Development and ssage of micro- and nanofluidic devices for nanoparticle trapping, sorting and biosensing

Microfluidics has revolutionized life sciences by introducing the tools to perform complex scientific studies in a simpler yet robust and reliable way. Miniaturization of bench-top processing tools using micro- and nanofluidic devices enables handling biological samples in a physiologically relevant environment to execute complex studies that were not possible before. Organ on a chip, lab on a chip, point-of-care diagnosis, biosensing, miniaturized PCR tools, etc., are some of the previously inconceivable examples in a portable device form. Due to the scale of the device dimensions in such microfluidic devices, small volume handling and processing have become noticeably effortless. Among various applications of micro- and nanofluidic devices, molecular sensing, nanoparticle separation, sorting, trapping, and processing are of significant impact due to their feasibility of implementation in most of the fluidic devices. Single-particle trapping is an effective approach to study the fundamental properties of molecules in their physiological environment. Various active and passive methods exist to execute single-particle studies, such as optical tweezers, magnetic tweezers, dielectrophoretic trapping, hydrodynamic trapping, geometrical trapping, and electrostatic trapping. In the case of active methods, such as optical and magnetic tweezers, precise control of molecular motion is possible at the cost of a complex setup with external force sources. However, high-throughput single-particle trapping and manipulation are not feasible in a way that can be achieved using passive methods such as geometry induced electrostatic (GIE) trapping and geometrical trapping. This thesis focuses on developing integrated micro and nanofluidic devices for 1) high throughput contact free electrostatic trapping of single nanoparticles and 2) size based nanoparticle separation, sorting, and trapping for biosensing applications. The high-throughput single-particle trapping was achieved by developing fluidic devices utilizing the GIE trapping. A GIE trapping fluidic device comprises nanochannels embedded with nanostructures, such as slits, cylinders, and grids. These nanostructures enable the formation of electrostatic potential traps inside the nanoindentations, forcing negatively charged nano objects to attain a position inside them to minimize their self-energy. In conventional GIE trapping devices, negatively charged molecules, such as DNA, viruses, and gold nanoparticles (Au NPs), can be easily trapped in the electrostatic traps. This thesis presents the development and fabrication of GIE trapping devices using 1) glass substrate and 2) polydimethylsiloxane (PDMS) polymer. These substrates attain a net negative surface charge density in an aqueous solution (pH > 2) due to the self-dissociation of terminal silanol groups. Therefore, glass and PDMS based fluidic devices are only usable for the confinement of negatively charged nano objects. In this work, the scope of these fluidic devices was extended to the trapping of positively charged nano objects by using surface modification methods for both glass and PDMS based fluidic devices. The surface modification of glass‑based nanofluidic devices was achieved by modifying the inside of the GIE-trapping device by the adsorption of a single layer of polyelectrolyte (poly(ethyleneimine), PEI). The PEI layer modifies the negatively charged glass surface to a positively charged surface and allows for the trapping of positively charged nanoparticles. However, the surface modifying procedure for the glass based GIE trapping device was demanding and required 4 to 5 days. To have an efficient surface modification process, PDMS based GIE trapping devices were introduced. The introduction of PDMS based fluidic devices for positively charged nano objects has improved the throughput for device fabrication and surface modification. Furthermore, two polyelectrolyte layers (1: poly(ethyleneimine) and 2: poly(styrenesulfonate)) deposition is presented in this work using PDMS based devices to demonstrate the possibility of achieving homogeneously charged surface using multi-polyelectrolyte layers. The efficiency of these devices with surface charge reversal was comparable to native GIE trapping devices, demonstrating the successful and homogeneous surface modification. The trapping efficiency and device performance of a GIE Trapping device rely on the geometry of the device and the interaction between the charged particle and the device surface. Therefore, extensive optimization of the device geometry is essential to achieve efficient GIE trapping in a fluidic device. In this work, two different approaches, 1) charged particle inclusive simulation and 2) point charge approximation simulation, are presented to optimize the geometrical parameters of a GIE trapping device numerically. To compare numerical results with experimental data, a cylindrical nanopocket design was used to represent a nanotrap to confine a charged gold nanoparticle. The charged-particle inclusive simulations are demanding, but provide more accurate results for attainable particle stiffness constant using crucial geometrical parameters of the device, size and charge of the particle of interest, and the salt concentration of the solution. Comparatively, point-charge approximation simulations are faster and give appropriate results of particle trapping stiffness constant, residence time, etc. Here, point-charge approximation simulations are used for efficiently identifying the trends of trapping strength of a device based on critical geometrical parameters, i.e., the height of the nanochannel and the nanopocket and the diameter of the nanopocket. The point charge approximation simulations demonstrated that the trapping strength of a particle inside a nanotrap could be enhanced by increasing the trap height or reducing the channel height. Additionally, the trapping strength of a nanotrap can be modified by changing the diameter of the nanopocket; however, reduction or enlargement of the pocket diameter from the optimum diameter reduces the trapping strength of the nanotrap. For effective GIE trapping, it is important to use a solution with low ionic or salt concentration ( 10-4 pN/nm) in order to avoid screening of the electrostatic field from the charged device surface. A detailed comparison of both approaches, numerical calculations, and experimental results are presented, demonstrating their advantages and disadvantages. While there are many advantages of GIE trapping devices for molecular trapping, one major disadvantage is the reduced functionality of the devices for body fluids that contain high salt concentrations. Due to the high ionic concentration in the body fluids, the electrostatic effect of the charged device surface gets screened, leading to no potential trap for the confinement of charged nano objects. Therefore, a new design of the fluidic device is developed for biosensing applications that can use body fluids to extract the target molecules for molecular sensing. The fluidic device exploited geometrical sieving, deterministic lateral displacement (DLD) arrays, and geometrical trapping for particle separation, sorting, and trapping, respectively. The separation of unwanted macro- and micro-particles was achieved in the separation chamber, followed by the size sorting of target molecule adsorbed nanoparticles and, later, the size based trapping of these nanoparticles in the detection area. The motion of the solution and nanoparticle throughout the device was observed using interferometric scattering detection (iSCAT) microscopy, whereas, for molecular sensing, Raman spectroscopy was used at the detection area to achieve a few pg/ml detection limit. The device has the potential for applications in early multi disease diagnosis for diseases that can be detected using antigen-antibody complex formation on antibody-coated nanoparticles. The presented GIE trapping devices can be used to achieve high-throughput single-nanoparticle trapping, whereas geometrical particle trapping devices can be used to perform size-selective nanoparticle trapping for molecular sensing. Both methods are effective for studies conducted in an aqueous environment and have the potential to be used in molecular studies, disease diagnosis, biological studies, etc., for research and commercial purposes. Demonstrated device fabrication methods and surface modification procedures allow improved productivity and yield of the GIE trapping devices. The device geometry of a GIE trapping device can be optimized further using the presented numerical calculations. Therefore, the work presented here advances the research in the field of GIE trapping and geometrical trapping and opens up new possibilities for both basic and applied research in several fields, such as biophysics, molecular dynamics, diagnostics, and molecular detection

EXCEL through IoT (Exploring Cognitive and Emotional Learning through IOT)

Cognitive Learning is a process that involves learner’s knowledge into consideration. It involves the use of human brain. These days understanding students’s emotional state of mind is one of the research area where student face problems in tackling academic tasks. It has been observed that emotions are a crucial part of students' psychosomatic life, and that they may strongly influence academic motivation, cognitive strategies of learning and achieving the desired results.So, our research is to augment student learning and teacher instruction by giving the real-time reaction of students' state of mind, so that teacher can engage students in the learning processes, help them to learn or use the brain in much and far better way to relate thing with the previous one while learning something new.

Non rhizobial endophytic bacteria from chickpea (Cicer arietinum L.) tissues and their antagonistic traits

Bacteria that colonize plant tissues other than rhizobia and are beneficial for plant growth referred to non rhizobial plant growth-promoting endophytic bacteria (PGPEB). This study was designed to assay the biocontrol activity of plant growth promoting endophytic bacterial isolates those found positive for P. solubilization, ACC deaminase, Indole acetic acid and Gibberelic acid production. These bacterial isolates were obtained from chickpea (Cicer arietinum L.) tissues (roots and nodules).  In a previous study a total of 263 non rhizobial endophytic bacterial isolates were isolated. Out of 263 isolates, 64.5% and 34.5% were Gram positive and negative, respectively. Further for biochemical characterization, catalase, oxidase, citrate utilization, nitrate reduction, methyl red and Voges Proskauer’s tests, were performed. On the basis of P solubilization, ACC deaminase, Indole acetic acid and Gibberelic acid production 75 potential isolates were selected and screened for their biocontrol activity viz. (production of cell wall degrading enzymes, production of HCN and fluorescent pigment). Out of 75 isolates, only 29 isolates produced cellulase, 64 isolates were able to produce protease and 28 were positive for both cellulose and protease. Of 75 endophytic isolates 12 isolates (7 from root tissue and 5 from nodules tissue, respectively) were positive for HCN production and 16 isolates were found to be fluorescent pigment producer under µv ligh. As chemical fertilizers and pesticides have detrimental effects on the environment. So these bacterial endophytic isolates will be used not only as a biofertilizer because of their plant growth promotional activities but also used as an alternative of synthetic chemicals for control of several plant diseases

Study of Satellite Object Detection Algorithms with Pixel Value and Otsu Method Algorithm

Object detection with the help of a satellite is always been a tough task. Normally satellites are used for communication of signals and cover entire earth and give us reliable information like TV signals, mobile communication, broadband, microwaves, internet and many more. In Signal processing, Image processing is the very important parameter. In this paper we will discuss about the various algorithms which are useful in image processing applications like vehicle detection, Storm detections etc. The main powerful algorithm are pixel level and otsu method algorithm system

Abdominal Obesity A Stepping Stone to Non Communicable Diseases in South Asia

This article provides an overview of the relationship between abdominal obesity (AO) and Non-Communicable Diseases (NCDs) in South Asia. A literature review has been conducted using key words: Abdominal obesity, Non-Communicable Diseases, Adipokines and South Asia, searching Scopus, Pubmed, Google scholar and Medline databases. South Asians suffer from abdominal obesity that results in systematic inflammation giving rise to excess production of harmful adipokines that eventually leads to the occurrence of NCDs. The incidence of NCDs related mortality ranges between 44 per cent - 84 per cent. Impaired developments during pregnancy may also have a linkage with AO and NCDs. Adipokines and fat derivatives produced in abundance by the abdominal fat tissues have a crucial implication in the progression of NCDs. South Asians have unhealthy metabolic profile leading to several forms of NCDs. Further research needs to be done in the population groups suffering from abdominal obesity to derive interventional strategies to prevent as well as manage NCDs in clinical settings

Radial Basis Neural Network for Availability Analysis

The appliance of radial basis neural network is demostrated in this paper. The method applies failure and repair rate signals to learn the hidden relationship presented into the input pattern. Statistics of availability of several years is considered and collected from the management of concern plant. This data is considered to train and calidate the radial basis neural network (RBNN). Subsequently validated RBNN is used to estimate the availability of concern plant. The main objective of using neural network approach is that it’s not require assumption, nor explicit coding of the problem and also not require the complete knowledge of interdependencies, only requirement is raw data of system functioning

Conservative management of placenta percreta

The incidence of placenta accreta, increta, percreta, collectively called placenta accrete spectrum disorders, has been rising dramatically over the last decade worldwide, mainly due to rising cesarean delivery rate. Antenatal diagnosis and making no attempt to remove any parts of placenta is associated with reduced levels of hemorrhage and therefore less blood transfusion. Although elective cesarean hysterectomy is the standard practice, the choice of conservative management has emerged into practice. Conservation of the uterus reduces numerous short- and long-term complications including massive blood transfusion, disseminated intravascular coagulopathy, high morbidity and mortality rates, adjacent pelvic organ damage, infection as well as long term psychological sequelae, due to loss of femininity and fertility. Hereby representing a model for the follow up of conservative management of placenta percreta. Sequential changes in symphysial fundal height, serum beta-HCG and ultrasonographic volume of the placenta mass were used as combined methods for the follow up of the case. The placental volume was calculated by using a 2-dimensional ultrasound scan by measuring the maximum length and anteroposterior and transverse diameters of the uterus and using the formula for the volume of prolate ellipsoid.

Development of Modified CNN Algorithm for Agriculture Product: A Research Review

Now a day, with the increase in world population, the demand for agricultural products is also increased. Modern days electronic technologies combined with machine vision techniques have become a good resource for precise weed and crop detection in the field. It is becoming prominent in precision agriculture and also supporting site-specific weed management. By reviewing as there are so many different kinds of weed detection algorithms that were already used in the weed removal process or in agriculture. By the comparative study of research papers on weed detection. In this paper, we have suggested advanced and improved algorithms which take care of most of the limitations of previous work. The main goal of this review is to study the different types of algorithms used to detect weeds present in crops for automated systems in agriculture. This paper used a method that is based on a convolutional neural network model, VGG16, to identify images of weeds. As the basic network, VGG16 has very good classification performance, and it is relatively easy to modify. Download the weed dataset. This image dataset has 15336 segments, being 3249 of soil, 7376 soybeans, 3520 grass, and 1191 broadleaf weeds. Our model fixes the first 16 layers of  VGG16 parameters for layer-by-layer automatic extraction of features, adding an average pooling layer, convolution layer, Dropout layer, fully connected layer, and softmax for classifiers. The results show that the final model performs well in the classification effect of 4 classes. The accuracy is 97.76 %. We will compare our result with the CNN model. It provides an accurate and reliable judgment basis for quantitative chemical pesticide spraying. The results of this study can provide an overview of the use of CNN-based techniques for weed detection

Secondary postpartum hemorrhage on day 58 of cesarean section due to uterine scar rupture

Secondary postpartum hemorrhage is rare and affect 0.23-3% of all pregnancies. It happens between 24 hours to 12 weeks of post-delivery. These postpartum hemorrhages occur more often during normal vaginal delivery only a small subset of postpartum hemorrhages occurs after cesarean section. Delayed postpartum hemorrhage is obstetrics emergencies that occurs following vaginal or cesarean delivery, in later condition may be caused by dehiscence of uterus incision after cesarean section which can lead to severe and fatal bleeding. We herein report a case of secondary postpartum hemorrhage after cesarean section