A NOVEL APPROACH FOR DELIVERY OF RISPERIDONE USING EURYALE FEROX BIOPOLYMER FOR TRANSVERMILLION DELIVERY FOR THE MANAGEMENT OF PSYCHOSIS

Objective: The purpose of the present study was to formulate and evaluate risperidone loaded bioflexi films for effective treatment of psychosis. For the preparation of bioflexi films, biopolymer was isolated from seeds of Euryale ferox (family Nymphaeaceae) by an economic method. The biopolymer recovered from the concentrate was subjected for various physiochemical properties such as color, solubility, color changing point, and chemical test. Methods: The bioflexi films were prepared using this biopolymer, dextrose as flexicizer, and sween 80 as permeation enhancer in methanol and water as solvent system. Results: The formulations were characterized including uniformity of weight, drug content, folding endurance, and thickness. To study the stability of the formulations and in vitro dissolution of the experimental formulations were performed to determine the amount of risperidone present in the patches and scanning electron microscopy of the prepared bioflexi films was taken to see the drug distribution pattern. Drug-excipient interaction studies were carried out using Fourier transform infrared spectroscopic technique. In vitro dissolution studies showed that the drug distribution in the bioflexi film was homogeneous and it was found that the maximum drug release in 24 h was 99.81% with formulation EF3. In vitro skin permeation study was also conducted in a modified Franz’s diffusion cell which shows that the maximum permeation with the formulation EF3 and it was 768.50 μg/cm2 after 24 h. Conclusion: Optimized formulations were found to be suitable for formulating in terms of physicochemical characteristics and there was no significant interaction noticed between the drug and biopolymer used

Pod indehiscence is a domestication and aridity resilience trait in common bean.

Plant domestication has strongly modified crop morphology and development. Nevertheless, many crops continue to display atavistic characteristics that were advantageous to their wild ancestors but are deleterious under cultivation, such as pod dehiscence (PD). Here, we provide the first comprehensive assessment of the inheritance of PD in the common bean (Phaseolus vulgaris), a major domesticated grain legume. Using three methods to evaluate the PD phenotype, we identified multiple, unlinked genetic regions controlling PD in a biparental population and two diversity panels. Subsequently, we assessed patterns of orthology among these loci and those controlling the trait in other species. Our results show that different genes were selected in each domestication and ecogeographic race. A chromosome Pv03 dirigent-like gene, involved in lignin biosynthesis, showed a base-pair substitution that is associated with decreased PD. This haplotype may underlie the expansion of Mesoamerican domesticates into northern Mexico, where arid conditions promote PD. The rise in frequency of the decreased-PD haplotype may be a consequence of the markedly different fitness landscape imposed by domestication. Environmental dependency and genetic redundancy can explain the maintenance of atavistic traits under domestication

The Goal Programming as a Tool for Measuring the Sustainability of Agricultural Production Chains of Rice

Agricultural activity is characterized by an intensive use of capital and a considerable dependence on external financing. Access to credit is often limited by the scarcity of resources and lack of guarantees, seriously affecting the productivity and economic performance of agricultural exploitations. The objective of this paper is to assess the sustainability of agricultural production chain of rice in Latin America using multi-criteria analysis tools to facilitate decision-making through a benchmarking process to contribute to their economic sustainability. The implementation of the model in an exploitation typy depending on financing sources (conservative, intermediate, and innovative) has revealed the conflict between the goals, being the intermediate exploitation, which gets the best results. The conclusions show that the flexibilization of financing options positively affects the economic performance

Machine Learning Approach for the Early Prediction of the Risk of Overweight and Obesity in Young People

Obesity is a major global concern with more than 2.1 billion people overweight or obese worldwide which amounts to almost 30% of the global population. If the current trend continues, the overweight and obese population is likely to increase to 41% by 2030. Individuals developing signs of weight gain or obesity are also at a risk of developing serious illnesses such as type 2 diabetes, respiratory problems, heart disease and stroke. Some intervention measures such as physical activity and healthy eating can be a fundamental component to maintain a healthy lifestyle. Therefore, it is absolutely essential to detect childhood obesity as early as possible. This paper utilises the vast amount of data available via UK’s millennium cohort study in order to construct a machine learning driven model to predict young people at the risk of becoming overweight or obese. The childhood BMI values from the ages 3, 5, 7 and 11 are used to predict adolescents of age 14 at the risk of becoming overweight or obese. There is an inherent imbalance in the dataset of individuals with normal BMI and the ones at risk. The results obtained are encouraging and a prediction accuracy of over 90% for the target class has been achieved. Various issues relating to data preprocessing and prediction accuracy are addressed and discussed

ARPES: A probe of electronic correlations

Angle-resolved photoemission spectroscopy (ARPES) is one of the most direct methods of studying the electronic structure of solids. By measuring the kinetic energy and angular distribution of the electrons photoemitted from a sample illuminated with sufficiently high-energy radiation, one can gain information on both the energy and momentum of the electrons propagating inside a material. This is of vital importance in elucidating the connection between electronic, magnetic, and chemical structure of solids, in particular for those complex systems which cannot be appropriately described within the independent-particle picture. Among the various classes of complex systems, of great interest are the transition metal oxides, which have been at the center stage in condensed matter physics for the last four decades. Following a general introduction to the topic, we will lay the theoretical basis needed to understand the pivotal role of ARPES in the study of such systems. After a brief overview on the state-of-the-art capabilities of the technique, we will review some of the most interesting and relevant case studies of the novel physics revealed by ARPES in 3d-, 4d- and 5d-based oxides.Comment: Chapter to appear in "Strongly Correlated Systems: Experimental Techniques", edited by A. Avella and F. Mancini, Springer Series in Solid-State Sciences (2013). A high-resolution version can be found at: http://www.phas.ubc.ca/~quantmat/ARPES/PUBLICATIONS/Reviews/ARPES_Springer.pdf. arXiv admin note: text overlap with arXiv:cond-mat/0307085, arXiv:cond-mat/020850

Big-Data-Driven Materials Science and its FAIR Data Infrastructure

This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201

Identifying the most appropriate classifier for underpinning assistive technology adoption for people with dementia: an integration of Fuzzy AHP and VIKOR methods

Recently, the number of People with Dementia (PwD) has been rising exponentially across the world. The main symptoms that PwD experience include AQ1 impairments of reasoning, memory, and thought. Owing to the burden faced by this chronic condition, Assistive Technology-based solutions (ATS) have been prescribed as a form of treatment. Nevertheless, it is widely acknowledged that low adoption rates of ATS have hampered their benefits within a health and social care context. It is then necessary to effectively discriminate between adopters and non-adopters of such solutions to avoid cost implications, improve the life quality of adopters, and find intervention alternatives for non-adopters. Several classifiers have been proposed as advancement towards the personalisation of self-management interventions for dementia in a scalable way. As multiple algorithms have been developed, an important step in technology adoption is to select the most appropriate classification alternative based on different criteria. This paper presents the integration of Fuzzy AHP (FAHP) and VIKOR to address this challenge. First, FAHP was used to calculate the criteria and sub-criteria weights under uncertainty and then VIKOR was implemented to rank the classifiers. A case study considering a mobile-based self-management and reminding solution for PwD is described to validate the proposed approach. The results revealed that Easiness of interpretation (GW = 0.192) and Handling of missing data (GW = 0.145) were the two most important criteria. Furthermore, SVM (Qj = 1.0) and AB (Qj = 0.891) were concluded to be the most suitable classifiers for supporting ATS adoption in PwD

Lack of PPARγ in Myeloid Cells Confers Resistance to Listeria monocytogenes Infection

The peroxisomal proliferator-activated receptor γ (PPARγ) is a nuclear receptor that controls inflammation and immunity. Innate immune defense against bacterial infection appears to be compromised by PPARγ. The relevance of PPARγ in myeloid cells, that organize anti-bacterial immunity, for the outcome of immune responses against intracellular bacteria such as Listeria monocytogenes in vivo is unknown. We found that Listeria monocytogenes infection of macrophages rapidly led to increased expression of PPARγ. This prompted us to investigate whether PPARγ in myeloid cells influences innate immunity against Listeria monocytogenes infection by using transgenic mice with myeloid-cell specific ablation of PPARγ (LysMCre×PPARγflox/flox). Loss of PPARγ in myeloid cells results in enhanced innate immune defense against Listeria monocytogenes infection both, in vitro and in vivo. This increased resistance against infection was characterized by augmented levels of bactericidal factors and inflammatory cytokines: ROS, NO, IFNγ TNF IL-6 and IL-12. Moreover, myeloid cell-specific loss of PPARγ enhanced chemokine and adhesion molecule expression leading to improved recruitment of inflammatory Ly6Chi monocytes to sites of infection. Importantly, increased resistance against Listeria infection in the absence of PPARγ was not accompanied by enhanced immunopathology. Our results elucidate a yet unknown regulatory network in myeloid cells that is governed by PPARγ and restrains both listeriocidal activity and recruitment of inflammatory monocytes during Listeria infection, which may contribute to bacterial immune escape. Pharmacological interference with PPARγ activity in myeloid cells might represent a novel strategy to overcome intracellular bacterial infection