Korelasi Antara Kadar Total Flavonoid Dan Fenolik Dari Ekstrak Dan Fraksi Daun Jati Putih (Gmelina Arborea Roxb.) Terhadap Aktivitas Antioksidan: Correlation Between Total Phenolic and Flavonoid Contents of Jati Putih (Gmelina Arborea Roxb.) Leaves Extract and Fraction Toward Antioxidant Activity

This experiment aims to determine the correlation of total phenolic and flavonoid content of jati putih leaves fraction (Gmelina arborea Roxb.) towards Antioxidant activity .  Sample was extracted by maceration method using ethanol 70% to obtain the ethanol extract (EE), followed by liquid-liquid extraction method to obtain fraction of ethyl acetate (EA) and n-Hexane (EH). The phytochemical screening  and determination of total phenolic and flavonoid content were done by colorimetric method. Antioxidant activity were done by DPPH, FRAP and ABTS methods. Phytochemical screening showed positive results for flavonoids, phenolic and saponins.  The largest total phenolic content was found on EA (11,59 µg/ml ± 0,3 %b/b EAG) and the largest total flavonoid content was on EA (3,88 µg/ml ± 0,02 %b/b EK). The total phenolic and flavonoid content of Jati putih leaves has a correlation with antioxidant activity. The coefficient correlation of activity on reducingDPPH radical was 56,7% (total of phenolic content) and 57,8% (total of flavonoid content) and on iron reduction power in FRAP method  was 99,9% (total of phenolics and flavonoids content). The relationship with the activity in reducing radical ABTS obtained coefficient correlation of 57,0% and 58,1% for total phenolic and flavonoids contents, respectively

Targeted kinase inhibition relieves slowness and tremor in a Drosophila model of LRRK2 Parkinson’s disease

Disease models: A reflex reaction A simple reflex in flies can be used to test the effectiveness of therapies that slow neurodegeneration in Parkinson’s disease (PD). Christopher Elliott and colleagues at the University of York in the United Kingdom investigated the contraction of the proboscis muscle which mediates a taste behavior response and is regulated by a single dopaminergic neuron. Flies bearing particular mutations in the PD-associated gene leucine-rich repeat kinase 2 (LRRK2) in dopaminergic neurons lost their ability to feed on a sweet solution. This was due to the movement of the proboscis muscle becoming slower and stiffer, hallmark features of PD. The authors rescued the impaired reflex reaction by feeding the flies l-DOPA or LRRK2 inhibitors. These findings highlight the proboscis extension response as a useful tool to identify other PD-associated mutations and test potential therapeutic compounds

An Introduction to EEG Source Analysis with an illustration of a study on Error-Related Potentials

International audienceOver the last twenty years blind source separation (BSS) has become a fundamental signal processing tool in the study of human electroencephalography (EEG), other biological data, as well as in many other signal processing domains such as speech, images, geophysics and wireless communication (Comon and Jutten, 2010). Without relying on head modeling BSS aims at estimating both the waveform and the scalp spatial pattern of the intracranial dipolar current responsible of the observed EEG, increasing the sensitivity and specificity of the signal received from the electrodes on the scalp. This chapter begins with a short review of brain volume conduction theory, demonstrating that BSS modeling is grounded on current physiological knowledge. We then illustrate a general BSS scheme requiring the estimation of second-order statistics (SOS) only. A simple and efficient implementation based on the approximate joint diagonalization of covariance matrices (AJDC) is described. The method operates in the same way in the time or frequency domain (or both at the same time) and is capable of modeling explicitly physiological and experimental source of variations with remarkable flexibility. Finally, we provide a specific example illustrating the analysis of a new experimental study on error-related potentials

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense