Spatial Analysis on the provision of Urban Amenities and their Deficiencies - A Case Study of Srinagar City, Jammu and Kashmir, India

The paper examined inequality in the distribution of urban amenities in Srinagar City. Inequality in the study area is manifested in the form of unequal provision of social amenities within the wards (municipal units) of the City. The spatial distribution and concentration of two social amenities, viz, educational institutions and fire service stations was studied. The study mainly relied on the secondary sources of data. The Z-score variate has been used to determine the spatial concentration pattern in the provision of these amenities. However, Lorenz Curve proved to be a useful tool in accessing and quantifying the spatial disparity. The results of the analysis indicate that inequalities exist in the provision of accessibility of these amenities among different wards in Srinagar city. The reasons for the uneven distribution of urban amenities are spurt urban growth in the last three decades and poor management planning. The paper suggests that planning body must keep pace with the urban sprawl in order to ensure the equitable distribution of urban amenities in the city. Keywords: Amenities, Wards, Srinagar City, Well-being, Accessibility, Lorenz Curv

Identification of Chimera using Machine Learning

Chimera state refers to coexistence of coherent and non-coherent phases in identically coupled dynamical units found in various complex dynamical systems. Identification of Chimera, on one hand is essential due to its applicability in various areas including neuroscience, and on other hand is challenging due to its widely varied appearance in different systems and the peculiar nature of its profile. Therefore, a simple yet universal method for its identification remains an open problem. Here, we present a very distinctive approach using machine learning techniques to characterize different dynamical phases and identify the chimera state from given spatial profiles generated using various different models. The experimental results show that the performance of the classification algorithms varies for different dynamical models. The machine learning algorithms, namely random forest, oblique random forest based on tikhonov, parallel-axis split and null space regularization achieved more than $96\%$ accuracy for the Kuramoto model. For the logistic-maps, random forest and tikhonov regularization based oblique random forest showed more than $90\%$ accuracy, and for the H\'enon-Map model, random forest, null-space and axis-parallel split regularization based oblique random forest achieved more than $80\%$ accuracy. The oblique random forest with null space regularization achieved consistent performance (more than $83\%$ accuracy) across different dynamical models while the auto-encoder based random vector functional link neural network showed relatively lower performance. This work provides a direction for employing machine learning techniques to identify dynamical patterns arising in coupled non-linear units on large-scale, and for characterizing complex spatio-temporal patterns in real-world systems for various applications.Comment: 20 Pages, 4 Figures; Comments welcom

Ensemble deep learning: A review

Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions

Moringa oleifera Lam. (family Moringaceae) leaf extract attenuates high-fat diet-induced dyslipidemia and vascular endothelium dysfunction in Wistar albino rats

Purpose: To investigate the protective effect of methanol extract of Moringa oleifera leaves (MEMO) in high-fat diet (HFD)-induced dyslipidemia and vascular endothelium dysfunction. Methods: Dose-dependent attenuating effect of MEMO was tested at doses of 200 and 400 mg/kg/day in an in vivo model of HFD-induced dyslipidemia using rats whereas vascular endothelial reactivity was assessed in isolated rat aorta using ex vivo organ bath setup. Results: MEMO administration in HFD-induced dyslipidemic rats for 3 consecutive weeks, resulted in significant decrease in rat body weight, LW/BW and RFPW/BW ratio when compared to rats treated with HFD only where an increase in body weight was observed. Decrease in the average daily feed intake and significant reductions in waist, Lee index and BMI was also observed after MEMO treatment in HFD-induced dyslipidemic rats. Lipid profile data indicate that HFD group showed significant increase in total cholesterol, triglyceride, LDL and VLDL levels while HDL levels decreased significantly. On the other hand, MEMO treatment improved lipid profile compared to HFD group. Ex-vivo isolated aorta results revealed that MEMO treatment reversed HFD-induced endothelium dysfunction when compared to SD group. Conclusion: MEMO treatment produces dose-dependent improvement in lipid profile and vascular endothelium protection, thereby rationalizing its traditional medicine use in the treatment of dyslipidemia and cardiovascular related endothelial disorders

Deep Learning for Brain Age Estimation: A Systematic Review

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning model

An ethnomedicinal survey of traditionally used medicinal plants from Charkhi Dadri district, Haryana: an attempt towards documentation and preservation of ethnic knowledge

Medicinal plants have remained an integral source of therapeutics for primary healthcare since antiquity. The information pertaining to usage of plants is either inherited from elders or acquired through trials or the experience of others but is not documented frequently. South Haryana is one such rich storehouse of ethnomedicinal knowledge. Hence, ethnomedicinally important plants from Charkhi Dadri district of Haryana state were documented in the present study. The data was collected through field surveys and in-depth interviews organized in the fields during the years 2018-19. Factor of informant consensus was also calculated. A total of 90 ethnomedicinal plants were identified, belonging to 41 families and 79 genera. Majority of plants were herbs (47.7%), followed by trees (30%). Leguminosae (10%) represented the maximum number of plants, followed by Solanaceae (6.6% each) and Amaranthaceae, Lamiaceae and Poaceae (5.5% each). A total of 64 ailments were reported to be treated traditionally by ethnomedicinal plants in the area. The most commonly treated diseases were menorrhagia, skin boils, typhoid, diabetes, piles and diarrhoea. It was observed that the majority of plants were used freshly to extract juice, followed by powder and decoction and rarely as tea or oil forms. The present study provides comprehensive ethnomedicinal data including vernacular and botanical names, names of the family, mode of preparation, administration and dosage of plant drugs and diseases treated. It was concluded that this region still possesses numerous useful ethnomedicinal knowledge and may contribute to further herbal drug development programs

An ethnomedicinal survey of traditionally used medicinal plants from Charkhi Dadri district, Haryana: an attempt towards documentation and preservation of ethnic knowledge

436-450Medicinal plants have remained an integral source of therapeutics for primary healthcare since antiquity. The information pertaining to usage of plants is either inherited from elders or acquired through trials or the experience of others but is not documented frequently. South Haryana is one such rich storehouse of ethnomedicinal knowledge. Hence, ethnomedicinally important plants from Charkhi Dadri district of Haryana state were documented in the present study. The data was collected through field surveys and in-depth interviews organized in the fields during the years 2018-19. Factor of informant consensus was also calculated. A total of 90 ethnomedicinal plants were identified, belonging to 41 families and 79 genera. Majority of plants were herbs (47.7%), followed by trees (30%). Leguminosae (10%) represented the maximum number of plants, followed by Solanaceae (6.6% each) and Amaranthaceae, Lamiaceae and Poaceae (5.5% each). A total of 64 ailments were reported to be treated traditionally by ethnomedicinal plants in the area. The most commonly treated diseases were menorrhagia, skin boils, typhoid, diabetes, piles and diarrhoea. It was observed that the majority of plants were used freshly to extract juice, followed by powder and decoction and rarely as tea or oil forms. The present study provides comprehensive ethnomedicinal data including vernacular and botanical names, names of the family, mode of preparation, administration and dosage of plant drugs and diseases treated. It was concluded that this region still possesses numerous useful ethnomedicinal knowledge and may contribute to further herbal drug development programs

Lrp1 is essential for lethal Rift Valley fever hepatic disease in mice

Rift Valley fever virus (RVFV) is an emerging arbovirus found in Africa. While RVFV is pantropic and infects many cells and tissues, viral replication and necrosis within the liver play a critical role in mediating severe disease. The low-density lipoprotein receptor-related protein 1 (Lrp1) is a recently identified host factor for cellular entry and infection by RVFV. The biological significance of Lrp1, including its role in hepatic disease in vivo, however, remains to be determined. Because Lrp1 has a high expression level in hepatocytes, we developed a mouse model in which Lrp1 is specifically deleted in hepatocytes to test how the absence of liver Lrp1 expression affects RVF pathogenesis. Mice lacking Lrp1 expression in hepatocytes showed minimal RVFV replication in the liver, longer time to death, and altered clinical signs toward neurological disease. In contrast, RVFV infection levels in other tissues showed no difference between the two genotypes. Therefore, Lrp1 is essential for RVF hepatic disease in mice

Oropouche orthobunyavirus infection is mediated by the cellular host factor Lrp1

Oropouche orthobunyavirus (OROV

An overview of the recent developments on fructooligosaccharide production and applications

Over the past years, many researchers have suggested that deficiencies in the diet can lead to disease states and that some diseases can be avoided through an adequate intake of relevant dietary components. Recently, a great interest in dietary modulation of the human gut has been registered. Prebiotics, such as fructooligosaccharides (FOS), play a key role in the improvement of gut microbiota balance and in individual health. FOS are generally used as components of functional foods, are generally regarded as safe (generally recognized as safe status—from the Food and Drug Administration, USA), and worth about 150€ per kilogram. Due to their nutrition- and health-relevant properties, such as moderate sweetness, low carcinogenicity, low calorimetric value, and low glycemic index, FOS have been increasingly used by the food industry. Conventionally, FOS are produced through a two-stage process that requires an enzyme production and purification step in order to proceed with the chemical reaction itself. Several studies have been conducted on the production of FOS, aiming its optimization toward the development of more efficient production processes and their potential as food ingredients. The improvement of FOS yield and productivity can be achieved by the use of different fermentative methods and different microbial sources of FOS producing enzymes and the optimization of nutritional and culture parameter; therefore, this review focuses on the latest progresses in FOS research such as its production, functional properties, and market data.Agencia de Inovacao (AdI)-Project BIOLIFE reference PRIME 03/347. Ana Dominguez acknowledges Fundacao para a Ciencia e a Tecnologia, Portugal, for her PhD grant reference SFRH/BD/23083/2005