In order to resolve this issue, a naïve backtracking algorithm has been developed in JAVA, determining the correspondence between two proteins by constructing a symmetrical matrix, containing pairwise angles between the helices for each protein, as described in Section 4.2.2. Using this correspondence one of the protein matrices is re-constructed and finally the distance between the two distance matrices is computed by taking the sum of the pair-wise differences of helices. This procedure is repeated for each of the PDBs, and the result of all of the calculated distances between the PDBS is stored in a symmetrical distance matrix which is then used by FITCH to construct the phylogenetic trees. Using the RF algorithm, the obtained phylogenetic trees are compared against the phylogenetic trees obtained from the other four structural alignment tools (TM-align, Lovoalign, CE, and LOCK2). Following this experiment, the HE-based phylogenetic trees have shown to have a low correlation in comparison to those constructed by the other alignment tools.
HBV RNase H primer grip. The primer grip of RNases H is a
group of residues that establishes contact with the bases of the RNA- DNA heteroduplex substrate ( 23 ). In particular, an aromatic residue (Y or F) within the B-helix plays a key role in substrate binding. According to our alignment, the residue in the HBV sequence that was equivalent to E. coli Y73 was the aliphatic V740, a residue unlikely to play a role in nucleic acid substrate binding. Instead, Y746 appeared to be the most appealing candidate for substrate binding, due to its conservation in the four data sets and its local- ization 12 positions before the C-helix. The F749 and W751 resi- dues could also be considered candidates for substrate binding, but the NSEs were higher at these positions in the Dudps data set. According to secondary structure predictions, a minor structural difference appeared to exist between the basic protrusions of the HBV RNase H and other enzymes: indeed, a ␤-strand was pre- dicted instead of the 〉-helix. This strand could be part of a larger ␤-sheet that could involve a second predicted ␤-strand (766- FVYV-769) in this region of the HBV RNase H.
selfing reduces e ffective population sizes and recombination,
which globally lessens the efficacy of selection. Selfing species are thus, more prone to the accumulation of deleterious muta- tions and less able to adapt to changing environments, which eventually drives them towards extinction (Wright et al., 2013). In agreement with this prediction and using the BiSSE model (Maddison et al., 2007), the net diversification rates are found to be higher in outcrossing than in selfing Solanaceae species (Goldberg et al., 2010). In parallel, the negative genetic e ffects of selfing, which can explain higher extinction rates, were tested by comparing the molecular evolutionary rates between self- ing and outcrossing lineages. The efficacy of selection can be assessed through the ratio of nonsynonymous to synonymous substitutions, dN/dS ; a higher ratio corresponding to less ef- ficient selection. Phylogenetic analyses have been carried out, but usually they either detect only weak e ffects, or fail at de-
However, despite the effectiveness ofphylogenetic approaches in concerted discovery, the technique is rarely used for the classification of conopeptides (but see Aguilar et al. 2009; Conticello et al. 2001; Wang et al. 2008; Zhangsun et al. 2006). Several statistical methods for conopeptide classification, such as Mahalanobis (Lin and Li 2007) or BLAST and Euclidian distances among others (Mondal et al. 2006) have been described; however, most of these approaches are primarily designed for classification of new sequences rather than for testing the current classification (i.e., checking the validity of each known group by a blind-exploratory approach). Conopeptide precursors are characterized by a typical structural organization consisting of a highly conserved signal region, followed by a more variable pro-region and a hyper-variable mature toxin containing a few conserved amino acids such as the cysteine residues required for disulfide bonds. Conopeptides are mainly named and classified according to three properties: first, they are characterized by their signal sequence, this short sequence (~20 amino-acids) is highly conserved, and has been used to define superfamilies; second, mature toxins structural families are characterized depending on their pattern of cysteines (the Cys-pattern), for example, the mature toxin can include a variable number of cysteines (most commonly 4 or 6), and their respective position can vary (4 cysteines can be organized as C-C-C-C or CC-C-C where “-” represents a variable number of amino-acids); finally,
A first process likely to render gene trees and species trees incongruent is hybridization. Although long suspected because of morphologically intermediate forms between putative parental species, hybridization has only fairly recently been evidenced in bryophytes (see Natcheva & Cronberg 2004 and McDaniel et al. 2010, for a review). One obvious example is in the case of allopolyploid speciation. Allopolyploids originate through reticulation rather than though “normal” divergent evolution, and for that reason do not fit the paradigm ofphylogenetic species delimitation. Moreover, many or perhaps most allopolyploid species appear to have originated multiple times and are therefore polyphyletic (Soltis & Soltis 1999). Nevertheless, most allopolyploid taxa appear to function as biologically and ecologically meaninful units of biodiversity and populations that may have originated independently can interbreed and exchange genes. It is neither practical nor biologically accurate to consider each independent derivation of an allopolyploid a separate species. Moreover, independent origins of allopolyploids (and in fact, species formed by other mechanisms) raises the question of what exactly constitutes monophyly; a single origin involving one individual, a group of closely- related individuals, a single population, etc.
L 共兵␥ k 其兲 ⫽ 写 i
P ⬘ i 共 ␤ i ; 兵t j 其 i 兲. (3) Values for the calibration parameters are obtained by maxi- mizing the likelihood function. The number of parameters and the complexity of the likelihood function depend on the dis- tributions used to describe each break order and on the num- ber of break orders considered. Here is an example of calcu- lations for the model consisting of a Weibull distribution for the first break and an exponential distribution for all subse- quent breaks.
B updated , since each relative position update has an impact on a bounded
number of terms.
In practice, the complexity of the algorithm depends on the local topol- ogy of the system and the bond update pattern. To demonstrate this, we generated a carbon nanotube of 8000 atoms thanks to TubeGen, and per- turbed the positions of a set of N atoms, 1 < N < 8000. This benchmark is presented in Figure 7 where the solid curves represent the computational cost of the forces and potential update. To illustrate the dependency of this cost on the activation pattern (i.e. the evolving set of active atoms), both random and continuous activations are compared. The curve with a continuous activation pattern shows a linear behavior. Indeed, continuous activation is equivalent to consider a nanotube system of length equal to the length of the active region plus a buffer zone. Thus, the linear behavior is a direct consequence of the linear dependency of the Brenner potential on the number of bonds. The computational cost of the random activation step is more important, since updated relative positions are isolated in space with a high probability, which results in a larger number of terms that have to be recomputed. In general, for any molecular system, there exists two constants
Indeed, molecularmodeling is a field of study that in interested in the behavior of atomic and molecular systems subject to energetic interactions. It is then a natural complement of experimental and modeling approaches to expand multiscale approaches towards smaller scales. Besides, process flows concern primarily molecules from raw materials to end products. Therefore, at any process development step, the challenge of knowing the physical properties and thermodynamical state of molecules is critical. But, the future of this challenge is dim when one thinks about the millions of chemical compounds referenced in the chemical abstract series. Neither experimental approaches nor current themodynamic models can handle the combination of properties needed. In some cases experiments are not even practical because of materials decomposition or safety issues. Universal group contribution methods are a pipe dream and existing ones are efficient but are restricted in use to specific areas like petrochemical and small molecular systems.
The aim of this study was to describe how structural equation modeling should be applied to analyse appropriately such common QOL issue.
As an illustration, those methods were applied to data from 4155 subjects participated in 2012 in a community based sample study in the French speaking part of Belgium. Volunteer participants were invited to complete a web-based questionnaire on their weight-related experience. The latent QOL was derived and direct and indirect effects of body mass index (BMI), body image discrepancy (BID), latent socio-economic (SOCIO) and latent subjective-norm (SN) variables were tested. Modeling was performed using the weighted least squares means and variance (WLSMV) estimator due to the presence of ordinal endogenous variables. The fit of models was analysed by χ² test, root mean square error of approximation (RMSEA), comparative fit index (CFI), standardized root mean square residual (SRMR) and Tucker-Lewis index (TLI).
Porphyrins combine readily with metal ions (Zn, Co, Cu etc.), coordinating them in the central "hole" to form metalloporphyrins. Generally, porphyrin molecules have multiple stable states. Redox processes occur primarily on the conjugated core, leading easily to mono radical or diradical cationic species and are dynamically reversible. Other redox activity may be brought by the metal ion if it is a redox ion (Mn, Fe, Co…). Generally, the redox potentials depend on their molecular properties, such as their basicity, their substituents, or the central metal ion and the possible presence of axial ligands [85-94]. In our work we have used used metalloporphyrin ZnTPP that exhibit two oxidation states (figure I.37). The behaviour of ZnTPP is well described by quantum chemistry and electrochemical analysis and impedance spectroscopy (ZnAB3P has almost the same structure) in the chapter 3.
positioning of yeasts and fission yeasts with mitochondrial data is plagued by a strong LBA artifact.
Ascomycota are currently subdivided into three major taxa (Hibbett et al. 2007): Saccharomycotina (Hemiascomycota; budding yeasts), Pezizomycotina (Euascomycota; for the most part filamentous fungi, e.g. Neurospora) and Taphrinomycotina (Archiascomycota). The taxon Taphrinomycotina was initially created based on rRNA phylogeny (Nishida and Sugiyama 1993), regrouping diverse fungal species of previously uncertain taxonomic affiliation: (i) Schizosaccharomyces species (fission yeasts; previously considered to be highly divergent yeast lineages), (ii) Taphrina (several fungal plant pathogens), (iii) the anamorphic yeast-like Saitoella, a suspected ascomycete or basidiomycete, and (iv) Neolecta irregularis, a fungus with filamentous cell growth that forms complex fruiting bodies (unique within this group of organisms). Yet, the statistical support for this grouping with rRNA data is well below standards (for details, see (Leigh et al. 2003)). Addition of potential taphrinomycotina taxa, for instance more
To achieve solutions to these two issues, researchers are taking their inspiration from nature. First, acknowledging that proton reduction is catalyzed in nature through enzymes which rely on the use of earth abundant metals only, scientists have followed this track to develop cheap catalysts for HER. The hydrogenase enzymes interconvert protons to hydrogen at thermodynamic equilibrium (i.e. with no loss of energy). 12 The core of their active site, where the reaction takes place, is constructed around iron and/or nickel atoms and also features a chemical entity, named the proton relay, able to take the proton very close to the metal actually performing catalysis. Chemists have derived such a strategy to create bio-inspired molecular catalysts. These species generally involve one or several non-noble metal center(s) surrounded by a chelating ligand, which can feature a proton relay function. Many studies have been reported on the development of these bio-inspired catalysts for the last 20 years. 12 This approach harnesses the versatility and the precision of inorganic and organic synthesis to create molecules of controlled shape. Among them, one of the most stable and active noble-metal free molecular catalysts for hydrogen evolution has been developed in our laboratory. This catalyst consists of cobalt, a relatively abundant element (20-30 ppm), which is the metallic center surrounded by an equatorial tetradentate diimine-dioxime ligand N 2 ,N 2 -propanediylbis(2,3-butanedione 2-imine 3-oxime),
and OH groups from the silanols seems to be fully completed at 600 C.
3.2. X-ray diﬀraction
The typical XRD pattern of lamellar mesostructured silica is shown on Fig. 3 (curve a: ‘‘untreated material’’). It exhibits two well-resolved peaks at low angle values that can be indexed as (001) and (002) reﬂections, therefore indicating a lamellar symmetry. These peaks correspond to a primary d-spacing of 38 A˚ (see Fig. 1 ). In the wide-angle region, there is only a very broad peak relative to the Si–O–Si bonds period as well as the tem- plate alkyl chain crystallization. The absence of sharp peak in this region indicates that the silica sheets are amorphous in the bulk, and also assesses that the tem-
We observed that sequences with a shorter random re- gion were enriched during this SELEX (Supplementary Ta- ble S1). We decided to recover all the sequences with a ran- dom region between 47 and 52 nucleotides to allow the de- tection of sequences with deletions or insertions. All the se- quences were clustered in families using a Levenshtein dis- tance of 10. Thus, every family was composed of similar sequences with no more than 10 substitutions, insertions, or deletions (Supplementary Table S2). The sequences of most families were separated by a maximum Levenshtein distance of four. We monitored the sequences that were en- riched during the selection, by analyzing those that could be detected in at least one round at a frequency >0.001% in the library (i.e. 10 copies per million sequences, Supple- mentary Table S3). We detected a few hundred sequences with a frequency >0.001% in the library until the round 5; but they collectively represented less than 0.1% of the population (solid lines in Figure 1 A and B and Supplemen- tary Table S3). Their number increases up to 8652 sequences from round 6 to round 12 before slowly decreasing down to 6680 sequences by round 15 (Figure 1 B). Simultaneously, their total prevalence in the pool increased exponentially and represented more than half the pool by round 7 and 83% by round 15 (Figure 1 A). The fact that the number of sequences >0.001% decreased after round 12 while their prevalence increased in the library demonstrates that some sequences started to disappear from the population due to greater amplification of others. Such extinction could be clearly seen for instance for the sequences of the ACE22 and ACE105 families, which were the most amplified families at round 7, but which progressively disappeared from the library thereafter (Supplementary Tables S2 and S3). This correlated with an increase in the number of families to 1737 at round 8 before a continuous decrease to 296 at round 15 (Figure 1 C). This demonstrates that there is predominant amplification of a few families relative to the others from round 8. Moreover, some of these families contained an in- creasing number of variants and, consequently, the number of sequences >0.001% decreased less rapidly than the num- ber of families. As an example of such an evolutionary pro- cess, the frequency of the family that contained the ACE4 aptamer steadily increased up to 9.5% at round 14 before decreasing slightly to 7.7% at round 15 (dotted line in Fig- ure 1 A).
D. Taylor expansion of the molecular SFA
1. Stationary points
As we explained, in Sec. II A we will calibrate our nu- merical results with the atomic ones in order to remove the quadratic harmonic phase. Moreover, since internuclear distances (a few Å) are small compared to the travel path of the freed electrons in the continuum (several tens of Å), the times for molecular trajectories are expected to remain close to those of an atomic trajectory. These considerations led us to perform Taylor expansions in powers of the internuclear distance R of the molecular SFA equations, Eq. ( 24 ). To remain consistent with our TDSE simulations, where we restricted the study to a 1D case, we will consider in the following the specific case of a model molecule, with only one degree of freedom, aligned along the laser polarization, i.e., R = R e x and p = p e x . Moreover, the “natural” absence of a perpendicular component of the momentum allows us to derive equations that reveal straightforward physical insights. The solutions of the molecular saddle point equations ( 24 ) are expanded as
Ray-tracing: Ray tracing methods provide a much more realistic simula-
tion of lighting by computing the paths and reflections that photons would travel from each light source to the view point. This produces photorealistic rendering that can be very helpful in conveying complex, 3D molecular structures (see figure 2). However, as these calculations require much computing power, many users only use ray tracing when finalizing high-quality, static images for pub-
Molecularly defined species remain phenotypically inconsistent
This is the most comprehensive phylogenetic study of Peltigera section Peltidea, where all five recognized morphospecies are represented by multiple individuals from distinct geographic localities (Figs. 1 and 2). In all contains the DNA extraction number (P) or voucher id (GenBank sequences) for the representative individual, mycobiont multilocus sequence type designation (H; after a slash), and the number of individuals represented by each sequence type (N) (see Fig. 1; Appendix 1). Asterisks in- dicate terminals assigned to polyphyletic or paraphyletic species delimited by bGMYC based on ITS and Structurama. Nostoc phylogroups were defined following O’Brien et al. (2013), Magain et al. (2017a), and this study (U indicated the placement outside of defined phylogroups; Fig. 3; Appendix 1). For the chemotype designations, aph refers to P. aphthosa, bri to P. britannica, chio to P. chionophila, fri to P. frippii, and mal to P. malacea, followed by the chemotype number (Fig. 4; see also Holtan-Hartwig 1993 and Vitikainen 1994). Black dots represent (single or multiple) specimens sampled in this study from the following geographic regions: 1) West Coast: Oregon, Washington, British Columbia, Alaska, Yukon; 2) Rocky Mountain Region: Montana, Wyoming, Colorado; 3) Midwest Region: Minnesota, South Dakota, Michigan, Alberta, Manitoba, Ontario; 4) Northeastern Region: Newfoundland, New Brunswick, Nova Scotia, Québec, Labrador; 5) Nunavut; 6) Central Northern Asia (Siberia): Krasnoyarsk Territory; 7) Eastern Northern Asia (Far Eastern District): Khabarovsk Territory, Kamchatka, Yakutia; 8) Fennoscandia; 9) Iceland; 10) remaining Europe including North Caucasus. Gray boxes show species delimited by bGMYC and Structurama and validated by BPP (for species abbreviations and posterior probability values see Appendix 2). White boxes within grey ones indicate nested species (e.g., species B1c is nested within species B1b as delimited by bGMYC based on ITS locus alone). Empty space indicates specimens that could not be assigned to any species because they were included in an alternative species delimitation with a higher posterior probability value or were not represented in a single gene matrix (missing data; Appendix 1). Singletons (species containing a single representative) are indicated by a unique DNA number (e.g., P275). Consensus species delimitations reflecting current species circumscriptions are indicated by black frames (notice that P. britannica as currently recognized, is nested within P. aphthosa s.l., but without strong bootstrap support).
Variovorax ) (Table 1) that were not determined in a previous study (26). The organisms utilized were chosen with the aim of covering a broad range of families and genera within a single bacterial subdivision. Three additional tmRNA sequences (B.pertussis, Neisseria meningitidis and N.gonorrhoeae), available at both the tmRNA and tmRDB Web sites (24,25), are also included in Table 1. All predicted proteolysis tags of the 13 tmRNA gene sequences presumably start with a non- coded alanine residue (from the chargeable tmRNA 3′-end) followed by 10 internally encoded amino acids, eight of which are strictly conserved among the subgroup (Table 1, bold letters). The two encoded amino acids that are not strictly conserved are centered in the tag at positions 5 and 6 (three different amino acids are found at both positions), suggesting a stronger selective pressure at both ends of the tag. Hydro- phobic amino acids (ALAA) at the C-terminus of the tag are required for efficient proteolysis of the tagged proteins (2), likely accounting for the sequence conservation at the C-terminus of the tag. The reason for the strict conservation of the first four amino acids in the tag (ANDE) within this bacterial subgroup is, however, unknown.