Demonstration of Multiple Mating in Heterodera glycines with Biochemical Markers

Controlled crosses of Heterodera glycines were carried out by placing one o r more virgin females of known esterase phenotype on an agar plate and adding, at various time intervals, one or more males of different esterase phenotypes. Progeny (second-stage juveniles) of such crosses were propagated on soybeans, and 30 days later young females were subjected to electrophoretic analysis to determine their esterase phenotype. Esterase phenotypes that represented the heterozygous state of the maternal and paternal genomes confirmed the hybrid nature of the progeny and identified their male parent. When each of 74 females was given the opportunity to mate successively with two males of different esterase phenotypes, 43 mated with a single male and 31 mated with both males. One female mated with three males, i.e., with a male of its own population (sib mating) and the two males provided for the cross. Inseminated females could mate for a second time soon after, or as late as 24 hours after, their first mating. When single males were given the opportunity to mate with many females, about equal numbers of them inseminated zero, one, two, or three females. In greenhouse tests, 12 females were given the opportunity to mate with many males of three different esterase phenotypes. Two females mated with one and possibly more males of the same phenotype, and 10 females mated with males of two different esterase phenotypes. In conclusion, multiple mating appears to be a common behavior of males and females of H. glycines.     

Quantitative Mass Spectrometry Reveals Plasticity of Metabolic Networks in Mycobacterium smegmatis

Mycobacterium tuberculosis has a remarkable ability to persist within the human host as a clinically inapparent or chronically active infection. Fatty acids are thought to be an important carbon source used by the bacteria during long term infection. Catabolism of fatty acids requires reprogramming of metabolic networks, and enzymes central to this reprogramming have been targeted for drug discovery. Mycobacterium smegmatis, a nonpathogenic relative of M. tuberculosis, is often used as a model system because of the similarity of basic cellular processes in these two species. Here, we take a quantitative proteomics-based approach to achieve a global view of how the M. smegmatis metabolic network adjusts to utilization of fatty acids as a carbon source. Two-dimensional liquid chromatography and mass spectrometry of isotopically labeled proteins identified a total of 3,067 proteins with high confidence. This number corresponds to 44% of the predicted M. smegmatis proteome and includes most of the predicted metabolic enzymes. Compared with glucose-grown cells, 162 proteins showed differential abundance in acetate- or propionate-grown cells. Among these, acetate-grown cells showed a higher abundance of proteins that could constitute a functional glycerate pathway. Gene inactivation experiments confirmed that both the glyoxylate shunt and the glycerate pathway are operational in M. smegmatis. In addition to proteins with annotated functions, we demonstrate carbon source-dependent differential abundance of proteins that have not been functionally characterized. These proteins might play as-yet-unidentified roles in mycobacterial carbon metabolism. This study reveals several novel features of carbon assimilation in M. smegmatis, which suggests significant functional plasticity of metabolic networks in this organism.The genus Mycobacterium comprises more than 100 known species of obligate and opportunistic pathogens as well as nonpathogenic saprophytes such as Mycobacterium smegmatis, which shares many characteristics with its pathogenic relatives (1, 2). Because of its similarity in basic cellular processes and the ease and safety in handling nonpathogenic bacteria, M. smegmatis is widely used as a model system for pathogenic Mycobacterium tuberculosis. These organisms share a common framework of carbon metabolism that is complemented with additional reactions and pathways suiting their pathogenic and saprophytic lifestyles. Although the M. smegmatis genome has been sequenced, understanding the many cellular processes is limited by the fact that a large number of genes, many of which are unique to mycobacteria, have not been functionally annotated. Furthermore, genes encoding proteins that mediate specialized functions may be expressed only in response to specific environmental cues. These gaps in our current understanding of mycobacterial metabolism warrant genome-scale studies aimed at delineating the adaptive mechanisms employed under specific growth conditions (3–6).Comparative proteome profiling is a powerful tool for investigating differences in global protein abundance that occur in response to different environmental stimuli (7, 8), and it can provide new insights into the metabolic and regulatory pathways involved in adaptation to the associated stimuli (9–11). Stable isotope dimethyl labeling is a technique that allows precise quantitative mass spectrometry-based analysis of proteome-wide changes within an organism (12, 13). Here, we demonstrate that this technique can also be exploited for deep mining of mycobacterial proteomes and simultaneous comparative analysis of proteomes originating from up to three different experimental conditions. We demonstrate this comparative approach by quantitative analysis of the M. smegmatis proteome in bacterial cells grown with one of three different carbon sources representing glycolytic (glucose) or gluconeogenic (acetate and propionate) substrates. Acetate and propionate are the immediate downstream products of fatty acid β-oxidation, a committed pathway for entry of long-chain fatty acids into the central carbon-metabolizing network.Using this approach, we identified 3,067 proteins in M. smegmatis with high confidence using at least two unique peptides per protein. Among the proteins that we identified, 162 proteins showed differential abundance in cells grown with either acetate or propionate as the carbon source compared with glucose-grown cells. The majority of these proteins clustered within the functional category related to energy metabolism, providing new insights into how these carbon sources are assimilated in M. smegmatis. An interesting feature that emerges from this study is the plasticity of metabolic networks in M. smegmatis, where multiple pathways can be co-utilized for assimilation of the same metabolite. This finding, along with the recent demonstration that mycobacteria can co-catabolize multiple carbon sources (14), underscores the flexibility and metabolic potential of M. smegmatis. In the future, this approach could be used to explore the metabolic adaptations that play a role in growth of M. tuberculosis on fatty acid substrates, which are thought to be an important carbon source for the bacteria in the lungs of the infected host (5, 6, 15, 16).     

FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks

It is important to cluster heterogeneous information networks. A fast clustering algorithm based on an approximate commute time embedding for heterogeneous information networks with a star network schema is proposed in this paper by utilizing the sparsity of heterogeneous information networks. First, a heterogeneous information network is transformed into multiple compatible bipartite graphs from the compatible point of view. Second, the approximate commute time embedding of each bipartite graph is computed using random mapping and a linear time solver. All of the indicator subsets in each embedding simultaneously determine the target dataset. Finally, a general model is formulated by these indicator subsets, and a fast algorithm is derived by simultaneously clustering all of the indicator subsets using the sum of the weighted distances for all indicators for an identical target object. The proposed fast algorithm, FctClus, is shown to be efficient and generalizable and exhibits high clustering accuracy and fast computation speed based on a theoretic analysis and experimental verification.     

WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks

In this paper, we propose an on-demand distributed clustering algorithm for multi-hop packet radio networks. These types of networks, also known as ad hoc networks, are dynamic in nature due to the mobility of nodes. The association and dissociation of nodes to and from clusters perturb the stability of the network topology, and hence a reconfiguration of the system is often unavoidable. However, it is vital to keep the topology stable as long as possible. The clusterheads, form a dominant set in the network, determine the topology and its stability. The proposed weight-based distributed clustering algorithm takes into consideration the ideal degree, transmission power, mobility, and battery power of mobile nodes. The time required to identify the clusterheads depends on the diameter of the underlying graph. We try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control (MAC) protocol. The non-periodic procedure for clusterhead election is invoked on-demand, and is aimed to reduce the computation and communication costs. The clusterheads, operating in dual power mode, connects the clusters which help in routing messages from a node to any other node. We observe a trade-off between the uniformity of the load handled by the clusterheads and the connectivity of the network. Simulation experiments are conducted to evaluate the performance of our algorithm in terms of the number of clusterheads, reaffiliation frequency, and dominant set updates. Results show that our algorithm performs better than existing ones and is also tunable to different kinds of network conditions.     

Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering

Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank.     

Multiple Manifold Clustering Using Curvature Constrained Path

The problem of multiple surface clustering is a challenging task, particularly when the surfaces intersect. Available methods such as Isomap fail to capture the true shape of the surface near by the intersection and result in incorrect clustering. The Isomap algorithm uses shortest path between points. The main draw back of the shortest path algorithm is due to the lack of curvature constrained where causes to have a path between points on different surfaces. In this paper we tackle this problem by imposing a curvature constraint to the shortest path algorithm used in Isomap. The algorithm chooses several landmark nodes at random and then checks whether there is a curvature constrained path between each landmark node and every other node in the neighborhood graph. We build a binary feature vector for each point where each entry represents the connectivity of that point to a particular landmark. Then the binary feature vectors could be used as a input of conventional clustering algorithm such as hierarchical clustering. We apply our method to simulated and some real datasets and show, it performs comparably to the best methods such as K-manifold and spectral multi-manifold clustering.     

Efficient and Anonymous Two-Factor User Authentication in Wireless Sensor Networks: Achieving User Anonymity with Lightweight Sensor Computation

A smart-card-based user authentication scheme for wireless sensor networks(hereafter referred to as a SCA-WSN scheme) is designed to ensure that onlyusers who possess both a smart card and the corresponding password are allowedto gain access to sensor data and their transmissions. Despite many researchefforts in recent years, it remains a challenging task to design an efficientSCA-WSN scheme that achieves user anonymity. The majority of published SCA-WSNschemes use only lightweight cryptographic techniques (rather than public-keycryptographic techniques) for the sake of efficiency, and have been demonstratedto suffer from the inability to provide user anonymity. Some schemes employelliptic curve cryptography for better security but require sensors with strictresource constraints to perform computationally expensive scalar-pointmultiplications; despite the increased computational requirements, these schemesdo not provide user anonymity. In this paper, we present a new SCA-WSN schemethat not only achieves user anonymity but also is efficient in terms of thecomputation loads for sensors. Our scheme employs elliptic curve cryptographybut restricts its use only to anonymous user-to-gateway authentication, therebyallowing sensors to perform only lightweight cryptographic operations. Ourscheme also enjoys provable security in a formal model extended from the widelyaccepted Bellare-Pointcheval-Rogaway (2000) model to capture the user anonymityproperty and various SCA-WSN specific attacks (e.g., stolen smart card attacks,node capture attacks, privileged insider attacks, and stolen verifierattacks).     

Estimating Relative Error in Nematode Numbers from Single Soil Samples Composed of Multiple Cores

Spatial distributions of several species of plant-parasitic nematodes were determined in each of three fallow vegetable fields and in smaller subunits of those fields. Goodness of fit to each of several theoretical distributions was tested hy means of a X² test. Distributions for most species showed good agreement with a negative binomial model. An exception occurred with Crictmemella sp., which showed a better fit to the Neyman Type A distribution. For nematodes distributed according to the negative binomial model, the number of cores per composite sample needed to achieve specified relative errors was calculated. For a given nematode species, such as Quinisulcius actus (Allen) Siddiqi or Meloidogyne incognita (Kofoid &White) Chitwood, the k values for the negative binomial distribution increased as field size decreased, with the result that fewer cores were needed to achieve the same level of precision in a smaller field. Best results were achieved when the single sample was used to estimate populations in fields of 0.25-0.45 ha in size. When using only a single composite sample to estimate mixed populations of the nematodes studied here in a field of that size, approximately 22 cores per composite sample would be needed to estimate all population means within a standard error to mean ratio of 25%. Considerably, more cores were needed to maintain a given level of precision in fields of 1.0 ha or greater, and it may be necessary to subdivide larger unils (ca. 1.5 ha and up) for accurate sampling.     

Multiple Pathways of Plasmid DNA Transfer in Helicobacter pylori

Many Helicobacter pylori (Hp) strains carry cryptic plasmids of different size and gene content, the function of which is not well understood. A subgroup of these plasmids (e.g. pHel4, pHel12), contain a mobilisation region, but no cognate type IV secretion system (T4SS) for conjugative transfer. Instead, certain H. pylori strains (e.g. strain P12 carrying plasmid pHel12) can harbour up to four T4SSs in their genome (cag-T4SS, comB, tfs3, tfs4). Here, we show that such indigenous plasmids can be efficiently transferred between H. pylori strains, even in the presence of extracellular DNaseI eliminating natural transformation. Knockout of a plasmid-encoded mobA relaxase gene significantly reduced plasmid DNA transfer in the presence of DNaseI, suggesting a DNA conjugation or mobilisation process. To identify the T4SS involved in this conjugative DNA transfer, each individual T4SS was consecutively deleted from the bacterial chromosome. Using a marker-free counterselectable gene deletion procedure (rpsL counterselection method), a P12 mutant strain was finally obtained with no single T4SS (P12ΔT4SS). Mating experiments using these mutants identified the comB T4SS in the recipient strain as the major mediator of plasmid DNA transfer between H. pylori strains, both in a DNaseI-sensitive (natural transformation) as well as a DNaseI-resistant manner (conjugative transfer). However, transfer of a pHel12::cat plasmid from a P12ΔT4SS donor strain into a P12ΔT4SS recipient strain provided evidence for the existence of a third, T4SS-independent mechanism of DNA transfer. This novel type of plasmid DNA transfer, designated as alternate DNaseI-Resistant (ADR) mechanism, is observed at a rather low frequency under in vitro conditions. Taken together, our study describes for the first time the existence of three distinct pathways of plasmid DNA transfer between H. pylori underscoring the importance of horizontal gene transfer for this species.     

Optogenetic Modulation and Multi-Electrode Analysis of Cerebellar Networks In Vivo

The firing patterns of cerebellar Purkinje cells (PCs), as the sole output of the cerebellar cortex, determine and tune motor behavior. PC firing is modulated by various inputs from different brain regions and by cell-types including granule cells (GCs), climbing fibers and inhibitory interneurons. To understand how signal integration in PCs occurs and how subtle changes in the modulation of PC firing lead to adjustment of motor behaviors, it is important to precisely record PC firing in vivo and to control modulatory pathways in a spatio-temporal manner. Combining optogenetic and multi-electrode approaches, we established a new method to integrate light-guides into a multi-electrode system. With this method we are able to variably position the light-guide in defined regions relative to the recording electrode with micrometer precision. We show that PC firing can be precisely monitored and modulated by light-activation of channelrhodopsin-2 (ChR2) expressed in PCs, GCs and interneurons. Thus, this method is ideally suited to investigate the spatio/temporal modulation of PCs in anesthetized and in behaving mice.     

Estimating Photosynthesis and Concurrent Export Rates in C3 and C4 Species at Ambient and Elevated CO2

The ability of 21 C₃ and C₄ monocot and dicot species to rapidly export newly fixed C in the light at both ambient and enriched CO₂ levels was compared. Photosynthesis and concurrent export rates were estimated during isotopic equilibrium of the transport sugars using a steady-state ¹⁴CO₂-labeling procedure. At ambient CO₂ photosynthesis and export rates for C₃ species were 5 to 15 and 1 to 10 μmol C m⁻² s⁻¹, respectively, and 20 to 30 and 15 to 22 μmol C m⁻² s⁻¹, respectively, for C₄ species. A linear regression plot of export on photosynthesis rate of all species had a correlation coefficient of 0.87. When concurrent export was expressed as a percentage of photosynthesis, several C₃ dicots that produced transport sugars other than Suc had high efflux rates relative to photosynthesis, comparable to those of C₄ species. At high CO₂ photosynthetic and export rates were only slightly altered in C₄ species, and photosynthesis increased but export rates did not in all C₃ species. The C₃ species that had high efflux rates relative to photosynthesis at ambient CO₂ exported at rates comparable to those of C₄ species on both an absolute basis and as a percentage of photosynthesis. At ambient CO₂ there were strong linear relationships between photosynthesis, sugar synthesis, and concurrent export. However, at high CO₂ the relationships between photosynthesis and export rate and between sugar synthesis and export rate were not as strong because sugars and starch were accumulated.