Intrapulmonary Cystic Lymphangioma in a 2-month-old Infant

Lymphangioma is an abnormal collection of lymphatics that are developmentally isolated from the normal lymphatic system. Lymphangioma rarely presents as a solitary pulmonary lesion. We report a rare case of intrapulmonary cystic lymphangioma involving the upper lobe of the right lung, which presented with dyspnea in a 2-month-old infant. High-resolution computed tomography (HRCT) of the chest demonstrated a well-circumscribed, multiseptate, cystic lesion in the upper lobe of the right lung, mimicking the feature of type I congenital cystic adenomatoid malformation. The tumor was removed by bilobectomy of the upper and middle lobes of the right lung, and its pathologic examination confirmed the diagnosis of an intrapulmonary cystic lymphangioma

Demonstration of Two-Qubit Algorithms with a Superconducting Quantum Processor

By harnessing the superposition and entanglement of physical states, quantum computers could outperform their classical counterparts in solving problems of technological impact, such as factoring large numbers and searching databases. A quantum processor executes algorithms by applying a programmable sequence of gates to an initialized register of qubits, which coherently evolves into a final state containing the result of the computation. Simultaneously meeting the conflicting requirements of long coherence, state preparation, universal gate operations, and qubit readout makes building quantum processors challenging. Few-qubit processors have already been shown in nuclear magnetic resonance, cold ion trap and optical systems, but a solid-state realization has remained an outstanding challenge. Here we demonstrate a two-qubit superconducting processor and the implementation of the Grover search and Deutsch-Jozsa quantum algorithms. We employ a novel two-qubit interaction, tunable in strength by two orders of magnitude on nanosecond time scales, which is mediated by a cavity bus in a circuit quantum electrodynamics (cQED) architecture. This interaction allows generation of highly-entangled states with concurrence up to 94%. Although this processor constitutes an important step in quantum computing with integrated circuits, continuing efforts to increase qubit coherence times, gate performance and register size will be required to fulfill the promise of a scalable technology.Comment: 6 pages, 1 table, 4 figures, and Supplementary Information (3 pages, 3 figures); Expanded author list, updated references, and minor improvements to text and figure

A Case of Traumatic Tricuspid Regurgitation Caused by Multiple Papillary Muscle Rupture

Traumatic tricuspid regurgitation is a rare complication of blunt chest trauma. With the increase in the number of automobile accidents, traumatic tricuspid regurgitation has become an important problem after blunt chest trauma. It has been reported more frequently because of better diagnostic procedures and a better understanding of the pathology. The early diagnosis of traumatic tricuspid regurgitation is important because traumatic tricuspid injury could be effectively corrected with reparative techniques, early operation is considered to relieve symptoms and to prevent right ventricular dysfunction. Echocardiography can reveal the cause and severity of regurgitation. We experienced a case of tricuspid regurgitation after blunt chest trauma early diagnosis and valve repair were performed. This case reminds the physicians in the emergency department should be aware of this potential complication following non-penetrating chest trauma and echocardiography is useful and should play an early role

ViennaRNA Package 2.0

<p>Abstract</p> <p>Background</p> <p>Secondary structure forms an important intermediate level of description of nucleic acids that encapsulates the dominating part of the folding energy, is often well conserved in evolution, and is routinely used as a basis to explain experimental findings. Based on carefully measured thermodynamic parameters, exact dynamic programming algorithms can be used to compute ground states, base pairing probabilities, as well as thermodynamic properties.</p> <p>Results</p> <p>The <monospace>ViennaRNA</monospace> Package has been a widely used compilation of RNA secondary structure related computer programs for nearly two decades. Major changes in the structure of the standard energy model, the <it>Turner 2004 </it>parameters, the pervasive use of multi-core CPUs, and an increasing number of algorithmic variants prompted a major technical overhaul of both the underlying <monospace>RNAlib</monospace> and the interactive user programs. New features include an expanded repertoire of tools to assess RNA-RNA interactions and restricted ensembles of structures, additional output information such as <it>centroid </it>structures and <it>maximum expected accuracy </it>structures derived from base pairing probabilities, or <it>z</it>-<it>scores </it>for locally stable secondary structures, and support for input in <monospace>fasta</monospace> format. Updates were implemented without compromising the computational efficiency of the core algorithms and ensuring compatibility with earlier versions.</p> <p>Conclusions</p> <p>The <monospace>ViennaRNA Package 2.0</monospace>, supporting concurrent computations <monospace>via OpenMP</monospace>, can be downloaded from <url>http://www.tbi.univie.ac.at/RNA</url>.</p

Topological Structure of the Space of Phenotypes: The Case of RNA Neutral Networks

The evolution and adaptation of molecular populations is constrained by the diversity accessible through mutational processes. RNA is a paradigmatic example of biopolymer where genotype (sequence) and phenotype (approximated by the secondary structure fold) are identified in a single molecule. The extreme redundancy of the genotype-phenotype map leads to large ensembles of RNA sequences that fold into the same secondary structure and can be connected through single-point mutations. These ensembles define neutral networks of phenotypes in sequence space. Here we analyze the topological properties of neutral networks formed by 12-nucleotides RNA sequences, obtained through the exhaustive folding of sequence space. A total of 412 sequences fragments into 645 subnetworks that correspond to 57 different secondary structures. The topological analysis reveals that each subnetwork is far from being random: it has a degree distribution with a well-defined average and a small dispersion, a high clustering coefficient, and an average shortest path between nodes close to its minimum possible value, i.e. the Hamming distance between sequences. RNA neutral networks are assortative due to the correlation in the composition of neighboring sequences, a feature that together with the symmetries inherent to the folding process explains the existence of communities. Several topological relationships can be analytically derived attending to structural restrictions and generic properties of the folding process. The average degree of these phenotypic networks grows logarithmically with their size, such that abundant phenotypes have the additional advantage of being more robust to mutations. This property prevents fragmentation of neutral networks and thus enhances the navigability of sequence space. In summary, RNA neutral networks show unique topological properties, unknown to other networks previously described

Efficient Algorithms for Probing the RNA Mutation Landscape

The diversity and importance of the role played by RNAs in the regulation and development of the cell are now well-known and well-documented. This broad range of functions is achieved through specific structures that have been (presumably) optimized through evolution. State-of-the-art methods, such as McCaskill's algorithm, use a statistical mechanics framework based on the computation of the partition function over the canonical ensemble of all possible secondary structures on a given sequence. Although secondary structure predictions from thermodynamics-based algorithms are not as accurate as methods employing comparative genomics, the former methods are the only available tools to investigate novel RNAs, such as the many RNAs of unknown function recently reported by the ENCODE consortium. In this paper, we generalize the McCaskill partition function algorithm to sum over the grand canonical ensemble of all secondary structures of all mutants of the given sequence. Specifically, our new program, RNAmutants, simultaneously computes for each integer k the minimum free energy structure MFE(k) and the partition function Z(k) over all secondary structures of all k-point mutants, even allowing the user to specify certain positions required not to mutate and certain positions required to base-pair or remain unpaired. This technically important extension allows us to study the resilience of an RNA molecule to pointwise mutations. By computing the mutation profile of a sequence, a novel graphical representation of the mutational tendency of nucleotide positions, we analyze the deleterious nature of mutating specific nucleotide positions or groups of positions. We have successfully applied RNAmutants to investigate deleterious mutations (mutations that radically modify the secondary structure) in the Hepatitis C virus cis-acting replication element and to evaluate the evolutionary pressure applied on different regions of the HIV trans-activation response element. In particular, we show qualitative agreement between published Hepatitis C and HIV experimental mutagenesis studies and our analysis of deleterious mutations using RNAmutants. Our work also predicts other deleterious mutations, which could be verified experimentally. Finally, we provide evidence that the 3′ UTR of the GB RNA virus C has been optimized to preserve evolutionarily conserved stem regions from a deleterious effect of pointwise mutations. We hope that there will be long-term potential applications of RNAmutants in de novo RNA design and drug design against RNA viruses. This work also suggests potential applications for large-scale exploration of the RNA sequence-structure network. Binary distributions are available at http://RNAmutants.csail.mit.edu/

Structural and Functional Analysis of Phytotoxin Toxoflavin-Degrading Enzyme

Pathogenic bacteria synthesize and secrete toxic low molecular weight compounds as virulence factors. These microbial toxins play essential roles in the pathogenicity of bacteria in various hosts, and are emerging as targets for antivirulence strategies. Toxoflavin, a phytotoxin produced by Burkholderia glumae BGR1, has been known to be the key factor in rice grain rot and wilt in many field crops. Recently, toxoflavin-degrading enzyme (TxDE) was identified from Paenibacillus polymyxa JH2, thereby providing a possible antivirulence strategy for toxoflavin-mediated plant diseases. Here, we report the crystal structure of TxDE in the substrate-free form and in complex with toxoflavin, along with the results of a functional analysis. The overall structure of TxDE is similar to those of the vicinal oxygen chelate superfamily of metalloenzymes, despite the lack of apparent sequence identity. The active site is located at the end of the hydrophobic channel, 9 Å in length, and contains a Mn(II) ion interacting with one histidine residue, two glutamate residues, and three water molecules in an octahedral coordination. In the complex, toxoflavin binds in the hydrophobic active site, specifically the Mn(II)-coordination shell by replacing a ligating water molecule. A functional analysis indicated that TxDE catalyzes the degradation of toxoflavin in a manner dependent on oxygen, Mn(II), and the reducing agent dithiothreitol. These results provide the structural features of TxDE and the early events in catalysis

The Ascent of the Abundant: How Mutational Networks Constrain Evolution

Evolution by natural selection is fundamentally shaped by the fitness landscapes in which it occurs. Yet fitness landscapes are vast and complex, and thus we know relatively little about the long-range constraints they impose on evolutionary dynamics. Here, we exhaustively survey the structural landscapes of RNA molecules of lengths 12 to 18 nucleotides, and develop a network model to describe the relationship between sequence and structure. We find that phenotype abundance—the number of genotypes producing a particular phenotype—varies in a predictable manner and critically influences evolutionary dynamics. A study of naturally occurring functional RNA molecules using a new structural statistic suggests that these molecules are biased toward abundant phenotypes. This supports an “ascent of the abundant” hypothesis, in which evolution yields abundant phenotypes even when they are not the most fit

Less Can Be More: RNA-Adapters May Enhance Coding Capacity of Replicators

It is still not clear how prebiotic replicators evolved towards the complexity found in present day organisms. Within the most realistic scenario for prebiotic evolution, known as the RNA world hypothesis, such complexity has arisen from replicators consisting solely of RNA. Within contemporary life, remarkably many RNAs are involved in modifying other RNAs. In hindsight, such RNA-RNA modification might have helped in alleviating the limits of complexity posed by the information threshold for RNA-only replicators. Here we study the possible role of such self-modification in early evolution, by modeling the evolution of protocells as evolving replicators, which have the opportunity to incorporate these mechanisms as a molecular tool. Evolution is studied towards a set of 25 arbitrary ‘functional’ structures, while avoiding all other (misfolded) structures, which are considered to be toxic and increase the death-rate of a protocell. The modeled protocells contain a genotype of different RNA-sequences while their phenotype is the ensemble of secondary structures they can potentially produce from these RNA-sequences. One of the secondary structures explicitly codes for a simple sequence-modification tool. This ‘RNA-adapter’ can block certain positions on other RNA-sequences through antisense base-pairing. The altered sequence can produce an alternative secondary structure, which may or may not be functional. We show that the modifying potential of interacting RNA-sequences enables these protocells to evolve high fitness under high mutation rates. Moreover, our model shows that because of toxicity of misfolded molecules, redundant coding impedes the evolution of self-modification machinery, in effect restraining the evolvability of coding structures. Hence, high mutation rates can actually promote the evolution of complex coding structures by reducing redundant coding. Protocells can successfully use RNA-adapters to modify their genotype-phenotype mapping in order to enhance the coding capacity of their genome and fit more information on smaller sized genomes

Facilitated Variation: How Evolution Learns from Past Environments To Generalize to New Environments

One of the striking features of evolution is the appearance of novel structures in organisms. Recently, Kirschner and Gerhart have integrated discoveries in evolution, genetics, and developmental biology to form a theory of facilitated variation (FV). The key observation is that organisms are designed such that random genetic changes are channeled in phenotypic directions that are potentially useful. An open question is how FV spontaneously emerges during evolution. Here, we address this by means of computer simulations of two well-studied model systems, logic circuits and RNA secondary structure. We find that evolution of FV is enhanced in environments that change from time to time in a systematic way: the varying environments are made of the same set of subgoals but in different combinations. We find that organisms that evolve under such varying goals not only remember their history but also generalize to future environments, exhibiting high adaptability to novel goals. Rapid adaptation is seen to goals composed of the same subgoals in novel combinations, and to goals where one of the subgoals was never seen in the history of the organism. The mechanisms for such enhanced generation of novelty (generalization) are analyzed, as is the way that organisms store information in their genomes about their past environments. Elements of facilitated variation theory, such as weak regulatory linkage, modularity, and reduced pleiotropy of mutations, evolve spontaneously under these conditions. Thus, environments that change in a systematic, modular fashion seem to promote facilitated variation and allow evolution to generalize to novel conditions