Effect of jicama (Pachyrhizus erosus L.) Fiber In hight-fat Diet On glucagon-like peptide 1 in mice (Mus musculus)

Pakan Berlemak Tinggi (PLT) adalah salah satu faktor merugikan yang berpengaruh dalam perkembangan obesitas. Penelitian ini bertujuan untuk menganalisis pengaruh dari serat bengkuang terhadap kadar hormone GLP-1 dan peran senyawa bioaktif yang dimiliki serat bengkuang untuk mencegah obesitas pada mencit yang diberi Pakan Berlemak Tinggi (PLT). Penelitian ini dilaksanakan selama 4 bulan, dari Mei hingga Agustus 2021 di Laboratorium Fisiologi Hewan, Jurusan Biologi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Andalas. Penelitian in menggunakan metode eksperimen dengan 4 perlakuan dan 6 kali ulangan selama 12 minggu. Mencit diberikan perlakuan pakan berbeda, Pakan Normal (PN), Pakan Berlemak Tinggi (PLT), PLT dikombinasikan dengan 10% dan 25% serat bengkuang. Hasil penelitian mengungkapkan bahwa serat bengkuang teruama pada dosis 25% memiliki perbedaan signifikan dalam menurunkan sekresi hormone GLP-1 dan menghambat hipersekresi hormone GLP-1. Selain itu serat bengkuang terdapat senyawa bioactive yaitu Cycloertanol yang berperan menjadi GLP-1 agonist yang mampu mengaktivasi reseptor GLP-1 sehingga mampu memberikan efek fisiologis untuk mencegah obesitas. Dengan demikian, serat bengkuang memberikan dampak kesehatan dalam menangkal efek negatif Pakan Berlemak Tinggi danobesitas

Informed consent in Sri Lanka: A survey among ethics committee members

<p>Abstract</p> <p>Background</p> <p>Approval of the research proposal by an ethical review committee from both sponsoring and host countries is a generally agreed requirement in externally sponsored research.</p> <p>However, capacity for ethics review is not universal. Aim of this study was to identify opinions and views of the members serving in ethical review and ethics committees in Sri Lanka on informed consent, essential components in the information leaflet and the consent form.</p> <p>Methods</p> <p>We obtained ethical approval from UK and Sri Lanka. A series of consensus generation meetings on the protocol were conducted. A task oriented interview guide was developed. The interview was based on open-ended questionnaire. Then the participants were given a WHO checklist on informed consent and requested to rate the items on a three point scale ranging from extremely important to not important.</p> <p>Results</p> <p>Twenty-nine members from ethics committees participated. Majority of participants (23), believed a copy of the information leaflet and consent form, should accompany research proposal. Opinions about the items that should be included in the information leaflets varied. Participants identified 18 criteria as requirements in the information leaflet and 19 for the consent form.</p> <p>The majority, 20 (69%), believed that all research need ethical approval but identified limited human resource, time and inadequate capacity as constraints. Fifteen (52%) believed that written consent is not required for all research. Verbal consent emerged as an alternative to written consent. The majority of participants rated all components of the WHO checklist as important.</p> <p>Conclusion</p> <p>The number of themes generated for the consent form (N = 18) is as many as for the information leaflet (N = 19) and had several overlaps. This suggests that the consent form should be itemized to reflect the contents covered in the information leaflet. The participants' opinion on components of the information leaflets and consent forms proved to be similar with WHO checklist on informed consent.</p

Ethics Review Committee approval and informed consent: an analysis of biomedical publications originating from Sri Lanka

<p>Abstract</p> <p>Background</p> <p>International guidelines on research have focused on protecting research participants. Ethical Research Committee (ERC) approval and informed consent are the cornerstones. Externally sponsored research requires approval through ethical review in both the host and the sponsoring country. This study aimed to determine to what extent ERC approval and informed consent procedures are documented in locally and internationally published human subject research carried out in Sri Lanka.</p> <p>Methods</p> <p>We obtained ERC approval in Sri Lanka and the United Kingdom. Theses from 1985 to 2005 available at the Postgraduate Institute of Medicine (PGIM) library affiliated to the University of Colombo were scrutinised using checklists agreed in consultation with senior research collaborators. A Medline search was carried out with MeSH major and minor heading 'Sri Lanka' as the search term for international publications originating in Sri Lanka during 1999 to 2004. All research publications from CMJ during 1999 to 2005 were also scrutinized.</p> <p>Results</p> <p>Of 291 theses, 34% documented ERC approvals and 61% documented obtaining consent. From the international journal survey, 250 publications originated from Sri Lanka of which only 79 full text original research publications could be accessed electronically. Of these 38% documented ERC approval and 39% documented obtaining consent. In the Ceylon Medical Journal 36% documented ERC approval and 37% documented obtaining consent.</p> <p>Conclusion</p> <p>Only one third of the publications scrutinized recorded ERC approval and procurement of informed consent. However, there is a positive trend in documenting these ethical requirements in local postgraduate research and in the local medical journal.</p

Math2.org (Formerly "Dave's Math Tables")

This general math site offers reference material on a host of math topics, plus a math message board and links to relevant material online. The tables cover a range of math skills, from basic fraction-decimal conversion to the more advanced calculus and discrete math. The information is presented in notation form, with diagrams, graphs, and tables. The site is available in English, Spanish, and French. This resource is part of the Teaching Quantitative Skills in the Geosciences collection. http://serc.carleton.edu/quantskills/ Educational levels: High school, Middle school, Undergraduate lower division, Undergraduate upper division

Scalable and accurate forecasting for smart cities

© 2018 Pasan Manura KarunaratneCities are getting bigger, better and smarter. The increased connectivity of people and devices and the availability of cheap sensors has led to a surge in public and government interest in smart city initiatives. This public interest, along with the recent increased interest in machine learning techniques has led to growing research focus into the mining and analysis of data in smart city settings. Much of the analysis in smart city settings is based on forecasting on time series data recorded by smart sensors for planning purposes. For example, utility companies can use electricity load forecasting on smart meter data for capacity planning, and prediction of pedestrian counts and passenger flow in public transportation systems can help in planning to reduce traffic congestion. Though forecasting in smart city settings yields such benefits, it also entails unique challenges, such as challenges related to multi-step prediction, challenges related to low quality training data due to sensors encountering vandalism, malfunction or communication failures, and challenges in maintaining predictive throughput in systems involving increasingly larger numbers of smart sensors. Improving accuracy is a primary goal in any forecasting task, which is especially challenging in multi-step prediction scenarios. We address this challenge by providing new methods to incorporate prior knowledge uniquely relevant to smart cities, such as the periodic behaviour of sensor time series data over the Monday-Friday working week. Specifically, we propose novel kernel function compositions which can incorporate such prior knowledge to kernel-based Bayesian forecasting techniques, with the goal of improving prediction accuracy and robustness to spurious data. We develop our kernel compositions for the state of the art Gaussian Process Regression technique. The new kernel compositions we develop enable prior knowledge relating to multiple periodic effects of the working week (e.g. daily, weekly, holiday effects) and their interactions to be incorporated in the same model. We also provide methods to mitigate the effects of convergence to local optima in the optimisation process over the hyperparameters used in the Gaussian Process models. We address the challenges relating to missing training data in smart city settings by making use of data of other related sensors (which may have more complete data) to mitigate the impact the low quality data has on prediction accuracy. To this end, we develop multi-task learning methods (which are able to learn joint representations from multiple sensors) to improve Gaussian Process Regression prediction accuracy with missing training data values. We also provide equivalent expressions to our multi-task learning methods as combinations of commonly used kernel functions in Gaussian Processes. This enables the straightforward implementation of these methods in popular machine learning toolkits. We address the scalability challenge of large volumes of sensor data in two steps. One, we focus on an interpretable label-based forecasting algorithm which allows for high-throughput predictions due to the minimal number of operations needed to be done in the forecasting stage. We perform numerous enhancements on this algorithm in order to improve its prediction accuracy, including filtering, windowing and ensembling methods as well as methods of incorporating exogenous variables. Our scalable forecasting methods are then developed using this enhanced base algorithm. We develop methods which enable the initial step of the algorithm to be performed using algorithms developed for stream processing, which not only allows for the algorithm to be parallelised across multiple machines, but also enables it to run on real-time data streams. We address the scalability challenges in scenarios with both a single fast stream and a large number of streams, especially with regard to synchronisation issues between multiple machines. We demonstrate the effectiveness of our methods on multiple real-world publicly available datasets to illustrate the potential generalisability of our techniques

Pengaruh Corporate Social Responsibility dan Good Corporate Governance Terhadap Nilai Perusahaan (Studi Empiris Pada Perusahaan Yang Terdaftar di BEI Tahun 2016-2018)

This study aims to determine the effect of Corporate Social Responsibility and Good Corporate Governance on corporate value. The research method used is the Structural Equation Modeling method. The sample of this research is companies listed on the Indonesia Stock Exchange in 2016-2018 using purposive sampling. 24 companies meet the criteria as a research sample. The results of this study indicate that CSR and GCG have a significant negative effect on firm value