\u3cem\u3eDrosophila\u3c/em\u3e Vitelline Membrane Assembly: A Critical Role for an Evolutionarily Conserved Cysteine in the “VM domain” of sV23

The vitelline membrane (VM), the oocyte proximal layer of the Drosophila eggshell, contains four major proteins (VMPs) that possess a highly conserved “VM domain” which includes three precisely spaced, evolutionarily conserved, cysteines (CX7CX8C). Focusing on sV23, this study showed that the three cysteines are not functionally equivalent. While substitution mutations at the first (C123S) or third (C140S) cysteines were tolerated, females with a substitution at the second position (C131S) were sterile. Fractionation studies showed that sV23 incorporates into a large disulfide linked network well after its secretion ceases, suggesting that post-depositional mechanisms are in place to restrict disulfide bond formation until late oogenesis, when the oocyte no longer experiences large volume increases. Affinity chromatography utilizing histidine tagged sV23 alleles revealed small sV23 disulfide linked complexes during the early stages of eggshell formation that included other VMPs, namely sV17 and Vml. The early presence but late loss of these associations in an sV23 double cysteine mutant suggests that reorganization of disulfide bonds may underlie the regulated growth of disulfide linked networks in the vitelline membrane. Found within the context of a putative thioredoxin active site (CXXS) C131, the critical cysteine in sV23, may play an important enzymatic role in isomerizing intermolecular disulfide bonds during eggshell assembly

Detecting the Possession of Harmful Weapons by Humans Through Surveillance System

Security Surveillance is a very tedious and time-consuming job. The system is to automate the task of analyzing video surveillance and alert systems. We will analyze the video feed in real-time and identify abnormal activities like gun and knife detection. There is much research going on in the industry about video surveillance, among them. The role of CCTV video has been overgrown, and CCTV cameras are placed all over the place for surveillance and security. The user gets notified for detecting the objects. It is crucial to proper surveillance for the safety and security of people and their assets. The libraries which have been used for detecting the object are TensorFlow, OpenCV, etc. The Convolutional Neural Network (CNN) is a deep learning algorithm that can take in an input image, assign importance to various aspects and objects in the image and be able to differentiate one from the other. The typical applications of deep surveillance are theft identification, violence detection, and detection of the chances of explosion

Most, but not All, Yeast Strains in the Deletion Library Contain the [PIN+] Prion

The yeast deletion library is a collection of over 5100 single gene deletions that has been widely used by the yeast community. The presence of a non-Mendelian element, such as a prion, within this library could affect the outcome of many large-scale genomic studies. We previously showed that the deletion library parent strain contained the [PIN+] prion. [PIN+] is the misfolded infectious prion form of the Rnq1 protein that displays distinct fluorescent foci in the presence of RNQ1–GFP and exists in different physical conformations, called variants. Here, we show that over 97% of the library deletion strains are [PIN+]. Of the 141 remaining strains that have completely (58) or partially (83) lost [PIN+], 139 deletions were able to efficiently maintain three different [PIN+] variants despite extensive growth and storage at 4 °C. One strain, cue2Δ, displayed an alteration in the RNQ1–GFP fluorescent shape, but the Rnq1p prion aggregate shows no biochemical differences from the wild-type. Only strains containing a deletion of either HSP104 or RNQ1 are unable to maintain [PIN+], indicating that 5153 non-essential genes are not required for [PIN+] propagation. Copyright © 2009 John Wiley & Sons, Ltd

An intelligent decision support system to prevent and control of dengue

Prevention and control of dengue fever are considered as a complex problem in day-to-day life. Noticeable changes in the human population growth, life style, and climate would cause more dengue outbreak in all over the world. The Government of India has developed a number of prevention and control strategies to protect individuals from dengue fever. Though, the strategies provided by the government are not identified based on people, space and time. In order to overcome this issue, the proposed approach presents various alternatives such as vaccination, disease surveillance, vector control, proper sanitation and increased accessed to safe drinking water, strengthening public health activities, awareness creation, and improving nutrition foods for women and child. The proposed alternatives are selected based on people, space and time criteria’s such as low temperature and heavy rain, high mean temperature and high humidity, water accumulation and rainfall resources and facilities, social culture variable and social demographic variable. The selection of alternatives based on multiple criteria’s is considered as a complex problem in decision-making framework. In general, decision makers and administrators are often used linguistic terms to give their opinions. This paper uses fuzzy logic based VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method to analyze the linguistic terms collected from the decision makers and rank the best alternatives based on multiple criteria’s

Integrating meta-heuristic with named data networking for secure edge computing in IoT enabled healthcare monitoring system

The advancement in technology, with the "Internet of Things (IoT) is continuing a crucial task to accomplish distance medical care observation, where the effective and secure healthcare information retrieval is complex. However, the IoT systems have restricted resources hence it is complex to attain effective and secure healthcare information acquisition. The idea of smart healthcare has developed in diverse regions, where small-scale implementations of medical facilities are evaluated. In the IoT-aided medical devices, the security of the IoT systems and related information is highly essential on the other hand, the edge computing is a significant framework that rectifies their processing and computational issues. The edge computing is inexpensive, and it is a powerful framework to offer low latency information assistance by enhancing the computation and the transmission speed of the IoT systems in the medical sectors. The main intention of this work is to design a secure framework for Edge computing in IoT-enabled healthcare systems using heuristic-based authentication and "Named Data Networking (NDN)". There are three layers in the proposed model. In the first layer, many IoT devices are connected together, and using the cluster head formation, the patients are transmitting their data to the edge cloud layer. The edge cloud layer is responsible for storage and computing resources for rapidly caching and providing medical data. Hence, the patient layer is a new heuristic-based sanitization algorithm called Revised Position of Cat Swarm Optimization (RPCSO) with NDN for hiding the sensitive data that should not be leaked to unauthorized users. This authentication procedure is adopted as a multi-objective function key generation procedure considering constraints like hiding failure rate, information preservation rate, and degree of modification. Further, the data from the edge cloud layer is transferred to the user layer, where the optimal key generation with NDN-based restoration is adopted, thus achieving efficient and secure medical data retrieval. The framework is evaluated quantitatively on diverse healthcare datasets from University of California (UCI) and Kaggle repository and experimental analysis shows the superior performance of the proposed model in terms of latency and cost when compared to existing solutions. The proposed model performs the comparative analysis of the existing algorithms such as Cat Swarm Optimization (CSO), Osprey Optimization Algorithm (OOA), Mexican Axolotl Optimization (MAO), Single candidate optimizer (SCO). Similarly, the cryptography tasks like "Rivest-Shamir-Adleman (RSA), Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC), and Data sanitization and Restoration (DSR) are applied and compared with the RPCSO in the proposed work. The results of the proposed model is compared on the basis of the best, worst, mean, median and standard deviation. The proposed RPCSO outperforms all other models with values of 0.018069361, 0.50564046, 0.112643119, 0.018069361, 0.156968355 and 0.283597992, 0.467442652, 0.32920734, 0.328581887, 0.063687386 for both dataset 1 and dataset 2 respectively

A novel energy-efficiency framework for UAV-assisted networks using adaptive deep reinforcement learning

In the air-to-ground transmissions, the lifespan of the network is based on the "unmanned aerial vehicle's (UAV)" life span because of the limited battery capacity. Thus, the enhancement of energy efficiency and the outage of the ground candidate's minimization are significant factors of the network functionality. UAV-aided transmission can highly enhance the spectrum efficacy and coverage. Because of their flexible deployment and the high maneuverability, the UAVs can be the best alternative for the situations where the "Internet of Things (IoT)" systems utilize more energy to attain the essential information rate, when they are far away from the terrestrial base station. Therefore, it is significant to win over the few troubles in the conventional UAV-aided efficiency approaches. Thus, this proposed work is aimed to design an innovative energy efficiency framework in the UAV-assisted network using a reinforcement learning mechanism. The energy efficiency optimization in the UAV offers better wireless coverage to the static and mobile ground user. Presently, reinforcement learning techniques effectively optimize the energy efficiency rate of the system by employing the 2D trajectory mechanism, which effectively removes the interference rate attained in the nearby UAV cells. The main objective of the recommended framework is to maximize the energy efficiency rate of the UAV network by performing the joint optimization using UAV 3D trajectory, with the energy utilized during interference accounting, and connected user counts. Hence, an efficient Adaptive Deep Reinforcement Learning with Novel Loss Function (ADRL-NLF) framework is designed to provide a better energy efficiency rate to the UAV network. Moreover, the parameter of ADRL is tuned using the Hybrid Energy Valley and Hermit Crab (HEVHC) algorithm. Various experimental observations are performed to observe the effectualness rate of the recommended energy efficiency model for UAV-based networks over the classical energy efficiency framework in UAV Networks

Implications of the Actin Cytoskeleton on the Multi-Step Process of [ PSI+] Prion Formation

Yeast prions are self-perpetuating misfolded proteins that are infectious. In yeast, [PSI+] is the prion form of the Sup35 protein. While the study of [PSI+] has revealed important cellular mechanisms that contribute to prion propagation, the underlying cellular factors that influence prion formation are not well understood. Prion formation has been described as a multi-step process involving both the initial nucleation and growth of aggregates, followed by the subsequent transmission of prion particles to daughter cells. Prior evidence suggests that actin plays a role in this multi-step process, but actin’s precise role is unclear. Here, we investigate how actin influences the cell’s ability to manage newly formed visible aggregates and how actin influences the transmission of newly formed aggregates to future generations. At early steps, using 3D time-lapse microscopy, several actin mutants, and Markov modeling, we find that the movement of newly formed aggregates is random and actin independent. At later steps, our prion induction studies provide evidence that the transmission of newly formed prion particles to daughter cells is limited by the actin cytoskeletal network. We suspect that this limitation is because actin is used to possibly retain prion particles in the mother cell

Big Data Analytics in Healthcare Internet of Things

Nowadays, wearable medical devices play a vital role in many environments such as continuous health monitoring of individuals, road traffic management, weather forecasting, and smart home. These sensor devices continually generate a huge amount of data and stored in cloud computing. This chapter proposes Internet of Things (IoT) architecture to store and process scalable sensor data (big data) for healthcare applications. Proposed architecture consists of two main sub-architecture, namely, MetaFog-Redirection (MF-R) and Grouping & Choosing (GC) architecture. Though cloud computing provides scalable data storage, it needs to be processed by an efficient computing platforms. There is a need for scalable algorithms to process the huge sensor data and identify the useful patterns. In order to overcome this issue, this chapter proposes a scalable MapReduce-based logistic regression to process such huge amount of sensor data. Apache Mahout consists of scalable logistic regression to process large data in distributed manner. This chapter uses Apache Mahout with Hadoop Distributed File System to process the sensor data generated by the wearable medical devices

Big Data Knowledge System in Healthcare

The health care systems are rapidly adopting large amounts of data, driven by record keeping, compliance and regulatory requirements, and patient care. The advances in healthcare system will rapidly enlarge the size of the health records that are accessible electronically. Concurrently, fast progress has been made in clinical analytics. For example, new techniques for analyzing large size of data and gleaning new business insights from that analysis is part of what is known as big data. Big data also hold the promise of supporting a wide range of medical and healthcare functions, including among others disease surveillance, clinical decision support and population health management. Hence, effective big data based knowledge management system is needed for monitoring of patients and identify the clinical decisions to the doctor. The chapter proposes a big data based knowledge management system to develop the clinical decisions. The proposed knowledge system is developed based on variety of databases such as Electronic Health Record (EHR), Medical Imaging Data, Unstructured Clinical Notes and Genetic Data. The proposed methodology asynchronously communicates with different data sources and produces many alternative decisions to the doctor

Optimized algorithm for speed-of-sound-based infant sulfur hexafluoride multiple-breath washout measurements.

INTRODUCTION Major methodological issues with the existing algorithm (WBreath) used for the analysis of speed-of-sound-based infant sulfur hexafluoride (SF6) multiple-breath washout (MBW) measurements lead to implausible results and complicate the comparison between different age groups and centers. METHODS We developed OASIS-a novel algorithm to analyze speed-of-sound-based infant SF6 MBW measurements. This algorithm uses known context of the measurements to replace the dependence of WBreath on model input parameters. We validated the functional residual capacity (FRC) measurement accuracy of this new algorithm in vitro, and investigated its use in existing infant MBW data sets from different infant cohorts from Switzerland and South Africa. RESULTS In vitro, OASIS managed to outperform WBreath at FRC measurement accuracy, lowering mean (SD) absolute error from 5.1 (3.2) % to 2.1 (1.6) % across volumes relevant for the infant age range, in variable temperature, respiratory rate, tidal volume and ventilation inhomogeneity conditions. We showed that changes in the input parameters to WBreath had a major impact on MBW results, a methodological drawback which does not exist in the new algorithm. OASIS produced more plausible results than WBreath in longitudinal tracking of lung clearance index (LCI), provided improved measurement stability in LCI over time, and improved comparability between centers. DISCUSSION This new algorithm represents a meaningful advance in obtaining results from a legacy system of lung function measurement by allowing a single method to analyze measurements from different age groups and centers