Approaches in biotechnological applications of natural polymers

Natural polymers, such as gums and mucilage, are biocompatible, cheap, easily available and non-toxic materials of native origin. These polymers are increasingly preferred over synthetic materials for industrial applications due to their intrinsic properties, as well as they are considered alternative sources of raw materials since they present characteristics of sustainability, biodegradability and biosafety. As definition, gums and mucilages are polysaccharides or complex carbohydrates consisting of one or more monosaccharides or their derivatives linked in bewildering variety of linkages and structures. Natural gums are considered polysaccharides naturally occurring in varieties of plant seeds and exudates, tree or shrub exudates, seaweed extracts, fungi, bacteria, and animal sources. Water-soluble gums, also known as hydrocolloids, are considered exudates and are pathological products; therefore, they do not form a part of cell wall. On the other hand, mucilages are part of cell and physiological products. It is important to highlight that gums represent the largest amounts of polymer materials derived from plants. Gums have enormously large and broad applications in both food and non-food industries, being commonly used as thickening, binding, emulsifying, suspending, stabilizing agents and matrices for drug release in pharmaceutical and cosmetic industries. In the food industry, their gelling properties and the ability to mold edible films and coatings are extensively studied. The use of gums depends on the intrinsic properties that they provide, often at costs below those of synthetic polymers. For upgrading the value of gums, they are being processed into various forms, including the most recent nanomaterials, for various biotechnological applications. Thus, the main natural polymers including galactomannans, cellulose, chitin, agar, carrageenan, alginate, cashew gum, pectin and starch, in addition to the current researches about them are reviewed in this article.. }To the Conselho Nacional de Desenvolvimento Cientfíico e Tecnológico (CNPq) for fellowships (LCBBC and MGCC) and the Coordenação de Aperfeiçoamento de Pessoal de Nvíel Superior (CAPES) (PBSA). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and COMPETE 2020 (POCI-01-0145-FEDER-006684) (JAT)

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The soft tissue covering the cranial vault forms the scalp. Which extends from supra orbital margins, anteriorly; external occipital protuberance and superior nuchal lines, posteriorly; and superior temporal lines laterally (Figure 1), (Krishna, 2010). SCALP is made up of five layers (Figure 2), (Yaremchuk, 2006) each word stand for one layer: (1) Skin, which is thick and hairy (Yaremchuk, 2006). It is adherent to the epicranial aponeurosis through the dense superficial fascia. Because of abundance of sebaceous glands, scalp is the common site for sebaceous cysts. (2) Subcutaneous tissue (Yaremchuk, 2006) and superficial fascia is fibrous and dense in the centre than at the periphery and binds skin to subjacent aponeurosis, and provides the proper medium for the passage of the vessels and nerves to skin. Scalp is very rich in blood supply and wounds of scalp bleeds profusely because the torn vessels are prevented from retracting by the fibrous fascia. Bleeding can be arrested by applying pressure against the bone. Because of density of fascia, subcutaneous hemorrhages are never extensive and inflammation in the layer cause little swelling but much pain. (3) Deep fascia in form of epicranial aponeurosis or galea aponeurotica with the occipitofrontalis muscle. Wounds of the scalp don’t gap unless aponeurosis is divided transversely. (4) Loose areolar tissue, it extends anteriorly into the eyelids as frontalis muscle does not have bony attachment. It is also known as dangerous area of scalp as emissary veins form here may transmit infection to cranial venous sinuses. Any collection here causes generalized swelling of scalp and blood may extend into root of none and eye lids causing black eye (Racoons eye). (5) Pericranium is loosel

Introducing Network Multi-Tenancy for Cloud-Based Enterprise Resource Planning: An IoT Application

© 2018 IEEE. The cloud service providers make a considerable investment in setting up the data centers backbone network with the aim to maximize the network resource. However, the actual utilization of the network resources is hard to predict. With the invent of Software Defined Networking (SDN) and OpenFlow protocol, the network control layer has got the capability to communicate with the applications or services which are offered by the service provider. Moreover, a Software Defined Data center suggests resource virtualization at computing, storage, and network layer. The multi-tenancy is a well-accepted architecture in cloud computing where a single instance of a software application serves multiple customers. This work is a first of its kind, which aims at maximizing the network resources with respect to multi-tenancy at the network layer. In this work, with network multitenancy, different customers IoT traffic flows are prioritized, and then network resources are allocated to the traffic flows dynamically based on the priority. We considered a scenario of Enterprise Resource Planning (ERP) solutions deployed in the cloud which offers services in the form of Software as a Service to the customers. The IoT devices deployed at the manufacturing site makes transactions on the cloud ERP. This work focuses on prioritizing the ERP- IoT traffic to meets the demands of a multi-tenant data center network. The ERP-IoT flows are prioritized using a regression based machine learning technique for predicting the response time for execution of a query caused by a traffic flow in the ERP backend server. Later, the ERP-IoT flows are assigned to multiple queues created on each network device in data center. This assignment is performed based on the traffic flow priority and Demand Supply scores, which aims at maximizing network resource utilization. During performance evaluation, we observed that the proposed work with network multi-tenancy shows more than 10% increase in service providers utility with respect to standard data center single queue operations