and pccE genes were inactivated by homologous recombination, the lipstatin production was reduced as much as 80%. In contrast, the accumulation of another compound, tetradeca-5.8-dienoic acid (the major lipstatin precursor), was 4.5-fold increased in disruptant compared with wild-type. It implies that PccB of S. toxytricini is involved in the activation of octanoic acid to hexylmalonic acid for lipstatin biosynthesis
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