Comparative Study of Perforated RF MEMS Switch

AbstractMEMS are the Micro Electronic mechanical system or in general terms it is also known as Micro electronic mechanical switch. MEMS have classified in two types of switch that is series switch and shunt switch .The cantilever is a series type switch whereas the fixed- fixed beam is shunt type switch. Fixed- Fixed beam is the element that is fixed at both anchor ends. The electrostatic actuation process occurs on the switch due to which switch deflects from its original position. The stiction problem occurs in MEMS switches which have been reduced by the proposed design. The perforation is used to reduce the squeeze film damping by decreasing the mass of the switch. As the voltage increases the switch moves to downward z-direction .The displacement is produced in the switch as direction of movement is towards negative z-axis. When the beam contacts with electrode, pull in voltage is achieved. This paper explores the perforation and meander concept with Fixed -fixed switch, which increases the flexibility, low actuation voltage and switching speed. The various types of perforations provide discrete displacement corresponding to voltage. In this paper we represent the design and simulation of Fixed-Fixed switch using perforation of size 2μm-5μm. The electrostatic actuation mechanism is applied on the Fixed-fixed switch which has a serpentine meanders and perforation at different voltages. The switch is designed and simulated by using COMSOL®MULTIPHYSICS 4.3b software

A Survey on Feature Selection Algorithms

One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations. DOI: 10.17762/ijritcc2321-8169.16043

The Global Distribution and Burden of Dengue and Japanese Encephalitis Co-Infection in Acute Encephalitis Syndrome

Dengue is widespread throughout the tropics globally in more than hundred countries and coincides with various climatic factors for co-infection with other flaviviral infections of the central nervous system (CNS). Dengue and Japanese encephalitis virus co-infection are highly prevalent, with diagnosis dilemma including significant mortality and morbidity in Southeast Asia. Both dengue and Japanese encephalitis transmissions intensify during the rainy season, during which the vector population increases. CNS involvement during dengue and Japanese encephalitis co-infection-associated acute encephalitis syndrome (AES) is still poorly understood, and therefore, there is a desperate need to understand the etiology, therapeutics, clinical management, and prevention of these tropically neglected diseases. AES can be differentiated from other etiologies of encephalopathy through considering its essential features: sudden onset of fever, cerebrospinal fluid (CSF) comprising inflammatory cells, magnetic resonance imaging (MRI)-based confirmation, and presence of pathogen or pathogen-specific antibodies. Complementary and alternative medicine is progressively being used globally and can be effective for the overall management of this co-infection

A Formal Specification Smart-Contract Language for Legally Binding Decentralized Autonomous Organizations

Blockchain- and smart-contract technology enhance the effectiveness and automation of business processes. The rising interest in the development of decentralized autonomous organizations (DAO) shows that blockchain technology has the potential to reform business and society. A DAO is an organization wherein business rules are encoded in smart-contract programs that are executed when specified rules are met. The contractual- and business semantics are sine qua non for drafting a legally-binding smart contract in DAO collaborations. Several smart-contract languages (SCLs) exist, such as SPESC, or Symboleo to specify a legally-binding contract. However, their primary focus is on designing and developing smart contracts with the cooperation of IT- and non-IT users. Therefore, this paper fills a gap in the state of the art by specifying a smart-legal-contract markup language (SLCML) for legal- and business constructs to draft a legally-binding DAO. To achieve the paper objective, we first present a formal SCL ontology to describe the legal- and business semantics of a DAO. Secondly, we translate the SCL ontology into SLCML, for which we present the XML schema definition. We demonstrate and evaluate our SLCML language through the specification of a real life-inspired Sale-of-Goods contract. Finally, the SLCML use-case code is translated into Solidity to demonstrate its feasibility for blockchain platform implementations

Pathogenesis and Host Immune Response during Japanese Encephalitis Virus Infection

Japanese Encephalitis Virus (JEV) is a mosquito borne flavivirus infection. Transmission of JEV starts with the infected mosquito bite where human dermis layer act as the primary site of infection. Once JEV makes its entry into blood, it infects monocytes wherein the viral replication peaks up without any cell death and results in production of TNF-α. One of the most characteristics pathogenesis of JEV is the breaching of blood brain barrier (BBB). JEV propagation occurs in neurons that results in neuronal cell death as well as dissemination of virus into astrocytes and microglia leading to overexpression of proinflammatory cytokines. JEV infection results in host cells mediated secretion of various types of cytokines including type-1 IFN along with TNF-α and IFN-γ. Molecule like nitrous oxide (NO) exhibits antiviral activities against JEV infection and helps in inhibiting the viral replication by blocking protein synthesis and viral RNA and also in virus infected cells clearance. In addition, the antibody can also acts an opsonizing agent in order to facilitate the phagocytosis of viral particles, which is mediated by Fc or C3 receptor. This chapter focuses on the crucial mechanism of JEV induced pathogenesis including neuropathogenesis viral clearance mechanisms and immune escape strategies

Trends in Molecular Aspects and Therapeutic Applications of Drug Repurposing for Infectious Diseases

The pharmaceutical industry has undergone a severe economic crunch in antibiotic discovery research due to evolving bacterial resistance along with enormous time and money that gets consumed in de novo drug design and discovery strategies. Nevertheless, drug repurposing has evolved as an economically safer and excellent alternative strategy to identify approved drugs for new therapeutic indications. Virtual high throughput screening (vHTS) and phenotype-based high throughput screening (HTS) of approved molecules play a crucial role in identifying, developing, and repurposing old drug molecules into anti-infective agents either alone or in synergistic combination with antibiotic therapy. This chapter briefly explains the process of drug repurposing/repositioning in comparison to de novo methods utilizing vHTS and HTS technologies along with ‘omics- and poly-pharmacology-based drug repurposing strategies in the identification and development of anti-microbial agents. This chapter also gives an insightful survey of the intellectual property landscape on drug repurposing. Further, the challenges and applications of drug repurposing strategies in the discovery of anti-infective drugs are exemplified. The future perspectives of drug repurposing in the context of anti-infective agents are also discussed

The expression of mismatched repair genes and their correlation with clinicopathological parameters and response to neo-adjuvant chemotherapy in breast cancer

BACKGROUND: The DNA mismatch repair (MMR) pathway is an important post-replicative repair process. It is involved in the maintenance of genomic stability and MMR genes have therefore been named the proofreaders of replicating DNA. These genes repair the replicative errors of DNA and are thus imperative for genomic stability. The MMR genes have been found to be involved in promoting cytotoxicity, apoptosis, p53 phosphorylation and cell cycle arrest following exposure to exogenous DNA damaging agents. Loss of MMR function prevents the correction of replicative errors leading to instability of the genome, and can be detected by polymorphisms in micro satellites (1–6 nucleotide repeat sequences scattered in whole of the genome). This phenomenon, known as micro satellite instability (MSI), is a hallmark of MMR dysfunction and can be used as a marker of MMR dysfunction in colorectal and other malignancies. An alternative method for detection of MMR dysfunction is to test the expression of protein products of the MMR genes by immunohistochemistry (IHC), as mutations in these genes lead to reduced or absent expression of their gene products. Correlation between loss of MMR function and clinical, histopathological, behavioral parameters of the tumor and its response to chemotherapy in breast cancers may be of value in predicting tumor behavior and response to neoadjuvant chemotherapy (NACT). Neoadjuvant chemotherapy is an integral part of multimodal therapy for locally advanced breast cancer and predicting response may help in tailoring regimens in patients for optimum response. MATERIALS: After approval by the IRB(Institutional Review Board) and ethical committee of the hospital, 31 cases of locally advanced breast carcinoma (LABC) were studied to assess the correlation between MMR dysfunction, clinicopathological parameters and objective clinical response to neoadjuvant chemotherapy using immunohistochemistry. The immunohistochemical analysis for four MMR protein products -MLH1, MSH2, MSH6 and PMS2 was done in the pre NACT trucut biopsy specimen and after three cycles of NACT with C AF (cyclophosphamide, adriamycin, 5-fluorouracil) regimen, in the modified radical mastectomy specimen. RESULTS AND CONCLUSION: There was no significant correlation observed between expression of MMR proteins and age, family history, tumor size or histological type. However there was a statistically significant negative correlation between MLH1, MSH2 expression and histological grade. There was also a negative correlation observed between PMS2 expression after neo-adjuvant chemotherapy and clinical response. Cases with high post NACT expression of PMS2 were poor responders to chemotherapy. MSH6 was the most frequently altered MMR gene, with a negativity rate of 48% and the patients with high expression responded poorly to NACT. The study highlights the possible role of MMR expression in predicting aggressive tumor behavior (histological grade) and response to neoadjuvant chemotherapy in patients with LABC

Hybrid Feature Selection Methods for High-Dimensional Multi-Class Datasets

Hybrid methods are very important for feature selection in case of the classification of high-dimensional datasets. In this paper, we proposed two hybrid methods which are the combination of filter-based feature selection, genetic algorithm, and sequential random search methods. The first proposed method is hybridisation of information gain and genetic algorithm. In this, first, the features are ranked based on the information gain and then a user defined features are selected from the ranked features. Genetic algorithm with these selected features is applied for the selection of optimal feature subset. It is applied for feature selection with two types of fitness functions which are single objective and multi-objective in nature. The second feature selection model is the hybridisation of information gain and sequential random K-nearest neighbour (SRKNN). In this method, again information gain is used to rank the features and a user defined top ranked number of features are selected. A set of binary population (having all feature selected by users) are generated and on each population sequential search method is applied for maximising the classification accuracy. These methods are applied to 21 high-dimensional multi-class datasets. Obtained results show that on some datasets first method\u27s performance is good and on some datasets second method\u27s performance is good. The results obtained by proposed methods are compared with results registered for other methods