Truee. The resulting spectrum covers an EIceCube is a neutrino observatory deployed in the glacial ice at the geographic South Pole. The  The neutrino flux, which can be observed with instruments such as IceCube, has its origin both in cosmic ray air showers (atmospheric neutrinos) –3 and exThis paper presents a measurement of the muon neutrino energy spectrum with IceCube during its deployment phase in the 79-string configuration (IC79). The spectrum was obtained from a highly pure sample of neutrino candidates by means of regularized unfolding.The IceCube detector, located at the geographic South Pole, is a neutrino observatory with an instrumented volume of ent}1km3 . It consEach DOM consists of a glass sphere of n system . The airn system .track-like events, which originate from muons propagating through the detector. These muons are either produced in charged current (CC) neutrino-nucleon interactions or in cosmic ray air showers. The second most frequent signature are cascade-like events, originating from CC interactions of Cascade-like events further originate from neutral-current (NC) interactions of neutrinos of all flavors within the instrumented volume.As neutrinos cannot be observed directly, they are detected via secondary particles produced in the interactions of neutrinos with nuclei in the ice or the bedrock. These secondary particles induce the emission of Cherenkov light, which is recorded by the DOMs. The majority of the events observed with IceCube are The atmospheric muon neutrino flux is expected to consist of two components distinguished by the lifetime of their hadronic parent particles. Conventional atmospheric muon neutrinos originate from the decay of charged pions and kaons in cosmic ray air showers. Due to their relatively long lifetime  . From thAs in , the RF To maintain computational feasibility, not all available variables can be used as input to the Random Forest. Following the approach in , the inpented in .k-th variable K of L of m variables is built up. A more detailed description of the approach can be found in [MRMR builds up a variable set in a sequential way. It starts with the variable with the highest correlation to the class variable. In the succeeding iterations, the fficient . This wafound in , 27.In this analysis, j-th tree assigns a label i-th event. Thus, the final classification is achieved by averaging the output of all decision trees in the forest:k randomly chosen variables are used to find the best cut. Before applying the RF to experimental data, the RF is applied to simulated events to evaluate the performance of the classification.A Random Forest is an ensemble of decision trees. It is trained with simulated events to build a model that can be applied to unclassified events. In the application the After the application of the forest, the vast majority of the simulated background muons (more than 99.9%) is found to be scored with a confidence echnique .In the bootstrapping, a total of 200 Random Forest models were trained, each built on a randomly chosen sample with 50% of the size of the full sample. Using this technique, each event is scored on average 100 times. By normalizing the resulting confidence distribution for each event, the approximation of the confidence distribution is improved by taking the variance of The cut on entclass1pt{minimaThis cut yields a total of 66,885 neutrino candidates in 319.6 days of detector livetime  is the distribution of a measured variable y and f(x) is the distribution of the sought-after variable x. A is generally referred to as the response function and gives the probability to measure a certain y given a specific x. The response function includes the physics of neutrino interactions, as well as the propagation of muons through the ice and all smearing effects introduced by the detector. The term b(y) is the distribution of y for any observed background. Due to the high purity (see Sect. b(y) is negligible in this analysis.This type of problem is generally referred to as an inverse problem. It is described by the Fredholm integral equation of the first kind:entclass1pt{minimaTruee [Several algorithms are available to obtain a solution to Eq. . In the Truee , which iTruee , was useTruee allows for the use of up to three input variables and generates a binned distribution f(x) and accordingly Truee, this leads to an equation of the form:x and y. Therefore, regularization is required to avoid unstable solutions.In this analysis, three variables were used as input to the unfolding. This is a trade-off between gaining information by using more input variables and the required number of simulated events, which grows drastically with the number of input variables. In this analysis, the reconstructed muon energy foldings .truncated meanThe energy proxy the muon . A diffecurately . \documeTruee and its implemented validation methods can be found in [The correlation of the individual variables with the energy of the incoming neutrino is depicted in Figs. found in , while ifound in .Fig. 4CoIn spectral measurements with IceCube, systematic uncertainties are dominated by two sources: The first source is associated with the amount of light detected in an event, which is affected by the light detection efficiency of the optical modules and by the muon interaction cross-sections . These two uncertainties cannot be disentangled on experimental data and are therefore combined in a single value associated with the efficiency of the DOMs. The common calibration error on the photomultiplier efficiency is 7.7% , whereasAs the unfolding result is based on simulated events which are used as input to TRUEE, the specific choices  used in the generation of these events affect the outcome of the spectral measurement. To estimate the impact of changes with respect to the so-called baseline simulation, systematic uncertainties were derived using a bootstrapping procedure. Within this bootstrapping, a number of simulated events corresponding to the number of neutrino candidates on experimental data was drawn at random from a systematic data set.In total, five systematic sets were available for the analysis at hand. In each of these sets, one property has been varied with respect to the baseline simulation. In one set, the efficiency for all DOMs was increased by 10%, while in a second, the efficiency for all DOMs was decreased by 10%. To account for uncertainties in the description of the ice, three sets of simulated events with different ice models were generated. The first set was produced with a scattering length increased by 10% with respect to the baseline simulation, whereas the second one was produced with an absorption length also increased by 10% with respect to the baseline simulation. In a third set, the effects of scattering and absorption were combined by simultaneously decreasing scattering and absorption length by 7.1%.Based on these simulated events the energy spectrum was obtained, while using the baseline simulation for the extraction of the response matrix. In a next step, the discrepancy between the unfolding result and the true distribution was computed. This procedure was repeated 1000 times on any of the five systematic data sets and yielded the contribution of the individual sources of uncertainty. The total systematic uncertainty was computed as the sum of squares of the individual contributions. Compared to the statistical uncertainty, the systematic uncertainty is found to be large, except for the last two bins, where both uncertainties are of approximately the same size.The unfolded flux of atmospheric muon neutrinos, including statistical and systematic uncertainties, is summarized in Table A comparison to theoretical predictions for the atmospheric neutrino flux is shown in Fig. 06 model , as well06 model  and QGSJ06 model  and is fThe largest deviation of RS model –46 are uDue to the relatively small number of events observed in the four highest energy bins, the observed correlation between those bins is rather large, especially in the case of neighbouring bins. This prohibits an accurate estimation of the spectral index of the diffuse flux of high energy astrophysical neutrinos, as well as its normalization. Furthermore, in such an estimate, two observables would be estimated on the basis of only four data points.A comparison of the unfolded spectrum with theoretical predictions for a purely atmospheric flux shows good compatibility up to energies of see Fig. . Due to mentclass2pt{minimsee Fig. .For energies above Figure ino flux , 48. We ino flux  (\docume.13) and  % at an event rate of % at an eFirstly, the quality criteria for tracks were chosen to be less exclusive, which results in a larger number of neutrino candidates available for a further selection with machine learning techniques. This mainly results from the more symmetric shape of the IC-79 detector, when compared to IC-59. Secondly, using a bootstrapping technique, the probability density function of contaminating muon events was estimated more accurately. Therefore, the final cut on the confidence distribution Fig.  was chosThe distribution of the reconstructed muon energy in the final sample Fig.  shows anThe excess at high energies was confirmed in the subsequent unfolding. The unfolded spectrum shows good compatibility with the atmospheric predictions up to neutrino energies of see Fig. . For higsee Fig. .This analysis presents the first observation of an astrophysical muon neutrino flux in a model independent spectral measurement.


We also propose a general probabilistic interpretation of genetic assimilation and specialize it to the Boltzmann ensemble of RNA sequences.The neutral theory of Motoo Kimura stipulates that evolution is mostly driven by neutral mutations. However adaptive pressure eventually leads to changes in phenotype that involve non-neutral mutations. The relation between neutrality and adaptation has been studied in the context of RNA before and here we further study transitional mutations in the context of degenerate (plastic) RNA sequences and genetic assimilation. We propose quasineutral mutations, i.e. mutations which preserve an element of the phenotype We show that degenerate sequences i.e. sequences with more than one structure at the MFE level have the highest evolvability among all sequences and are central to evolutionary innovation. Degenerate sequences also tend to cluster together in the sequence space. The selective pressure in an evolutionary simulation causes the population to move towards regions with more degenerate sequences, i.e. regions at the intersection of different neutral networks, and this causes the number of such sequences to increase well beyond the average percentage of degenerate sequences in the sequence space. We also observe that evolution by quasineutral mutations tends to conserve the number of base pairs in structures and thereby maintains structural integrity even in the presence of pressure to the contrary.We conclude that degenerate RNA sequences play a major role in evolutionary adaptation. An RNA molecule is a linear polymer in the nucleotides Adenine, Cytosine, Guanine, Uracil. RNA molecules play various vital roles in the cell ranging from working as messengers mediating between genes and the protein that the genes encode (coding RNA) to functioning as enzymes (non-coding RNA) , 2. Moresecondary structure which is given by the pairs of bases that form hydrogen bonds with each other. The most widely used method for predicting RNA secondary structure in silico is the thermodynamic model 41] it isTo remedy this problem we use a fitness function which is given by the maximum of the fitnesses of the MFE structures of a sequence. Using the maximum gives advantage to the phenotype with higher fitness and accelerates adaptation by means of genetic assimilation which in this case is moving through the degenerate boundary of two neutral networks. As opposed to  our simuWe also observe that even if mutations are restricted to being quasineutral, i.e. restricting evolution only to genetic assimilation through degenerate sequences, the population still reaches its target.In all our simulations the target is the Phenylalanine tRNA and fitness of a genotype is given by the secondary structure distance of its folded structure(s) to the target. MFE-set simulations take all the MFE structures of a sequence into account for measuring distance whereas single-MFE simulations use only the one picked by the ViennaRNA package, as in , see “Me6 steps to finish whereas MFE-set runs reached the target after the average of 5.7×106 steps. The standard deviations for the two quantities were 5.5×106 and 3.8×106 respectively with the t-test p-value being 0.09. This difference can be explained as follows. Since the single-MFE fitness function neglects all but one of the MFE structures of degenerate sequences, the single-MFE fitness of a sequence is always less than or equal its MFE-set fitness, see the inequality displayed in  3.7% were quasineutral. Paired student t-test for the two quantities yielded a p-value of 0.06. This suggests that quasineutral mutations play a larger role in adaptation compared to their share of all mutations.Only 2.6The spikes in the distance of the dominant structure to the target in the MFE-set run are the result of degenerate sequences dominating the population and hence two or more structures, to which these sequences fold, competing for dominance. This of course does not happen in the single-MFE runs.In both types of simulation, the drops in the distance to the target coincide with drops in the number of sequences folding to the dominant structure. This happens when a beneficial mutation starts becoming established and the population begins to migrate to a new neutral network.In Fig. Thus in almost all simulations of either type, degenerate sequences dominate the population at least at one point during the run.In order to understand this phenomenon better we ran 40 runs of each type. The highest number of degenerate sequences during each MFE-set run was at least 940. In the single-MFE runs this number was at least 700 in all but four of the runs. non-adaptive runs. In such a simulation all sequences have the same fitness, see Methods. In each of the non-adaptive runs the mean number of degenerate sequences was close to 210 which agrees with the percentage of the degenerate sequences in the sequence space of length 73.We next show that the surge in the number of degenerate sequences is a meaningful signal instead of random noise. To this end we ran simulations without a fitness function, called p-value of less than 2.2×10−16 with the averages for the two types of run being 912 ± 146 and 412 ± 64 respectively.  This suggests that it is the selection pressure that forces the population to move towards areas of the sequence space with higher concentration of degenerate sequences.The difference is seen when comparing the highest number of degenerate sequences during a run, between single-MFE and non-adaptive runs. The t-test yielded a We next investigate the reason behind the surge in the number of degenerate sequences in simulations. We show that degenerate sequences contribute to transitions more than their share of the population.To this end we studied the number of dominant structures with degenerate parents. Note that not all beneficial mutations result in a transition, since the result of such a mutation may die before becoming dominant. We thus recorded the degeneracy (the number of MFE structures) of the parent of each dominant structure. Note that a structure may be born and die several times before becoming dominant. Thus by the parent of such a structure we mean the last sequence that gave birth to it (i.e. introduced it to the current pool of structures) before it becoming dominant.%±13% and 56%±18% respectively. This means that, even in single-MFE runs degenerate sequences contribute to shape innovation (transitions) more than their 21% share of the population. The difference is much more pronounced for MFE-set simulations as they take advantage of the full MFE-set of degenerate sequences.In Fig. k to be a neighbor of a sequence of degeneracy l. This plot is obtained by folding the one-point mutants of 105 random sequences of length 73 (the same length as Phe-tRNA). We can see that it is most likely for a sequence to be a neighbor of a sequence of the same degeneracy, except for sequences of degeneracy 5,7,9,… which are most likely to have non-degenerate neighbors. This implies that degenerate sequences cluster together and possibly form networks in the sequence space. As a consequence once the number of degenerate sequences in the population starts to increase, it teds to accelerate.In the last subsection we demonstrated that degenerate sequences contribute to transitions more and this was possibly the reason for the surge in their numbers in the population. In this section we verify this by studying their neutrality and evolvability as well as their spread in the sequence space. In Fig. What causes the number of degenerate sequences to start to increase? In Figs. p=0.001 and p=0.01. Because of the quasineutrality condition, in order to pass from one neutral network to another, the population has to go through the degenerate boundary between the two neutral networks. Therefore the number of degenerate sequences spikes before each transition.In quasineutral simulations only quasineutral mutations are allowed during replication, see “Note that most quasineutral mutations are actually neutral (92% for length 100 ) and thuWe analyze the genotypic and phenotypic diversity of the elements of the population. There are different ways to quantify phenotypic diversity. One such quantification is to count the number of structures to which the sequences in the population fold. However degenerate sequences fold into more than one structure, therefore it is more suitable to consider the number of neutral networks in the interior of which at least one sequence lies. This is equivalent to counting the number of structures to which the non-degenerate sequences in the population fold. For the free run depicted in Fig. As a measure of the genotypic diversity of the population we consider the number of sequences folding to the dominant structure counted without multiplicity.  For the quasineutral run the mean of this number over time was 41.2 while for the free MFE-set run it was 30.2. This may be attributed to the fact that in quasineutral runs the sequences can only exit a neutral network through the minority degenerate sequences and thus they evolve neutrally inside a neutral network more than in free simulations.A question remains at to what extent the properties of quasineutral and free runs depend on the fitness function or in other words on the selective pressure. In , Fontanaquasineutral structure network. We also introduce probabilities ρ and ρ∗ that inform us how likely it is for an inverse fold of S to mutate to a sequence folding to S′ freely and quasineutrally, respectively.In the “The consequences of the absence of selective pressure can be studied via simulations which have no specific target and therefore all sequences have the same fitness. We refer to such simulations as “non-adaptive runs”, see “43 elements and thus it is unexpected for a simulation starting from a random initial population to evolve into a clustered population. However it follows from general properties of stochastic processes [One major difference between the non-adaptive free and quasineutral runs is the size of the pool of the dominant structure. Note that the sequence space of length 73 has more than 8.9×10rocesses  that thefS  to be zero if s[i] is a dot, s[ i] is the start of an arc that ends at l and s[ i] is the end of an arc that started at location l. One associates a vector to s whose k’th entry is given by Secondary structure metrics are used for measuring the dissimilarity of structures of the same length. Let distance  between dWM-distance between the open chain and any structure with only one arc is the same. Tree edit distance is widely used for comparing secondary structures such as in the simulations done by Fontana and Schuster. However it is slow to compute as one needs to find the shortest edit path connecting the trees associated to the two structures. According to [dWM and the tree-edit distance exhibit similar distributions of  distances between random structures. Therefore dWM represents a suitable alternative to the tree-edit distance when comparing a large number of secondary structures.In contrast to the Motzkin distance, the rding to  dWM and σ is degenerate if it has more than one structure at the minimum free energy level. The set of MFE-structures is called the MFE-set of the sequence and denoted by MFE(σ). The cardinality of this set is the degeneracy of the sequence. In the ViennaRNA package [σ. Two sequences are called quasineutral if their MFE-sets have non-empty intersection, or in other words if they share a phenotype. They are called neutral if their MFE-sets are identical. A quasineutral mutation of σ is a sequence σ′ of Hamming distance one to σ which is quasineutral to σ. For example two sequences with MFE-sets {S} and {S,S′} are quasineutral but not neutral.Following , an RNA  package , one eleS in their MFE-sets the neutral network of S and denote it by NS. We refer to the set of non-degenerate and degenerate sequences in NS as the interior and the boundary of the neutral networkNS belong to (the boundaries of) other neutral networks as well.We call the set of all sequences which have a structure neutrality  of an RNA sequence σ is the percentage of its one point mutants that fold into the same structure as σ. Since in this paper we take the multiplicity of MFE-structures into account, as in [with respect to a given structure S. This means that both σ and its mutant have S in their respective MFE-sets. The evolvability of σ is on the other hand is the number of all structures two which the one point mutants of σ fold. These structures are counted without multiplicity i.e. a structure appearing in the MFE-sets of several one point mutants is counted only once.The t, as in  we consisingle-MFE evolvability i.e. instead of all the elements of the MFE-set, only the one chosen by the ViennaRNA package as the MFE-structure is counted for each sequence.For the purpose of comparison we also consider the We conducted two different types of population simulations: free simulations similar to those of  in whichIn an experimental setting the fitness of an RNA genotype may be assessed by its binding capacity to a target site or, in the case of RNA viruses, its adaptability to a new host. Here however, as in , our goaσ of the population is defined to be the reciprocal of the secondary structure distance d of its MFE-structure to the target structure. One has a choice of whether to take all the structures in the MFE set MFE(σ) of a sequence σ into account for measuring this distance or not. In the former case fitness of a sequence is defined to be the maximum fitness of its MFE-structures: For the free runs two different types of fitness function were considered: the single-MFE fitness and the MFE-set fitness functions. In both cases the fitness of a member MFE-set fitness function. The reason for using the maximum is that a degenerate sequence lies at the intersection of the neutral networks of all the structures in its MFE-set and so is able to mutate to a sequence in each one of them.V(σ) to measure fitness: We refer to this fitness function as the single-MFE fitness function. This method is equivalent to choosing an MFE structure from the MFE set at random and measuring its distance to the target. By construction for each sequence σ holds We call this function the σ i.e. having only one structure at the MFE level. This condition is however not necessary. As we will see fS results in more efficient simulations.A sufficient condition for equality is non-degeneracy of σ of the population is chosen at random to replicate. The members are always ranked by their fitness and the likelihood to be chosen is linearly proportional to rank. As in [p=10−3, meaning that during replication, each base is mutated with the probability of 10−3. A mutation from a sequence σ to σ′ is called beneficial if σ′ is more fit than σ.At each step a member k. As in  we choseFS only.In free runs no condition is imposed on this (possibly mutated) copy. However in the quasineutral simulations we only want the quasineutral mutations to contribute to evolution therefore, after a sequence is chosen for replication, it is replicated until a quasineutral mutant is obtained. Only this quasineutral mutant is added to the pool. For quasineutral runs we use the MFE-set fitness function fM-simulations all the MFE-structures of each sequence contribute to the pool whereas in the fV runs only the Vienna MFE-structure contributes. The dominant structure is the one into which the majority of the population folds. Note that in fM-runs, degenerate sequences can cause the majority of the population to fold to more than one structure at once. A transition is a step at which the dominant structure changes to a structure which has not been dominant before.By the number of steps we mean the number of replications in the simulation. In the quasineutral runs the (rejected) non-quasineutral mutations do not count towards the number of steps. After each replication a random member of the population (regardless of its fitness) is removed to keep the population constant. At any given step the structure pool of the population is the set of all structures (counted without multiplicity) to which the population folds. In the Simulations terminate as soon as the target structure becomes the dominant structure.To see whether the increase in the number of degenerate sequences is caused by adaptive pressure or not, we ran non-adaptive simulations. The free or quasineutral non-adaptive simulations are the same as the free and quasineutral simulations discussed above except that in the former all members of the population have the same fitness. This means that a population of RNA sequences mutates freely (in free simulations) or quasineutrally  and choosing for replication is completely random. The population is kept constant as in targeted simulations.S one considers a sample Σ of inverse folds of S and list all the structures to which the one point mutants of the elements of Σ fold. If S′ is a structure obtained in this way then they consider the weight σ∈Σ that have at least one one-point mutant that folds into S′.In  Fontana S they then sort these structures S′ according to their weights For each n drops very fast and they choose a threshold ε and consider a directed edge from S to each free structure network.S to S′ is continuous if the weight of the edge from S to S′ is large compared to the other edges emanating from S. In practice in [S2 existed in the pool when S1 was dominant then it is likely that the mutants of the sequences folding to S1 fold to S2 and vice versa.They use these likelihoods to decide whether a transition in a simulation is continuous or discontinuous: a transition from ctice in  continuicharacteristic set) of the Phenylalanine tRNA in this graph. Moreover it is observed in [S and S′, give us the likelihood that a free simulation would move from being dominated by S to being dominated by S′.In  the autherved in  that theS which have at least one quasineutral one-point mutant which folds into S′. One then uses the same method as above to discard structures S′ of low weight and build a directed graph which we call quasineutral structure network.In this paper we consider a variant of this graph in which the edge weights, denoted by S,S′, Note that since only a fraction of one-point mutants of a sequence are quasineutral (on average 8% for length 100 ), for a ρ=0. In contrast to ρ computes the probability of entering the neutral network of S′, by means of one-point mutations, when leaving the neutral network of S.We set ρ∗ from ρ and ρ∗ to see how the likelihood of passing from S to S′ by means of a single free vs quasineutral mutation are different. Note that since most quasineutral mutations are neutral (about 92% for sequences of length 100), in general We obtain We showed that degenerate sequences have the highest evolvability among all sequences and are central to evolutionary innovation. This is important given that the multiplicity of folded structures of RNA sequences is generally ignored.The selective pressure in an evolutionary simulation causes the population to move towards regions with more degenerate sequences and this causes the number of such sequences to increase well beyond the average percentage of degenerate sequences in the sequence space. We conclude that degenerate RNA sequences play a major role in evolutionary adaptation and further experimental research is needed to better understand this role.


In this article we propose a novel mathematical description of biomass growth that combines poroelastic theory of mixtures and cellular population models. The formulation, potentially applicable to general mechanobiological processes, is here used to study the engineered cultivation in bioreactors of articular chondrocytes, a process of Regenerative Medicine characterized by a complex interaction among spatial scales (from nanometers to centimeters), temporal scales (from seconds to weeks) and biophysical phenomena . The principal contribution of this research is the inclusion of the concept of cellular “force isotropy” as one of the main factors influencing cellular activity. In this description, the induced cytoskeletal tensional states trigger signalling transduction cascades regulating functional cell behavior. This mechanims is modeled by a parameter which estimates the influence of local force isotropy by the norm of the deviatoric part of the total stress tensor. According to the value of the estimator, isotropic mechanical conditions are assumed to be the promoting factor of extracellular matrix production whereas anisotropic conditions are assumed to promote cell proliferation. The resulting mathematical formulation is a coupled system of nonlinear partial differential equations comprising: conservation laws for mass and linear momentum of the growing biomass; advection–diffusion–reaction laws for nutrient (oxygen) transport, delivery and consumption; and kinetic laws for cellular population dynamics. To develop a reliable computational tool for the simulation of the engineered tissue growth process the nonlinear differential problem is numerically solved by: (1) temporal semidiscretization; (2) linearization via a fixed-point map; and (3) finite element spatial approximation. The biophysical accuracy of the mechanobiological model is assessed in the analysis of a simplified 1D geometrical setting. Simulation results show that: (1) isotropic/anisotropic conditions are strongly influenced by both maximum cell specific growth rate and mechanical boundary conditions enforced at the interface between the biomass construct and the interstitial fluid; (2) experimentally measured features of cultivated articular chondrocytes, such as the early proliferation phase and the delayed extracellular matrix production, are well described by the computed spatial and temporal evolutions of cellular populations. In thiseview in –32. In teview in , 45, 46.eview in  and refeIn order to work in a continuum mechanics framework, we propose an extension of the above concepts to aggregates of cells. Figure To translate the mechanobiological description of the processes illustrated in Fig. The paper is organized as follows: in Sect. the growing biomass is treated as a mixture composed by a multiphase solid mass and an interstitial fluid, the latter representing a fraction of the order of 65–80% in mass of the total biomass. The multiphase solid consists of ACCs and of ECM. ACCs are pooled in different populations according to their life cycle status , as in the works of Sengers  and Ducrthe poroelasticity theory is used to model the interaction of deformation and fluid flow in the fluid-saturated porous, elastic solid , 22;the kinematics of the solid phase of the mixture is based on an infinitesimal–deformation approach, including the effect on the stress field of biological growth, according to the formulation proposed by Klisch and co–authors –33;the mass conservation balance for each single constituent and for the mixture are written according to the formulation introduced by Lemon and co-authors , 36 and the mass exchange terms, including the rate of switch of cells from a population to the other, are tuned according to the nutrient level, the latter being itself an unknown of the problem, and to the stress state locally experienced by the mixture, which may drive cells into a certain functional behavior pool.In this section we develop a mathematical model based on the representation of the ensemble of the growing cartilaginous biomass by the mixture theory. In this framework, equations are postulated for the balance of mass and momentum for each constituent and then for the entire mixture according to the following ideas:In the following, we use the term “phase” when we refer to the solid or to the fluid part of the mixture, while the term “component” is used to refer to any of the constituents of the solid phase (cell populations and ECM). When it is not necessary to distinguish between phase and component, we simply use the term “species”. The meaning of the subscripts used throughout the article is as follows: s=solid phase, fl=fluid phase, cells=cell component of the solid phase, ECM= extracellular component of the solid phase.t denote the space and time variables, respectively. We use the convention that the dependence of all variables and model parameters on t is left understood except otherwise stated.We let t due to cell proliferation and matrix deposition. This is the precise sense of the concept of “growing mixture”. From now on, we denote by The geometrical configuration of the mixture is identified by the open bounded set d-dimensional volume. Then, for each component i-th component in the REV and the volume of the REV itself. We also let Referring to Fig. }Vx (see ) and denAccording to the biochemical hypothesis a), we consider three ACC populations: proliferating, ECM secreting state and quiescent state, denoted by the letters The following assumptions on the mixture are also considered.Fully saturated mixture) The mixture is fully saturated, i.e. All species constituting the growing mixture have the same (constant) mass density whole mixture is incompressible  The mixture is closed, this meaning that the system does not exchange mass with external mass sources or sinks \documentt at point The quantity 2.2.4 of , the groin vitro cartilage tissue growth. We assume the relative velocity in Eq.  geometrical configuration. This is a first step toward the simulation of a realistic structure such as the 3D scaffolded bioreactor used in the experimental analysis discussed in . The 1D-The 1D mechanobiological model consists of the following PDE subsystems:Poroelastic IV-BVP for biomass for given d in Eq.  (right) Mass balance IV-BVP for nutrient concentration for given u, V and Mass conservation IV-BVP for cellular populations for given u, p and c, find the volume fractionsx of the mixture and at any time t. Namely, we define the parameter r asx, t). The principal components of x, t) has center x, t)In the 1D configuration, the total stress tensor gime. In , 58 it iSolving in closed form the mechanobiological model proposed in this article is a very difficult task because of the strong nonlinear nature of the problem. Therefore, in this section we illustrate the approximation methods that are used to solve numerically the equation system in the 1D setting of Fig. m of the inner loop we solve in sequence three linear PDE system blocks. For the convenience of the reader, for each substep of the inner loop ,we indicate in Fig. given inputs whereas the updated values of the dependent variables that are returned as outputs of the block are indicated on the right of the downward arrow that exits out the block. We notice that as soon as a newly updated variable is available such variable is immediately plugged into the successive block as input variable. For this reason the algorithm of Fig. Prior to discretization, we need to reduce the solution of the whole coupled system to the solution of a sequence of linearized equations of simpler form. For this purpose we set Two remarks are in order about the above described solution map. The first remark concerns the linear poroelastic system. The weak formulation of this problem leads to solving a saddle-point problem in block symmetric form to which the abstract analysis of , Chapt. ple (see ) allows h being the discretization parameter , rather, it is the solution of an elliptic Darcy problem . In theved . In. In\docu-1 as in . These v-1 as in , 58, 63.A of cell density at the beginning of the culture process (A: (IC1)(IC2) The above values of The first investigated question is the effect of the input model parameter amount The second investigated question is the effect of the input model parameter cellular growth rate mentclass2pt{minim used in  in such The third investigated question is the effect of the input model parameter maximum value of the external oxygen concentration mentclass2pt{minimr, we define the following  parameter For a synthetic representation of the isotropy indicator ng using . Simulatmentclass2pt{minimThe illustrated numerical results indicate that the in vitro cell cultivation process is strongly sensitive to variations of (1) the initial seeding density of cells, (2) the value of the maximum growth rate and (3) the mechanical boundary conditions. In particular, the amount of seeded cells turns out to be determinant for cell responsiveness: initial cell density should be high enough to ensure optimal conditions for proliferation, but not so high that grow factors are rapidly depleted from the medium and the contact inhibition phenomenon prevents the formation of new colonies see Fig. . To furtre Figs.  top. On re Figs.  bottom. see Fig.  top. In ugh Fig.  bottom.The behavior of the solid mixture components is in excellent agreement with the experimental trends obtained by cultivation of engineered tissues in bioreactors. In particular, the temporal evolution of construct cellularity and ECM content, especially for an under-threshold value of l papers , 48, 72.The characterization of the (an)isotropicity of the biomass intrinsic stress state through the equivalent parameter sue (see ). Model In this section we address a critical discussion of the more significant outcomes of the simulations of the model illustrated in Sect. In the present article we propose a novel mathematical formulation based on the continuum assumption to describe the biomechanical sensitivity of articular chondrocytes. The natural application of our model is Tissue Engineering, a continuously growing discipline within the wider area of Regenerative Medicine, in which the control of cell response to multi-factorial stimuli is of utmost importance to obtain products suitable to clinical practice. However, it is worth noting that the proposed scheme may be used as well to describe more general settings in Cellular Biology, for example, the expansion of staminal cells.The principal novelty of our contribution is the development of a model based on the use of Partial Differential Equations (PDEs) that incorporates the concept of “force isotropy” on the cell within the general and well established framework of poroelastic theory of mixtures and of cell population models. Specifically, the model translates into a simplified mathematical formalism, based on the use of suitably parametrized Heaviside functions, how the induced cytoskeletal tensional states trigger signalling transduction cascades regulating functional cell behavior, for example, the traslocation of specific transcription factors in the nucleus. According to the concept of force isotropy, it turns out that if cell adhesion-mediated traction forces have approximately the same strength over the cell surface, then the cell nucleus tends to maintain a roundish morphology, otherwise the cell nucleus tends to elongate. In the first case, the cell tensile condition is defined as “isotropic cytoskeletal tension” whereas in the second case the cell tensile condition is defined as “anisotropic cytoskeletal tension”.Having defined the cytoskeletal stress characterization at the single cellular level, the next step of our approach is to build upon the concept of continuum-based approach to extend the above described description to the local stress tensor associated with the biological construct to mathematically represent the isotropic or anisotropic cell adhesion state. To this purpose, we generalize in a natural manner the previous definitions prescribing that if the anisotropic part of the local stress tensor is lower than a fixed threshold then the local stress state of the system is isotropic otherwise the local stress state of the system is anisotropic.The final step of our model construction is to incorporate the above illustrated mechanobiological scheme within the setting of the theory of poroelasticity of a mixture composed by a solid and a multi-component fluid phases. The mixture represents the cellular construct in which several different cellular populations are well-mixed and oxygen delivery and consumption is taken into account to regulate in a dynamical manner the progressive fate of the evolving (macroscopic) tissue. The overall mathematical formulation consists of a system of conservation laws (mass and linear momentum) for the phases and components of the mixture that includes stress state and oxygen tension as main determinants of cellular culture evolution.A thorough investigation of the PDE system is critically performed in a simplified 1D setting to allow an easy preliminary validation of the formulation. Extensive simulation tests outline a generally sound response of the computational model with respect to biophysical conjectures. In particular, numerical results indicate that the in vitro cell cultivation process is strongly sensitive to variations of (1) the initial seeding density of cells, (2) the value of the maximum growth rate and (3) the mechanical boundary conditions.A stability analysis of the homogeneous steady states of the dynamical system that describes the conservation of mass of the solid mixture components. Such a study will allow us to characterize the admissible range of values of model constitutive parameters that ensures the biophysical consistency of the proposed mathematical representation, in the same spirit as in  and 37]37].The introduction of a visco-elastic component in the constitutive law for the total stress (as recently done in ). This eThe inclusion of other mixture constituents, such as proteoglycan and collagen as done in . This exin silico scheme to simulate different levels of isotropy/anisotropy that otherwise should be reproduced in vitro at the price of complex engineering strategies, as described in [in vivo, so that the behavior predicted by 3D computations could be directly compared with the in vivo cellular processes. Of course, passing to a 1D implementation to a fully 3D simulation tool requires to face and solve, at least, four computational challenges. The first challenge is the need of a flexible tool for the generation of an accurate geometrical description of the structure to be simulated. The preferable choice is to use tetrahedral elements and to this purpose a very good 3D mesh generator is the open-source program gmesh. The second challenge is the selection of a stable and accurate time-advancing discretization method. The obvious choice is to continue to emply the Backward Euler scheme. If a more accurate method is in order, the choice might fall on the second-order Trapezoidal or TR-BDF2 methods ribed in  to mimicsis, see , Chaptersis, see  is a versis, see , 40, 61.Below, we mention several future steps that we intend to take in the prosecution of this promising research activity.


The use of natural sustainable resources such as wood in green industrial processes is currently limited by our poor understanding of the impact of moisture on their thermodynamic and mechanical behaviors. Here, a molecular dynamics approach is used to investigate the physical response of a typical hydrophilic biopolymer in softwood hemicellulose—xylan—when subjected to moisture adsorption. A unique moisture-induced crossover is found in the thermodynamic and mechanical properties of this prototypical biopolymer with many quantities such as the heat of adsorption, heat capacity, thermal expansion and elastic moduli exhibiting a marked evolution change for a moisture content about 30 wt%. By investigating the microscopic structure of the confined water molecules and the polymer–water interfacial area, the molecular mechanism responsible for this crossover is shown to correspond to the formation of a double-layer adsorbed film along the amorphous polymeric chains. In addition to this moisture-induced crossover, many properties of the hydrated biopolymer are found to obey simple material models.The online version of this article (10.1007/s10570-019-02808-z) contains supplementary material, which is available to authorized users. Wood and its various hydrophilic cellulosic components are the most abundant polymers on Earth  to investigate the role of molecular interactions, e.g. HBs, which are critical to explaining the microscopic mechanisms leading to the coupling between the polymer properties and water adsorption. MD has shown its capability of simulating cellulosic material, such as cellulose and xylan, in agreement with existing experimental studies   is shown in Fig. m ~ 30%.The integral heat of adsorption −5 K−1 . Both #HB/V0 for AGX–AGX and AGX–water—shown in black and white dots respectively in Fig. m > 18%, which is below the transition point of m ~ 30%. The number of HBs cannot explain the crossover observed for the material properties in Fig. To explain the transition behavior observed around entclass1pt{minimam > 18%, further decrease of moduli is seen to occur without further breaking of HBs. This indicates that there must be another mechanism explaining the mechanical weakening behavior of AGX for m > 18%, and the crossover happening around m ~ 30%. It is abnormal to find that hydrogen bond is not dominating such important behaviors of the hydrophilic polymer.In previous work, for another wood polymer, amorphous cellulose (AC), a linear relationship between the number of polymer–polymer HBs and the mechanical moduli was found. The breakage of HBs was then understood to be the main mechanism of the mechanical weakening of AC  is used. The water population along the polymer chains is given as a function of the polymer–water distance and moisture content  is the time average of the number of water molecules at a specific m and dPW. From Fig. m > 30%, at least two major peaks, centered at 2.8 Å and 5.6 Å, can be identified. Between the two peaks, there is a “valley”, i.e. a local minimum, at 4.5 Å. Water population is practically zero for dPW > 15 Å. We define the water that resides within 4.5 Å of the polymer chains as the 1st adsorbed water layer and water from 4.5 to 20 Å as the 2nd adsorbed water layer. The occurrence of double-layer adsorption is also supported by the sigmoidal shape of the AGX sorption isotherm (type II isotherm) .Finding that material properties like the heat of adsorption and the heat capacity follow a mixture rule model is logical, since these are scalar properties. Our findings show that the polymer material, first and second adsorbed water layer act as a layered composite material loaded mainly normally to the layers as in a series model. The parallel model shows that the polymers, as well as layers of water, mainly expand along their longest direction which is along the layer direction as in a parallel model. According to all these analyses, we conclude that the saturation of the 1st water layer and the development of the 2nd water layer is the mechanism inducing the crossover occurring around There is considerable interest in understanding the thermodynamic and mechanical responses of hydrophilic plant biopolymers upon moisture adsorption. In particular, this could allow better utilizing plant-based sustainable resources. In this study, molecular dynamics simulation is used to investigate the influence of moisture on the properties of a prototypical hydrophilic polymer: softwood xylan. The heat of adsorption, heat capacity, thermal expansion, elastic moduli and Poisson’s ratio are yielded as a function of moisture content. All these mechanical and thermodynamic properties show a crossover occurring around 30 wt% moisture content, which cannot be explained by the alteration of the hydrogen bond network which plateaus at 18 wt%. A study of water population distribution and polymer–water contact area leads to the identification of a double-layer adsorption. The saturation of the first adsorption layer and the growth of the second adsorption layer of water are identified as the main mechanism of the moisture-induced crossover. This decomposition of two types of water also enables a further material model study, where properties of the composite can be attributed to the contributions of the three major components, i.e. polymer, first and second layer of water. The universality and diversity of the observed crossover in the current study still require further investigation. Nevertheless, the simulation framework and the statistical analysis of the layering structure of water could be extended to other material systems potentially explaining phenomenon associated with it. These new insights provide an important missing piece of information for the full understanding of the structure–property relationship of polymers, which may interest many research fields and industries.The detailed description of modeling and measurement methods.Supplementary material 1 (DOCX 87 kb)Below is the link to the electronic supplementary material.


A personalised transportable folding device for seating (DATP) on a standard seat was developed by an occupational therapist at the Toulouse University Hospital Centre (patent no. WO 2011121249 A1) based on the hypothesis that the use of a seat to assist with better positioning on any chair during meals modifies the sitting posture and has an impact on cervical statics which increases the amplitude of movements of the axial skeleton (larynx and hyoid bone) and benefits swallowing. The aim of this work is to demonstrate that an improvement in sitting posture with the help of the DATP, through Hyoid bone motion, has an impact on the quality of swallowing in a dysphagic population which benefits from the device in comparison to a dysphagic population which does not benefit from the device after 1 month of care. The secondary endpoints concern the evaluation of the impact on other characteristics of swallowing, posture, the acceptability of the device and the quality of life.This is a randomised comparative clinical trial. The blind was not possible for the patients but the examiner who evaluated the outcome criterion was blinded to the group to which the patient belonged. The outcome criterion was the measurement of the hyoid bone movement during swallowing. The other criteria were collected during the videofluoroscopic examination of swallowing and by use of a questionnaire. Fifty-six (56) patients were included: 30 in the group without device (D−) and 26 in the group with the device (D+). All the patients benefited from a training course on seating. Only the D+ patients participated in this course where the use of the device was explained and the device was then kept for use at home for 1 month.p < 0.001) and more hyoid bone motion in the D+ group. The difference was significant in the bivariate analysis for horizontal movement (p = 0.04). After adjustment of potential factors of confusion, we noted a significant mean difference for the three distances in the D+ group in comparison to the D− group, of + 0.33  for horizontal movement, + 0.22  for vertical movement and + 0.37  for horizontal movement. However, the other parameters, and notably the other swallowing markers were not significantly modified by the use of the device.A significant improvement was noted in the postural criteria before and after use, in favour of a better posture for the two groups (The personalised transportable folding device for seating developed to reduce dysphagia has an action on hyoid bone motion during swallowing. However, this positive effect on an intermediate outcome criterion of the quality of swallowing was not associated with an improvement in swallowing efficiency in the study population. The diversity of diseases with which the patients in this study were afflicted is a factor to be controlled in future studies with this device. Many swallowing rehabilitation methods are used in clinical practice: stimulation, dietary adaptations (texture and volume), swallowing exercises, posture and swallowing manoeuvres. Postural adaptations in terms of the positioning of the head are one of the key elements of care regardless of the aetiology –4. To reThe most obvious method of action is the adaptation of the seat according to the morphology and the deficiencies. When the seat is a vehicle for a disabled person, the postural adaptation method is known . For subA personalised transportable folding device for seating (DATP) placed on a standard seat is a solution that could find relevance in the home, in adult disability care homes or in elderly care facilities. It is in this context that a device was developed by an occupational therapist in our deprtment and patented (WO 2011121249 A1). Prior to the creation of this device, training sessions were carried out. However, the patients did not benefit from technical assistance to implement them at home . The DATThis work is based on the hypothesis that assistance with better positioning by using a seat on any standard chair during meals modifies the sitting posture with an effect on cervical statics and provides greater amplitude in axial skeleton (larynx and hyoid bone) movements which helps with swallowing and changing postures.Our aim is to demonstrate that an improvement in sitting posture with a sitting device has an impact on the quality of swallowing.The main endpoint of this work is to compare hyoid bone motion during swallowing in a dysphagic population which benefits from the device in comparison to a dysphagic population which does not benefit from the device after 1 month of care.The secondary endpoints concern the evaluation of the impact on other characteristics of swallowing, posture, the acceptability of the device and the quality of life.This is a prospective, monocentric controlled study with the design of a superiority randomised controlled clinical trial (1:1) in two parallel groups. The blind is not possible for patients,s but the examiner who evaluated the outcome criterion was blinded to the group to which the patients belonged.The patients in this study were required to have a sitting abnormality determined by the seated postural control measure SPCM, , 15) and and15]) The device that was tested is a sitting aid prototype. It targets a population with postural disorder and dysphagia that has the capacity to ambulate which justifies a device that can be easily and rapidly installed on several types of seats.It concerns a transportable folding device for maintaining a seated posture, which includes: a bottom lining suitable for a seat, where the said bottom lining includes a rigid seat and a backrest, with the seat and backrest connected to each other in alignment by a link that forms a hinge; at least one orthopaedic support element suitable for the bottom lining and an attachment to fit the orthopaedic support element to the bottom lining by adjusting the position.This device was used as technical support in a training programme to improve the sitting position.All the patients benefited from a training session including an evaluation of their needs, information on the impact of head positioning on swallowing, on how to facilitate an adapted position of the head through global positioning of the body with practice sessions using occupational therapy cushions or the DATP according to the randomisation.During a visit to the unit for a swallowing assessment, the patients who met the inclusion criteria were scheduled for an inclusion visit and the first data were collected: age, gender, weight and BMI, dysphagia history, dysphagia aetiology, DHI, the treatment implemented for dysphagia  and 3 items of the SPCM. This corresponded with the pre-inclusion visit (T-1 month).The complement to the SPCM items by the occupational therapist.A videofluoroscopy of swallowing.The Functional Oral Intake Scale (FOIS ) and theThe inclusion visit corresponds to T0. After verification of the inclusion criteria, the patients were randomly distributed into the arm with the device (D+) or without the device (D−). All the patients benefited from the same assessment:The two patient groups were then interviewed to establish a diagnosis for educational purposes and the objectives of the training session were thereby specified and personalised.The D+ group was then taken in charge to determine the characteristics of the device required for the order so that they could have them during the training session.All the patients were re-invited for the therapeutic training session run by the occupational therapist 1 month after (T + 1 month). This deadline was justified by the deadline for manufacture of the device by the company for the patients in the D+ group. The instruction for patients at the end of this training session was to put the personalised instructions into practice by using the device only for the D+ group.At the control visit 1 month after at T + 2 months, all the outcome criteria at T0 were collected in addition to the DHI which was collected at T-1 month and the device satisfaction questionnaire for the patients in the D+ group , during inclusion (T0) and in 2 months of follow-up (T2). The effectiveness of the device was evaluated by the improvement in swallowing based on the laryngeal rise criterion, which is recognized as a reliable indication of laryngeal closure during swallowing .The main outcome criterion was the measurement of laryngeal movement during swallowing, which is one of the parameters that describe the biomechanics of swallowing . For thiPositioning of the patient side-on seated on a standard chair.A 10c Euro coin stuck to the skin in the cervical region under the chin served as a reference for measurement.A bolus of 5 ml barium sulphate diluted to 35% W/V repeated five times.The VFS was carried out as follows for all the patients:Y axis defined by a straight line passing through the following two points:the inferior and anterior edge of C4 = point 0,the inferior and anterior edge of C2,X axis strictly perpendicular to the y axis at point 0 (inferior and anterior edge of C4),The maximum hyoid bone excursion was calculated after location of the anterior and superior edge of the hyoid bone. The movement in cm was measured on a vertical axis and on a horizontal axis with the following marking:All the swallowing could not be exploited for the main measurement due to errors in centring and errors in synchronisation between the start of swallowing and recording.At T0 and T2, the swallowing measurements were taken based on three videofluoroscopic recordings that provided conditions for satisfactory marking and selected in a standardised manner from among all the videofluoroscopic recordings of swallowing.The outcome criteria were the mean of these three measurements for three dimensions: vertical, horizontal and total movements (hypotenuse), after evaluation of the measurement concordance. Technically speaking, the video recordings were manual .These criteria were measured several times. Therefore, the measurement concordance was evaluated by an intra-class coefficient of correlation (ICC). This was satisfactory for the measurements of hyoid bone motion: the ICC for the mean of the measurements for horizontal movement was 0.85 at T0 and 0.90 at T2, for vertical movement 0.89 at T0 and 0.88 at T2 and for global movement (hypotenuse) 0.87 at T0 and 0.88 at T2.It was also satisfactory for the NIHSS (0.96 at T0 and 0.96 at T2). However, it was marginal for the PAS measurement (0.66 at T0 and 0.69 at T2).The NIH Swallowing Safety Scale (NIH-SSS) which estimates the global swallowing efficacy  for whicThe Penetration Aspiration Scale (PAS) which evaluates aspiration with an ordinal scale at 8 levels: 1 (no aspiration and 8 (severe aspiration) .The other outcome criteria collected during the videofluoroscopic examination were:FOIS : ordinalThe Deglutition Handicap Index  which mThe other swallowing and feeding markers were:A seated postural control measure validated for subjects seated in wheelchairs was used. The SPCM includes 21 items rated on the equivalent of an ordinal scale at 7 levels. The scores can be grouped together.For the acceptability of the device, satisfaction with a technical aid was evaluated with a Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST ) questioFor posture, anomalies of the seated position and the impact of the device were evaluated,For the quality of life the SF36  questionThe other criteria made it possible to complete the analysis of the impact on swallowing, confirm the impact on posture and verify the acceptability of the device and the quality of life.N = 44) were required to detect differences greater than 0.3 cm, at an α level of 5% and power of 90%. Assuming a 30% loss of the patients to follow-up, the sample size was increased to 32 in each group .Paik et al.  showed tralloc package for Stata 12 SE, in blocks of sizes that vary between 2, 4 and 6The random allocation sequence was obtained using the The evaluation of the outcome criteria in videofluoroscopy was blinded. The video recording of each swallowing sequence related to a bolus was prepared so that it could be identified by a random number independent of the evaluation time (T0 or T2) and the patient’s identity. The practitioner then analysed the recordings in the order of this random numbering. The intervention was open for the patients (a blind is impossible with this device) and for the evaluation of the other outcome criteria.All the variables measured at inclusion or pre-inclusion (characteristics of the patient and the expectation) were described for the total population. The initial comparability of the groups was verified by the description of these same variables by randomisation group (with and without the positioning device).t + 2 months) with a Student test or a Mann–Whitney–Wilcoxon test. Then, for the main outcome criteria, three linear regressions were performed to model the movement  according to the intervention group, after adjustment of the factors of confusion identified in principle: the quality of initial swallowing , malnutrition measured by weight, severity of the postural effect , the prognosis through age and the type of disease (degenerative or non-degenerative), and the associated treatment .The main and secondary outcome criteria were compared at the last visit .This study received the approval of the committee for the protection of individuals CCP SOOM 2  on 05/05/2014 under no. 2-14-08.The RCB number (HPS Study) is 2013-A01351-44.Of the 64 patients included in the study, 32 were randomised in the group with DPAT and 32 in the group without DPAT. Eight withdrew from the study after randomisation: two in the “D+” group and six in the “D−” group: two due to the occurrence of adverse effects, one due to a rapid worsening of dysphagia, four due to a decision by the patient to withdraw = one refused to continue and 1 was lost to follow-up  of the patients and included the following neurological diseases: extrapyramidal syndromes and Huntington’s disease (n = 12) neuromuscular disease and myopathy such as ALS, Steinert’s disease (n = 9) Scleroderma (n = 3). The other diseases  were brain lesions  (n = 3), peripheral neuropathy and bilateral cranial nerve palsy (n = 7), head and neck cancer (n = 8), respiratory or digestive disease (n = 7), laryngeal or cervical disease (n = 7). It should be noted that 11 (19.6%) patients had no disease associated with the cause of the dysphagia, 15 had an associated disease (26.8%), 30 patients had more than one associated disease. The history of the disorder was between 3 months and 57 months with a mean of 10.1 months (SD 11.2). No other care was proposed in addition to postural training during the study period for 56.4% of the patients. 12.7% continued physical therapy alone, 16.4% speech therapy alone and 14.6% both.Dysphagia was considered to be degenerative, i.e., with a tendency to worsen over time, for 43%  ,the difference was significant in the bivariate analysis for horizontal movement  for horizontal movement, + 0.22  for vertical movement and + 0.37  for global movement  were not significantly modified by the use of the device. In addition, no significant improvement in the different dimensions of quality of life were noted (SF36).The acceptability of the device was very good with a mean total score of 4.0 out of 5 (SD 0.7). All the dimensions evaluated had a mean score of more than 3.5 . We found no publication that enabled us to compare the results. However, in relation to the impact of the changes from the upright seated position and the lateral decubitus position, the difference appears to be inferior: 2 mm in bivariate analyses in our study and 3 mm in the study by Byung-Mo Oh et al. [The impact on the axis change measurement in the vertebral column induced by postural correction could influence the results. Sia et al.  showed tFinally, the latest works on hyoid bone kinematics put forward as more pertinent  the measp = 0.059) was found in a study population of 13 patients who presented with aspiration versus 29 patients who presented no aspiration. In our study, the poor reproducibility of the PAS measurement on the patient recordings does not make it possible to conclude on this question. This raises the point concerning the sensitivity of the measurement scales available to evaluate swallowing.The absence of impact on the swallowing parameters is probably related to the inadequacy of the effect on swallowing efficiency. In fact, the only positive result of our study concerns an intermediate outcome criterion. We have no data concerning the minimum threshold of hyoid bone motion that makes it possible to judge swallowing efficiency. Molfenter and Steele  were unaFinally, as the studies that have been conducted attest, the reduction in hyoid bone motion is dependent on the pathological context. Wide variations are possible as the study by Paik  shows. IThe personalised transportable folding device for seating developed to reduce dysphagia has an action on hyoid bone motion during swallowing. However, this positive effect on an intermediate outcome criterion of the quality of swallowing was not associated with an improvement in swallowing efficiency in the study population. The diversity of diseases with which the patients in this study were afflicted is a factor to be controlled in future studies with this device.


Simulation of a left ventricle has become a critical facet of evaluating therapies and operations that interact with cardiac performance. The ability to simulate a wide range of possible conditions, changes in cardiac performance, and production of nuisances at transition points enables evaluation of precision medicine concepts that are designed to function through this spectrum. Ventricle models have historically been based on biomechanical analysis, with model architectures constituted of continuous states and not conducive to deterministic processing. Producing a finite-state machine governance of a left ventricle model would enable a broad range of applications: physiological controller development, experimental left ventricle control, and high throughput simulations of left ventricle function.® and Stateflow tools.A method for simulating left ventricular pressure-volume control utilizing a preload, afterload, and contractility sensitive computational model is shown. This approach uses a logic-based conditional finite state machine based on the four pressure-volume phases that describe left ventricular function. This was executed with a physical system hydraulic model using MathWorks’ SimulinkThe approach developed is capable of simulating changes in preload, afterload, and contractility in time based on a patient’s preload analysis. Six pressure–volume loop simulations are presented to include a base-line, preload change only, afterload change only, contractility change only, a clinical control, and heart failure with normal ejection fraction. All simulations produced an error of less than 1 mmHg and 1 mL of the absolute difference between the desired and simulated pressure and volume set points. The acceptable performance of the fixed-timestep architecture in the finite state machine allows for deployment to deterministic systems, such as experimental systems for validation.The proposed approach allows for personalized data, revealed through an individualized clinical pressure–volume analysis, to be simulated in silico. The computational model architecture enables this control structure to be executed on deterministic systems that govern experimental left ventricles. This provides a mock circulatory system with the ability to investigate the pathophysiology for a specific individual by replicating the exact pressure–volume relationship defined by their left ventricular functionality; as well as perform predictive analysis regarding changes in preload, afterload, and contractility in time. Every year since 1919, cardiovascular disease (CVD) accounted for more deaths than any other major cause of death in the United States . The organization of the state transition diagram follows FSM convention: the single curved arrow donates the initial time-dependent conditions of the model, the oval shapes are the states of the model, the dotted hoop arrows denote the output of the state until a specific condition is met, and the straight arrows are the transition direction once the condition annotated is satisfied. Time (t) is an input variable that discretely changes at the Fundamental Sampling Time of the simulation, EDV to start the initialization of the Phase 2 state to compensate for the radius of curvature created due to transitioning from fill to eject, as well as the means by which end-diastolic pressure and volume are clinically quantified. The Systolic Ejection Rate is defined as the rate of change in the output variable, F, during systolic ejection. Systolic Ejection Offset is defined as the value subtracted from the LVESP to start the initialization of the Phase 3 state, establishing LVEICP.Utilizing Simulink™ Stateflow® and Simscape™ block library provided all the necessary components needed to create a hydraulic testing platform capable of simulating this application. All modified parameter values are noted in the diagram, while any parameters not noted were left standard to the block’s original parameter values. Additionally, for any element parameter denoted as ‘Variable’, these values were not left constant for all simulations presented. The values utilized in each simulation, not explicitly declared in Fig. A hydraulic testing model was developed for simulating hydraulic performance as presented in Fig. ® control architecture. The outputs are simulated left ventricular volume (LVV) [mL], simulated left ventricular pressure (LVP) [mmHg], simulated aortic pressure (AoP) [mmHg], and left atrial pressure (LAP) [mmHg]. LVP and LVV are utilized by the Stateflow® control logic to govern state transitions while AoP and LAP are used for system fidelity and plotting purposes. The input force is applied to the Ideal Force Source block element which is then directed to an Ideal Translational Motion Sensor which converts an across variable measured between two mechanical translational nodes into a control signal proportional to position. The position signal is then converted into volume [mL] based on a piston diameter of 2 inches, thus a cross-sectional area of π × 2.542 = 20.27 cm2. The input force [N] is also applied to a Translational Hydro-Mechanical Converter which converts hydraulic energy into mechanical energy in the form of translational motion of the converter output member. Two check valves , positioned in opposing directions, regulate the fluid flow direction as seen in the left ventricular section of an MCS. A Constant Volume element is positioned between the two check valves to simulate a constant volume filling chamber. A Hydraulic Pressure Sensor is positioned between the opposing check valves to monitor LVP, then outputs the simulated values to the Stateflow® control logic.The hydraulic testing model is a one-input, four-output system. The input is the force [N] applied to the piston and is regulated by means of the StateflowUpstream to the mitral valve is a Hydraulic Reference source block governed by the EDPVR curve function with respect to simulated volume, LVV, and increased by an offset of 2 mmHg to ensure proper flow through the mitral check valve. This establishes a dynamic LAP, the initial pressure condition of the left heart. LAP is outputted from the model here for plotting purposes. Downstream to the aortic valve is a Spring-Loaded Accumulator block. This block element consists of a preloaded spring and a fluid chamber. As the fluid pressure at the inlet of the accumulator becomes greater than the prescribed preload pressure, fluid enters the accumulator and compresses the spring, creating stored hydraulic energy. A decrease in the fluid pressure causes the spring to decompress and eject the stored fluid into the system. The spring motion is restricted by a hard stop when the fluid volume becomes zero, as well as when the fluid volume is at the prescribed capacity of the fluid chamber. These settings are utilized to regulate the compliance,Additionally, a needle valve was positioned downstream to the aortic valve to simulate the resistance to flow contributed to the branching arteries of the aortic arch, as well as provide the capability to simulate the effects of increasing and decreasing resistance with time. As was previously stated, all block element parameter values that were modified are noted in the diagram presented in Fig. 3 and a kinematic viscosity of 3.3 centistoke  can additionally be derived from the volume time plot by means of A LV-PV loop; LVP, LAP, and AoP versus time; and volume versus time graphs for the 10 s total simulation time was presented for each simulation. Note, the driving force [N] produced by the FSM can be derived from the presented LVP and LVV plots by means of a curves, the computational model successfully provided the correct LVESP, LVESV, LVEDP, LVEDV, LVEIRP, and LVEIRV transition points within the state transition logic to produce the prescribed LV-PV relationship. Table The LV-PV loop critical point computational model and FSM approach was effective at driving the hydraulic testing model to produce the characteristic LV-PV relationship as presented in Fig. The system takes one cycle to initialize from a rest state before the control topology functions consistently for the remainder of the simulation. Additionally, the isovolumetric and systolic offsets and rates, necessary to achieve this response are noted in Table As presented in Fig. a was initially defined by the equation As shown in Fig. a is initially defined by the equation a was decreased from − 1.7504 mmHg/mL concluding with a slope of − 1.0408 mmHg/mL. This rate of change for the Ea slope was derived from the 15 mmHg per cycle y-axis rate of increase to achieve a consistent x-intercept, as shown in Fig. Presented in Fig. mentclass2pt{minimAs presented in Fig. a, and EDPVR curve coefficients were developed utilizing DataThief to find the associated LVESP, LVESV, LVEDP, and LVEDV for the initial and final loops, as well as evaluate the EDPVR curve. These datasets were analyzed over a 10 s total simulation time and for each simulation are the LV-PV loop; LVP, LAP, and AoP versus time; and volume versus time graphs. Both simulations reflect a mean heart rate [bpm] within the range of mean values noted in the reference material. All parameter values are presented in Table Figure a is initially defined by the equation a was divided into evenly-spaced increments to constitute 4 intermediate discrete steps between the initial and final cycle parameters. The results indicate an error of less than 1 mmHg and 1 mL for all targeted datasets.The Control is presented in Fig. a is initially defined by the equation a was divided into evenly-spaced increments to constitute 4 intermediate discrete steps between the initial and final cycle parameters. The results produced an error of less than 1 mmHg and 1 mL for all targeted datasets.HFNEF is presented in Fig. a. The acquired setpoints drive the FSM controller to perform the prescribed PV relationship. Then the hydraulic testing system, which reproduces conditions comparable to those found in a left heart MCS with cardiac piston actuation, simulates the PV relationship defined by the inputs to the computational model.A novel method for simulating LV-PV control functionality utilizing explicitly defined preload, afterload, and contractility was delivered for cardiovascular intervention assessment. The proposed aggregate model consists of three subsystems which include a preload, afterload, and contractility sensitive computational setpoint calculator “” section® and its supplemental product library was effective at developing reproducible clinical conditions, which would be determined through an individualized clinical PV analysis, simulated in silico for this work with ability to translate to future in vitro investigations. This provides an MCS with the capabilities to investigate the pathophysiology for a specific individual, with or without VAD support, by reproducing the precise PV relationship defined by their left ventricular functionality.The resulting solution was capable of being compiled for hardware control in a MCS through the architecture and solver type employed; deterministic processing is achievable and runtime setpoint changes can be made. SimulinkIn silico verification of the LV-PV loop critical point computational model, FSM control architecture, and hydraulic testing system support this modeling approach as an effective means of simulating the LV-PV relationship. In this work, a novel method for simulating the characteristic EDPVR curve and LAP during diastolic filling was presented. This approach proved to be an effective means of capturing the nuisances in those sections of the PV curve that are critical for diastolic operation of mechanical circulatory support systems and not found in prior computational models , 41.As shown in Fig. a curves. Displayed in Fig. A key result of this investigation is a novel in silico method for simulating LV-PV relationships based on an analysis of a patient’s ESPVR, EDPVR, and EESP during ejection.The limitations of this approach are mainly the ideal hydraulic testing system and use of anticipatory limits in transition points of the PV loop. If a force is exerted into this computational model of the hydraulic system, the system responds with the corresponding pressure instantaneously within that sample period. There was no modeled delay or rise time in the actuation components. This consideration is made in the FSM by increasing force incrementally instead of applying a constant desired force. Some parameters which define the hydraulic system, such as the parameters within the Spring-Loaded Accumulator are ideal assumptions based off a desired performance of the system. The focus of this work was on the control architecture that can be adjusted to a variety of hardware platforms through manipulation of the output signal magnitude and response characteristics. Additionally, pressure sensor feedback is ideal using this modeling approach. The sensor sampling rate was set to 512 Hz and assumed an ideal sensor with low noise. Additionally, a manual offset was made to the transition from diastolic filling to isovolumetric contraction of the system; enabling a ramping from the transition of fill to eject. Moreover, an offset was utilized in the transition from isovolumetric contraction to ejection in order to allow the pressure to slowly increase to the desired LVFuture work includes a sensitivity analysis regarding resistance, compliance, and force rates. This analysis will be useful in that it will quantify the exact limitations of the hydraulic testing system as well as the range of accuracy of the FSM approach. Isolated in vitro testing of this approach will be conducted on a nested-loop hydraulic system before being incorporated into an MCS for investigating accurate cardiovascular hemodynamic considerations, such as the accuracy of pressure and flowrate sensor feedback. Additionally, what-if scenarios will be conducted on a MCS in order to create feasible scenarios to which a patient may experience.This research will assist in producing an investigatory method and MCS control logic that will advance the medical community by improving left ventricular in vitro analysis capabilities. The ability of an MCS to be able to replicate the exact PV relationship that define the pathophysiology allows for a robust in vitro analysis to be completed. This ventricular model for ventricular function could also be coupled with aortic and left atrium computational fluid dynamics (CFD) models that require inlet and outlet conditions manifested by the left ventricle. The FSM approach is computationally efficient due to the explicit computation, and simple transition logic, which is preferential when small time steps and high iteration solvers are being employed. It was this efficiency and portability in the outcome that has made this work impactful for a variety of investigative purposes.


This deterioration is largest during Scandinavian Blocking episodes, during which: (a) error growth is enhanced in the European‐Arctic, as a result of increased baroclinicity in the region, and (b) high‐amplitude planetary waves allow errors to propagate from the Arctic into midlatitudes. The important role played by Scandinavian Blocking, in modulating the influence of the Arctic on midlatitudes, is also corroborated in relaxation experiments, and through a diagnostic analysis of the ERA5 reanalysis and reforecasts.Recent studies have suggested that Arctic teleconnections affect the weather of the midlatitudes on time‐scales relevant for medium‐range weather forecasting. In this study, we use several numerical experimentation approaches with a state‐of‐the‐art global operational numerical weather prediction system to investigate this idea further. Focusing on boreal winter, we investigate whether the influence of the Arctic on midlatitude weather, and the impact of the current Arctic observing system on the skill of medium‐range weather forecasts in the midlatitudes is more pronounced in certain flow regimes. Using so‐called Observing System Experiments, we demonstrate that removing  Change in RMS error (%) for z500 in the IN‐SITU OSE (top) and Arctic relaxation experiment (bottom) at a lead time of 1 day  and 4 days , with respect to the control for each experiment. Forecasts were started at 0000 and 1200 UTC between 1 December 2017 and 31 March 2018. The operational ECMWF HRES analysis is used to verify the forecast skill. The regions where  in situ data are denied and  relaxation is performed, are shown in magenta. The north Asia region is shown in red. As the usage of observations to produce an accurate initial state is crucial for a skilful forecast, denying Arctic observations provides another type of numerical experiment that can be used to investigate how improved initial conditions over the Arctic could lead to improved forecasts in midlatitudes. Such experiments can also be used to quantify the impact of specific Arctic observations on the initial conditions in the Arctic, and subsequently on the forecast skill in the midlatitudes. However, until recently such OSEs have only been performed for Arctic observations in a reduced complexity forecasting system , in which specific observations are not assimilated (or are denied) when creating the initial conditions for weather forecasts. The accuracy of weather forecasts depends on the three key ingredients of a modern NWP system , north of 60°N, were run for both a winter and a summer season. A thorough assessment of the impacts of different Arctic observation types over both seasons is presented by Lawrence et al. . This aspect was not examined in Lawrence et al. . Specifi , at a horizontal resolution of TCo399 (≈25 km), which is lower than the resolution of the ECMWF operational high‐resolution deterministic analyses and 10‐day forecasts . The vertical resolution is the same as in the operational HRES, i.e. 137 levels.et al., et al., in situ observations in two 12 h assimilation windows (0900 UTC to 2100 UTC and 2100 UTC to 0900 UTC). The analyses are then used as initial conditions for 10‐day weather forecasts initialized each day at 0000 and 1200 UTC respectively.As in the operational HRES, the incremental 4D‐Variational (4D‐Var) data assimilation system  in order to investigate the importance of the polar observations. In this study, we focus on the OSEs performed for the period December 2017–March 2018 (DJFM 2017/2018) and on the role of Arctic observations. This period contains the Year of Polar Prediction (YOPP) First Special Observing Period (SOP1), February–March 2018, during which many of the Arctic radiosonde stations doubled or tripled the frequency of radiosonde launches All All microwave radiance observations (MW hereafter), from AMSU‐A (6 instruments), ATMS (1 instrument), MHS (4 instruments), GMI , SSMI/S , MWHS (1 instrument), and MWHS‐2 (1 instrument),All infrared radiance observations (IR hereafter), from IASI (2 instruments), CrIS (1 instrument), and AIRS (1 instrument)The comprehensive set of OSEs performed for DJFM 2017/2018 is described in detail in Lawrence et al. . In thiset al.  represents the model's prognostic equations. The value of α controls the strength of the relaxation and is set to 1/3 (per time step) above 75°N. This means that at each time step the model is corrected using 1/3 of the departure of x from xana for all variables and levels. To achieve a smooth transition between the relaxed and free running regions, α is smoothed between 75°N and 65°N by an exponential function that approaches 0N at 65°N. Note that the southernmost extent of the relaxation zone, 65°N, is slightly further north than the 60°N cut‐off used in the OSEs. The choice of the value of α is based on the consideration of both the strength of the relaxation and the model stability. The relaxation of the atmospheric variables is carried out by interpolating from the 6 h ECMWF operational analyses (xana) to the actual time step. The atmospheric state variables: surface pressure, temperature, humidity and horizontal winds are relaxed at each model time step throughout the 10‐day forecasts.A relaxation experiment was carried out with the ECMWF IFS for the same period as the OSEs, i.e. DJFM 2017/2018. This experiment consists of 10‐day weather forecasts initialized each day at 0000 and 1200 UTC from the ECMWF operational analyses, in which the relaxation methodology described in Jung et al. , Jung  at a horizontal resolution of TCo639 (≈18 km) with 137 levels in the vertical. The relaxation experiments use a slightly older version of the IFS and a higher spatial resolution than the OSEs, but these differences in the model version and resolution with respect to the OSEs are not expected to affect the interpretation of the outcomes.As in previous studies, the relaxation approach is used to determine whether improving the representation of the atmospheric circulation in the Arctic would improve the skill in the midlatitudes. In this study, we use this technique to see whether we get similar insights about the regime dependence of the Arctic's influence on the midlatitudes compared to OSEs.2.3expt−RMSEctrl)/RMSEctrl), multiplied by 100 to arrive at the percentage change in forecast error. Throughout the text we will refer to this as the change in RMSE. A paired t‐test is used to calculate a 95% significance threshold. In order to account for temporal autocorrelation the error bars are inflated by 1.22 and an additional “Sidak correction” is applied to further inflate the error bars to account for the multiple tests (i.e. multiple OSEs), which would otherwise increase the chances of a false positive , the largest impact is within the Arctic itself, where the IN‐SITU OSE increases the error in geopotential height at 500 hPa (z500) compared to the control experiment , due to combined changes to all components of the NWP system .To put these changes in skill into context, the performance of the ECMWF operational medium‐range weather forecasts improves at a rate of approximately 2% per year in key metrics such as et al., North Asia is a region that is also significantly affected in the relaxation experiment Figure c,d, likeOutside the region of relaxation, the reduced errors may reflect causal links in the real atmosphere from the Arctic into the midlatitudes, but one should keep in mind that these could be over‐emphasized if the relaxation term represents a strong forcing on e.g. planetary waves (so that an Arctic “tail” could be wagging a midlatitude “dog” to some extent). In contrast, the differences in errors between the OSEs and the control tend to diminish with lead‐time Figure a,b and bet al., et al., Previous relaxation experiments have also highlighted North America as one of the regions that would benefit the most from improved Arctic forecasts  in medium‐range forecast skill over north Asia obtained in the OSEs (relaxation experiments) is related to specific flow regimes. As in the previous section, we focus our analysis on the north Asia region  and a pre‐defined Scandinavian Blocking pattern . The Scandinavian Blocking pattern we used is one of the four standard Euro‐Atlantic regimes used in ECMWF products and was originally computed using a “k‐means” clustering algorithm, based on daily z500 reanalysis fields  with respect to the control. Then we select the forecast start times for which this metric is high (above 50th percentile) and low (below 50th percentile), respectively. We then plot the difference in the mean z500 field  between the cases in which overall impact of the OSEs on forecast skill in this region is higher or lower than average .The 24 February 2018 case coincided with the Sudden Stratospheric Warming event. The meteorological situation was characterized by a period of exceptional warming in the Arctic, with a ridge and associated warm‐anomaly extending high into the Arctic. The pressure pattern on 13 January 2018 is more typical of a period of Scandinavian Blocking with the region of high pressure confined to northern Europe. Although there are some differences, the cases are quite similar to each other and to other Arctic warm episodes, such as those described in Moore  and Messet al.  have grown most rapidly around a developing trough north of Greenland , will have a larger impact than during other periods.et al., To test this, we calculate climatological forecast errors from the ERA5 reanalysis and reforecasts  to the composites of cases where the Arctic has a strong influence on the midlatitudes shown in section To begin, we separated the ERA5 forecast start times into two groups: a Scandinavian Blocking group and a “normal” group. These were selected based on whether the forecast start time was in the highest and middle tercile of the Scandinavian Blocking index introduced in section et al.  is to examine the “EDA ensemble variance” (EnsVar), and “analysis increments” from the data assimilation system that was used to create the initial conditions of the forecasts  between the Scandinavian Blocking cases and the average conditions Figure b,c. Notes Figure b,c. Overs Figure b. Furthe4.2z500 RMSE in the 10‐day forecasts initialised from ERA5 show that at short ranges (days 1–2) the errors are larger in the Arctic itself during Scandinavian Blocking events, as one would expect based on the analysis of increments , is highly significant for forecasts initialised during Scandinavian Blocking periods. During non‐blocking periods this drops  and the difference between the two is significant at the 5% level using a Fisher r‐to‐z transformation.Given the connection between errors in the European Arctic and north Asia shown in section s Figure . At dayss Figure , in an as Figure  can alsoThese results corroborate the main findings from the OSE and relaxation experiments by demonstrating that the European‐Arctic has an increased sensitivity to the initial conditions during episodes of Scandinavian Blocking. It also shows that the magnitude of short‐range forecast error in the European‐Arctic and medium‐range error in northern Asia are causally linked during Scandinavian Blocking episodes. Further, it shows that these findings of section 5In this study, we used three approaches to understand the influence of the Arctic on medium‐range weather forecasts in north Asia, and how this impact depends on flow type during the winter season. The central questions were: (a) does the Arctic observing system have an impact on the skill of medium‐range weather forecasts in north Asia in all cases, or does the strength of the influence vary as a result of changes in flow regime, (b) are insights about the regime dependence of Arctic‐to‐midlatitude linkages, gained from the Arctic OSEs, consistent with those derived from relaxation experiments? And (c) to what extent are they also reflected in the climatology of forecast errors?in situ, microwave and infrared satellite observations when creating the initial conditions of the forecasts, leads to a decrease in forecast skill over northern Asia in the medium‐range. A broadly similar pattern, of albeit opposite sign, is found when the Arctic is relaxed toward our best estimate of the atmospheric state in relaxation experiments. However, in general the impact of the relaxation on midlatitudes is more widespread and typically larger than the impact found in the OSEs.The degradation of the initial conditions in the Arctic, induced by removing Arctic Short‐range error growth in the European‐Arctic is larger than average during Scandinavian Blocking episodes, when warm‐moist intrusions lead to higher baroclinicity in the region. As a result, the sensitivity to removing observation when creating the initial conditions is larger then.The high‐amplitude stationary waves that occur during Scandinavian Blocking episodes allow efficient propagation of forecast errors out of the European‐Arctic into north Asia.The medium‐range impact in north Asia, induced either through the Arctic observing system or relaxation experiments, is largest during episodes of Scandinavian Blocking. This is because:Analysis of medium‐range forecasts initialized from ERA5 (for the period 2013–2018) show that points 2a and 2b are general features of the forecast error climatology over a longer period.In summary, we found that during boreal winter:The results from this study provide some guidance on how, when and where improving the representation of the Arctic, for example through improvements to the observing system or the use of observations in NWP, would improve forecasts in the midlatitudes during winter. By comparing Arctic relaxation experiments and OSEs for the first time, the study also allows us to interpret the results from relaxation experiments, which have been used recently to understand the influence of the Arctic on midlatitudes from a forecast perspective. The relaxation experiments provide an upper bound of the forecast improvement that one would get in the midlatitudes by improving the representation of the Arctic because they limit growth of errors in the initial conditions as well as errors arising from forecast model error. As a result, they cannot be directly used to inform of the design or better use of the observing systems in the same way as the OSEs. However, our comparison shows that they do provide a consistent picture on how the Arctic‐to‐midlatitude linkages are modulated by the Scandinavian Blocking regime, suggesting that they could also be used to identify other situations in which improvements in the initial conditions, or other aspects of forecast, in the Arctic might improve forecasts in midlatitudes.et al. (The idea that the influence of the Arctic on midlatitudes is flow‐dependent was recently proposed in the study of Semmler et al. . This stet al. , then itA corollary of our findings is that increasing the observational coverage in regions of high error growth in the European Arctic during Scandinavian Blocking events should improve forecast errors, not just in this part of the Arctic, but also downstream over north Asia. Indeed, such flow‐dependent error growth suggests that a more dynamic observing network, where more observations are taken in regions where error growth is fast might be advantageous.