Effect of race on outcome after kidney and kidney-pancreas transplantation in type 1 diabetic patients

OBJECTIVE: The racial impact on graft outcome is not well defined in diabetic recipients. The purpose of this study is to analyze our experience with kidney-alone (A) and kidney-pancreas (KP) transplantation in type 1 diabetic recipients and evaluate the impact of racial disparity on outcome. RESEARCH DESIGN AND METHODS: The records of 217 kidney transplants (118 KA, 99 KP) performed on type 1 diabetic patients between 1985 and 1995 at the Medical University of South Carolina and the University of Texas Medical Branch were reviewed. RESULTS: A total of 53 (31%) white patients and 15 (33%) black patients experienced at least one episode of biopsy-proven acute rejection of the renal graft (NS). Patient survival at 1, 2, and 5 years was similar in white (92, 87, 69%) and black (91, 91, 69%) patients (NS). Kidney graft survival at 1, 2, and 5 years in the KA group was 72, 62, and 42% in blacks, compared with 79, 76, and 53% in whites (NS). Kidney graft survival at 1, 2, and 5 years in the KP group was 92, 92, and 74% in blacks, compared with 83, 77, and 58% in whites (NS). Pancreas graft survival at 1, 2, and 5 years was 81, 81, and 81% in blacks, compared with 81, 75, and 62% in whites (NS). Cox regression analysis revealed that donor age > or = 40 years increased the risk of renal graft failure 6.2-fold (P = 0.0001), whereas the addition of a pancreas transplant to a kidney and a living-related transplant decreased the risk of failure of the kidney graft 0.2 (P = 0.005) and 0.1 times (P = 0.005). CONCLUSIONS: Our results suggest that when compared with whites, there may be a trend toward an improved kidney and pancreas graft outcome in blacks undergoing KP transplants. These findings suggest that diabetes may override the risk factors that account for the pronounced disparity in outcome observed between nondiabetic white and black recipients.     

Hydrothermal upgrading of wood biomass: Influence of the addition of K2CO3 and cellulose/lignin ratio

The hydrothermal treatment of two different wood biomass samples such as cherry (hard wood) and cypress (soft wood), whose composition is different i.e. lignin, cellulose and hemicellulose were performed at 280 °C for 15 min with aq. K₂CO₃ with different concentrations (0–1 M). The soft wood biomass contains higher lignin content than hard wood biomass. The cellulose rich cherry wood biomass produced higher proportion of acetic acid than cypress. The lignin rich cypress produced the hydrocarbons with major portion of phenolic hydrocarbons and derivatives than cherry. The total oil yields from both cherry and cypress wood biomass produced 50 wt% of liquid hydrocarbons at 280 °C for 15 min with 0.5 M K₂CO₃ solution. The volatility distribution of liquid hydrocarbons showed the characteristic features of soft and hard wood biomasses.     

TES modeling for analysis of a video multiplexer

The transport of video images is likely to be a major application of high-speed networks of the future. These applications are capable of utilizing the high bandwidths that will become available with the advent of B-ISDN. In this paper, we propose a new methodology (called TES) for modeling the frame bitrate stream generated by compressed video sources. The main characteristic of this method is that it can model an arbitrary marginal distribution and approximate the autocorrelation structure of the random bitrate process generated by compressed frames. We present a modeling example utilizing this method, based on data from a video coding algorithm employing a layering technique. Using the source model, we develop a simulation model of a multiplexer of video sources. We show that this model can be used to address a number of design issues that arise in this class of problem. In particular, we show that for a layered video coding algorithm, it is possible to have a gain in the number of sources multiplexed at the cost of some loss of low priority packets.     

Plastic deformation of forearm

The unusual occurrence of plastic deformation in an adult is described.     

On the Computational Complexity of Measuring Global Stability of Banking Networks

Threats on the stability of a financial system may severely affect the functioning of the entire economy, and thus considerable emphasis is placed on the analyzing the cause and effect of such threats. The financial crisis in the current and past decade has shown that one important cause of instability in global markets is the so-called financial contagion, namely the spreadings of instabilities or failures of individual components of the network to other, perhaps healthier, components. This leads to a natural question of whether the regulatory authorities could have predicted and perhaps mitigated the current economic crisis by effective computations of some stability measure of the banking networks. Motivated by such observations, we consider the problem of defining and evaluating stabilities of both homogeneous and heterogeneous banking networks against propagation of synchronous idiosyncratic shocks given to a subset of banks. We formalize the homogeneous banking network model of Nier et al. (J. Econ. Dyn. Control 31:2033–2060, 2007) and its corresponding heterogeneous version, formalize the synchronous shock propagation procedures outlined in (Nier et al. J. Econ. Dyn. Control 31:2033–2060, 2007; M. Eboli Mimeo, 2004), define two appropriate stability measures and investigate the computational complexities of evaluating these measures for various network topologies and parameters of interest. Our results and proofs also shed some light on the properties of topologies and parameters of the network that may lead to higher or lower stabilities.     

Evaluating the role of composition and local structure on alkali out-diffusion in glasses for thin-film solar cells

The presence of alkali ions has reportedly improved the performance of CIGS/CZTS–based thin-film solar cells. The out-diffusion of the alkali ion, in particular, Na, from the glass substrate offers a facile scalable route of supplying the alkali ions during the growth of the absorber layer. In this work, we demonstrate the diffusion of different alkali ions (Li/Na/K) from composition tuned glasses with intentionally incorporated excess alkali ions into a thin Mo film, typically used as a bottom electrode in solar cells. We also evaluate the physical, mechanical, and thermal properties of the glasses for suitability as a substrate in thin-film deposition. The out-diffusion of alkali ions to the overlayer is found to be critically influenced by the composition and the local structure of the glasses. The Na ions exhibit the highest extent of diffusion among the alkali ions present in glass substrates, while that for the K-ions is the lowest. For the glasses with mixed alkali ions, the presence of Li facilitated the out-diffusion of Na, whereas K ions appear to inhibit the same. Differently with the existing reports, we show that the activation energy and the presence of Ca ions as additional modifiers play a crucial role in the transport mechanism of the ions. In addition, the synthesized glasses exhibit hardness of the order 5-7 GPa, density ~2.55 g cm^-3. The glass transition temperature lies between 535 and 580°C and the coefficient of thermal expansion 8.5-10 ppm/K, which is highly suitable for use as substrates in thin-film solar cells.     

Inferring (Biological) Signal Transduction Networks via Transitive Reductions of Directed Graphs

In this paper we consider the p-ary transitive reduction (TR _p ) problem where p>0 is an integer; for p=2 this problem arises in inferring a sparsest possible (biological) signal transduction network consistent with a set of experimental observations with a goal to minimize false positive inferences even if risking false negatives. Special cases of TR _p have been investigated before in different contexts; the best previous results are as follows:

A Dynamic Hybrid Decoder Apprroach Using EG-LDPC Codes for Signal Processing Applications

The Low-Density Parity Check (LDPC) codes of Euclidean Geometry (EG) are encrypted and decrypted in numerous ways, namely Soft Bit Flipping (SBF), Sequential Peeling Decoder (SPD), Belief Propagation Decoder (BPD), Majority Logic Decoder/Detector (MLDD), and Parallel Peeling Decoder (PPD) decoding algorithms. These algorithms provide aextensive range of trade-offs between latency decoding, power consumption, hardware complexity-required resources, and error rate performance. Therefore, the problem is to communicate a sophisticated technique specifying the both soft and burst errors for effective information transmission. In this research, projected a technique named as Hybrid SBF (HSBF) decoder for EG-LDPC codes, which reduces the decoding complexity and maximizes the signal transmission and reception. In this paper, HSBF is also known as Self Reliability based Weighted Soft Bit Flipping (SRWSBF) Decoder. It is obvious from the outcomes that the proposed technique is better than the decoding algorithms SBF, MLDD, BPD, SPD and PPD. Using Xilinx synthesis and SPARTAN 3e, a simulation model is designed to investigate latency, hardware utilization and power consumption. Average latency of 16.65 percent is found to be reduced. It is observed that in considered synthesis parameters such as number of 4-input LUTs, number of slices, and number of bonded IOBs, excluding number of slice Flip-Flops, hardware utilization is minimized to an average of 4.25 percent. The number of slices Flip-Flops resource use in the proposed HSBF decoding algorithm is slightly higher than other decoding algorithms, i.e. 1.85%. It is noted that, over the decoding algorithms considered in this study, the proposed research study minimizes power consumption by an average of 41.68%. These algorithms are used in multimedia applications, processing systems for security and information.

     

A new approach for generating efficient sample from market basket data

Classical data mining algorithms require expensive passes over the entire database to generate frequent items and hence to generate association rules. With the increase in the size of database, it is becoming very difficult to handle large amount of data for computation. One of the solutions to this problem is to generate sample from the database that acts as representative of the entire database for finding association rules in such a way that the distance of the sample from the complete database is minimal. Choosing correct sample that could represent data is not an easy task. Many algorithms have been proposed in the past. Some of them are computationally fast while others give better accuracy. In this paper, we present an algorithm for generating a sample from the database that can replace the entire database for generating association rules and is aimed at keeping a balance between accuracy and speed. The algorithm that is proposed takes into account the average number of small, medium and large 1-itemset in the database and average weight of the transactions to define threshold condition for the transactions. Set of transactions that satisfy the threshold condition is chosen as the representative for the entire database. The effectiveness of the proposed algorithm has been tested over several runs of database generated by IBM synthetic data generator. A vivid comparative performance evaluation of the proposed technique with the existing sampling techniques for comparing the accuracy and speed has also been carried out.     

Unsupervised strategies for shilling detection and robust collaborative filtering

Collaborative filtering systems are essentially social systems which base their recommendation on the judgment of a large number of people. However, like other social systems, they are also vulnerable to manipulation by malicious social elements. Lies and Propaganda may be spread by a malicious user who may have an interest in promoting an item, or downplaying the popularity of another one. By doing this systematically, with either multiple identities, or by involving more people, malicious user votes and profiles can be injected into a collaborative recommender system. This can significantly affect the robustness of a system or algorithm, as has been studied in previous work. While current detection algorithms are able to use certain characteristics of shilling profiles to detect them, they suffer from low precision, and require a large amount of training data. In this work, we provide an in-depth analysis of shilling profiles and describe new approaches to detect malicious collaborative filtering profiles. In particular, we exploit the similarity structure in shilling user profiles to separate them from normal user profiles using unsupervised dimensionality reduction. We present two detection algorithms; one based on PCA, while the other uses PLSA. Experimental results show a much improved detection precision over existing methods without the usage of additional training time required for supervised approaches. Finally, we present a novel and highly effective robust collaborative filtering algorithm which uses ideas presented in the detection algorithms using principal component analysis.