Saccharomyces cerevisiae cells were immobilized in calcium alginate and chitosan-covered calcium alginate beads and studied in the fermentation of glucose and sucrose for ethanol production. The batch fermentations were carried out in an orbital shaker and assessed by monitoring the concentration of substrate and product with HPLC. Cell immobilization in calcium alginate beads and chitosan-covered calcium alginate beads allowed reuse of the beads in eight sequential fermentation cycles of 10 h each. The final concentration of ethanol using free cells was 40 g L-1 and the yields using glucose and sucrose as carbon sources were 78% and 74.3%, respectively. For immobilized cells in calcium alginate beads, the final ethanol concentration from glucose was 32.9 ± 1.7 g L-1 with a 64.5 ± 3.4% yield, while the final ethanol concentration from sucrose was 33.5 ± 4.6 g L-1 with a 64.5 ± 8.6% yield. For immobilized cells in chitosan-covered calcium alginate beads, the ethanol concentration from glucose was 30.7 ± 1.4 g L-1 with a 61.1 ± 2.8% yield, while the final ethanol concentration from sucrose was 31.8 ± 6.9 g L-1 with a 62.1 ± 12.8% yield. The immobilized cells allowed eight 10 h sequential reuse cycles to be carried out with stable final ethanol concentrations. In addition, there was no need to use antibiotics and no contamination was observed. After the eighth cycle, there was a significant rupture of the beads making them inappropriate for reuse. Due to the gradual depletion of crude oil and environmental problems caused as a result of the growing consumption of oil and its derivatives, biomass and bioenergy have recently been attracting more attention. Therefore, it is important to study and develop alternatives that are both renewable and environmentally friendly. Ethanol has been considered an alternative to oil, since it is more environmentally friendly and is a renewable energy source , a higher substrate conversion, less inhibition by products, reduced reaction time, and control of cell replication (biomass growth) -linked-D-glucosamine (deacetylated unit) and N-acetyl-D-glucosamine (acetylated unit)  and chitosan , from Sigma-Aldrich; and calcium chloride, from Aldrich Chemical Company, Inc.-1 glucose, 5 g L-1 peptone, 3 g L-1 yeast extract, and 3 g L-1 malt extract. After one day, the mixture was centrifuged  and suspended in sterile water (0.10 L).The yeast cells were grown in a sterile solution  containing 10 g L-1 glucose, 20 g L-1 peptone, and 10 g L-1 yeast extract. This solution was used in the batch fermentation experiments.A sterile solution  was prepared with 100 g L-1 sucrose, 20 g L-1 peptone, and 10 g L-1 yeast extract. This solution was also used in the batch fermentation experiments.A sterile solution  was prepared with 100 g LS. cerevisiae in a beaker. The solution was mildly shaken. A CaCl2 solution with a final concentration of 2% (w/v) was prepared in a separate beaker. The mixture containing the cells and the sodium alginate was added dropwise to 0.150 L of the CaCl2 solution using a 0.010 L syringe. The beads were hardened in this solution for 1 h. After hardening in the CaCl2 solution, the beads were rinsed with sterile water to be used thereafter in the fermentation experiments. The beads were got with a diameter approximately between 3 to 4 mm.For the immobilization in beads, 3% (w/v) sodium alginate was dissolved in 0.10 L water and added to a 0.10 L suspension of S. cerevisiae in a beaker. The solution was mildly shaken. A CaCl2 solution with a final concentration of 2% (w/v) was prepared in a separate beaker. A solution of 0.25% (w/v) chitosan in 5% acetic acid was prepared in another beaker. The mixture containing the cells and the sodium alginate was added dropwise to 0.150 L of the CaCl2 solution using a 0.010 L syringe. The beads were hardened in this solution for 1 h. After hardening in the CaCl2 solution, the beads were rinsed with sterile water and then added to the 0.25% chitosan solution and kept under mild shaking for 30 minutes. The beads were then rinsed with sterile water to be used thereafter in the fermentation experiments. The beads were got with a diameter approximately between 3 to 4 mm.For the immobilization in beads, 3% (w/v) sodium alginate was dissolved in 0.10 L water and added to a 0.10 L suspension of Approximately 100 g of calcium alginate beads was added to a 1.0 L Erlenmeyer flask containing 0.20 L of the fermentation medium with glucose or sucrose as carbon source. All steps prior to fermentation were carried out under sterile conditions. The procedure described above was carried out for chitosan-covered calcium alginate beads. The four flasks were sealed with a cotton plug and placed in an orbital shaker for 10 h at 30°C and 200 rpm. Samples were collected during the fermentation period. After 10 h, the beads were filtered and rinsed with sterile water and added to a fresh fermentation medium  as described above. This procedure was repeated eight times for the successive fermentation cycles.In order to compare the performance of the immobilized cells, a parallel experiment using free cells for the fermentation was carried out using the same mass of cells as that used in the immobilized beads. The experiments were carried out under the same experimental conditions with either glucose or sucrose as carbon source.Ethanol, glucose, and sucrose concentrations were determined using high-performance liquid chromatography equipment (Agilent Technologies 1200 series) equipped with a Aminex ® HPX-87H column (Biorad) and refractive index detector (RID). Cell growth was determined by optical density at 600 nm in an Agilent HP 8453 spectrophotometer using the fermentation medium as blank.-1 glucose or sucrose as carbon source. The cycles lasted 10 h each and every experiment used approximately 6 g of wet yeast cells. When mixed with the alginate, these cells had a final weight of 100 g, so the immobilized cell concentration in the beads was approximately 60 g L-1. Ethanol yield and cell growth in aqueous medium are slightly different in fermentation systems for free cells from those for immobilized cells. The ethanol concentration, yield, and cell growth in aqueous medium for glucose or sucrose fermentation are shown in Tables Eight sequential fermentation cycles using immobilized cells in calcium alginate and chitosan-covered calcium alginate beads were carried out with 100 g LCell immobilization in beads offers important advantages, such as ease of cell separation from the medium, a decrease in costs due to cell reuse in subsequent reaction cycles, and a reduced possibility of contamination. Furthermore, the results of cell immobilization showed a stable final ethanol concentration throughout the cycles Figures  and 6 so-1, but this experiment was carried out without cell reuse. For immobilized cells, it was about 30 g L-1 in each cycle with cell reuse. Our results show an improvement over those observed by Ghorbani et al.  with a feed containing 150 g L-1 of molasses. Using a 10% initial glucose concentration Birol et al. (-1, which was similar to the one we obtained for free cell fermentation. Our results for immobilized cells are also similar to those reported by Shafaghat et al. (-1 in an experiment with an initial sugar concentration of 100 g L-1. It was observed (Eiadpum et al. Kluyveromyces marxianus and Saccharomyces cerevisiae immobilized on thin-shell silk cocoon was effective for ethanol fermentation at a high-temperature, generating ethanol concentrations of 81.4 and 77.3 g L-1 when using temperatures of 37 and 40°C and an initial sugar concentration of 220 g L-1. Our results show that ethanol conversion time was longer on chitosan-covered calcium alginate beads than on calcium alginate beads. It was noticed that the chitosan-covered calcium alginate beads were slightly more robust than the calcium alginate beads after eight sequential fermentation cycles. However, this difference was not significant. Final ethanol concentration is due mostly to the immobilized cells since the cell concentration in aqueous medium due to the release from beads is negligible when compared to the cell concentration in the beads. It was possible to perform eight sequential cycles of 10 h each using immobilized cells. Since the maximum ethanol concentration was reached in 6 h for the chitosan-covered beads, it may be possible to increase significantly the number of cycles if the cycle time is reduced to 6 h.The final ethanol concentration and yield were higher for free cell fermentation systems using glucose or sucrose than for systems carried out with immobilized cells. Chitosan-covered calcium alginate beads were not mechanically more resistant than beads without chitosan. When using chitosan, cell growth in aqueous medium was slightly lower than that for the beads without it. This had been expected, since chitosan acts as a barrier to cell release. Figures i et al. , who shol et al.  observedt et al. , where cIn conclusion, with immobilized cells it was possible to carry out eight sequential reuse cycles, generating a stable final ethanol concentration in each cycle. Therefore, this study suggests that the beads' robustness allows potential application in a continuous process in a tubular reactor. In addition, there was no need to use antibiotics and contamination was not observed. In comparing calcium alginate beads and chitosan-covered calcium alginate beads, it was observed that the chitosan cover did not improve the beads' robustness and, moreover, substrate consumption and ethanol production were slower because chitosan acted as a barrier to substrates and products.The authors declare that they have no competing interests.


Understanding the persistence of rabies in multiple canine hosts in southern Africa requires applying the principles of metapopulation biology. Rabies is a widespread disease in African domestic dogs and certain wild canine populations. Canine rabies became established in Africa during the 20th century, coinciding with ecologic changes that favored its emergence in canids. I present a conceptual and terminologic framework for understanding rabies ecology in African canids. The framework is underpinned by 2 distinct concepts: maintenance and persistence. Maintenance encompasses the notion of indefinite transmission of infection within a local population and depends on an average transmission ratio >1. Maintenance in all local populations is inherently unstable, and the disease frequently becomes extinct. Persistence, the notion of long-term continuity, depends on the presence of rabies in >1 local population within the canine metapopulation at any time. The implications for understanding rabies ecology and control are reviewed, as are previous studies on rabies ecology in African canids. In southern Africa, jackals  are also hosts, although their role has been controversial; some studies indicate that they can support rabies cycles  must be >1. At the beginning of an epidemic, this number is expressed mathematically as the basic reproductive number, Individuals of species other than the maintenance host may also become infected; they are usually dead-end hosts because of low transmission ratios, which are caused by factors such as the failure to induce biting behavior, inefficient salivary shedding, and absence of other hosts with which to interact. Occasionally, nonmaintenance hosts successfully transmit the infection to conspecifics, which may lead to the establishment of a new cycle if conditions for continual, effective transmission to conspecifics are favorable. The emergence of a new cycle requires some genetic adaptation of the virus in the new host. Lyssaviruses can probably adapt with relative ease because their broad host range allows adaptive selection to take place, as evidenced by the emergence of many new cycles in the last 100 years. A mechanism to explain how such adaptation may arise is the quasispecies concept, where the inherently high mutation rates of RNA viruses produce variant populations of viruses through which selection can act  The average transmission ratio must be >1 for virus maintenance to be successful. A maintenance host is a member of a population of susceptible individuals that can replicate, shed, and transmit virus efficiently to conspecifics. Maintenance hosts live in local populations, which support indefinite transmission of virus independently of other local populations.Individual local populations are unlikely to maintain rabies continuously because of the inherently high instability of the disease in any single local population; the disease is normally reintroduced from other infected local populations. Persistence encompasses the concept of long-term and continuous presence of disease within a metapopulation. Successful persistence requires that virus is maintained in >1 local population at any time.,,–Many viral pathogens depend on a constant supply of susceptible hosts because the viral infection causes either host death or durable immunity. The unstable pattern caused by depletion and renewal of host local populations is a prominent feature of all carnivore rabies cycles, particularly when studied at a relatively fine geographic resolution , the black-backed jackal (C. mesomelas), and the bat-eared fox   (C. simensis)  in 1902, were in domestic dogs, and their origin was traced to distant lands , on the basis of rabies virus RNA detected in saliva of healthy hyenas by polymerase chain reaction, although virus isolation, arguably the more important test, was unsuccessful from these samples  and persistence . To clearly conceptualize the ecology of canine rabies, we must use lucid, appropriate definitions for virus-host interactions and epidemiologic patterns.The ecology of many ecosystems has changed dramatically in recent centuries because of the increase in human populations, the introduction of large-scale commercial agriculture, urbanization, loss of biodiversity within the human biosphere, and other changes. The new ecologic landscapes have been exploited by species that can adapt favorably to them, including many of the prominent rabies maintenance hosts. Rabies viruses have recently become prominent in the African ecosystem because of transmission in mammals that have exploited ecologic changes that have occurred in much of the continent. Such change is set to continue into the future, and those species that can flourish under the new conditions will be candidate hosts for the maintenance of pathogens.


Hapi is a new dynamic programming algorithm that ignores uninformative states and state transitions in order to efficiently compute minimum-recombinant and maximum likelihood haplotypes. When applied to a dataset containing 103 families, Hapi performs 3.8 and 320 times faster than state-of-the-art algorithms. Because Hapi infers both minimum-recombinant and maximum likelihood haplotypes and applies to related individuals, the haplotypes it infers are highly accurate over extended genomic distances. The emergence of high throughput genotyping technologies has enabled rapid, low-cost assays of single nucleotide polymorphisms (SNPs) in large datasets of human subjects. These genotype data provide two unordered allele values at each queried genomic position, with each allele derived from the two homologous chromosomes in a diploid cell. However, genotype data do not identify which variant is present on each homologous chromosome.A haplotype is an assignment of each allele to the homologous chromosome it resides on, and the haplotypes of a set of individuals can be determined, with varying levels of accuracy, from their genotype data using haplotype inference or 'phasing' techniques. Haplotypes are essential for many important genetic applications, including: 1) imputation of genotypes at loci that were originally untyped in a set of samples  imputati, a technGenome scale haplotypes cannot be discovered using direct molecular means at present, so computational methods must be used to infer them. Algorithms for inferring haplotypes can be separated into three classes. One class of haplotyping algorithms applies to unrelated individuals, and techniques of this class use probabilistic constraints governed by mathematical models of population dynamics to infer haplotypes. Available algorithms ,12 include novo meiotic recombinations and gene conversions (which appear as short double crossovers), and have been used to build genetic maps of recombination rates [de novo meiotic recombinations and gene conversions enables the study of differences in location and number [The second class of haplotyping algorithms applies to individuals with known family relationships -26. Theson rates . Consided number  of such d number . Importade novo meiotic recombinations or gene conversions, and therefore suffer from the same limitations for studies of the results of meiosis as do haplotypes from unrelated individuals.The third class of haplotyping algorithms applies to many family trios which contain data for a father, mother, and one child; approaches in this class leverage techniques from the other two classes outlined above. In particular, algorithms for haplotyping trio data use the laws of Mendelian inheritance to resolve the haplotypes of the trio individuals at every locus where one of the individuals is homozygous. For the remaining ambiguous loci, these algorithms utilize the mathematical models that govern haplotype expectations for unrelated individuals, with adaptations to apply to trio data. PHASE , BEAGLE Hapi is a new dynamic programming algorithm that infers both minimum-recombinant and maximum likelihood haplotypes, and performs substantially faster than all other haplotyping algorithms for the nuclear family problem. Nuclear family derived genotypic data identifies parents and their children, but provides no information about relationships within a larger pedigree. Minimum-recombinant haplotypes assign family members' genotypes to homologs such that the number of recombinations that occur in the homologs the parents transmitted to the children is minimized. Maximum likelihood haplotypes utilize recombination frequencies between successive loci from a genetic map to calculate the most likely haplotype reconstruction.Maximum likelihood haplotypes are often substantially similar or identical to minimum-recombinant haplotypes. Both approaches to haplotype estimation have strengths and weaknesses.Minimum-recombinant haplotyping may yield suboptimal results when the recombination frequencies between loci in some region varies widely.  Maximum-likelihood haplotyping reports only the most likely haplotype, a feature that can be misleading to a user when the difference in probability to alternate haplotypes is small. Typically this occurs when the number of recombinations across the alternate haplotypes are the same, and in such a case, minimum-recombinant haplotyping reports the ambiguities. Historically, geneticists have manually performed minimum-recombinant haplotype assignment to analyze small datasets. Hapi enables this approach to be applied to the very large datasets currently produced by high-throughput SNP genotyping.n 2possible values, where n is the number of non-founders or individuals with at least one parent in the pedigree.Several existing programs for haplotyping related individuals are based on the Lander-Green algorithm , includin 2states in order to perform multipoint analyses, including haplotyping.Although Merlin, GENEHUNTER, and Allegro all employ techniques to reduce computational space and time requirements of this basic algorithm, all are relatively inefficient; in general, each requires exponential time in the number of non-founders in the pedigree. One technique that all these algorithms employ is to avoid representing inheritance vectors that are inconsistent with Mendelian inheritance. In addition, Merlin  uses spaSuperlink  is anothHapi's optimizations reduce the state space that it must examine both at a single locus and across multiple loci, as it is able to avoid considering transitions between all possible states at adjacent loci. The optimizations we introduce in Hapi represent a leap forward in reducing the algorithm runtime and space complexity compared to existing algorithms. The following is a summary of Hapi's optimizations; further details appear in Materials and methods:p is homozygous at a locus l, Hapi only builds states for l in which the homolog that parent p transmitted does not exhibit recombination relative to the previous locus. In connection with this, Hapi does not build states at loci where both parents are homozygous since recombination cannot be observed at these loci. This optimization is natural for minimum-recombinant haplotyping, but it requires special consideration in the context of maximum likelihood haplotypes.1. When a parent 2. At loci where Mendelian inheritance cannot unambiguously infer for a set of children which parent transmitted each allele, Hapi uses a novel, concise representation of the ambiguities instead of forming an exponential number of states for all possible transmissions to the children. Hapi also avoids building any states that represent recombinations on both homologs for the ambiguous children and later evaluates whether that decision is consistent with nearby loci.s states, there are s2 possible state transitions . Kruglyak and Lander [O(s·log s), but Hapi's transition runtime is only O(s), that is, linear in the number of states at a locus.3. To transition between states at adjacent loci, Hapi considers a state at the previous locus as possibly transitioning to either two or four states at the next locus, depending on the genotypes and possible phase assignments of the parents at that locus. This optimization is actually a by-product of the first two optimizations mentioned above, but deserves separate consideration. Normally if two adjacent loci each have d Lander  introducet al. [4. Some states encode the same transmissions of homologs from the parents to the children and differ only in the parents' phase. These states are equivalent downstream of the current locus and Hapi only retains the state with minimum recombinations or maximum likelihood. Kruglyak et al.  first di5. The previous optimization is most effective when none of the children are missing genotype data. We devised a mechanism for comparing nearly equivalent states in the presence of children with missing data that often enables the detection and elimination of suboptimal states.6. At each locus, Hapi only considers states that are consistent with Mendel's laws for the genotypes of the individuals and spends no time processing any inconsistent states. Other algorithms also employ similar optimizations that help reduce the number of states they examine ,21,24.To demonstrate the efficacy of Hapi's optimizations in the context of real genotype data, we ran Merlin, Allegro, Superlink, PedPhase 2.0  and HapiExisting algorithms have limits on the size and number of families they can haplotype. Hapi makes possible the efficient haplotyping of very large numbers of families as well as families with large numbers of individuals. Because of the relative ease of gathering genotypes for nuclear families, we expect that the number of nuclear families within datasets will continue to grow and that Hapi will provide the opportunity to haplotype this large quantity of data. The techniques Hapi implements to efficiently haplotype nuclear families also apply to general pedigrees, and thus promise to extend the size of pedigree datasets beyond the limitations of roughly 20 non-founders inherent in existing algorithms (see Conclusions). Hapi is freely available for non-profit use .The remainder of this paper describes our experimental results (Results and discussion), gives a summary of our contributions (Conclusions), and describes our algorithm in detail .We have evaluated Hapi's runtime performance compared to three state-of-the-art algorithms: Merlin , AllegroThe derived nuclear family dataset contains 103 nuclear families where both parents have data. These families have a total of 438 individuals. Note that because we analyzed the families separately, we double counted individuals that appear in more than one family .These families range in size from one to eleven children, with an average of 2.23 children per family. There are 86 families with three or fewer children , with an average of 1.56 children for that subset of families. Using the Illumina linkage IV_v3 SNP panel, genotypes at 5,456 SNPs covering the whole genome were obtained for each individual in the dataset . The numTable Superlink ran for over six hours without finishing when we used it haplotype chromosome 1 for all families in the dataset. At that time, the program reported that 0% of the haplotyping was complete. We found that Superlink uses an excessive amount of memory (>24 GB) to haplotype a family with nine or ten children. The times for Superlink therefore reflect its haplotyping a modified set of families, with three of the children removed from the original eleven child family. Superlink used less than 8 GB of memory when analyzing this modified dataset.We include times for haplotyping all families in the dataset (modified for Superlink), as well as the subset of families with three or fewer children in Table Besides these maximum likelihood systems, we compared Hapi's minimum-recombinant haplotyping to PedPhase 2.0, which uses an Integer Linear Programming algorithm to calculate minimum-recombinant haplotypes for pedigrees . PedPhasAs we discuss in Additional file Hapi produces output in text or CSV format, suitable for import into a spreadsheet. It can output either the actual haplotypes with allele values or the children's inheritance vector values. The latter is useful for inspecting the results of meioses, including recombination patterns. Figure Assignment of haplotypes is an important element in a number of significant areas of genetic analysis, including locating genes involved in human disease, analyzing the products of meiosis to locate recombination hotspots and gene conversions, and studying population dynamics and history for humans and other species. Because of their importance, researchers have developed computational algorithms for inferring haplotypes from genotypes. The most effective approach to this problem is to use data for individuals whose family relationships are known.Inferring minimum-recombinant haplotypes for the individuals in a pedigree is known to be NP-hard in general ,35. ProbHapi is an efficient algorithm for inferring both minimum-recombinant and maximum likelihood haplotypes for nuclear families. Hapi runs in polynomial time in practice r), where s is the maximum states the algorithm would produce to evaluate any of the nuclear families individually, r is the number of nuclear families in the pedigree, and i* is the maximum number of shared individuals in any nuclear family. This bound, while exponential, compares favorably against the bound of O(2n-f2) states per locus of existing techniques since r·i* <n . With this reduction in the bound on the number of states, the optimizations Hapi employs make possible the haplotyping of larger pedigrees than can be handled by existing techniques.The optimizations Hapi uses to efficiently haplotype nuclear families can also be extended to pedigrees. A detailed discussion of this problem is available elsewhere , but we As time passes and technology improves, genotype datasets will continue to grow in size, both numbers of individuals and numbers of loci assayed. As such, faster tools for haplotype analysis will be essential. Existing algorithms for haplotyping related individuals have hard limits on the size of families they can analyze because of their exponential complexity. These algorithms are consequently ineffective for datasets with thousands of families or for families with large numbers of children. Hapi provides a solution that is able to meet many of these future challenges.Hapi performs both minimum-recombinant and maximum likelihood haplotyping for nuclear families. These two haplotyping approaches are similar, and we first present the minimum-recombinant algorithm. Later we will describe how to extend this approach to calculate maximum likelihood haplotypes. This paper describes an algorithm for haplotyping nuclear families that have genotype data for both parents and some number of children. We elsewhere describe how to generalize the algorithm to infer haplotypes for nuclear families with data for only one parent or to sets of siblings only  .globally minimal across the chromosome length rather than locally minimal between successive pairs of loci. Thus, a solution may contain a locus that has an alternate assignment of individuals' alleles to homologous chromosomes that yields fewer recombinations from the previous locus , but not over the entire chromosome length . An example of such a locus from real data for a family of human subjects is described in the Example subsection.Hapi seeks to find a minimum-recombinant haplotype solution that is c bits, where c is the number of children in the nuclear family.Hapi uses inheritance vectors, represented using bit strings, to encode which chromosome homolog each parent transmitted to each child at a locus. These bit strings are composed of 2l:A dynamic programming equation for calculating minimum-recombinant haplotypes is given below. The function l-1. l = 0 is defined naturally as Here, c 2possible inheritance vectors at locus l = 0 and would model most or all of these vectors at successive loci. Hapi functions differently: the initial locus has only one inheritance vector, and successive loci model a very small number of inheritance vectors.A naive implementation of the above dynamic programming recurrence would initialize all 2state to store the information computed in the above dynamic programming equation. A locus state stores: (1) an inheritance vector; (2) the assignment of the heterozygous parent's or parents' genotype alleles to homologs that is consistent with this inheritance vector; (3) the minimal number of recombinations necessary to reach this state/inheritance vector value; (4) a pointer to the state or states at the previous locus that yields this minimal number of recombinations; and (5) a bit string encoding which children have ambiguous inheritance values (necessary for some kinds of loci as we describe later). Because the parents' allele to homolog assignments imply part or all of the inheritance vector values, there is only one consistent parent assignment for each inheritance vector.Hapi uses a locus After evaluating equation (1) by building the necessary states for all loci, it is straight forward to deduce haplotypes. Hapi does this by performing the assignments of alleles to homologs as dictated by the minimum-recombinant state at the final locus and then back tracing to states at previous loci. Rather than waiting until the final locus to make these assignments and perform back tracing, Hapi does this work whenever a locus yields only one state (which happens frequently). The one state at that locus and those leading to it at previous loci are guaranteed to have minimum recombinations. Performing this process before the final locus allows the system to reclaim the memory used to store states.We give an illustrative example of what a graph of states generated by our algorithm might look like in Figure Hapi implements six optimizations that allow it to very efficiently infer minimum-recombinant haplotypes, and it uses these same optimizations to calculate maximum likelihood haplotypes, as we describe later. The goal of each optimization is to reduce the number of states and state transitions that Hapi must consider and store. Below, we give details about five of the optimizations Hapi implements. The last optimization applies at loci where one or more children are missing data, a scenario we discuss later. Note that Hapi builds states for a locus based on the states at the previous locus and the genotypes of the individuals at the locus being considered. Considering states at the previous locus is necessary for two of Hapi's optimizations. The initial state for a chromosome cannot depend on previous locus states and is therefore built differently as we discuss later.c inheritance vector values for each homozygous parent. Instead of building and processing this exponential number of states, Hapi copies the inheritance vector values corresponding to the homozygous parent from the states at the previous locus. Typically the number of unique inheritance vector values for the homozygous parent at the previous locus is small, though it is possible for this number to be large. In general, other optimizations aid in keeping the number of states small, and our experimental results demonstrate that the number of states is small in practice.When one or both of the parents at a locus are homozygous, which homolog the homozygous parent(s) transmitted is ambiguous. A naive implementation of the Lander-Green algorithm builds states corresponding to all possible homolog transmissions for the homozygous parent, yielding 2This approach of copying inheritance vector values for the homozygous parent assumes a lack of recombination for this uninformative case, and this will always yield minimal recombinations. The next locus that is heterozygous for the parent in question will indicate if a recombination has occurred within any region of homozygosity for that parent.c 2possible inheritance vectors are consistent with the genotypes. Rather than building all states or copying every state from the previous locus, Hapi simply skips these loci. Subsequent loci utilize the states located at the most recent locus for which states exist. Table p1 are shown as the second element in each of the ordered pairs in the rows labeled a and b are the same as those in the previous state since Hapi copies these values. Without this copying optimization, the locus would have 2·2c states rather than two. Merlin [For loci where both parents are homozygous, all 2. Merlin  and Alle. Merlin  also inca priori ambiguous as to which parent transmitted each of their alleles: either parent could have transmitted either allele.At loci where both parents are heterozygous with the same genotype (which we later term 'partly informative'), a heterozygous child will have the same genotype as its parents. As a result, these heterozygous children are h heterozygous children, there are 2h possible inheritance vectors for each of the four possible assignments of parents' alleles to homologs, or 4·2h total inheritance vectors/states consistent with the individuals' genotypes at these loci.Existing algorithms build states corresponding to all possible inheritance vector values for these ambiguous children, and for a given assignment of the parents' alleles to homologs, each heterozygous child has two possible inheritance vector values. Thus, for Instead of building this exponential number of states, Hapi again uses the states at the previous locus to reduce the number of states it must build. The system maps each previous state to four states corresponding to each assignment of parents' alleles to homologs. Note that multiple previous states can map to the same state, so the number of states usually does not quadruple. Also note that homozygous children have only one inheritance vector value that is consistent with a given assignment of parents' alleles, so they do not affect the number of necessary states.Heterozygous children have two consistent inheritance vector values for a given assignment of parents' alleles to homologs, and these two values are opposite each other. If the inheritance value in the previous state is equivalent to one of these two values, Hapi uses the value equivalent to the previous state in the state being built. The other inheritance value results in two crossovers for the child, one from each parent. Such an event is extremely unlikely, yet if it were to take place, downstream loci that are fully informative would reveal its occurrence. In that rare case, Hapi will mark the partly informative locus as ambiguous during back tracing, since it is impossible to know whether these two recombinations took place at the earlier partly informative locus or at the later fully informative locus. (Maximum likelihood haplotyping determines the location of the recombinations based on recombination frequencies.)In the case that the inheritance value in the previous state is not equal to one of the two ambiguous inheritance values, the previous inheritance value must differ from these two values in exactly one bit. For example, if the previous value is 〈0, 0〉 and is not equal to either of the values at the current locus, they must be 〈0, 1〉 and 〈1, 0〉. The differences between the two consistent values and the previous one represent a recombination in one or the other parent. Which parent recombined is ambiguous at this locus and can only be determined at later loci.Rather than creating separate states for these two inheritance values - which would yield an exponential number of states across multiple children - Hapi instead marks the child as having ambiguous inheritance. A child's inheritance being marked as ambiguous means that its inheritance vector value can be inverted without inducing additional recombinations - both possibilities result in the exactly one recombination.The choice of which of the two inheritance values to store in the state is arbitrary, and Hapi indicates that a child is ambiguous using another bit vector. For our explanation, we designate ambiguous values with the? symbol. One can view an ambiguous inheritance value as a set of values, so 〈0, 0〉? = 〈1, 1〉? = {〈0, 0〉, 〈1, 1〉}. For the earlier example with a previous inheritance value of 〈0, 0〉, the resulting inheritance value would be 〈0, 1〉?. The use of these ambiguous values effectively merges the exponential number of states that would otherwise result. Merging the states in this way suffices because (1) Hapi can later resolve which of the unambiguous inheritance vectors is optimal, and (2) the number of recombinations remains the same regardless of which unambiguous inheritance vector ultimately results. If the previous inheritance value is itself ambiguous, the resulting value must also be ambiguous, and when there is a recombination, the resulting value is unequal to the previous value, such as with 〈0, 0〉? and 〈0, 1〉?.Hapi resolves ambiguous inheritance values for a state during the back tracing process. While back tracing, if the system encounters a state that has one or more ambiguous inheritance values, it compares these values to the corresponding values at the next  locus. If the unambiguous form of this value  or its opposite is equal to the inheritance value at the next locus, the system assigns the equivalent value at the current locus. If neither is equal, recombinations occur on either side of this locus and the inheritance value is truly ambiguous. In this rare case, Hapi's output reports the child's haplotype at this locus as ambiguous.This optimization significantly improves Hapi's efficiency. Removing this optimization would cause the number of states to grow unwieldy whenever Hapi encountered a locus that has heterozygous parents with the same genotype. Even with all the other optimizations in place, the increase in the number of states would propagate to subsequent loci that have one parent that is heterozygous and the other homozygous.In general, any state at a previous locus can transition to any state at the next locus. However, because Hapi does not consider state transitions that include recombinations from a parent that is homozygous, and because it uses ambiguous inheritance values, the number of possible state transitions is limited. The state transitions optimization actually comes as a by-product of the two optimizations we have already outlined, yet the effects of these optimizations on the complexity of state transition calculations merit a separate discussion.At each locus, Hapi considers transitions from the states at the previous locus to either two or four states. If only one parent is heterozygous at the locus, each state at the previous locus can transition to only two states at the current locus. These two states correspond to the two possible phase assignments for the heterozygous parent. A particular phase assignment for the heterozygous parent uniquely defines the inheritance vector bits that that parent transmits. The system copies the other inheritance vector bits from the previous state.If both parents are heterozygous at a locus, then the parents have four possible phase assignments, and each state at the previous locus can transition to four states at the next locus. The ambiguous inheritance vector optimization makes this possible, since loci in which both parents have the same heterozygous genotype would otherwise produce an exponential number of states. Instead, for a given phase assignment for the parents, a state at previous locus uniquely determines the inheritance vector it transitions to. If the parents are heterozygous with differing genotypes, the children's genotypes at the locus unambiguously imply the complete inheritance vectors corresponding to each parent phase assignment. Thus, exactly four inheritance vectors are possible, and each previous state can transition to these four states.s states, other algorithms compute transition probabilities corresponding to all s2 state transitions. Use of a fast Fourier transformation reduces the computational burden of these optimizations from a quadratic O(s2) to O(s·log s) [s or 4s possible transitions, so the computational burden is linear, O(s). The speed of computing state transitions - in addition to and in connection with tracking of very few states at each locus - enable Hapi to perform haplotyping calculations very efficiently.The efficiency gains of our approach are significant. Without these optimizations, haplotyping algorithms must consider all possible state transitions between loci. If two adjacent loci each have s·log s) . With HaAt many loci, it is possible to unambiguously deduce which allele each heterozygous parent transmitted to each child. In that case, the inheritance bits that correspond to transmissions from this parent can take on exactly two values depending on the parent's phase assignment. The inheritance bits in these two values are opposite each other, since the parent transmits the same allele in each case, but the alleles reside on opposite homologs for the opposite phase assignments. The locus in Table a and b in Table b and eliminates state a from further consideration.Two inheritance vectors with opposite bits corresponding to one parent and equivalent bits for the other parent are equivalent in terms of the number and locations of recombinations that will occur at downstream loci. Hapi uses inheritance vectors to detect recombinations. A recombination occurs when the homolog a parent transmitted to a child differs between two loci. Because the parent's inheritance values in these states are exactly opposite each other, each of these inheritance vectors encodes the same set of children as receiving a given homolog. The two states merely use opposite labels for the homologs as implied by the parent's opposite phase assignments. Choosing one of the states instead of the other results in all downstream loci having opposite phase assignments for the parent, consistent with the chosen phase assignment in the upstream locus. The number and location of downstream recombinations are the same regardless of which state the system chooses at this locus because the sets of children that share a common homolog same between states. The two states In general, any states with opposite inheritance values for one parent and either equivalent or opposite inheritance values for the other parent are equivalent. This means that, if both parents are heterozygous with differing genotypes, there are only four possible states, and these have equivalent downstream affects. When two or more states are equivalent at a locus, Hapi only retains the state with the fewest recombinations.et al. [n 2possible inheritance vectors in a pedigree, where n is the number of non-founders, this optimization reduces the state space to 2n-f 2inheritance vectors, where f is the number of founders. For a nuclear family, f = 2, so this optimization reduces the state space by a factor of 4.Kruglyak et al.  first dic 2possible inheritance vectors for every locus, the genotypes of the individuals at a locus often make many of these inheritance vectors inconsistent with the Mendelian laws of inheritance. For example, a parent that has a genotype of a/b cannot transmit its b allele to a child with genotype a/a. Hapi builds states based explicitly on the genotypes at each locus and spends no time processing any inheritance vectors that are inconsistent with Mendelian inheritance. Merlin [Although there are 2. Merlin , GENEHUN. Merlin , and All. Merlin  all contHapi's optimizations apply in different contexts, and in particular, we have identified four types of loci with different parents' genotypes for which different technical issues arise and different optimizations apply. Table Loci that we term to be fully informative for both parents are those in which both parents are heterozygous but with differing genotypes. In this case there are exactly four possible states and the equivalent states optimization enables Hapi to retain only one state. Note that although this locus type is advantageous, most SNPs are bi-allelic, and therefore this locus type will not occur in SNP genotype datasets. A fully informative for one parent locus is one that has one heterozygous parent and one homozygous parent. Two successive loci that are fully informative for each of the parents is analogous to one fully informative for both parents locus. Each such locus produces only two possible inheritance vector values corresponding to each parent and, at the second locus, the states are all equivalent.Often the states at the locus preceding a fully informative for one parent locus do not contain ambiguous inheritance values. When that is the case, because Hapi does not introduce any states with recombinations for the homozygous parent, and the because of the equivalent states optimization, the system retains at most s states. We discuss the case in which the previous locus has states with ambiguous inheritance below. Partly informative loci are those in which both parents are heterozygous with the same genotype. The number of states at these loci may increase by a factor of four from the previous locus, but typically the number of states does not grow large. As Table Uninformative loci are those in which both parents are homozygous, and yield no information about meiosis. Hapi does not produce any states for these loci, and only deduces the children's phase if they are heterozygous.Ambiguous inheritance values complicate the handling of fully informative for one parent loci. At this locus type, we apply an optimization to propagate the inheritance vector bits for the homozygous parent from the previous locus. This requires only copying in the case of unambiguous inheritance values, and results in two equivalently labeled states.The situation is different when a previous state has children with ambiguous inheritance values. In that case, the corresponding two inheritance vectors that Hapi builds are not equivalent because, for children with ambiguous inheritance values, the homozygous parent's inheritance bits are opposite each other rather than equivalent. At the same time, the homozygous parent's inheritance bits for any unambiguous children remain identical across the two values.Consider the example in Table Ambiguous inheritance values in states at a locus preceding a fully informative for one parent locus can produce double the number of states at that locus, but does not always do so. While the two inheritance vectors produced by a particular previous state with ambiguous inheritance values are not equivalent, some other previous state may yield an inheritance value that is equivalent to one of these states, thereby enabling the elimination of some states.To build the initial state from which to haplotype a given chromosome, Hapi uses either a fully informative for both parent locus or two loci that are fully informative for opposite parents. Hapi begins at the first locus on a chromosome, scanning for these types of loci, and skips any partly informative loci. Later, after defining an initial state and haplotyping the remainder of the chromosome, Hapi resolves haplotypes at these early partly informative loci by performing reverse haplotyping from the locus that established the initial state.A locus that is fully informative for both parents completely defines an initial state. This locus type has exactly four possible inheritance vectors, and because they are equivalent, Hapi arbitrarily chooses one of them.A fully informative for one parent locus defines half of an inheritance vector, giving information only for the bits that correspond to the heterozygous parent. Hapi again arbitrarily chooses one of the two possible values to assign. The initial state is then partially defined with values for the heterozygous parent. Later, when the system encounters a locus that is fully informative for the undefined parent (or a locus fully informative for both parents), it fills in the inheritance vector values for the undefined parent, and haplotyping proceeds forward normally from this point. The system handles any intervening loci that are fully informative for the already-defined parent in the normal way, while still leaving the homozygous parents' inheritance vector bits undefined. Table Missing genotype data can result either because of quality control mechanisms associated with genotyping technologies or because of non-Mendelian errors . To x be the number of children with missing data, and let r be the value of the smallest number of recombinations among this set of states. The states in this set are x or 2x recombinations away from having equivalent inheritance vectors, depending on whether the inheritance values are opposite each other for transmissions from one or both of the parents.  Considering states that are separated by x recombinations, a state that has more than r + x recombinations will always be less optimal than the minimal state and can therefore be removed. Even if all the missing data children later recombine relative to the state with r recombinations - which would produce an inheritance vector equivalent to the larger state - that minimal state would yield r + x recombinations - that is, fewer than that for the larger state.The following constitutes Hapi's sixth and final optimization. Consider a set of states that have equivalent inheritance vectors when the missing data children are ignored and with identical inheritance values for those missing data children . Let Although this technique will not always eliminate the same number of states as if full data were available, it is quite effective. Our experimental results demonstrate this as Hapi very efficiently analyzes a real dataset that includes missing data (see Results). Often one state at a locus will have zero or one recombinations compared to another state that has all or all but one child recombining. In such a case, the technique just described will typically be able to eliminate the state with more recombinations.Hapi does not currently handle loci that are missing data for one or both parents. It can be modified to do so by building states corresponding to all possible parent genotypes consistent with the children's genotypes .We give a brief example illustrating some aspects of our algorithm in Table The left side state at locus 14 has two recombinations. It transitions to two states at locus 16, one with a total of three recombinations and one with five; the table shows the state with fewer recombinations. These two states at locus 16 both transition to the same two states at locus 17, and we include the state with fewer recombinations in the table.The right side state for locus 14 has three recombinations. Although this is greater than the two local recombinations shown for the left side state, this state actually yields fewer recombinations globally. It transitions to two states at locus 16, one of which produces no additional recombinations, and likewise that non-recombinant state produces zero recombinations at locus 17. This path of states therefore has only three recombinations, which is minimal across these loci.Although this discussion considered the downstream effects of each state at locus 14 separately, Hapi considers all states at successive loci at the same time and does not revisit loci. The four states at locus 14 each transition to two non-equivalent  states at locus 16, for a total of eight states. Because locus 16 is fully informative for parent 1, the inheritance vector values for that parent are equivalently labeled in these states. Locus 17 is heterozygous for parent 0 and produces exactly four equivalently labeled states, and the state with the fewest recombinations must be globally minimal. This globally minimal state is on the right side of the table.We now formulate the problem of maximum likelihood haplotyping and show how to solve it using the same techniques as those we employ for minimum-recombinant haplotyping.L for each member of a nuclear family with c children, and assigned inheritance vectors l. Let l θbe the recombination frequency between locus l and l - 1 for all 0 <l ≤ L. Also let l - 1 and l. Then the probability of the assigned inheritance vectors is:Suppose we have genotyped loci numbered 0,...,Using log likelihoods, this can be written as:θ < 0:5, then the maximum likelihood solution is the same as the minimum-recombinant one since ln(θ) < ln(1 - θ) across all loci, so decreased r(l) values increase the overall likelihood. However, when the recombination frequencies differ across loci, more recombinations at one locus may have higher likelihood than fewer recombinations at another.This formulation of the maximum likelihood problem shows clearly the relationship of the maximum likelihood problem to the minimum-recombinant one. If all loci have the same recombination frequency l is a locus, and A dynamic programming equation computing maximum likelihood haplotypes can be written as follows, where Using log likelihoods, the dynamic programming formulation becomes:2c/4 non-equivalent inheritance vectors at uninformative loci, and at least 2c/2 inheritance vectors at loci that are fully informative for one parent.Immediate application of the above formula is problematic because we cannot completely ignore uninformative loci: they have non-zero recombination frequencies that affect the overall probability of a solution. Without some novel insight, it is necessary to model most or all of the 2In order to account for recombination frequencies at loci where both parents are homozygous - that is, at uninformative loci - Hapi computes modified recombination frequencies at all other informative loci, including fully informative for one parent loci where one parent is homozygous. These modified recombination frequencies include the recombination frequencies for all uninformative loci that occur between a given informative locus and the nearest upstream informative locus. In calculating these probabilities, the algorithm is pre-computing the effects of recombination frequencies at uninformative loci, allowing it to avoid directly processing such loci.l as l φand the frequency of non-recombination (expressed above as (1 - lθ)) as lψ. To calculate l φand l ψfor a locus l, let l0 be the nearest upstream informative locus and let l1, ..., ln-1 be the uninformative loci that appear between l0 and l. Let ln = l and let l* be the locus with the highest recombination frequency, that is, find l* ∈ {l1, ..., nl} such that We denote Hapi's modified recombination frequency at a locus l0 and ln = l is equal to the maximum between-marker recombination frequency within the region spanned by these loci, or l*θ, multiplied by the probability of not recombining anywhere else. Note that θl* is the probability of recombining between locus l* and l* - 1, and either or both of these loci can be uninformative. Hapi stores the locus number l* so that the final haplotype solution includes any recombinations in their most likely positions.Thus, the probability of recombination between locus l θ< 0.5 means recombination is less likely than non-recombination. Therefore, even if we were to model such events, they would not ultimately appear in the haplotype solution, so we lose nothing by not modeling them.A consequence of this formula is that at most one recombination can occur between any two informative loci on a given homolog. Thus, within a region of uninformative loci, we do not model the possibility of intervening gene conversions or double recombinations. Not modeling such events is sensible because it is impossible to observe or verify them. Furthermore, haplotypes that include additional recombinations or gene conversions not directly implied by the data are less likely than those without these events since l0 and n l= l is the product of non-recombination across each of the locus intervals:The probability of not recombining between locus l φand l ψutilize the recombination frequencies between each pair of loci rather than a single recombination frequency spanning the region between l0 and l. This is the case because the haplotyping output must place every recombination at some discrete location between a pair of markers. There must exist a pair of markers flanking every recombination, and sometimes one or both of these will be uninformative. This matches the maximum likelihood approach employed by other algorithms, which calculate the probability of recombining (or not) between each pair of markers, not just those that are informative. A consequence of this formulation is that l φ+ l ψ≠ 1. This occurs because, as we earlier noted, these probabilities account for the possibility of only one recombination on a given homolog between any two informative loci - more than one recombination will always be less likely. Some applications - notably linkage analysis; see below - may benefit from using a single recombination frequency between the region spanned by l0 to l. Our algorithm functions the same regardless of how we calculate φ and ψ. To increase numerical stability and efficiency, Hapi uses the log likelihood formulation of this dynamic programming problem. This formula substitutes multiplication for exponentiation and uses the values ln(lφ) and ln(lψ) which requires summation instead of multiplication to calculate. The dynamic programming equation for maximum log likelihood haplotypes at a locus l is thus given by the following:The equations for The formulation just presented solves the problem of needing to track states at uninformative loci, but does not resolve another important issue. One of Hapi's key optimizations is to avoid modeling states that exhibit recombination from a parent that is homozygous at a locus, including at fully informative for one parent loci. This approach suffices in order to produce minimum-recombinant haplotypes since recombinations only occur at the informative locus that reveals them. For maximum likelihood haplotyping, if some informative locus exhibits recombination, the most likely location of that recombination might be upstream of an earlier fully informative for one parent locus where the transmitting parent is homozygous.A further complication to this issue of fully informative for one parent loci is the interactions between such loci and partly informative loci. A child may exhibit an ambiguous recombination at a partly informative locus immediately after a fully informative for one parent locus. In this case, the recombination might occur at the partly informative locus or upstream of the fully informative for one parent locus on the homozygous parent's homolog. Confounding this issue is the possibility of additional recombinations occurring downstream of the partly informative locus in this scenario. Although we could immediately evaluate the relative likelihoods of placing the ambiguous recombination upstream or at the partly informative locus, a downstream recombination may affect the overall likelihood.alternate inheritance value for each child at a locus. In the case of fully informative for one parent loci, this is the probability of inverting the inheritance value transmitted by the homozygous parent. For partly informative loci, the alternate inheritance has inverted homolog transmissions from both parents. The alternate inheritance probability at a locus depends on the surrounding loci. For example, the system can assign the alternate inheritance at a fully informative for one parent locus by recombining at that locus or, if the previous locus is fully informative for the same parent or partly informative, the system can apply the alternate inheritance (and associated probability) at the previous locus.  Hapi evaluates the probabilities for all possible ways of assigning the alternate inheritance for each child at a locus and stores those probabilities and the information relating to how they are assigned. Because of space considerations, we omit the details of how Hapi handles alternate inheritance probabilities; a complete description is available in another document [Complicated dependence across loci can also occur when a fully informative for one parent locus appears downstream of a partly informative locus where the child is heterozygous. The earlier partly informative locus may be the result of an ambiguous recombination and the opposite inheritance value may have equivalent or nearly equivalent likelihood. Choosing the opposite inheritance at the partly informative locus introduces a recombination but also inverts the inheritance value that occurs for the homozygous parent at the subsequent fully informative for one parent locus. Making such a change affects the inheritance values at downstream loci and may be more likely than some other downstream recombination. To address all these possibilities, Hapi tracks the probability of an document .We note briefly that because the alternate inheritance probabilities will differ across states at a locus, whenever multiple states transition to the same state at the next locus, Hapi must sometimes track multiple probabilities. To do so, it stores a range of probabilities - the maximum and minimum alternate probability for each child across all previous states. The alternate probability is therefore ambiguous during haplotyping and can only be determined during back tracing as Hapi explores the paths yielding the alternate inheritance values. Determining the maximum likelihood haplotyping assignment therefore requires back tracing to determine which path of states yields the highest likelihood and then forward tracing to assign those states.The basic Hapi algorithm is applicable not only to haplotype reconstruction but also to linkage analysis and LOD score calculations. This paper provides an overview of how to apply Hapi to linkage analysis; for more details, see Williams . BrieflyHapi's dramatic efficiency gains over other algorithms make it attractive to apply to linkage analysis for disease gene studies. Its optimizations make possible analyses of datasets for which current algorithms either fail or have significant time and storage requirements.HMM: Hidden Markov Model; LOD score: Logarithm base 10 of Odds score; MTBDD: Multi-Terminal Binary Decision Diagram; SNP: Single Nucleotide Polymorphism.ALW devised and implemented the Hapi algorithm and wrote this paper under the supervision of DEH, MCR, and DKG.Analysis of Hapi's runtime complexity. A detailed discussion of Hapi's runtime complexity, including descriptions of inputs that can yield runtime that is exponential in the size of the family. Also a probabilistic analysis of the likelihood of one class of these inputs occurring in real data.Click here for file


In vitro experiments showed that NL/2631-I223R replicated as well as NL/602 in MDCK cells. In a ferret pathogenesis model, body weight loss was similar in animals inoculated with NL/2631-R223 or NL/602. In addition, pulmonary lesions were similar at day 4 post inoculation. However, at day 7 post inoculation, NL/2631-R223 caused milder pulmonary lesions and degree of alveolitis than NL/602. This indicated that the mutant virus was less pathogenic. Both NL/2631-R223 and a recombinant virus with a single I223R change (recNL/602-I223R), transmitted among ferrets by aerosols, despite observed attenuation of recNL/602-I223R in vitro. In conclusion, the I223R mutated virus isolate has comparable replicative ability and transmissibility, but lower pathogenicity than the reference virus based on these in vivo studies. This implies that the 2009 pandemic influenza A/H1N1 virus subtype with an isoleucine to arginine change at position 223 in the neuraminidase has the potential to spread in the human population. It is important to be vigilant for this mutation in influenza surveillance and to continue efforts to increase the arsenal of antiviral drugs to combat influenza.Only two classes of antiviral drugs, neuraminidase inhibitors and adamantanes, are approved for prophylaxis and therapy against influenza virus infections. A major concern is that influenza virus becomes resistant to these antiviral drugs and spreads in the human population. The 2009 pandemic A/H1N1 influenza virus is naturally resistant to adamantanes. Recently a novel neuraminidase I223R mutation was identified in an A/H1N1 virus showing cross-resistance to the neuraminidase inhibitors oseltamivir, zanamivir and peramivir. However, the ability of this virus to cause disease and spread in the human population is unknown. Therefore, this clinical isolate (NL/2631-R223) was compared with a well-characterized reference virus (NL/602).  Recently, a 2009 pandemic A/H1N1 influenza virus was isolated from an immune compromised patient, with antiviral resistance to the neuraminidase inhibitor class of drugs. This virus had an amino acid change in the viral neuraminidase enzyme; an isoleucine at position 223 was substituted for an arginine (I223R). Patients infected with a pandemic virus that is resistant to all neuraminidase inhibitors, would leave physicians without antiviral treatment options, since these viruses are naturally resistant to the other class of antivirals, the adamantanes. To date, it is unknown if this I223R mutant virus is affected in its ability to cause severe disease and to transmit to other humans. Therefore, we have addressed this question by comparing the I223R mutant virus with a wild type reference virus in a ferret pathogenicity and transmission model. We found that the I223R mutant virus was not severely affected in its pathogenicity, although fewer lung lesions and alveolitis scores were found for the I223R mutant virus. In addition, we demonstrated that this virus transmitted efficiently to naïve ferrets. Consequently, we conclude that this I223R mutant virus has the potential to cause disease and may spread among humans. Therefore, influenza surveillance for this resistance pattern is advised. Two classes of antiviral drugs are approved for prophylaxis and therapy of influenza virus infected patients To date, the incidence of NAI resistant 2009 pandemic A/H1N1 viruses is very low. Nevertheless, 565 cases of patients infected with an  oseltamivir (OS) resistant virus have been reported to the World Health Organization in vitro and in vivo experiments Within the first years after approval of the NAIs in 1999, antiviral resistance in influenza viruses at a population level was rare (0.4%). In clinical trials, the incidence of resistant viruses was higher, varying from 0.4 to 1% in adults and up to 18% in young children In vitro analysis showed that reversion of the arginine to isoleucine fully restored NAI susceptibility. In another case, an I223R/H275Y double mutant virus was isolated that showed high resistance to the NAIs in vitro replication kinetics and its pathogenicity and transmissibility in the ferret model. We here show that this 2009 pandemic influenza A/H1N1 clinical isolate, harboring a neuraminidase I223R substitution retains its virulence and transmissibility, but is less pathogenic than a virus prototype without this mutation. In addition, recombinant NL/602/09 with a single I223R amino acid substitution transmitted as well as its recombinant parental virus, suggesting that no additional mutations are needed to compensate for the presence of this I223R mutation in the 2009 pandemic A/H1N1 virus backbone.Recently, the identification of a novel multidrug resistant 2009 pandemic A/H1N1 virus was reported, isolated from an immune compromised child with reduced susceptibility to all NAIs A pandemic 2009 influenza virus with reduced susceptibility to all NAIs that was isolated from a Dutch immune compromised child was studied here. Full genome sequencing of this clinical isolate A/NL/2631_1202/2010  harboring an I223R mutation in the neuraminidase was performed. Since no drug susceptible virus had been isolated from this patient before start of antiviral therapy, the well-characterized NAI-susceptible virus isolate A/NL/602/2009  was used as a reference virus in all experiments. This reference virus is a representative of pandemic H1N1 viruses that circulated in 2009, with only amino acid changes I108V and V407I (N1 numbering) in NA being unusual among the deposited sequences in the Influenza Research Database in vitro by multi-cycle replication kinetics of the viruses of interest. For this purpose, MDCK or MDCK-SIAT1 cell cultures were inoculated at a multiplicity of infection of 0.001 TCID50 per cell and at fixed time points supernatants were harvested to determine viral titers  replicated to lower peak titers in both cell lines compared to recNL/602 and NL/2631-R223. In addition, initial virus replication of recNL/602-I223R was delayed by 6 to 12 hours in MDCK-SIAT1 cells.6 TCID50 of virus. The animals were weighed daily as an indicator of disease. Over the 7-day period, no significant differences were observed in weight loss between the two groups inoculated with either virus. At day 4 post infection (p.i.), when there were still 6 animals present in each group, the mean percentage of weight loss was 8,2±2,4% and 7,6±6,7% for NL/602 and NL/2631-R223-inoculated animals respectively, not statistically significant , or 3 (NL/2631-R223) out of 4 animals . The expin vitro replication capacity could be extrapolated to reduced replication in vivo, the ability of recNL/602-I223R to transmit in the ferret model was studied. It was expected that reduced replication in ferrets would impede the virus to transmit to naïve animals, thereby suggesting that compensatory mutations are needed to balance the fitness loss induced by the I223R mutation. In contrast to the results obtained in MDCK cells, recNL/602-I223R replicated and transmitted as well as recNL/602 when evaluated in the ferret transmission model. Inoculated animals started to shed virus from the upper respiratory tract from day 1 p.i. onwards and transmission was detected in 4 out of 4 (recNL/602), or 2 out of 2 (recNL/602-I223R) naïve animals from day 2 onwards  influenza viruses with reduced susceptibility to the NAIs or mutations that may facilitate the emergence of circulating multi drug resistant influenza viruses.Mustella putorius furo), weighing 800–1000 g., were obtained from a commercial breeder.Animals were housed and experiments were conducted in strict compliance with European guidelines  and Dutch legislation . All animal experiments were approved by the independent animal experimentation ethical review committee ‘stichting DEC consult’ (Erasmus MC permit number EUR1821) and were performed under animal biosafety level 3+ conditions. Animal welfare was observed on a daily basis, and all animal handling was performed under light anesthesia using ketamine to minimize animal suffering. Influenza virus seronegative 6-month-old female ferrets (Madin-Darby Canine Kidney (MDCK) cells were obtained from American Type Culture Collection. MDCK-SIAT1 cells, constitutively expressing the human 2,6-sialyltransferase (SIAT1), were kindly provided by Professor H.D. Klenk, Philipps University Marburg Virus titers in nasal and throat swabs, homogenized tissue samples, or samples for replication curves were determined by endpoint titration in MDCK cells. MDCK cells were inoculated with 10-fold serial dilutions of each sample, washed 1 hour after inoculation with phosphate-buffered saline (PBS), and grown in 200 µl of infection medium, consisting of EMEM supplemented with 100 U/ml penicillin, 100 µg/ml streptomycin, 2 mM glutamine, 1.5 mg/ml sodium bicarbonate, 10 mM HEPES, nonessential amino acids, and 20 µg/ml trypsin (Lonza). Three days after inoculation, the supernatants of inoculated cell cultures were tested for agglutinating activity using turkey erythrocytes as an indicator of virus replication in the cells. Infectious-virus titers were calculated from 4 replicates by the method of Spearman-Kärber 50) per cell. One hour after inoculation, at time point 0, the cells were washed once with PBS, and fresh infection medium was added. The supernatants were sampled at 6, 12, 24, and 48 h post infection and the virus titers in these supernatants were determined by means of endpoint titration in MDCK cells.Multi-cycle replication curves were generated by inoculating MDCK or MDCK-SIAT1 cells at a multiplicity of infection (MOI) of 0.001 50% tissue culture infectious dose  were evaluated. The extent of consolidation was estimated by visual assessment. The lungs were weighed after the trachea was removed at its bifurcation. The relative lung weights were calculated as proportion of the body weight on day of death (lung weight/body weight x 100). Tissues (∼0.4 g) from the right lung were collected for determination of lung virus titers at day 4 and 7 p.i. The left lung and trachea were collected for histological examination, and immersed for fixation in 10% neutral-buffered formalin. All samples were sectioned in a standardized way  and routinely processed, paraffin embedded and cut to 4 µm hematoxylin and eosin (H&E) stained slides. The samples were histologically examined for the character and severity of influenza virus–associated lesions without knowledge of the identity of the animals. The extent of alveolitis/alveolar damage  and the severity of alveolitis, bronchi(oli)tis  and tracheitis  were scored per slide. The overall histology score for alveolitis is the sum of the scores for the extent and severity of the alveolitis (score 0 to 6).6 TCID50 of NL/602, NL/2631-R223, recNL/602 or recNL/602-I223R respectively, divided over both nostrils (2×250 µl). On day 1, 4 or 2 naïve female ferrets were individually placed in a transmission cage adjacent to an inoculated ferret, separated by two stainless steel grids. Negative pressure within the isolator cage is used to direct a modest (<0.1 m/sec) flow of high efficiency particulate air (HEPA) filtered air from the inoculated to the naive ferret. This experimental setup was designed to prevent direct contact or fomite transmission, but to allow airflow, thereby permitting transmission via aerosol or respiratory droplets. Nasal and throat swabs were collected on day 0, 1, 2, 3, 5 and p.i. from the inoculated ferrets and on days 0, 1, 2, 3, 5 and 7 p.e. from the naïve ferrets. Inoculated ferrets were euthanized at day 7 p.i., and naive ferrets that were found positive by reverse transcription polymerase chain reaction at day 7 p.e. were also euthanized The transmission experiments were done as described previously P-values less than 0.05 were considered significant.For the pathogenesis experiment, statistical analysis was done for each time point, until 4 days after inoculation . The Mann-Whitney-U test was used to compare weight losses and virus shedding of the six animals in both groups.


The responsiveness of the human central nervous system can change profoundly with exercise, injury, disuse, or disease. Changes occur at both cortical and spinal levels but in most cases excitability of the motoneuron pool must be assessed to localize accurately the site of adaptation. Hence, it is critical to understand, and employ correctly, the methods to test motoneuron excitability in humans. Several techniques exist and each has its advantages and disadvantages. This review examines the most common techniques that use evoked compound muscle action potentials to test the excitability of the motoneuron pool and describes the merits and limitations of each. The techniques discussed are the H-reflex, F-wave, tendon jerk, V-wave, cervicomedullary motor evoked potential (CMEP), and motor evoked potential (MEP). A number of limitations with these techniques are presented. The motoneuron was described as the “final common path” of the nervous system , do more or fewer motoneurons generate action potentials after the intervention? A bigger or smaller output would represent a net increase or decrease in motoneuron excitability, respectively. The change in excitability will reflect the balance of inhibition and facilitation but it is difficult to determine the mechanism in a given situation; e.g., a decrease in excitability could be due to either an increase in inhibition or a decrease in facilitation. Further, any change in “excitability” may not apply uniformly across the whole motoneuron pool.Regardless of the methodology or the intervention studied , the goal of testing human motoneuron excitability is the same: to know more about the status of the motoneuron pool. The aim of this review is to discuss briefly the existing methodologies which test motoneuron excitability by evoking compound muscle action potentials with particular focus on the benefits and limitations of each. Discussion of the H-reflex, F-wave, tendon jerk, V-wave, cervicomedullary motor evoked potential (CMEP) and motor evoked potential (MEP) is included below and summarized in Table Hmax. Comparison of H-reflex size to the maximal M-wave enables an estimate of the motoneuron pool involved in the H-reflex. For the soleus muscle in most subjects the percentage of involved motoneurons is ~50% . However, this list of muscles expands greatly if stimulation is given while the subject performs a weak voluntary contraction. This ensures some motoneurons are discharging repetitively, brings others closer to threshold and thus increases motoneuron excitability. Burke and colleagues  identifiThus, far, this section has described homonymous connections, that is from the stimulated Ia afferents to the motoneuron pool of the same muscle. However, there are also heteronymous links between Ia afferents of one nerve to the motoneuron pools of muscles supplied by a different nerve. For example, stimulation of the femoral nerve delivers monosynaptic Ia excitation not only to the quadriceps motoneurons but also to the soleus motoneuron pool  and the consequent difficulty in interpreting a change to the response raises questions about its usefulness as an independent measure of motoneuron excitability. During a maximal voluntary contraction, the size of the V-wave is proposed to indicate the level of descending voluntary drive conveyed by the motoneurons  will be activated by the stimulus, but there is strong evidence that the CMEP is primarily the result of motoneuron activation by a single descending volley elicited by excitation of corticospinal axons  if a given stimulus is delivered during a weak voluntary contraction.Corticospinal (transmastoid) stimulation has recently been paired with transcranial magnetic stimulation (TMS) as a novel means to test motoneuron responsiveness during ongoing muscle activity and fatigue without the confounding influence of unknown levels of descending voluntary drive , is the same change evident when other methods  are applied? We recently compared the effects of fatigue on CMEPs (TMEPs) and F-waves (Giesebrecht et al., The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cotton leaf curl Multan virus (CLCuMV), associated with the disease. Three hairpin RNAi constructs were produced containing either complementary-sense genes essential for replication/pathogenicity or non-coding regulatory sequences of CLCuMV. In transient assays all three RNAi constructs significantly reduced the replication of the virus in inoculated tissues. However, only one of the constructs, that targeting the overlapping genes involved in virus replication and pathogenicity (the replication-associated protein (Rep), the transcriptional activator protein and the replication enhancer protein) was able to prevent systemic movement of the virus, although the other constructs significantly reduced the levels of virus in systemic tissues. In the presence of CLCuMB, however, a small number of plants co-inoculated with even the most efficient RNAi construct developed symptoms of virus infection, suggesting that the betasatellite may compromise resistance. Further analyses, using Rep gene sequences of distinct begomoviruses expressed from a PVX vector as the target, are consistent with the idea that the success of the RNAi approach depends on sequence identity to the target virus. The results show that selection of both the target sequence, as well as the levels of identity between the construct and target sequence, determine the outcome of RNAi-based resistance against geminivirus complexes.Cotton leaf curl disease is caused by a geminivirus complex that involves multiple distinct begomoviruses and a disease-specific DNA satellite, cotton leaf curl Multan betasatellite (CLCuMB), which is essential to induce disease symptoms. Here we have investigated the use of RNA interference (RNAi) for obtaining resistance against one of the viruses,  Bemisia tabaci [Begomovirus, family Geminiviridae) that interact with a disease-specific DNA satellite, Cotton leaf curl Multan betasatellite (CLCuMB) [Cotton leaf curl is a serious disease of cotton and several other malvaceous plant species that is transmitted by the whitefly a tabaci . The disa tabaci ,3. Affeca tabaci ,4. Addit(CLCuMB) -7.Begomovirus which encompasses many of the agriculturally most destructive geminiviruses. All begomoviruses native to the New World have bipartite genomes, with components known as DNA A and DNA B. In the Old World, although a few bipartite begomoviruses have been identified, the majority of begomoviruses have genomes consisting of a single component, homologous to the DNA A component of the bipartite viruses [Tomato leaf curl virus and Tomato yellow leaf curl Sardinia virus (TYLCSV) [Cotton leaf curl Multan virus (CLCuMV), which require CLCuMB to efficiently infect cotton and induce bona fide disease symptoms [The geminiviruses are a rapidly emerging group of plant viruses, which can be attributed to various factors, including increased insect vector populations, the presence of alternate hosts and recombination among viruses ,9. Gemin viruses ,12. A sm(TYLCSV) ,14. Howe(TYLCSV) . The betsymptoms ,16. The symptoms -19. The symptoms -22. Theysymptoms .The genomes of monopartite begomoviruses encode six proteins with genes in the virion and complementary-sense separated by a non-coding intergenic (IR) region that contains control sequences as well as the virion-sense origin of replication . The genThe control of CLCuD, as is the case for most geminivirus diseases, is mainly based on control of the vector using insecticides and the cultivation of resistant crop varieties . ResistaRNAi is a ubiquitous phenomenon in eukaryotic organisms that is triggered by double-stranded RNA (dsRNA) that plays important roles in diverse biological processes. The key features of RNA silencing include the production of 21-25 nucleotide small interfering RNAs (siRNA) by enzymes known as Dicers and the formation of RNA-induced silencing complexes (RISCs) which contain Argonaute proteins that directly carry out gene silencing at the transcriptional or posttranscriptional levels . RNA silSeveral studies investigating the use of RNAi for obtaining resistance against geminiviruses have been reported, with different levels of success . HowThree hairpin gene constructs targeting CLCuMV were produced. These were based on sequences of the Rep and C4 genes (CLCRNAiRepC4/pFGC), the Rep, TrAP and REn genes (CLCRNAiRepTrAPREn/pFGC), and on the IR (CLCRNAiIR/pFGC). The primers for PCR-mediated amplification of the respective sequences were designed to the published sequence of CLCuMV (AJ496287) and are given in Table Agrobacterium tumefaciens strain GV3101 cultures harbouring the pFGC5941 constructs were co-infiltrated to young N. benthamiana plants with partial, direct-repeat constructs of CLCuMV and CLCuMB [d CLCuMB . Replicad CLCuMB .Potato virus X (PVX) vector constructs expressing the Rep gene sequences of heterologous begomoviruses. The production of PVX vector  construNicotiana benthamiana and their correlation with the levels of developmental miRNAs, submitted). A. tumefaciens strain GV3101 cultures harbouring the CLCRNAiRepTrAPREn/pFGC construct and the PVX vector constructs were co-infiltrated to N. benthamiana plants as described above.Comparison of phenotypes produced in response to transient expression of genes encoded by four distinct begomoviruses in N. benthamiana and induces mild symptoms in the absence of the betasatellite but induces severe, CLCuD-like symptoms when co-inoculated with CLCuMB [N. benthamiana plants. For each construct 10 plants were used for agroinfiltration. Additionally 15 plants of the same age, grown under the same conditions were inoculated with only CLCuMV. The experiment was repeated three times. All control plants inoculated with only CLCuMV developed typical leaf curl symptoms in systemic leaves, emerging subsequent to inoculation, within 16 days of inoculation. N. benthamiana plants co-infiltrated with RNAi constructs and CLCuMV did not exhibit symptoms of infection, even after 45 days  and open circular (oc) virus replication forms in the inoculated tissues Figure . This cobona fide disease symptoms in cotton. The satellite encodes an essential pathogenicity determinant that has suppressor of RNA silencing activity [N. benthamiana plants. The symptoms in control plants, inoculated with only CLCuMV and CLCuMB, appeared within 12-15 days after agroinfiltration. However, co-inoculation of CLCuMV and CLCuMB with CLCRNAiRepC4/pFGC or CLCRNAiIR/pFGC failed to inhibit virus replication and symptoms appeared in all plants within 12 days after agroinfiltration (results not shown). This shows that these two constructs were not effective in controlling CLCuMV in the presence of CLCuMB. In contrast, for co-inoculation with CLCRNAiRepTrAPREn/pFGC, only 2 out of 10 plants inoculated developed symptoms at 20 days after inoculation and the remainder of the plants remained asymptomatic, thus showing the silencing efficiency of the construct even in the presence of CLCuMB  or African cassava mosaic virus (ACMV); two begomoviruses only distantly related to CLCuMV whose Rep genes show 82% and 86% nucleotide sequence identity, respectively, to the Rep gene of CLCuMV.In investigate whether the CLCRNAiRepTrAPREn/pFGC RNAi construct containing sequences derived from CLCuMV would provide resistance against heterologous viruses, the construct was co-inoculated with a PVX vector expressing the Rep genes of N. benthamiana plants with PVX expressing the Rep genes of CLCuMV (PVX/CLCuMV-Rep) and ACMV (PVX/ACMV-Rep) induced severe necrosis of inoculated tissues  in many cases means that this is not an option. This is unfortunately the case for cotton in Pakistan, which is the main foreign exchange earner of the country, where an epidemic of CLCuD in the 1990 s led to massive losses  was rapiN. benthamiana by CLCuMV, only one (CLCRNAiRepTrAPREn/pFGC) was able prevent systemic infection. The reason for the superior performance of the CLCRNAiRepTrAPREn/pFGC construct is possibly that it spans three virus-encoded genes; Rep and REn, which are important for viral DNA replication [The study here was initiated to determine which sequences of the genome of a monopartite begomovirus are the most efficient at delivering RNAi-mediated resistance and what effects the presence of a betasatellite might have on the outcome of RNAi-mediated resistance. The results show that, although all three constructs spanning the complementary-sense sequences of the genome are able to prevent symptoms in lication ,39, and lication ,41, can lication , may coulication , conditilication , interaclication  and may lication , possibllication ,48.m gene sequences yielded resistance [The results obtained here thus show that not all virus derived sequences will deliver efficient silencing mediated resistance to begomoviruses. A similar conclusion was reached for RNAi-mediated resistance to TSWV where only expression of either the N or NSsistance . Our resAlthough the CLCRNAiRepTrAPREn/pFGC construct was able to prevent symptomatic infection of CLCuMV, in the presence of CLCuMB a significant proportion of plants exhibited symptoms. This suggests that the betasatellite may compromise the resistance. Betasatellites encode a single protein, βC1, which is a dominant pathogenicity (symptom) determinant which also has suppressor of RNAi activity ,36,50. TTomato spotted wilt virus (TSWV) provide protection against the homologous virus but not against the related viruses Groundnut ring spot virus and Tomato chlorotic spot virus, despite the fact that they share 78 to 80% nucleotide sequence identity across their genomes [A major drawback of RNA-mediated resistance, being sequence-based, is its high degree of specificity. Thus the resistance will only be functional against viruses with very similar sequences across the targeted region. For example, transgenically expressed sequences of the tospovirus  genomes . This isThe ultimate aim of this work is to develop a broad spectrum virus-resistance that can be deployed in a number of crops but particularly in cotton. Efforts are now underway to assess whether the results with the CLCRNAiRepTrAPREn/pFGC construct results can be reproduced in transgenic plants and particularly whether the betasatellite is going to be a problem for RNAi-mediated resistance to begomoviruses.The authors declare that they have no competing interests.MM and MH conducted all the experiments and wrote the paper. SM and RWB edited the paper and coordinated the research efforts. All authors have read and approved the manuscript.