Effect of transition metal on stability and activity of La-Ca-M-(Al)-O (M = Co,Cr, Fe and Mn) perovskite oxides during partial oxidation of methane

Perovskite oxides of the type of La_xCa_1-xM_yAl_1-yO_3-δ (M = Co, Cr, Fe, Mn; x = 0.5; y = 0.7–1.0) were prepared using the polymerization methods and evaluated via N₂ adsorption, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning transmission electron microscopy (STEM), energy dispersive X-ray (EDX) spectroscopy, temperature-programmed reduction by hydrogen (TPR-H₂) and temperature-programmed oxidation by oxygen (TPO-O₂). Catalytic behaviour of the perovskite oxides during methane oxidation was studied using a tubular fixed-bed reactor. In a partial oxidation, which proceeded in two steps, there was total oxidation in the first step and CO₂ and H₂O were formed; in the second step, the total oxidation products oxidized methane by (dry and wet) reforming reactions to yield CO and H₂. Total oxidation and the two reforming reactions proceeded on two types of an active centre formed by transition metal ions, oxygen vacancies and oxide ions. The catalytic system La-Ca-Co-Al-O which contained aluminium, decomposed in partial oxidation of methane (POM) into a composite that contained firmly bonded cobalt nanoparticles in the surface of a substrate made up of La₂O₃, CaO and Al₂O₃ which catalysed POM with a high methane conversion and hydrogen selectivity.     

Finding the Consensus Shape for a Protein Family

We define and prove properties of the consensus shape for a protein family, a protein-like structure that provides a compact summary of the significant structural information for a protein family. If all members of the protein family exhibit a geometric relationship between corresponding -carbons, then that relationship is preserved in the consensus shape. In particular, distances and angles that are consistent across family members are preserved. For the consensus shape, the spacing between successive -carbons is variable, with small distances in regions where the members of the protein family exhibit significant variation and large distances (up to the standard spacing of about 3.8 Å) in regions where the family members agree. Despite this non-protein-like characteristic, the consensus shape preserves and highlights important structural information. We describe an iterative algorithm for computing the consensus shape and prove that the algorithm converges. As a by-product we produce a multiple structure alignment for the family members. We present the results of experiments in which we build consensus shapes for several known protein families.     

Maximizing Lifetime for Data Aggregation in Wireless Sensor Networks

This paper studies energy efficient routing for data aggregation in wireless sensor networks. Our goal is to maximize the lifetime of the network, given the energy constraint on each sensor node. Using linear programming (LP) formulation, we model this problem as a multicommodity flow problem, where a commodity represents the data generated from a sensor node and delivered to a base station. A fast approximate algorithm is presented, which is able to compute (1−ε)-approximation to the optimal lifetime for any ε > 0. Then along this baseline, we further study several advanced topics. First, we design an algorithm, which utilizes the unique characteristic of data aggregation, and is proved to reduce the running time of the fastest existing algorithm by a factor of K, K being the number of commodities. Second, we extend our algorithm to accommodate the same problem in the setting of multiple base stations, and study its impact on network lifetime improvement. All algorithms are evaluated through both solid theoretical analysis and extensive simulation results. Yuan Xue received her B.S. in Computer Science from Harbin Institute of Technology, China in 1994 and her M.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2002, and 2005. Currently she is an assistant professor at the Department of Electrical Engineering and Computer Science of Vanderbilt University. Her research interests include wireless and sensor networks, mobile systems, and network security. Yi Cui received his B.S. and M.S. degrees in 1997 and 1999, from Department of Computer Science, Tsinghua University, China, and his Ph.D. degree in 2005 from the Department of Computer Science, University of Illinois at Urbana-Champaign. Since then, he has been with the Department of Electrical Engineering and Computer Science at Vanderbilt University, where he is currently an assistant professor. His research interests include overlay network, peer-to-peer system, multimedia system, and wireless sensor network. Klara Nahrstedt (M ' 94) received her A.B., M.Sc degrees in mathematics from the Humboldt University, Berlin, Germany, and Ph.D in computer science from the University of Pennsylvania. She is an associate professor at the University of Illinois at Urbana-Champaign, Computer Science Department where she does research on Quality of Service(QoS)-aware systems with emphasis on end-to-end resource management, routing and middleware issues for distributed multimedia systems. She is the coauthor of the widely used multimedia book ‘Multimedia:Computing, Communications and Applications’ published by Prentice Hall, and the recipient of the Early NSF Career Award, the Junior Xerox Award and the IEEE Communication Society Leonard Abraham Award for Research Achievements, and the Ralph and Catherine Fisher Professorship Chair. Since June 2001 she serves as the editor-in-chief of the ACM/Springer Multimedia System Journal. An erratum to this article is available at .     

Sense of coherence as a predictor of work reentry following multidisciplinary rehabilitation for individuals with chronic musculoskeletal pain.

This study investigated the association between sense of coherence (SOC) and work reentry. One hundred and fifty-three patients with musculoskeletal pain (mean age=45.6 years, SD=9.1) were included. SOC, demographics, personal characteristics, work status, pain intensity, pain experience, anxiety, and depression were collected during a 57-week rehabilitation period. SOC significantly improved, and pain experience, anxiety, and depression significantly decreased during the rehabilitation period. SOC was found to significantly predict anxiety and depression in the nonwork reentry subsample (n=44). No significant association was found between SOC and work reentry. These data clarify the role of SOC in chronic pain and emotional distress and question the role of SOC in predicting work reentry in long-term chronic musculoskeletal pain conditions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Geometric pattern matching for point sets in the plane under similarity transformations

We consider the following geometric pattern matching problem: Given two sets of points in the plane, P and Q, and some (arbitrary) δ>0, find a similarity transformation T (translation, rotation and scale) such that h(T(P),Q)<δ, where h(⋅,⋅) is the directional Hausdorff distance with L_∞ as the underlying metric; or report that none exists. We are only interested in the decision problem, not in minimizing the Hausdorff distance, since in the real world, where our applications come from, δ is determined by the practical uncertainty in the position of the points (pixels). Similarity transformations have not been dealt with in the context of the Hausdorff distance and we fill the gap here. We present efficient algorithms for this problem imposing a reasonable separation restriction on the points in the set Q. If the L_∞ distance between every pair of points in Q is at least 8δ, then the problem can be solved in O(mn²logn) time, where m and n are the numbers of points in P and Q respectively. If the L_∞ distance between every pair of points in Q is at least cδ, for some c, 0<c<1, we present a randomized approximate solution with expected runtime O(n²c−4ε−8log⁴mn), where ε>0 controls the approximation. Our approximation is on the size of the subset, B⊆P, such that h(T(B),Q)<δ and |B|>(1−ε)|P| with high probability.     

Lipid Nanoparticles for Broad-Spectrum Nucleic Acid Delivery

Lipid nanoparticles (LNPs) are the most advanced nonviral modality for nucleic acid (NA) delivery, and have recently gained enormous attention in the fields of RNA therapeutics and vaccine development. Here, ionizable adamantane-based lipidoids named XMaNs, which circumvent the usual need for laborious optimization of LNP components for highly diverse types of NAs, are described. The non-toxic XMaN6 lipidoid is highly versatile in entrapment and delivery of siRNA, mRNA, plasmid DNA, and a cyclic dinucleotide. XMaN6-based LNPs efficiently deliver: 1) siRNA into human primary hepatocytes and cell lines that are hard-to-transfect; 2) mRNA into mouse liver; 3) plasmid DNA; 4) 2′,3′-cGAMP into cells and activated the cGAS-STING pathway three orders of magnitude more efficiently than 2′,3′-cGAMP alone. To our knowledge, such universality in delivering different NA types has not been previously described and can accelerate translation of LNPs into the clinic.