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Plant metabolites are the products produced during metabolism which occurs naturally in cells. Different types of secondary metabolites have their different therapeutic effects. Herbal medicines have been used for a long time to cure neurodegenerative disorders. Two of major types of neurodegenerative diseases which are of huge concern nowadays are- Parkinson's disease and Alzheimer's disease. During the past few decades, plant secondary metabolites have shown a large variety of beneficial effects against lots of neurodegenerative disorders including Parkinson's disease and Alzheimer's disease. More research is required to discover new bioactive compounds from plants which can be used to treat these neurodegenerative diseases.

Soft bioinspired fiber networks offer great potential in biomedical engineering and material design due to their adjustable mechanical behaviors. However, existing strategies to integrate modeling and manufacturing of bioinspired networks do not consider the intrinsic microstructural disorder of biopolymer networks, which limits the ability to tune their mechanical properties. To fill in this gap, we developed a method to generate computer models of aperiodic fiber networks mimicking type I collagen ready to be submitted for additive manufacturing. The models of fiber networks were created in a scripting language wherein key geometric features like connectivity, fiber length, and fiber cross section could be easily tuned to achieve desired mechanical behavior, namely, pretension-induced shear stiffening. The stiffening was first predicted using finite element software, and then a representative network was fabricated using a commercial 3D printer based on digital light processing technology using a soft resin. The stiffening response of the fabricated network was verified experimentally on a novel test device capable of testing the shear stiffness of the specimen under varying levels of uniaxial pretension. The resulting data demonstrated clear pretension-induced stiffening in shear in the fabricated network, with uniaxial pretension of 40% resulting in a factor of 2.65 increase in the small strain shear stiffness. The strategy described in this article addresses current challenges in modeling bioinspired fiber networks and can be readily integrated with advances in fabrication technology to fabricate materials truly replicating the mechanical response of biopolymer networks.

Global warming is a great threat to the very survival of our planet.

Fiber networks are the primary structural components of many biological structures, including the cell cytoskeleton and the extracellular matrix. These materials exhibit global nonlinearities, such as stiffening in extension and shear, during which the fibers bend and align with the direction of applied loading. Precise details of deformations at the scale of the fibers during strain stiffening are still lacking, however, as prior work has studied fiber alignment primarily from a qualitative perspective, which leaves incomplete the understanding of how the local microstructural evolution leads to the global mechanical behavior. To fill this gap, we studied how axial forces are transmitted inside the fiber network along paths called force chains, which continuously evolve during the course of deformation. We performed numerical simulations on two-dimensional networks of random fibers under uniaxial extension and shear, modeling the fibers using beam elements in finite element software. To quantify the force chains, we identified all chains of connected fibers for which the axial force was larger than a preset threshold and computed the total length of all such chains. To study the evolution of force chains during loading, we computed the derivative of the total length of all force chains with respect to the applied engineering strain. Results showed that the highest rate of evolution of force chains coincided with the global critical strain for strain stiffening of the fiber network. Therefore, force chains are an important factor connecting understanding of the local kinematics and force transmission to the macroscale stiffness of the fiber network.

As fibrous collagen is the most abundant protein in mammalian tissues, gels of collagen fibers have been extensively used as an extracellular matrix scaffold to study how cells sense and respond to cues from their microenvironment. Other components of native tissues, such as glycosaminoglycans like hyaluronic acid, can affect cell behavior in part by changing the mechanical properties of the collagen gel. Prior studies have quantified the effects of hyaluronic acid on the mechanical properties of collagen gels in experiments of uniform shear or compression at the macroscale. However, there remains a lack of experimental studies of how hyaluronic acid changes the mechanical properties of collagen gels at the scale of a cell. Here, we studied how addition of hyaluronic acid to gels of collagen fibers affects the local field of displacements in response to contractile loads applied on length scales similar to those of a contracting cell. Using spherical poly(N-isopropylacrylamide) particles, which contract when heated, we induced displacement in gels of collagen and collagen with hyaluronic acid. Displacement fields were quantified using a combination of confocal microscopy and digital image correlation. Results showed that hyaluronic acid suppressed the distance over which displacements propagated, suggesting that it caused the network to become more linear. Additionally, hyaluronic acid had no statistical effect on heterogeneity of the displacement fields, but it did make the gels more elastic by substantially reducing the magnitude of permanent deformations. Lastly, we examined the effect of hyaluronic acid on fiber remodeling due to localized forces and found that hyaluronic acid partially - but not fully - inhibited remodeling. This result is consistent with prior studies suggesting that fiber remodeling is associated with a phase transition resulting from an instability caused by nonlinearity of the collagen gel.

In predictive modeling, firms often deal with high-dimensional data that span multiple channels, websites, demographics, purchase types, and product categories. Traditional customer response models rely heavily on feature engineering, and their performance depends on the analyst's domain knowledge and expertise to craft relevant predictors. As the complexity of data increases, however, traditional models grow exponentially complicated. In this paper, we demonstrate that long-short term memory (LSTM) neural networks, which rely exclusively on raw data as input, can predict customer behaviors with great accuracy. In our first application, a model outperforms standard benchmarks. In a second, more realistic application, an LSTM model competes against 271 hand-crafted models that use a wide variety of features and modeling approaches. It beats 269 of them, most by a wide margin. LSTM neural networks are excellent candidates for modeling customer behavior using panel data in complex environments (e.g., direct marketing, brand choices, clickstream data, churn prediction).

Depth estimation from a single RGB image has been one of the most important research topics in recent days as it has several important applications in self-supervised driving in autonomous cars, image reconstruction, and scene segmentation. Depth estimation from a single monocular image has been challenging as compared to stereo images due to the lack of spatio-temporal features per frame that makes 3D depth perception easier. Existing models and solutions in monocular depth estimation often resulted in low resolution and blurry depth maps and often fail to identify small object boundaries. In this paper, we propose a simple encoder-decoder based network that can predict high-quality depth images from single RGB images using transfer learning. We have utilized important features extracted from pre-trained networks, and after initializing the encoder with fine-tuning and important augmentation strategies, the network decoder part computes the high-end depth maps. The network has fewer trainable parameters and small iterations, though it outperforms the existing state-of-the-art methods and captures accurate boundaries when evaluated on two standard datasets, KITTI, and NYU Depth V2.

The best-selling book ‘Silent Spring’ (1962, by Rachel Carson) quivered the international awareness about the role of pesticides for fatally damaging the avian population, but, after more than five decades of such information and advent of various other functionally similar chemicals, still there are overwhelming recent reports of toxicity of pesticides. Globally, over 5 billion pounds of conventional pesticides are used annually for various purposes; these contaminants may act as silent killer of birds. Several studies suggest that the different populations of birds such as songbird, peregrine falcons, ospreys, and Swainson’s hawks are unwitting victims of pesticide contamination.

Organophosphates (OPs) are most widely used pesticides and primarily induce toxicity by inhibition of acetylcholinesterase (AChE) in the nerve terminals of central and peripheral nervous system, leading to a variety of short-term and chronic effects in the non-target animals. In addition to acetylcholinesterase, OPs are known to potent inhibitors of serine hydrolases which are vital component of the immune system and therefore influence the immune functions. OPs induce several immunomodulatory effects in vertebrates by altering neutrophil function, macrophage production, antibody production, immunosuppression, reduced interleukin production and T cell proliferation. Immunotoxicity due to OP exposure is mediated through perturbation of the cholinergic response of lymphocytes, altering signal transduction, mutilating granule exocytosis pathway and impairing FasL/Fas pathway of natural killer cell and other immune related cells. Apoptosis of lymphocytes or immune related cells is promoted through mitochondrial pore formation and DNA fragmentation. In this review an attempt has been made to document the immunomodulatory effects of organophosphate pesticides using mammals as potential model with an additional information on the probable mechanism of immunotoxicity induced by OPs.

Using the classic moral hazard problem with limited liability we characterize the optimal incentive contracts when first an other-regarding principal interacts with a self-regarding agent. The optimal contract differs considerably when the principal is ‘inequity averse’ vis-a-vis the self-regarding case. Also the agent is generally (weakly) better-off under an ‘inequity averse’ principal compared to a ‘status seeking’ principal. Then we extend our analysis and characterize the optimal contracts when both other-regarding principal and other-regarding agent interact.

The author investigates the success of the recently launched intraday auction at the London Stock Exchange (LSE) along various metrics such as liquidity, price deviation, and so on. This initiative is important because of the coming Markets in Financial Instruments Directive (MIFID II) regulations, which will set a limit on dark trading and seek to encourage larger trade sizes. The author finds that this initiative has been successful for finding liquidity (block trading) for midcap stocks but less so for the FTSE 100 stocks. Interestingly, he also finds that alternative venues such as BATS, Chi-X, and Turquoise stop being good sources for liquidity during the auction phase on the LSE.

We study the numerical accuracy and computational efficiency of alternative formulations of the finite element solution procedure for the monodomain equations of cardiac electrophysiology, focusing on the interaction of spatial quadrature implementations with operator splitting and examining both nodal and Gauss quadrature methods and implementations that mix nodal storage of state variables with Gauss quadrature. We evaluate the performance of all possible combinations of 'lumped' approximations of consistent capacitance and mass matrices. Most generally, we find that quadrature schemes and lumped approximations that produce decoupled nodal ionic equations allow for the greatest computational efficiency, this being afforded through the use of asynchronous adaptive time-stepping of the ionic state variable ODEs. We identify two lumped approximation schemes that exhibit superior accuracy, rivaling that of the most expensive variationally consistent implementations. Finally, we illustrate some of the physiological consequences of discretization error in electrophysiological simulation relevant to cardiac arrhythmia and fibrillation. These results suggest caution with the use of semi-automated free-form tetrahedral and hexahedral meshing algorithms available in most commercially available meshing software, which produce nonuniform meshes having a large distribution of element sizes.

The human development impact of decentralization is the central focus of this paper, which addresses evolving patterns of fiscal decentralization in Argentina based on health and education indicators. The authors use previously unavailable data to look at decentralization in Argentina over time, and to document the positive impact of devolutionary decentralization on health and education, and the empirical relationship between fiscal decentralization and human development. The aim is to shift the focus of the general debate on decentralization away from purely budgetary issues.

This article seeks to estimate the impact of the ban imposed on short selling by various market regulators across Europe in late 2008, at the peak of the financial crisis. The authors select the STOXX 600 constituents as their universes and do an event study of the ban period, in contrast with the preand post ban-period for the same universe of stocks. In particular, the article reports what impact the ban had, if any, on the efficiency of the market (abnormal returns, volatility, etc.) and market microstructure variables (spread, shape of the order book, etc.), using tick data from Reuters. The authors find a deleterious effect on both sets of variables during the ban, even after controlling for the general crisis like market environment (using STOXX 600 stocks not under the purview of the ban).

We compare the financial performance of three Indian companies, Tata Motors, Maruti, and Mahindra & Mahindra with two MNCs, Honda and Hyundai. While it would be desirable to include other MNCs in the study, data on only these two MNCs are available in the PROWESS database as these are the only ones listed on the stock exchange. In order to compare the financial performance, we use ten ratios. There are four profitability ratios, four liquidity ratios and two solvency ratios. The profitability ratios used are profit margin, asset turnover, return on assets, and return on equity. The liquidity ratios used are current ratio, quick ratio, debtor turnover and inventory turnover. The solvency ratios used are debt to equity ratio and interest coverage ratio. For each ratio we find the average performance for the three Indian companies and the average performance for the two MNCs. The averages are compared. We also find the coefficient of variation for Indian companies and for MNCs for each of the ten ratios. A high average performance on a particular ratio combined with a lower coefficient of variation would definitely indicate a better performance by a particular group. Based on this criterion, MNCs have a better performance than Indian companies on return on assets and interest coverage ratio. On the other hand, Indian companies have a better performance than MNCs on return on equity. For each of the ten ratios, we also look at data from 2002 to 2006 for each of the five companies. Using regression, we see if the trend in each ratio for each company is statistically significant. This would indicate whether a company is on an improvement path, based on a particular ratio.

The aim of this study was to determine the diversity of zooplankton in a local pond ecosystem. Zooplankton plays a very important role in the food chain of pond ecosystem and its diversity is one of the determinant of shaping the food chain. Its diversity will ultimately decide the nature of the pond community. During our investigation in local pond, we have gathered various types of zooplanktons like Cyclops, Daphnia, Cypris , and Moina . Their number was influenced by the effect of pH (measured by digital pH meter), temperature (measured by thermometer), free CO 2 level (estimated by APHA method), and dissolved O 2 (estimated by Winkler’s method). The season wise variation of zooplankton in pond indicate the different environmental condition in water bodies.

Using the classic moral hazard problem with limited liability we characterize the optimal contracts when an other-regarding principal interacts with a self-regarding agent. The optimal contract differs considerably when the principal is ‘inequity averse’ vis-a-vis the self-regarding case. Also the agent is generally (weakly) better-off under an ‘inequity- averse’ principal compared to a ‘status seeking’ principal.

We study the numerical accuracy and computational efficiency of alternative formulations of the finite element solution procedure for the monodomain equations of cardiac electrophysiology, focusing on the interaction of spatial quadrature implementations with operator splitting and examining both nodal and Gauss quadrature methods and implementations that mix nodal storage of state variables with Gauss quadrature. We evaluate the performance of all possible combinations of 'lumped' approximations of consistent capacitance and mass matrices. Most generally, we find that quadrature schemes and lumped approximations that produce decoupled nodal ionic equations allow for the greatest computational efficiency, this being afforded through the use of asynchronous adaptive time-stepping of the ionic state variable ODEs. We identify two lumped approximation schemes that exhibit superior accuracy, rivaling that of the most expensive variationally consistent implementations. Finally, we illustrate some of the physiological consequences of discretization error in electrophysiological simulation relevant to cardiac arrhythmia and fibrillation. These results suggest caution with the use of semi-automated free-form tetrahedral and hexahedral meshing algorithms available in most commercially available meshing software, which produce nonuniform meshes having a large distribution of element sizes.

Human development, reflected in the status of people's levels of health and education, affects future growth and, in turn, is affected by decentralization. Unlike earlier exclusive emphasis on budgetary issues, this study focuses on the impact of fiscal decentralization on the level of human development. It traces the origin and recent development of revenue-sharing arrangements across Argentina's provinces over time (1970-94). The study regresses two indicators of health and educational status on two decentralization measures. It highlights the link between decentralization and human development outcomes and suggests that devolutionary decentralization has a positive influence on the effectiveness of public policy directed towards an improvement in the level of human development. Decentralization is shown to reduce intra- regional disparities and increase levels of human development. While the paper also recognizes problems associated with decentralization, including addressing inter-regional disparities, the positive impact of decentralization schemes on human development is seen to be of relevance in evaluating the Argentine co-participation regime which is currently under negotiation.

As markets are becoming more and more competitive and consumers have an abundance of information about products available at the tips of their fingers, marketing professionals must carefully decide which information channels to prioritise. Additionally, the trends of being “sustainable” and “eco-friendly” are becoming increasingly popular. The concept of customer journey is also widely researched, proving to be a valuable tool for identifying the need and desires of the consumer and guiding them down the path to purchase a product or service. However, the studies surrounding how this journey should differ for a consumer focused on sustainability compared to a consumer with little interest in the subject, is limited. This study is an initial attempt to investigate the relationship between two phases of the customer journey (i.e., consideration and purchase) and the chosen information channels regarding sustainability. Data for this study were collected with the help of a self-reported questionnaire. The survey was mostly distributed through social media channels. By conducting a regression analysis on the results of the survey, the data supports our theory that there may be a relationship between the two chosen phases of the customer journey (i.e., consideration and purchase) and common communication channels. The general findings in our analysis indicate that respondents with an interest in sustainability have higher values for the consideration stage than respondents with a lower interest in sustainability. For the purchase stage we found opposite to be true. For either group the most influential channels of communication for the consideration phase were Reviews and Word-of-Mouth. As for the purchase stage, both sustainable and nonsustainable groups showed the highest values for Physical Store as their purchase channel of choice. Results obtained in this thesis provide insights into the factors that influence consumer behaviour in relation to interest in sustainability and the stages of the customer journey. The results of our research can benefit marketing professionals wishing to increase the efficiency of their advertising and may prompt further studies of the concepts presented within this thesis. As markets are becoming more and more competitive and consumers have an abundance of information about products available at the tips of their fingers, marketing professionals must carefully decide which information channels to prioritise. Additionally, the trends of being “sustainable” and “eco-friendly” are becoming increasingly popular. The concept of customer journey is also widely researched, proving to be a valuable tool for identifying the need and desires of the consumer and guiding them down the path to purchase a product or service. However, the studies surrounding how this journey should differ for a consumer focused on sustainability compared to a consumer with little interest in the subject, is limited. This study is an initial attempt to investigate the relationship between two phases of the customer journey (i.e., consideration and purchase) and the chosen information channels regarding sustainability. Data for this study were collected with the help of a self-reported questionnaire. The survey was mostly distributed through social media channels. By conducting a regression analysis on the results of the survey, the data supports our theory that there may be a relationship between the two chosen phases of the customer journey (i.e., consideration and purchase) and common communication channels. The general findings in our analysis indicate that respondents with an interest in sustainability have higher values for the consideration stage than respondents with a lower interest in sustainability. For the purchase stage we found opposite to be true. For either group the most influential channels of communication for the consideration phase were Reviews and Word-of-Mouth. As for the purchase stage, both sustainable and nonsustainable groups showed the highest values for Physical Store as their purchase channel of choice. Results obtained in this thesis provide insights into the factors that influence consumer behaviour in relation to interest in sustainability and the stages of the customer journey. The results of our research can benefit marketing professionals wishing to increase the efficiency of their advertising and may prompt further studies of the concepts presented within this thesis.