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Integration of light-harvesting chlorophyll binding proteins into the thylakoid membrane requires a specific chaperone, being the cpSRP43 subunit, of the signal recognition particle pathway in chloroplasts. cpSRP43, unique to the chloroplast, is responsible for transport of LHCPs through the stroma as well as assisting in the correct folding, assembly and disaggregation of these proteins for the acquisition of light energy. cpSRP43 is a highly flexible, multidomain protein capable of binding distinct partners in the cpSRP pathway. cpSRP43 is an irreplaceable component, necessary for the accurate and successful integration of LHCPs. It can act as a disaggregase without any input of external energy. Its action is based on the ability to associate with variable regions of different proteins owing to the domains and flexibility within its distinctive structure. Understanding the unique capabilities of cpSRP43 in the chloroplast begs the question of its usefulness outside of the plant cell, as well as its yet unknown roles still within the plant cell. Although the capabilities of cpSRP43 as a hub protein, adept to binding many unknown partners, has been alluded to in other works, it has yet to be thoroughly investigated. In this study we discover that cpSRP43 can act as a generic chaperone for proteins other than LHCP/not native to the chloroplast. The high thermal stability of cpSRP43 has been demonstrated in the previous chapter by its ability to retain its secondary structure as well as withstand aggregation upon heating and cooling cycles as confirmed by absorbance, intrinsic tryptophan fluorescence and far UV circular dichroism spectroscopy. This property gives cpSRP43 the basis to act as a generic chaperone and provide protection like that of typical heat shock proteins. Carbonic anhydrase, Concanavalin A and hFGF1 (acidic human fibroblast growth factor), were selected as candidates for chaperoning activity by cpSRP43. In all three cases, heat-induced aggregation of the candidate protein was either eliminated or significantly reduced in the presence of cpSRP43. In the case of hFGF1, the bioactivity was preserved after heat-treatment in the presence of cpSRP43. We have proposed a mechanism by which cpSRP43 is able to execute this action however further investigation is warranted to determine the exact mechanism(s) which may vary dependent on the target protein.

Induration of magnetite pellet involves three important phenomena, namely oxidation, sintering and associate heat transfer; these phenomena are interdependent on determining the final quality of the pellet. In order to study the sintering a novel optical dilatometric method was used. These studies were performed separately for magnetite and completely oxidized magnetite. Further, the sintering phenomena was modeled taking cues from powder metallurgy literature. In order to study the oxidation kinetics, TGA analysis were conducted both at powder and pellet scales at sufficiently low enough temperatures so that sintering effects are minimized. Interesting results that has not been reported earlier have been observed and these results could be explained using available gas-solid reactions models both at powder and pellet scales. Once the sintering and oxidation models were validated independently, they were integrated along with heat transfer model to realize a comprehensive induration model. These efforts have resulted in a Single Magnetite Pellet Induration Model (SPIM) which in principle can be used for optimizing raw material mix as well as the process parameters at the reactor scale.

As microblogs such as Twitter become commonplace, greater importance needs to be given to data generated in those media as sources for information mining. This paper proposes a computationally simple model for future outcome prediction at current time using shot-term data generated from microblogs (tweets in particular). The model adopts an asset price bubble model, which is non-probabilistic. A threshold approach is used for predictions. A method to generate time series data from tweets is also presented. The methodology is illustrated using Twitter data. It is simple and easy to implement.

Proper management of the occlusal plane is an essential consideration when multiple long span posterior restorations are designed. When restorations are added to an existing tooth arrangement characterized by rotated, tipped or extruded teeth, excursive interferences may be incorporated. The curve of Spee which exists in natural dentition, allows harmony to exist between the anterior teeth and the condylar guidance. Broadrick flag or occlusal plane analyzer is used to assist in the reproduction of tooth morphology that is commensurate with the curve of Spee when posterior restorations are designed; its use prevents the introduction of protrusive interferences. The current study determines the relationship of the center of anteroposterior curve of occlusion in the cranium and its relationship to other cephalometric landmarks and also evaluates the relationship of anteroposterior curve of occlusion to the condyle. How to cite this article Kumar TK, Thomas V, Nilawar S, Balamurugan R, Marwaha BS, Vinod V. A Cephalometric Study to Determine the Center of Anteroposterior Curve of Occlusion in the Cranium. J Contemp Dent Pract 2013;14(5):836-841.

In Europe, Sweden has the richest magnetite ore deposits. The mined magnetite ore is ground, beneficiated and pelletized to make the process sustainable and environment friendly. These pellets are subsequently processed in blast furnaces, and hence the optimum pellet quality is of utmost important. Magnetite green pellets are indurated (heat hardened) in either rotary kiln or straight grate induration furnace to attain the quality standards in terms of strength and other metallurgical properties. The quality of magnetite pellet is primarily determined by the physico-chemical changes the pellet undergoes as it makes excursion through the gaseous and thermal environment in the induration furnace. Among these physico-chemical processes, the oxidation of magnetite phase and the sintering of oxidized magnetite (hematite) and magnetite (non-oxidized) phases are vital. Rates of these processes not only depend on the thermal and gaseous environment the pellet gets exposed in the induration reactor but are also interdependent on each other. Therefore, a systematic study has been done to understand these processes in isolation to the extent possible and quantify them seeking the physics.Optical Dilatometer was used in a novel way to design the experiments on single pellets, exposed to different thermal profiles, in order to quantify the sintering of oxidized magnetite and non-oxidized magnetite, independently. Power law (Kt^n) and Arrhenius (푙n(TK(1^n) = ln K' - Q/RT) equations quantifies sintering behavior by estimating three isothermal kinetic parameters, namely – activation energy (Q), pre-exponential factor (K’) and time exponent (n). The values of activation energy and time exponent derived suggests that sintering of oxidized magnetite (hematite) is a single dominant diffusion mechanism, whereas sintering of unoxidized magnetite might be a combination of two distinct mechanisms; one operating at lower temperatures and the other at higher temperatures. The isothermal sintering kinetic equation is also extended to predict the non-isothermal sintering, and validated with the laboratory experiments. This will be further useful in predicting the sintering state of pellets during induration in the plant scale operations. Godkänd; 2016; 20160616 (kamsan)

Twitter is a powerful real-time micro-blogging service and a platform where users communicate with each other instantaneously. Thus, tweets form an integral part of big data ecosystem. While this platform serves as an efficient information diffusion medium, it can also be used to spread misinformation intentionally or unintentionally, which can damage the reputation of an individual or a corporation. Misinformation could also be harmful to society in general. As veracity in big data gains more attention, it is also important to develop methods to estimate veracity of tweets. There are no definitive measures to determine the veracity of tweets from tweets themselves. Other information that are required to verify tweets may not be readily available. Hence, there is a need for such mechanisms to determine the level of accuracy of tweets from available data. In this paper we propose three quantitative measures we name as topic diffusion, geographic dispersion, and spam index as indicators of veracity of tweets. These measures are derived from tweets themselves independent of any corroborating data. The proposed measures are tested using tweets about oil companies as validators. To validate the proposed measures, information extracted from tweets are compared with information collected from official data sources. Our experiments show that the proposed measures were able to estimate the level of veracity among tweets in most topics we tested. We also found the measures useful to compare the veracity of different topics as points in a 3-dimensional space. Another application of veracity indices to positions of political candidates is also described.

As Twitter usage increases worldwide, it becomes an important part of the big data ecosystem. People from across the globe tweeting and retweeting a large number of tweets instantaneously results in exponential growth of the information diffusion. This in turn can cause information bubbles. As data from Twitter and other microblogs are used in predictive analytics in many areas such as stock price prediction, varied interpretations of tweets would be necessary for adaptive diffusion analysis. This research is based on the fact that diffusion of information in Twitter has contributed to predictive analytics in other fields. Our research is concerned with the volume and velocity aspects of big data. We propose a new diffusion model for "topics" in twitter data. For the sake of simplicity, we assume (without providing formal reasoning) that all properties such as user interest, user familiarity, user curiosity, etc. affecting information diffusion are encoded in the tweet/retweet itself. We suggest a concept of potential for topics and build a time series for analysis. The definition of potential and time series allows to model different scenarios in information diffusion contributed by both volume and velocity of data (tweets). We also introduce the idea of bubble in information diffusion and propose a method to detect bubbles.

Objective: Acute kidney injury (AKI) following cardiac surgery is a serious complication with a reported incidence of 30% to 50%. This study sought to determine the prevalence and risk factors for AKI among neonates and young infants undergoing repair of cardiac defects at an institution using novel perioperative strategies. Methods: A retrospective analysis of 102 consecutive infants (Results: Mean age was 24 ± 19 days. Eighteen patients were less than 7 days of age at the time of surgery. Patient distribution according to STAT categories was as follows: 1 (n = 21), 2 (n = 12), 3 (n = 22), 4 (n = 28), and 5 (n = 19). The incidence of stages 1, 2, and 3 AKI in the population was 8% (n = 9), 2% (n = 2), and 0% (n = 0), respectively. On multivariate analysis higher STAT category was the only significant risk factor for AKI. Conclusion: Current incidence of AKI following cardiac surgery in young infants at our institution is low. Novel perioperative strategies may have contributed to the low observed incidence of AKI in our patient population. Increased complexity of heart disease was a risk factor for AKI.

Networks are prone to intrusions and detecting intruders on the Internet, is a major problem. Many Intrusion Detection Systems have been proposed to detect these intrusions. However, as the Internet grows day by day, there is a huge amount of data (big data) that needs to be processed to detect intruders. For this reason, intrusion detection has to be done in real-time before intruders can inflict damage, and previous detection systems do not satisfy this need for big data. Using Apache Storm, a Real time Hybrid Intrusion Detection System has been developed in our work. Apache Storm serves as a distributed, fault tolerant, real time big data stream processor. The hybrid detection system consists of two neural networks. The CC4 instantaneous neural network acts as an anomaly-based detection for unknown attacks and the Multi Layer Perceptron neural network acts as a misuse-based detection for known attacks. Based on the outputs from these two neural networks, the incoming data will be classified as "attack" or "normal." We found the average accuracy of hybrid detection system is 89% with a 4.32% false positive rate. This model is appropriate for real time detection since Apache Storm acts as a real time streaming processor, which can also handle big data.

Recent growth in big-data is raising security and privacy concerns. Organizations that collect data from various sources are at a risk of legal or business liabilities due to security breach and exposure of sensitive information. Only file-level access control is feasible in current Hadoop implementation and the sensitive information can only be identified manually or from the information provided by the data owner. The problem of identifying sensitive information manually gets complicated due to different types of data. When sensitive information is accessed by an unauthorized user or misused by an authorized person, they can compromise privacy. This paper is the first part of our intended access control framework for Hadoop and it automates the process of identifying sensitive data items manually. To identify such data items, the proposed framework harnesses data context, usage patterns and data provenance. In addition to this the proposed framework can also keep track of the data lineage.

Magnetite pellet induration is a combination of complex physicochemical phenomena – oxidation, sintering and theheat transfer associated with them. Depending on the pellet properties and the environment it encounters duringthe induration, the oxidation and sintering course may vary and the mechanisms will interact. To be able to predict their course and control it, the kinetics of these phenomena needs to be understood. One approach is to determine the kinetics of the phenomena in isolation. The present investigation is aimed to predict and studying the sintering behavior of oxidized magnetite (hematite) pellets exposed to different heating rates. Experiments have been carefully performed at three different heating rates to capture the sintering behavior during induration using an optical dilatometer, and also used for validation. Godkänd; 2015; 20150617 (kamsan)

One of the measures of development and economy of a nation is its per capita consumption of steel, and the demand is fulfilled by iron ore. In the context of increasing environmental constraints and ores becoming leaner and leaner, recycling and utilization of ore fines becomes necessary. Pelletization, being one of the major agglomeration techniques is increasingly practiced across the world to produce agglomerates that can be fed into the metallurgical furnaces (say blast furnaces) for subsequent processing. In Europe, Sweden has the richest iron ore deposits, and mining and metals production contributes majorly to its net export. LKAB operates with magnetite ore bodies in the northern Sweden to produce magnetite pellets (26 MTPA) exports about 70 % of its product to the European Steel producers. Therefore, constant efforts are necessary to maintain and improve the quality of magnetite pellets, and it is necessary to enhance the understanding on the reaction kinetics and mechanisms responsible while producing pellets.Magnetite pellets prepared from the fines are indurated (heat hardened) to attain the quality standards in terms of strength and other metallurgical properties. The quality of magnetite pellet is primarily determined by the physico-chemical changes the pellet undergoes as it makes excursion through the gaseous and thermal environment in the induration furnace. Among these physico-chemical processes, the oxidation of magnetite phase and the sintering of oxidized magnetite (hematite) and magnetite (non-oxidized) phases are vital. Rates of these processes not only depend on the thermal and gaseous environment the pellet gets exposed in the induration reactor but are also interdependent on each other. Therefore, a doctorate project is undertaken to systematically understand these processes in isolation to the extent possible and quantify them seeking the physics. With this motivation, the current study is focused on investigating the sintering phenomena involved during induration of magnetite pellet.Experiments with single pellets were designed to understand and quantify the sintering behavior of oxidized magnetite (hematite) and magnetite independently. The kinetics of sintering can be described using power law (Ktn) and Arrhenius (ln⁡(TK^((1/n)) )=ln⁡K' - Q/RT ) equations. In the experiments, a single pellet was exposed to different thermal profiles in a controlled atmosphere, and their in-situ shrinkage was captured continuously by a novel technique using Optical Dilatometer. It was found that the sintering behavior captured by shrinkage of the pellet can be quantified using three isothermal kinetic parameters, namely – activation energy (Q), pre-exponential factor (K’) and time exponent (n). The values of activation energy and time exponent derived suggests that sintering of oxidized magnetite (hematite) is dominated by a single diffusion mechanism, whereas sintering of magnetite showed two distinct mechanisms; one operating at lower temperatures and the other at higher temperatures. The isothermal sintering kinetic equation is also extended to predict the non-isothermal sintering for both oxidized magnetite and magnetite, and validated with the laboratory experiments. This is further useful in predicting the sintering state of pellets during induration in the plant scale operations. Godkänd; 2015; 20150613 (kamsan); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: T. Kamesh Sandeep Kumar Ämne: Processmetallurgi/Process Metallurgy Uppsats: Investigation of Sintering Kinetics of Magnetite Pellets During Induration Examinator: Professor Åke Sandström Institutionen för samhällsbyggnad och naturresurser Avdelning Mineralteknik och metallurgi Luleå tekniska universitet Diskutant: Professor Lawrence Hooey Swerea MEFOS AB Tid: Tisdag 20 oktober 2015 kl 10.00 Plats: F341, Luleå tekniska universitet

Pelletization is by far the leading agglomeration technique practiced in Sweden and also across the world for magnetite fines. Magnetite pelletization provides an added advantage in terms of energy generated from exothermic nature of magnetite oxidation. Swedish steel industries pioneered in operating their blast furnaces with cent percent pellets. This makes it necessary to understand the entire process of pelletization, where green pellets are strengthened through heat hardening process known as induration for subsequent use in iron making units such as blast furnace and direct reduced iron processes. Godkänd; 2015; 20150507 (kamsan)