Polymer assisted hydroxyapatite microspheres suitable for biomedical application

Hollow Microspheres of hydroxyapatite-polymer composite can be used as carriers in drug delivery and fillers in tissue engineering. Based on the concept of soft chemistry, a battery of technique is available in the literature to synthesize hollow microspheres, however, an economically viable synthesis route, having good control over the microarchitect and easy to be scaled up, is yet to be developed. Polymer matrix mediated synthesis of inorganic nanoparticles is known to synthesize nanoparticles with controlled morphology and dimensions. It is termed as biomimetic synthesis. Integrating the biomimetic synthesis of nano-particles and spray drying techniques, a novel process of producing hydroxyapatite-polymer composite hollow microspheres is briefly discussed here.

Simultaneous material selection and geometry design of statically determinate trusses using continuous optimization

In this work, we explore simultaneous geometry design and material selection for statically determinate trusses by posing it as a continuous optimization problem. The underlying principles of our approach are structural optimization and Ashby’s procedure for material selection from a database. For simplicity and ease of initial implementation, only static loads are considered in this work with the intent of maximum stiffness, minimum weight/cost, and safety against failure. Safety of tensile and compression members in the truss is treated differently to prevent yield and buckling failures, respectively. Geometry variables such as lengths and orientations of members are taken to be the design variables in an assumed layout. Areas of cross-section of the members are determined to satisfy the failure constraints in each member. Along the lines of Ashby’s material indices, a new design index is derived for trusses. The design index helps in choosing the most suitable material for any geometry of the truss. Using the design index, both the design space and the material database are searched simultaneously using gradient-based optimization algorithms. The important feature of our approach is that the formulated optimization problem is continuous, although the material selection from a database is an inherently discrete problem. A few illustrative examples are included. It is observed that the method is capable of determining the optimal topology in addition to optimal geometry when the assumed layout contains more links than are necessary for optimality.

Examination of EXOSC4 as a new prognostic marker and a novel therapeutic avenue in lung adenocarcinoma

Although prevention and early detection of the disease greatly improved over the past few years, lung cancer remains the leading cause of cancer deaths. In order to be able to treat a larger population, we are in urgent need for novel treatments. While it is known that DNA repair genes play a major role in the oncogenic transformation, they also represent a weakness of cancers that constitute a therapeutic opportunity. To identify novel DNA repair genes implicated in Lung cancers, we conducted an in silico investigation to identify genes co-regulated with two DNA repair factors, BRCA2 and hSSB1. This approach allowed for the identification of EXOSC4, a component of the RNA Exosome machinery, as a potential factor involved in the maintenance of genome stability and that is deregulated in lung cancer.

Splitting Algorithms for Fast Relay Selection: Generalizations, Analysis, and a Unified View

Relay selection for cooperative communications promises significant performance improvements, and is, therefore, attracting considerable attention. While several criteria have been proposed for selecting one or more relays, distributed mechanisms that perform the selection have received relatively less attention. In this paper, we develop a novel, yet simple, asymptotic analysis of a splitting-based multiple access selection algorithm to find the single best relay. The analysis leads to simpler and alternate expressions for the average number of slots required to find the best user. By introducing a new contention load' parameter, the analysis shows that the parameter settings used in the existing literature can be improved upon. New and simple bounds are also derived. Furthermore, we propose a new algorithm that addresses the general problem of selecting the best Q >= 1 relays, and analyze and optimize it. Even for a large number of relays, the scalable algorithm selects the best two relays within 4.406 slots and the best three within 6.491 slots, on average. We also propose a new and simple scheme for the practically relevant case of discrete metrics. Altogether, our results develop a unifying perspective about the general problem of distributed selection in cooperative systems and several other multi-node systems.

Selection of intrusion detection system threshold bounds for effective sensor fusion

The motivation behind the fusion of Intrusion Detection Systems was the realization that with the increasing traffic and increasing complexity of attacks, none of the present day stand-alone Intrusion Detection Systems can meet the high demand for a very high detection rate and an extremely low false positive rate. Multi-sensor fusion can be used to meet these requirements by a refinement of the combined response of different Intrusion Detection Systems. In this paper, we show the design technique of sensor fusion to best utilize the useful response from multiple sensors by an appropriate adjustment of the fusion threshold. The threshold is generally chosen according to the past experiences or by an expert system. In this paper, we show that the choice of the threshold bounds according to the Chebyshev inequality principle performs better. This approach also helps to solve the problem of scalability and has the advantage of failsafe capability. This paper theoretically models the fusion of Intrusion Detection Systems for the purpose of proving the improvement in performance, supplemented with the empirical evaluation. The combination of complementary sensors is shown to detect more attacks than the individual components. Since the individual sensors chosen detect sufficiently different attacks, their result can be merged for improved performance. The combination is done in different ways like (i) taking all the alarms from each system and avoiding duplications, (ii) taking alarms from each system by fixing threshold bounds, and (iii) rule-based fusion with a priori knowledge of the individual sensor performance. A number of evaluation metrics are used, and the results indicate that there is an overall enhancement in the performance of the combined detector using sensor fusion incorporating the threshold bounds and significantly better performance using simple rule-based fusion.

The selection, optimization, and compensation model in the work context: A systematic review and meta-analysis of two decades of research

Over the past two decades, the selection, optimization, and compensation (SOC) model has been applied in the work context to investigate antecedents and outcomes of employees' use of action regulation strategies. We systematically review, meta-analyze, and critically discuss the literature on SOC strategy use at work and outline directions for future research and practice. The systematic review illustrates the breadth of constructs that have been studied in relation to SOC strategy use, and that SOC strategy use can mediate and moderate relationships of person and contextual antecedents with work outcomes. Results of the meta-analysis show that SOC strategy use is positively related to age (rc = .04), job autonomy (rc = .17), self-reported job performance (rc = .23), non-self-reported job performance (rc = .21), job satisfaction (rc = .25), and job engagement (rc = .38), whereas SOC strategy use is not significantly related to job tenure, job demands, and job strain. Overall, our findings underline the importance of the SOC model for the work context, and they also suggest that its measurement and reporting standards need to be improved to become a reliable guide for future research and organizational practice.

Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment

This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.

On Guo and Nixon's Criterion for Feature Subset Selection: Assumptions, Implications,and Alternative Options

Guo and Nixon proposed a feature selection method based on maximizing I(x; Y),the multidimensional mutual information between feature vector x and class variable Y. Because computing I(x; Y) can be difficult in practice, Guo and Nixon proposed an approximation of I(x; Y) as the criterion for feature selection. We show that Guo and Nixon's criterion originates from approximating the joint probability distributions in I(x; Y) by second-order product distributions. We remark on the limitations of the approximation and discuss computationally attractive alternatives to compute I(x; Y).

Measurement of the tt̅ cross section in pp̅ collisions at √s=1.96 TeV using dilepton events with a lepton plus track selection

This paper reports a measurement of the cross section for the pair production of top quarks in ppbar collisions at sqrt(s) = 1.96 TeV at the Fermilab Tevatron. The data was collected from the CDF II detector in a set of runs with a total integrated luminosity of 1.1 fb^{-1}. The cross section is measured in the dilepton channel, the subset of ttbar events in which both top quarks decay through t -> Wb -> l nu b where l = e, mu, or tau. The lepton pair is reconstructed as one identified electron or muon and one isolated track. The use of an isolated track to identify the second lepton increases the ttbar acceptance, particularly for the case in which one W decays as W -> tau nu. The purity of the sample may be further improved at the cost of a reduction in the number of signal events, by requiring an identified b-jet. We present the results of measurements performed with and without the request of an identified b-jet. The former is the first published CDF result for which a b-jet requirement is added to the dilepton selection. In the CDF data there are 129 pretag lepton + track candidate events, of which 69 are tagged. With the tagging information, the sample is divided into tagged and untagged sub-samples, and a combined cross section is calculated by maximizing a likelihood. The result is sigma_{ttbar} = 9.6 +/- 1.2 (stat.) -0.5 +0.6 (sys.) +/- 0.6 (lum.) pb, assuming a branching ratio of BR(W -> ell nu) = 10.8% and a top mass of m_t = 175 GeV/c^2.

Cystatin C, a measure of renal function, as prognostic risk marker in acute heart failure : Studies on the cardiorenal syndrome

Acute heart failure (AHF) is a complex syndrome associated with exceptionally high mortality. Still, characteristics and prognostic factors of contemporary AHF patients have been inadequately studied. Kidney function has emerged as a very powerful prognostic risk factor in cardiovascular disease. This is believed to be the consequence of an interaction between the heart and kidneys, also termed the cardiorenal syndrome, the mechanisms of which are not fully understood. Renal insufficiency is common in heart failure and of particular interest for predicting outcome in AHF. Cystatin C (CysC) is a marker of glomerular filtration rate with properties making it a prospective alternative to the currently used measure creatinine for assessment of renal function. The aim of this thesis is to characterize a representative cohort of patients hospitalized for AHF and to identify risk factors for poor outcome in AHF. In particular, the role of CysC as a marker of renal function is evaluated, including examination of the value of CysC as a predictor of mortality in AHF. The FINN-AKVA (Finnish Acute Heart Failure) study is a national prospective multicenter study conducted to investigate the clinical presentation, aetiology and treatment of, as well as concomitant diseases and outcome in, AHF. Patients hospitalized for AHF were enrolled in the FINN-AKVA study, and mortality was followed for 12 months. The mean age of patients with AHF is 75 years and they frequently have both cardiovascular and non-cardiovascular co-morbidities. The mortality after hospitalization for AHF is high, rising to 27% by 12 months. The present study shows that renal dysfunction is very common in AHF. CysC detects impaired renal function in forty percent of patients. Renal function, measured by CysC, is one of the strongest predictors of mortality independently of other prognostic risk markers, such as age, gender, co-morbidities and systolic blood pressure on admission. Moreover, in patients with normal creatinine values, elevated CysC is associated with a marked increase in mortality. Acute kidney injury, defined as an increase in CysC within 48 hours of hospital admission, occurs in a significant proportion of patients and is associated with increased short- and mid-term mortality. The results suggest that CysC can be used for risk stratification in AHF. Markers of inflammation are elevated both in heart failure and in chronic kidney disease, and inflammation is one of the mechanisms thought to mediate heart-kidney interactions in the cardiorenal syndrome. Inflammatory cytokines such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) correlate very differently to markers of cardiac stress and renal function. In particular, TNF-α showed a robust correlation to CysC, but was not associated with levels of NT-proBNP, a marker of hemodynamic cardiac stress. Compared to CysC, the inflammatory markers were not strongly related to mortality in AHF. In conclusion, patients with AHF are elderly with multiple co-morbidities, and renal dysfunction is very common. CysC demonstrates good diagnostic properties both in identifying impaired renal function and acute kidney injury in patients with AHF. CysC, as a measure of renal function, is also a powerful prognostic marker in AHF. CysC shows promise as a marker for assessment of kidney function and risk stratification in patients hospitalized for AHF.

Moment-Matched Lognormal Modeling of Uplink Interference with Power Control and Cell Selection

We develop an alternate characterization of the statistical distribution of the inter-cell interference power observed in the uplink of CDMA systems. We show that the lognormal distribution better matches the cumulative distribution and complementary cumulative distribution functions of the uplink interference than the conventionally assumed Gaussian distribution and variants based on it. This is in spite of the fact that many users together contribute to uplink interference, with the number of users and their locations both being random. Our observations hold even in the presence of power control and cell selection, which have hitherto been used to justify the Gaussian distribution approximation. The parameters of the lognormal are obtained by matching moments, for which detailed analytical expressions that incorporate wireless propagation, cellular layout, power control, and cell selection parameters are developed. The moment-matched lognormal model, while not perfect, is an order of magnitude better in modeling the interference power distribution.

Template-Assisted Sol-Gel Synthesis of Nanocrystalline BaTiO3

Nanocrystalline perovskite barium titanate with an average particle size less than similar to 10 nm is produced using sol-gel route involving ligand-assisted templating. BaTiO3 is obtained by the controlled hydrolysis and condensation reaction of barium acetate (Ba(CH3COO)(2)) with titanium tetra chloride (TiCl4) in the reverse micelles of dodecylamine (DDA) which is used as the template. Our attempts to produce mesoporous BaTiO3 have resulted in the formation of nanocrystalline BaTiO3. The synthesis of nanostructured BaTiO3 is carried out using the ligand-assisted templating approach which proceeds through the sol-gel route. Dodecylamine is used as the template. The sol-gel process in general presents inherent advantages because the nanostructure of the desired materials can be controlled together with their porous structure. Ligand-assisted templating approach involves the formation of covalent bond between the inorganic analogue and the template. Ba(CH3COO)(2) and TiCl4 are used as barium-source and titanium-source respectively. The reaction between Ba(CH3COO)(2) and TiCl4 is found to take place deliberately on the pre-assembled species which acts as the template or occurring with in them which in turn will lead to the generation of the desired nanoscale structure (nanopores or nanoparticles).