Noise-induced burst and spike synchronizations in an inhibitory small-world network of subthreshold bursting neurons

We are interested in noise-induced firings of subthreshold neurons which may be used for encoding environmental stimuli. Noise-induced population synchronization was previously studied only for the case of global coupling, unlike the case of subthreshold spiking neurons. Hence, we investigate the effect of complex network architecture on noise-induced synchronization in an inhibitory population of subthreshold bursting Hindmarsh–Rose neurons. For modeling complex synaptic connectivity, we consider the Watts–Strogatz small-world network which interpolates between regular lattice and random network via rewiring, and investigate the effect of small-world connectivity on emergence of noise-induced population synchronization. Thus, noise-induced burst synchronization (synchrony on the slow bursting time scale) and spike synchronization (synchrony on the fast spike time scale) are found to appear in a synchronized region of the J–D plane (J: synaptic inhibition strength and D: noise intensity). As the rewiring probability p is decreased from 1 (random network) to 0 (regular lattice), the region of spike synchronization shrinks rapidly in the J–D plane, while the region of the burst synchronization decreases slowly. We separate the slow bursting and the fast spiking time scales via frequency filtering, and characterize the noise-induced burst and spike synchronizations by employing realistic order parameters and statistical-mechanical measures introduced in our recent work. Thus, the bursting and spiking thresholds for the burst and spike synchronization transitions are determined in terms of the bursting and spiking order parameters, respectively. Furthermore, we also measure the degrees of burst and spike synchronizations in terms of the statistical-mechanical bursting and spiking measures, respectively.     

Dynamical responses to external stimuli for both cases of excitatory and inhibitory synchronization in a complex neuronal network

For studying how dynamical responses to external stimuli depend on the synaptic-coupling type, we consider two types of excitatory and inhibitory synchronization (i.e., synchronization via synaptic excitation and inhibition) in complex small-world networks of excitatory regular spiking (RS) pyramidal neurons and inhibitory fast spiking (FS) interneurons. For both cases of excitatory and inhibitory synchronization, effects of synaptic couplings on dynamical responses to external time-periodic stimuli S(t) (applied to a fraction of neurons) are investigated by varying the driving amplitude A of S(t). Stimulated neurons are phase-locked to external stimuli for both cases of excitatory and inhibitory couplings. On the other hand, the stimulation effect on non-stimulated neurons depends on the type of synaptic coupling. The external stimulus S(t) makes a constructive effect on excitatory non-stimulated RS neurons (i.e., it causes external phase lockings in the non-stimulated sub-population), while S(t) makes a destructive effect on inhibitory non-stimulated FS interneurons (i.e., it breaks up original inhibitory synchronization in the non-stimulated sub-population). As results of these different effects of S(t), the type and degree of dynamical response (e.g., synchronization enhancement or suppression), characterized by the dynamical response factor \(D_f\) (given by the ratio of synchronization degree in the presence and absence of stimulus), are found to vary in a distinctly different way, depending on the synaptic-coupling type. Furthermore, we also measure the matching degree between the dynamics of the two sub-populations of stimulated and non-stimulated neurons in terms of a “cross-correlation” measure \(M_c\). With increasing A, based on \(M_c\), we discuss the cross-correlations between the two sub-populations, affecting the dynamical responses to S(t).     

Defining and Mapping Mammalian Coat Pattern Genes: Multiple Genomic Regions Implicated in Domestic Cat Stripes and Spots

Mammalian coat patterns (e.g., spots, stripes) are hypothesized to play important roles in camouflage and other relevant processes, yet the genetic and developmental bases for these phenotypes are completely unknown. The domestic cat, with its diversity of coat patterns, is an excellent model organism to investigate these phenomena. We have established three independent pedigrees to map the four recognized pattern variants classically considered to be specified by a single locus, Tabby; in order of dominance, these are the unpatterned agouti form called “Abyssinian” or “ticked” (T^a), followed by Spotted (T^s), Mackerel (T^M), and Blotched (t^b). We demonstrate that at least three different loci control the coat markings of the domestic cat. One locus, responsible for the Abyssinian form (herein termed the Ticked locus), maps to an ∼3.8-Mb region on cat chromosome B1. A second locus controls the Tabby alleles T^M and t^b, and maps to an ∼5-Mb genomic region on cat chromosome A1. One or more additional loci act as modifiers and create a spotted coat by altering mackerel stripes. On the basis of our results and associated observations, we hypothesize that mammalian patterned coats are formed by two distinct processes: a spatially oriented developmental mechanism that lays down a species-specific pattern of skin cell differentiation and a pigmentation-oriented mechanism that uses information from the preestablished pattern to regulate the synthesis of melanin profiles.PATTERNED coats are typical of many mammalian groups, whose spots, stripes, and other markings have been hypothesized to play important adaptive roles in camouflage, predator evasion, and social communication (Cott 1940; Searle 1968; Ortolani and Caro 1996). Many mammals bear striped or spotted coats, and these phenotypes have historically drawn attention from many fields of human science and culture (e.g., the leopard''s spots, or the stripes seen in tigers and zebras). Although several theoretical studies have proposed mathematical models that could underlie the developmental dynamics of coat pattern formation in mammals (Murray and Oster 1984; Oyehaug et al. 2002), no direct investigation of the genetic basis of these phenotypes has yet been performed, so that their mechanistic causes remain a mystery. Recent advances in genomics, molecular biology, and evolutionary developmental biology (Evo-Devo) have revealed genes and pathways involved in skin pattern formation in Drosophila (Schug et al. 1998; Gompel et al. 2005; Prud''homme et al. 2006; Parchem et al. 2007), butterflies (Joron et al. 2006a,b), and zebrafish (Iwashita et al. 2006; Watanabe et al. 2006; Svetic et al. 2007). In contrast, despite the relevance of characterizing equivalent processes in mammals, little progress toward this goal has been accomplished, perhaps due to the lack of adequate mammalian models exhibiting variation in skin pattern and for which genetic and genomic tools were available.The domestic cat is a very promising model in this regard, as it presents several coat pattern variants and a growing body of genetic and genomic tools suitable for gene identification (Menotti-Raymond et al. 2003; Murphy et al. 2007; Pontius et al. 2007; Pontius and O''Brien 2007; Davis et al. 2009). Classic work on domestic cat coat color (Robinson 1958; Lomax and Robinson 1988) has suggested that there is a monogenic allelic series of coat patterns in the domestic cat, controlled by the Tabby (T) locus: in order of dominance, the four recognized alleles would be Abyssinian or “ticked” (T^a), Spotted (T^s), Mackerel (T^M), and Blotched (t^b) (Figure 1). Although there has been little doubt among breeders that the “mackerel” and “blotched” forms segregate as a single autosomal locus, this may not be the case for the other two phenotypes (T^a and T^s), which so far have not been tested thoroughly for allelism relative to the more common Tabby variants T^M and t^b. Some breeding data have suggested that these variants may not be allelic with the main Tabby locus (Lorimer 1995), but further scrutiny is required to test this hypothesis. A recent genetic study (Lyons et al. 2006) considered the Abyssinian variant as an allele of Tabby, reflecting the prevalent perception that they are coded by the same locus. Testing this hypothesis, and identifying the implicated genomic region (or regions), is a first step in the process of dissecting the molecular and developmental basis for these pattern-formation phenotypes.Open in a separate windowFigure 1.—Major coat pattern phenotypes of the domestic cat (Felis silvestris catus). A “hierarchy” of pelage patterns is observed in this species, with the absence of markings seen in Abyssinian cats (A) dominating over a spotted coat (B), which dominates over a “mackerel” (striped) coat (C), itself dominant over the blotched pattern (D). The classical, single-locus model for this phenotypic variation proposed the allelic series T^a > T^s > T^M > t^b for these respective variants.Aiming to investigate the genetic basis of pattern formation on the domestic cat pelage by genomic, positional methods, we established three separate pedigrees segregating for different combinations of coat pattern phenotypes. Our results demonstrate that at least three different loci underlie the striping and spotting patterns observed in domestic cats and identify the genomic location of two of them.     

Genomewide identification of PPR gene family and prediction analysis on restorer gene in <Emphasis Type="BoldItalic">Gossypium</Emphasis>

Pentatricopeptide repeat (PPR) gene family plays an essential role in the regulation of plant growth and organelle gene expression. Some PPR genes are related to fertility restoration in plant, but there is no detailed information in Gossypium. In the present study, we identified 482 and 433 PPR homologues in Gossypium raimondii (\(\hbox {D}_{5}\)) and G. arboreum (\(\hbox {A}_{2}\)) genomes, respectively. Most PPR homologues showed an even distribution on the whole chromosomes. Given an evolutionary analysis to PPR genes from G. raimondii (\(\hbox {D}_{5}\)), G. arboreum (\(\hbox {A}_{2}\)) and G. hirsutum genomes, eight PPR genes were clustered together with restoring genes of other species. Most cotton PPR genes were qualified with no intron, high proportion of \(\upalpha \)-helix and classical tertiary structure of PPR protein. Based on bioinformatics analyses, eight PPR genes were targeted in mitochondrion, encoding typical P subfamily protein with protein binding activity and organelle RNA metabolism in function. Further verified by RNA-seq and quantitative real-time PCR (qRT-PCR) analyses, two PPR candidate genes, Gorai.005G0470 (\(\hbox {D}_{5}\)) and Cotton_A_08373 (\(\hbox {A}_{2}\)), were upregulated in fertile line than sterile line. These results reveal new insights into PPR gene evolution in Gossypium.     

A new indicator-based many-objective ant colony optimizer for continuous search spaces

In this paper, we propose a novel multi-objective ant colony optimizer (called iMOACO\(_{\mathbb {R}}\)) for continuous search spaces, which is based on ACO\(_{\mathbb {R}}\) and the R2 performance indicator. iMOACO\(_{\mathbb {R}}\) is the first multi-objective ant colony optimizer (MOACO) specifically designed to tackle continuous many-objective optimization problems (i.e., multi-objective optimization problems having four or more objectives). Our proposed iMOACO\(_{\mathbb {R}}\) is compared to three state-of-the-art multi-objective evolutionary algorithms (NSGA-III, MOEA/D and SMS-EMOA) and a MOACO algorithm called MOACO\(_{\mathbb {R}}\) using standard test problems and performance indicators taken from the specialized literature. Our experimental results indicate that iMOACO\(_{\mathbb {R}}\) is very competitive with respect to NSGA-III and MOEA/D and it is able to outperform SMS-EMOA and MOACO\(_{\mathbb {R}}\) in most of the test problems adopted.     

Study of biological communities subject to imperfect detection: bias and precision of community <Emphasis Type="Italic">N</Emphasis>-mixture abundance models in small-sample situations

Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance \((\bar{\lambda }\): 0.1, 0.5, 1, 5), detection probability \((\bar{p}\): 0.1, 0.2, 0.5), and number of sampling sites (n _site : 10, 20, 40) and visits (n _visit : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables \((\bar{\lambda }\), \(\bar{p}\), n _site , n _visit ) increased. Detection probability \(\bar{p}\) was most important for the estimates of mean abundance, while \(\bar{\lambda }\) was most influential for covariate effect and species richness estimates. For all parameters, increasing n _site was more beneficial than increasing n _visit . Minimal conditions for obtaining adequate performance of community abundance models were n _site  ≥ 20, \(\bar{p}\) ≥ 0.2, and \(\bar{\lambda }\) ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N-mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.     

Glutathione <Emphasis Type="BoldItalic">S</Emphasis>-transferase P1 gene polymorphisms and susceptibility to coronary artery disease in a subgroup of north Indian population

The present study aimed to investigate the association of \(\hbox {g}.313\hbox {A}{>}\hbox {G}\) and \(\hbox {g}.341\hbox {C}{>}\hbox {T}\) polymorphisms of GSTP1 with coronary artery disease (CAD) in a subgroup of north Indian population. In the present case–control study, CAD patients (\(n = 200\)) and age-matched, sex-matched and ethnicity-matched healthy controls (\(n = 200\)) were genotyped for polymorphisms in GSTP1 using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Genotype distribution of \(\hbox {g}.313\hbox {A}{>}\hbox {G}\) and \(\hbox {g}.341\hbox {C}{>}\hbox {T}\) polymorphisms of GSTP1 gene was significantly different between cases and controls (\(P = 0.005\) and 0.024, respectively). Binary logistic regression analysis showed significant association of A/G (odds ratio (OR): 1.6, 95% CI: 1.08–2.49, \(P = 0.020\)) and G/G (OR: 3.1, 95% CI: 1.41–6.71, P \(=\) 0.005) genotypes of GSTP1 \(\hbox {g}.313\hbox {A}{\!>\!}\hbox {G}\), and C/T (OR: 5.8, 95% CI: 1.26–26.34, \(P = 0.024\)) genotype of GSTP1 \(\hbox {g}.341\hbox {C}{>}\hbox {T}\) with CAD. The A/G and G/G genotypes of \(\hbox {g}.313\hbox {A}{>}\hbox {G}\) and C/T genotype of \(\hbox {g}.341\hbox {C}{>}\hbox {T}\) conferred 6.5-fold increased risk for CAD (OR: 6.5, 95% CI: 1.37–31.27, \(P = 0.018\)). Moreover, the recessive model of GSTP1 \(\hbox {g}.313\hbox {A}{>}\hbox {G}\) is the best fit inheritance model to predict the susceptible gene effect (OR: 2.3, 95% CI: 1.11–4.92, \(P = 0.020\)). In conclusion, statistically significant associations of GSTP1 \(\hbox {g}.313\hbox {A}{>}\hbox {G}\) (A/G, G/G) and \(\hbox {g}.341\hbox {C}{>}\hbox {T}\) (C/T) genotypes with CAD were observed.     

Effect of spike-timing-dependent plasticity on stochastic burst synchronization in a scale-free neuronal network

We consider an excitatory population of subthreshold Izhikevich neurons which cannot fire spontaneously without noise. As the coupling strength passes a threshold, individual neurons exhibit noise-induced burstings. This neuronal population has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). However, STDP was not considered in previous works on stochastic burst synchronization (SBS) between noise-induced burstings of sub-threshold neurons. Here, we study the effect of additive STDP on SBS by varying the noise intensity D in the Barabási–Albert scale-free network (SFN). One of our main findings is a Matthew effect in synaptic plasticity which occurs due to a positive feedback process. Good burst synchronization (with higher bursting measure) gets better via long-term potentiation (LTP) of synaptic strengths, while bad burst synchronization (with lower bursting measure) gets worse via long-term depression (LTD). Consequently, a step-like rapid transition to SBS occurs by changing D, in contrast to a relatively smooth transition in the absence of STDP. We also investigate the effects of network architecture on SBS by varying the symmetric attachment degree \(l^*\) and the asymmetry parameter \(\Delta l\) in the SFN, and Matthew effects are also found to occur by varying \(l^*\) and \(\Delta l\). Furthermore, emergences of LTP and LTD of synaptic strengths are investigated in details via our own microscopic methods based on both the distributions of time delays between the burst onset times of the pre- and the post-synaptic neurons and the pair-correlations between the pre- and the post-synaptic instantaneous individual burst rates (IIBRs). Finally, a multiplicative STDP case (depending on states) with soft bounds is also investigated in comparison with the additive STDP case (independent of states) with hard bounds. Due to the soft bounds, a Matthew effect with some quantitative differences is also found to occur for the case of multiplicative STDP.     

Rate of Adaptation in Large Sexual Populations

Adaptation often involves the acquisition of a large number of genomic changes that arise as mutations in single individuals. In asexual populations, combinations of mutations can fix only when they arise in the same lineage, but for populations in which genetic information is exchanged, beneficial mutations can arise in different individuals and be combined later. In large populations, when the product of the population size N and the total beneficial mutation rate U_b is large, many new beneficial alleles can be segregating in the population simultaneously. We calculate the rate of adaptation, v, in several models of such sexual populations and show that v is linear in NU_b only in sufficiently small populations. In large populations, v increases much more slowly as log NU_b. The prefactor of this logarithm, however, increases as the square of the recombination rate. This acceleration of adaptation by recombination implies a strong evolutionary advantage of sex.IN asexual populations, beneficial mutations arising on different genotypes compete against each other and in large populations most of the beneficial mutations are lost because they arise on mediocre genetic backgrounds or acquire further beneficial mutations less rapidly than their peers—the combined effects of clonal interference and multiple mutations (Gerrish and Lenski 1998; Desai and Fisher 2007). Exchange of genetic material between individuals allows the combination of beneficial variants that arose in different lineages and can thereby speed up the process of adaptation (Fisher 1930; Muller 1932). Indeed, most life forms engage in some form of recombination, e.g., lateral gene transfer or competence for picking up DNA in bacteria, facultative sexual reproduction in yeast and plants, or obligate sexual reproduction in most animals. Some benefits of recombination for the rate of adaptation have recently been demonstrated experimentally in Caenorhabditis reinhardtii (Colegrave 2002), Escherichia coli (Cooper 2007), and Saccharomyces cerevisiae (Goddard et al. 2005); for a review of older experiments, see Rice (2002).Yet the benefits of sex become less obvious when one considers its disadvantageous effects: recombination can separate well-adapted combinations of alleles and sexual reproduction is more costly than asexual reproduction due to resources spent for mating and, in some cases, the necessity of males. The latter—in animals often termed the twofold cost of sex—implies that sexual populations can be unstable to the invasion of asexual variants. As a result, the pros and cons of sex have been the subject of many decades of debate in the theoretical literature (Crow and Kimura 1965; Maynard Smith 1968; Felsenstein 1974; Barton 1995a; Barton and Charlesworth 1998), and several different potentially beneficial aspects of sex have been identified, including the pruning of detrimental mutations (Peck 1994; Rice 1998) and host–parasite coevolution or otherwise changing environments (Charlesworth 1993; Ladle et al. 1993; Bürger 1999; Waxman and Peck 1999; Gandon and Otto 2007; Callahan et al. 2009). In the opposite situation of relatively static populations, it has been proposed that recombination is favored in the presence of negative epistasis (Feldman et al. 1980; Kondrashov 1984, 1988)—a situation when the combined detrimental effect of two unfavorable alleles is greater than the sum of the individual effects. While this may sometimes be a significant effect, most populations, especially microbes, are likely to be under continuing selection and the benefits of sex for speeding up adaptation are likely to dominate.The Fisher–Muller hypothesis is that sex speeds up adaptation by combining beneficial variants. Moreover, it has been demonstrated by Hill and Robertson (1966) that linkage decreases the efficacy of selection. This detrimental effect of linkage, known as the “Hill–Robertson effect,” causes selection for higher recombination rates, which has been shown by analyzing recombination modifier alleles at a locus linked to two competing segregating loci (Otto and Barton 1997; Iles et al. 2003; Barton and Otto 2005; Roze and Barton 2006; Martin et al. 2006). Hitchhiking of the allele that increases the recombination rates with the sweeping linked loci results in effective selection for increased recombination.Experiments and simulation studies suggest that the Hill–Roberston effect is more pronounced and selection for recombination modifiers is stronger in large populations with many sweeping loci (Felsenstein 1974; Colegrave 2002; Iles et al. 2003). However, the quantitative understanding of the effect of recombination in large populations is limited. Rouzine and Coffin have studied the role of recombination in the context of evolution of drug resistance in human immunodeficiency virus, finding that recombination of standing variation speeds up adaptation by producing anomalously fit individuals at the high fitness edge of the distribution (Rouzine and Coffin 2005; Gheorghiu-Svirschevski et al. 2007). The effects of epistatic interactions between polymorphisms and recombination on the dynamics of selection have recently been analyzed by Neher and Shraiman (2009). Yet none of these works consider the effects of new beneficial mutations. In the absence of new mutations (and in the absence of heterozygous advantage that can maintain polymorphisms) the fitness soon saturates as most alleles become extinct and standing variation disappears. Thus the crucial point that must be addressed is the balance between selection and recombination of existing variation and the injection of additional variation by new mutations.Here, we study the dynamics of continual evolution via new mutations, selection, and recombination using several models of recombination. Our primary models most naturally apply when periods of asexual reproduction occur between matings, so that they approximate the life style of facultatively outcrossing species such as S. cerevisiae, some plants, and C. elegans, which reproduce asexually most of the time but undergo extensive recombination when outcrossing. The models enable us to study analytically the explicit dependence of the rate of adaptation and of the dynamics of the beneficial alleles on the important parameters such as the outcrossing rate and population size. In an independent study N. H. Barton and J. Coe (personal communication) calculate the rate of adaptation for obligate sexual organisms using several different multilocus models of recombination, including the free recombination model studied here. The relation of our work to theirs, as well as to that of Cohen et al. (2005, 2006) who have also studied the effects of recombination with multiple new mutations, is commented on in the discussion.When deleterious mutations can be neglected, the rate of adaptation is the product of the rate of production of favorable mutations NU_b (N being the population size and U_b the genomewide beneficial mutation rate), the magnitude of their effect, and their fixation probability. The fixation probability is dominated by the probability that the allele becomes established, i.e., that it rises to high enough numbers in the population that it is very unlikely to die out by further stochastic fluctuations. In a homogeneous population a single beneficial mutation with selective advantage s has a probability of establishment and eventual fixation of (in discrete generation models, P_e≈2s) (Moran 1959). In a heterogeneous population, however, a novel beneficial mutation can arise on different genetic backgrounds and its establishment probability will thus vary, being greater if it arises in a well-adapted individual. But even well-adapted genotypes soon fall behind due to sweeps of other beneficial mutations and combinations. To avoid extinction, descendants of the novel mutation thus have to move to fitter genetic backgrounds via recombination in outcrossing events (Rice 2002). As a result the establishment probability decreases as the rate of average fitness gain, v, in the population increases. But the rate of average fitness gain or, equivalently, the rate of adaptation itself depends on the establishment probability. These two quantities therefore have to be determined self-consistently.In this article we analyze several models via self-consistent calculations of the fixation probability of new mutations. For a given production rate of beneficial mutations NU_b, we find that interference between mutations is of minor importance if the recombination rate r exceeds . In this regime, the rate of adaption is v ≈ NU_bs² as found for sequential mutations or in the absence of linkage. At recombination rates below , however, v grows only logarithmically with log NU_b. We find this behavior in all our models and argue that it obtains more generally. The prefactor of the log NU_b increases with the square of the recombination rate, implying a strong benefit of recombination in large populations.     

Multistage affinity cross-flow filtration: mathematical modelling and analysis

A multistage affinity cross-flow filtration (mACFF) process for protein purification is proposed. The process is mathematically modelled taking into account a case of rapid equilibrium binding of a target protein to its macroligand. The process performance, i.e., dimensionless breakthrough volume (Q _b ⁺ )and recovery yield (REC) to obtain a desired purity is analysed by computer simulations. The results indicate that Q _b ⁺ increases with the increase of stage number (n) due to the increase of affinity binding efficiency. In addition, REC also increases with the increase of n, especially for lower affinity systems, even though the feed loading is the same as the corresponding breakthrough volume that increases with n. Thus both feed loading and recovery yield can be enhanced by raising the stage number. Incompletely permeable membranes reject the target and contaminant proteins. So they delay the appearance of the breakthrough point and compromise the contaminant washing efficiency. Hence although Q _b ⁺ increases with the increase of membrane rejection coefficient (R), REC decreases when the feed loading equals that of Q _b ⁺ . However, when the feed loading is kept unchanged and equals Q _b ⁺ at R=0, REC does not decrease, but slightly increases with the increase of R. This result indicates that incompletely permeable membranes may also be employed for the mACFF process. In general, the model gives a predictive evaluation of the mACFF process successfully.     

Attomolar detection of botulinum toxin type A in complex biological matrices

Background

A highly sensitive, rapid and cost efficient method that can detect active botulinum neurotoxin (BoNT) in complex biological samples such as foods or serum is desired in order to 1) counter the potential bioterrorist threat 2) enhance food safety 3) enable future pharmacokinetic studies in medical applications that utilize BoNTs.

Methodology/Principal Findings

Here we describe a botulinum neurotoxin serotype A assay with a large immuno-sorbent surface area (BoNT/A ALISSA) that captures a low number of toxin molecules and measures their intrinsic metalloprotease activity with a fluorogenic substrate. In direct comparison with the “gold standard” mouse bioassay, the ALISSA is four to five orders of magnitudes more sensitive and considerably faster. Our method reaches attomolar sensitivities in serum, milk, carrot juice, and in the diluent fluid used in the mouse assay. ALISSA has high specificity for the targeted type A toxin when tested against alternative proteases including other BoNT serotypes and trypsin, and it detects the holotoxin as well as the multi-protein complex form of BoNT/A. The assay was optimized for temperature, substrate concentration, size and volume proportions of the immuno-sorbent matrix, enrichment and reaction times. Finally, a kinetic model is presented that is consistent with the observed improvement in sensitivity.

Conclusions/Significance

The sensitivity, specificity, speed and simplicity of the BoNT ALISSA should make this method attractive for diagnostic, biodefense and pharmacological applications.     

On the minimum electron transport coefficients in tokamaks in the range of low collision frequencies

There are two close empirical scalings, namely, the T-11 and neo-Alcator ones, that provide correct estimates for the energy confinement time in tokamaks in ohmic heating regimes in the linear part of the dependence τ _E (\(\bar n_e \)) in the range of low values of \(\bar n_e \) and 〈ν _e ^* 〉 ≤ 1. The similar character of electron energy confinement in this range, which expands with increasing magnetic field B ₀, has stimulated the search for dimensionless parameters and simple physical models that would explain the experimentally observed dependences χ _e ～ 1/n _e and τ _Ee ～ \(\bar n_e \). In 1987, T. Okhawa showed that the experimental data were satisfactorily described by the formula χ_e⊥ = (c ²/ω _pe ² )ν _e /qR, in deriving of which the random spatial leap along the radius r on the electron trajectory was assumed to be the same as that in the coefficient of the poloidal field diffusion, while the repetition rate of these leaps was assumed to be ν _e /qR. In 2004, J. Callen took into account the decrease in the fraction of transient electrons with increasing toroidal ratio ? = r/R and corrected the coefficient c ²/ω _pe ² in Okhawa equation by the factor σ _‖ ^Sp /σ _‖ ^neo . If one takes into account this correction and assumes that the frequency of the stochastic process is equal to the reciprocal of the half-period of rotation of a trapped electron along its banana trajectory, then the resulting expression for χ_e⊥ will coincide with the T-11 scaling: χ _e ^an ∞ ?^1.75(T _e /A _i )^0.5/(n _e qR) at A i = 1. If the same stochastic process also involves ions, it may result in the opening of the orbit of a trapped ion at the distance ～(c/ω _pe )(m _i /m _e )^1/4. In this case, the calculated coefficient of electron and ion diffusion D is close to D ^an ≈ χ _e ^an /2.     

Optimal Therapy Scheduling Based on a Pair of Collaterally Sensitive Drugs

Despite major strides in the treatment of cancer, the development of drug resistance remains a major hurdle. One strategy which has been proposed to address this is the sequential application of drug therapies where resistance to one drug induces sensitivity to another drug, a concept called collateral sensitivity. The optimal timing of drug switching in these situations, however, remains unknown. To study this, we developed a dynamical model of sequential therapy on heterogeneous tumors comprised of resistant and sensitive cells. A pair of drugs (DrugA, DrugB) are utilized and are periodically switched during therapy. Assuming resistant cells to one drug are collaterally sensitive to the opposing drug, we classified cancer cells into two groups, \(A_\mathrm{R}\) and \(B_\mathrm{R}\), each of which is a subpopulation of cells resistant to the indicated drug and concurrently sensitive to the other, and we subsequently explored the resulting population dynamics. Specifically, based on a system of ordinary differential equations for \(A_\mathrm{R}\) and \(B_\mathrm{R}\), we determined that the optimal treatment strategy consists of two stages: an initial stage in which a chosen effective drug is utilized until a specific time point, T, and a second stage in which drugs are switched repeatedly, during which each drug is used for a relative duration (i.e., \(f \Delta t\)-long for DrugA and \((1-f) \Delta t\)-long for DrugB with \(0 \le f \le 1\) and \(\Delta t \ge 0\)). We prove that the optimal duration of the initial stage, in which the first drug is administered, T, is shorter than the period in which it remains effective in decreasing the total population, contrary to current clinical intuition. We further analyzed the relationship between population makeup, \(\mathcal {A/B} = A_\mathrm{R}/B_\mathrm{R}\), and the effect of each drug. We determine a critical ratio, which we term \(\mathcal {(A/B)}^{*}\), at which the two drugs are equally effective. As the first stage of the optimal strategy is applied, \(\mathcal {A/B}\) changes monotonically to \(\mathcal {(A/B)}^{*}\) and then, during the second stage, remains at \(\mathcal {(A/B)}^{*}\) thereafter. Beyond our analytic results, we explored an individual-based stochastic model and presented the distribution of extinction times for the classes of solutions found. Taken together, our results suggest opportunities to improve therapy scheduling in clinical oncology.     

Analysis of two susceptibility SNPs in HLA region and evidence of interaction between rs6457617 in <Emphasis Type="BoldItalic">HLA-DQB1</Emphasis> and <Emphasis Type="BoldItalic">HLA-DRB1</Emphasis>*04 locus on Tunisian rheumatoid arthritis

Previous genomewide association studies (GWAS) and meta-analyses have enumerated several genes/loci in major histocompatibility complex region, which are consistently associated with rheumatoid arthritis (RA) in different ethnic populations. Given the genetic heterogeneity of the disease, it is necessary to replicate these susceptibility loci in other populations. In this case, we investigate the analysis of two SNPs, rs13192471 and rs6457617, from the human leukocyte antigen (HLA) region with the risk of RA in Tunisian population. These SNPs were previously identified to have a strong RA association signal in several GWAS studies. A case–control sample composed of 142 RA patients and 123 healthy controls was analysed. Genotyping of rs13192471 and rs6457617 was carried out using real-time PCR methods by TaqMan allelic discrimination assay. A trend of significant association was found in rs6457617 TT genotype with susceptibility to RA (\(P = 0.04\), \(p_{c} = 0.08\), \(\hbox {OR} = 1.73\)). Moreover, using multivariable analysis, the combination of rs6457617*TT–HLA-DRB1*\(04^{+}\) increased risk of RA (\(\hbox {OR} = 2.38\)), which suggest a gene–gene interaction event between rs6457617 located within the HLA-DQB1 and HLA-DRB1. Additionally, haplotypic analysis highlighted a significant association of rs6457617*T–HLA-DRB1*\(04^{+}\) haplotype with susceptibility to RA (\(P = 0.018\), \(p_{c} = 0.036\), \(\hbox {OR} = 1.72\)). An evidence of association was shown subsequently in \(\hbox {antiCCP}^{+}\) subgroup with rs6457617 both in T allele and TT genotype (\(P = 0.01\), \(p_{c} = 0.03\), \(\hbox {OR} = 1.66\) and \(P = 0.008\), \(p_{c} = 0.024\), \(\hbox {OR} = 1.28\), respectively). However, no association was shown for rs13192471 polymorphism with susceptibility and severity to RA. This study suggests the involvement of rs6457617 locus as risk variant for susceptibility/severity to RA in Tunisian population. Secondly, it highlights the gene–gene interaction between HLA-DQB1 and HLA-DRB1.     

Nitric-oxide Dioxygenase Function of Human Cytoglobin with Cellular Reductants and in Rat Hepatocytes

Cytoglobin (Cygb) was investigated for its capacity to function as a NO dioxygenase (NOD) in vitro and in hepatocytes. Ascorbate and cytochrome b₅ were found to support a high NOD activity. Cygb-NOD activity shows respective K_m values for ascorbate, cytochrome b₅, NO, and O₂ of 0.25 mm, 0.3 μm, 40 nm, and ∼20 μm and achieves a k_cat of 0.5 s⁻¹. Ascorbate and cytochrome b₅ reduce the oxidized Cygb-NOD intermediate with apparent second order rate constants of 1000 m⁻¹ s⁻¹ and 3 × 10⁶ m⁻¹ s⁻¹, respectively. In rat hepatocytes engineered to express human Cygb, Cygb-NOD activity shows a similar k_cat of 1.2 s⁻¹, a K_m(NO) of 40 nm, and a k_cat/K_m(NO) (k′_NOD) value of 3 × 10⁷ m⁻¹ s⁻¹, demonstrating the efficiency of catalysis. NO inhibits the activity at [NO]/[O₂] ratios >1:500 and limits catalytic turnover. The activity is competitively inhibited by CO, is slowly inactivated by cyanide, and is distinct from the microsomal NOD activity. Cygb-NOD provides protection to the NO-sensitive aconitase. The results define the NOD function of Cygb and demonstrate roles for ascorbate and cytochrome b₅ as reductants.     

Strong Comma-Free Codes in Genetic Information

Comma-free codes constitute a class of circular codes, which has been widely studied, in particular by Golomb et al. (Biologiske Meddelelser, Kongelige Danske Videnskabernes Selskab 23:1–34, 1958a, Can J Math 10:202–209, 1958b), Michel et al. (Comput Math Appl 55:989–996, 2008a, Theor Comput Sci 401:17–26, 2008b, Inf Comput 212:55–63, 2012), Michel and Pirillo (Int J Comb 2011:659567, 2011), and Fimmel and Strüngmann (J Theor Biol 389:206–213, 2016). Based on a recent approach using graph theory to study circular codes Fimmel et al. (Philos Trans R Soc 374:20150058, 2016), a new class of circular codes, called strong comma-free codes, is identified. These codes detect a frameshift during the translation process immediately after a reading window of at most two nucleotides. We describe several combinatorial properties of strong comma-free codes: enumeration, maximality, self-complementarity and \(CF^3\)-property (comma-free property in all the three possible frames). These combinatorial results also highlight some new properties of the genetic code and its evolution. Each amino acid in the standard genetic code is coded by at least one strong comma-free code of size 1. There are 9 amino acids \(S=\{Asn,Asp,Gln,Gly,Lys,Met,Phe,Pro,Trp\}\) among 20 such that for each amino acid from S, its synonymous trinucleotide set (excluding the necessary periodic trinucleotides \(\{AAA,CCC,GGG,TTT\}\)) is a strong comma-free code. The primeval comma-free RNY code of Eigen and Schuster (Naturwissenschaften 65:341–369, 1978) is a self-complementary \(CF^3\)-code of size 16. Furthermore, it is the union of two strong comma-free codes of size 8 which are complementary to each other.