Surface acoustic wave enabled pipette on a chip

Mono-disperse droplet formation in microfluidic devices allows the rapid production of thousands of identical droplets and has enabled a wide range of chemical and biological studies through repeat tests performed at pico-to-nanoliter volume samples. However, it is exactly this efficiency of production which has hindered the ability to carefully control the location and quantity of the distribution of various samples on a chip – the key requirement for replicating micro well plate based high throughput screening in vastly reduced volumetric scales. To address this need, here, we present a programmable microfluidic chip capable of pipetting samples from mobile droplets with high accuracy using a non-contact approach. Pipette on a chip (PoaCH) system selectively ejects (pipettes) part of a droplet into a customizable reaction chamber using surface acoustic waves (SAWs). Droplet pipetting is shown to range from as low as 150 pL up to 850 pL with precision down to tens of picoliters. PoaCH offers ease of integration with existing lab on a chip systems as well as a robust and contamination-free droplet manipulation technique in closed microchannels enabling potential implementation in screening and other studies

A Lumped Parameter Approach for GEROTOR Pumps: Model Formulation and Experimental Validation

This paper describes a high fidelity simulation model for GEROTOR pumps. The simulation approach is based on the coupling of different models: a geometric model used to evaluate the instantaneous volumes and flow areas inside the unit, a lumped parameter fluid dynamic model for the evaluation of the displacing action inside the unit and mechanical models for the evaluation of the internal micro-motions of the rotors axes. This paper particularly details the geometrical approach, which takes into account the actual geometry of the rotors, given as input as CAD files. This model can take into account the actual location of the points of contact between the rotors as well for the actual clearances between the rotors. The potentials of the model are shown by considering a particular GEROTOR design. A specific test set-up was developed within this research for the model validation, and comparisons in terms of steady-state pressure versus flow curves and instantaneous pressure ripples are shown for the reference pump

Brain tumor segmentation and prediction on MRI images using deep learning network

Brain Tumor is caused when the anomalousl cells that form within the brain and these could be of any size, shape in nature, so it is one of the difficult tasks to detect the presence of tumor. This could be found using MRI scans. In this paper, suitable algorithms have been developed to detect the MRI image has a brain tumor or not. The dataset used here has been taken from kaggle competition. Data augmentation is performed to maximize the data in dataset and this could results in having huge data. Since tumor area can overlap with non-tumor area of the MRI image, preprocessing steps is used to differentiate the images. So the proposed idea is to recognize tumors, this utilizes pre-processing strategies like filters, image enhancements, cropping, dilation, erosion, etc and for image classification pre-trained model InceptionResNetv2 is used as an CNN algorithm to detect whether the tumor is present or not. Various combination of pre-processing steps has been performed to find the effective pipeline for the classification. With the image pre processing techniques like  cropped , median filter and CLAHE is gives a accuracy of 98.03% after the classification

A Lumped Parameter Approach for GEROTOR Pumps: Model Formulation and Experimental Validation

This paper describes a high fidelity simulation model for GEROTOR pumps. The simulation approach is based on the coupling of different models: a geometric model used to evaluate the instantaneous volumes and flow areas inside the unit, a lumped parameter fluid dynamic model for the evaluation of the displacing action inside the unit and mechanical models for the evaluation of the internal micro-motions of the rotors axes. This paper particularly details the geometrical approach, which takes into account the actual geometry of the rotors, given as input as CAD files. This model can take into account the actual location of the points of contact between the rotors as well for the actual clearances between the rotors. The potentials of the model are shown by considering a particular GEROTOR design. A specific test set-up was developed within this research for the model validation, and comparisons in terms of steady-state pressure versus flow curves and instantaneous pressure ripples are shown for the reference pump

BIO-SYNTHESIS OF SILVER NANO CUBES FROM ACTIVE COMPOUND QUERCETIN-3-O-β-D-GALACTOPYRANOSIDE CONTAINING PLANT EXTRACT AND ITS ANTIFUNGAL APPLICATION

In this study, the biosynthesis of silver nano cubes was carried out using leaf extract of Peltophorum pterocarphum (P. pterocarphum) containing Quercetin-3-O-β-D-Galactopyranoside compound. Simple organic compound extraction and reaction with silver nitrate was carried out. Synthesized nano cubes were characterized by various  techniques like UV-Visible spectroscopy, Scanning Electron Microscopy (SEM), Fourier Transform Infra Red (FTIR) spectroscopy and X-Ray Diffractometer (XRD). The antifungal assay of silver nano cubes was performed, which shows potential effect on plant pathogenic fungi Rhizoctonia solani (R. solani). It shows increasing inhibitory action when compared to commercially obtainable anti fungal agent fluconazole. Thus for first time it was revealed that the active compound extracted helps in synthesis of silver nano cubes from the plant source and its application as an antimicrobial agent against plant morbific fungi.Keywords: Silver nano cubes; Characterization; Quercetin-3-O-β-D-Galactopyranoside; P. pterocarphum; Antifungal activity

Thermal Characterization of Urea Phthalic Acid Material with Thermal Kinetic Calculations

Urea Phthalic acid crystalline material was grown by slow evaporation method. The material was studied by Coats – Redfern method to calculate the thermal kinetic parameters. By analyzing the results, the nature of decomposition reaction of Urea Phthalic acid was investigated. The calculated values of thermal parameters indicate that the material is suitable for device fabrication applications

Effective prediction on music therapy using hybrid SVM-ANN approach

In this world, people are moving with lightning speed. Stress has become a usual thing we experience in our day to day routine. Some factors like work tension, emotional obstacles, brutality, etc lead to stress. Many health issues like headaches, heart problems, depression, etc and psychological issues arise in human beings due to stress. Music therapy gives qualitative results in balancing the physical and psychological issues. Music therapy is an expressive type of art therapy. There are many beneficial effects achieved through music therapy like relaxation, maintain blood pressure level, cure on medical disorders, stability in mood, and improve memory and sleep. Here we aimed to establish the main predictive factors of music listening’s relaxation and the prediction of music for music therapy using various machine learning algorithms such as Decision tree, Random Forest, Artificial Neural Network (ANN), Support Vector Machine (SVM) and hybrid of SVM ANN algorithm. The accuracy of these different methods is critically examined with the help of the accuracy performance metric. Various factors like age, gender, education level, music choice, visual analog scale score before and after listening to music for both individual and therapist suggestions on music are considered for prediction. Our study revealed that SVM-ANN hybrid classifier performance is much better than other machine learning algorithms

Gemcitabine (A Chemotherapy Medication) Vs. Phytochemicals Against Non-Small Cell Lung Cancer – A Computational Approach

Non-small cell lung cancer is a type of lung cancer, where healthy cells in the lungs grow out of control forming a tumor or a nodule. Although small cell lung cancer and non-small cell lung cancer share few common symptoms and causes, their rate of spread or metastasis differs significantly. Targeted gene therapies involving uses of targeted drugs against specific gene or protein are increasingly being used for the treatment of lung cancer. This study aims at understanding the efficacy of the bioactive compounds over the already existing chemotherapy drug- Gemcitabine. For this study, the target receptor proteins of Non-Small Cell Lung Cancer (NSCLC) were retrieved from Protein Data Bank (PDB) and the ligand compounds were retrieved using PubChem NCBI. Based on various research studies, four target proteins from NSCLC and sixteen bioactive compounds of Curculigoorchioides (Black musale) were selected. And their effect of bioactive compounds has been studied by means of in-silico approach and further the identified potential active compounds have been compared with control. This comparative in-silico study has predicted that the bioactive principle of Curculigoorchioides has better efficacy against cancer receptors and can considered as an effective alternative drug for cancer treatment. Concluding, the present study will be useful in future for designing novel therapeutic plant-based drug with higher efficacy for the treatment of lung cancer

Texture Analysis Using Gabor filter Based on Transcranial Sonography Image

Abstract. Transcranial sonography (TCS) is a new tool for the diagnosis of Parkinson’s disease (PD) at a very early state. The TCS image of the mesencephalon shows a distinct hyperechogenic pattern in about 90 % PD patients. This pattern is usually manually segmented and the substantia nigra (SN) region can be used as an early PD indicator. However this method is based on manual evaluation of examined images. We propose a texture analysis method using Gabor filters for the early PD risk assessment. The features are based on the local spectrum, which is obtained by a bank of Gabor filters, and the performance of these features is evaluated by feature selection method. The results show that the accuracy of the classification with the feature subset is reaching 92.73 %.