ABSTRACT The tumour suppressor, Lethal (2) giant larvae [Lgl; also known as L(2)gl], is an evolutionarily conserved protein that was discovered in the vinegar fly Drosophila, where its depletion results in tissue overgrowth and loss of cell polarity. Lgl links cell polarity and tissue growth through regulation of the Notch and the Hippo signalling pathways. Lgl regulates the Notch pathway by inhibiting V-ATPase activity via Vap33. How Lgl regulates the Hippo pathway was unclear. In this current study, we show that V-ATPase activity inhibits the Hippo pathway, whereas Vap33 acts to activate Hippo signalling. Vap33 physically and genetically interacts with the actin cytoskeletal regulators RtGEF (Pix) and Git, which also bind to the Hippo protein (Hpo) and are involved in the activation of the Hippo pathway. Additionally, we show that the ADP ribosylation factor Arf79F (Arf1), which is a Hpo interactor, is involved in the inhibition of the Hippo pathway. Altogether, our data suggest that Lgl acts via Vap33 to activate the Hippo pathway by a dual mechanism: (1) through interaction with RtGEF, Git and Arf79F, and (2) through interaction and inhibition of the V-ATPase, thereby controlling epithelial tissue growth.
Abstract The tumour suppressor, Lethal (2) giant larvae (Lgl), is an evolutionarily conserved protein that was discovered in the vinegar fly, Drosophila , where its depletion results in tissue overgrowth and loss of cell polarity and tissue architecture. Our previous studies have revealed a new role for Lgl in linking cell polarity and tissue growth through regulation of the Notch (proliferation and differentiation) and the Hippo (negative tissue growth control) signalling pathways. Moreover, Lgl regulates vesicle acidification, via the Vacuolar ATPase (V-ATPase), and we showed that Lgl inhibits V-ATPase activity through Vap33 (a Vamp (v-SNARE)-associated protein, involved in endo-lysosomal trafficking) to regulate the Notch pathway. However, how Lgl acts to regulate the Hippo pathway was unclear. In this current study, we show that V-ATPase activity inhibits the Hippo pathway, whereas Vap33 acts to activate Hippo signalling. Using an in vivo affinity-purification approach we found that Vap33 binds to the actin cytoskeletal regulators RtGEF (Pix, a Rho-type guanine nucleotide exchange factor) and Git (G protein-coupled receptor kinase interacting ArfGAP), which also bind to the Hpo protein kinase, and are involved in the activation of the Hippo pathway. Vap33 genetically interacts with RtGEF and Git in Hippo pathway regulation. Additionally, we show that the ADP ribosylation factor Arf79F (Arf1), which is a Hpo interactor, is involved in the inhibition of the Hippo pathway. Altogether our data suggests that Lgl acts via Vap33 to activate the Hippo pathway by a dual mechanism, 1) through interaction with RtGEF/Git/Arf79F, and 2) through interaction and inhibition of the V-ATPase, thereby controlling epithelial tissue growth.
Over the years it has been known that Acetic Acid Bacteria (AAB) has a role in fermentation. These obligate aerobes oxidize variety of sugars and alcohols. But, despite this role, some acetic acid bacteria too have a role in controlling malaria by regulating the growth and development of Plasmodium falciparum in Anopheles stephensi. These AAB seem to play a role in the stimulation of the immune system and the protection of the host against pathogens. One among them is a α-Proteobacterium, Asaia bogorensis that belong to the Asaia family. Being present in the midgut of Anopheles stephensi, it lowers the load of Plasmodium falciparum pathogen by secreting anti-plasmodium factors like Drosomycin, Cecropin, Defensin and Gambicin. Consequently, it blooms and its growth is not hampered neither by the immune system of the host nor by the pathogen. It also maintains an interconnected relationship with FREP proteins that are present in the abdomen of the insect host to further lower the pathogen concentration by acting as two barriers. Genetically engineered Asaia bogorensis can be injected into male Anopheles stephensi mosquito. While mating, this genetically engineered bacterium gets transferred to the female Anopheles stephensi mosquito in the wild. Through trans-ovarian transmission, this genetically engineered bacterium also passes to the next generation male and female mosquitoes. Thus, this genetically engineered Asaia bogorensis can acts as a tool for the management of falciparum malaria by impeding the development of malaria parasite inside the vector. So, the vector might fail to attain the infective stage for transmission and thereby blocking its transmission to humans. Further studies in this regard are required to prove this hypothesis.
This chapter highlights the total structure and capabilities of robotic systems. This chapter then discusses the invocation of cloud technology in robotics technology empowering the whole system with higher processing power and bigger storage unit which was not possible earlier in the conventional robotic system being restricted in on-board manipulation. The flexibility of handling big data, ability to perform cloud computing, crowed sourcing and collaborative robot learning using the cloud robotics technology has been discussed briefly. This chapter describes concepts of Cloud Enabled Standalone Robotic System (CeSRS), Cloud Enabled Networked Robotic System (CeNRS), Cloud Robotic Networking System (CRNS), Standalone Robotic System (SRS), Common Networked Robotic (CNRS), Infrastructure As A Service (IAAS), Multi Robot System, R/R and R/C Network, ROS, Tele Operated Robotic System, Quality of Service (QoS), Virtual Machine (VM) and Cloud Datacenter. The existing applications of the cloud robotics technology are also described. However, the chapter focuses on the problems either inherited from the parent technology or appeared in the child technology. This chapter further recommends some solutions, new future directions and research aspects of the cloud robotics technology depending on the applications.
In the contemporary world, with the emergence of information technologies, Big data is one of the rising IT trends. An overwhelming amount of data and information is generated every day throughout the world. Storing and processing this huge volume of data is named by a ubiquitous term: Big Data Management. The traditional database architectures are not designed to face the challenges associated with huge data. The Apache Hadoop is the well-known software library framework that allows distributed processing of large data sets across clusters of low-end computers using simple programming models. Hadoop is designed to scale up from single servers to thousands of machines, each offering local computation and storage. When HDFS takes in data, it breaks the raw data into separate blocks and distributes them to different nodes(DataNode) in a cluster for parallel processing. But there is no scope to determine, which block is stored in which DataNode. Thus if there is a need to run a query on a specific item/keyword/key, the Hadoop runs the query or analytics program to all its cluster, which somehow increase the overall cost. To overcome these issues, We propose a scheme where we store the data in such a way that the system responses faster to each query and overall processing time decreases. Our motivation is to deliver an efficient and reliable storage and retrieval system in the health-care industry.
The various services that are offered by IoT and Cloud Service Providers (CSPs) to the customers today feature a pay-per-use service-charging policy. Customers can choose and avail these services when they want, how they want, and from where they want on demand. Demand for these services has increased drastically over the years among individuals and enterprises worldwide, and thus, it is very important to keep up good Quality of Service (QoS). This chapter highlights the history of internet, the gradual evolution of cloud computing, the reasons behind it, evolution and concepts of the Internet of Things (IoT), CloudIoT and its necessities, and various applications and service fields of CloudIoT. This chapter also precisely highlights various concepts regarding maintenance of good QoS, controversies in QoS maintenance, different parameters that the QoS depends on, various problems faced in maintaining those parameters, and the possible solutions for overcoming those problems. Possible directions towards future works are also highlighted in this chapter.
Cloud computing is the ubiquitous on demand service that has brought remarkable revolution in the commercialization of High Performance Computing (HPC) [1]. Quality of Service (QoS) is the vital factor that always seeks high attention. Efficient Resource allocation and management techniques along w ith advance load balancing approaches make a bigger difference in terms of total system throughput. Several frameworks and algorithmic approaches are proposed in these areas to improve the throughput. Cloudlets are the tasks formed as the requirements of cloud users and submitted to the Local Queues (LQ) of Virtual Machines (VM) by the Datacenter Broker (DCB) to be processed. In this paper the main focus is given to this cloudlet scheduling policy which is nothing but an enhancement of the existing Improved Round Robin Cloudlet Scheduling Algorithm (IRRCSA) and Round Robin Algorithm (RRA). CloudSim 3.0.3 is used to implant the modelling and several parameters like Context Switching (CS), waiting Time (WT), Turnaround Time (TAT) are taken into account to highlight the QoS improvement in comparison with the IRRCSA and RRA approaches.
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